As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

• Top 5 IoT Trends
  1. IoT Cybersecurity Concerns Grow
  2. IoT Advancements In Health Care
  3. 5G Enables More IoT Opportunities
  4. Demand For Specialized IoT Data Management
  5. Bundled IoT For The Enterprise Buyer
• The Future Of IoT
• Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and  IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

• Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
• Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
• Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

• Managing, analyzing, and obtaining helpful insights from customer data
• Offering quick and accurate analysis
• Adding personalization with data privacy
• Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

• Remote monitoring and managing
• Process optimization
• Workload forecasting
• Grid balancing
• Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

• Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
• Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
• Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
• Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices

For businesses considering a data warehouse solution, a number of competing providers offer a range of features and prices. This article will compare the top seven solutions and explain the features that differentiate them, making it easier to match them to specific needs.

Table Of Contents

• Top Data Warehouse Providers And Solutions
• Data Warehouse Providers And Solutions Comparison Table
• Amazon Redshift: Best For Deployment Optimization
• Google BigQuery: Best For Serverless Data Warehousing
• IBM Db2 Warehouse: Best For Analytic Workloads
• Azure Synapse Analytics: Best For Code-Free Offering
• Oracle Autonomous Data Warehouse: Best For Integration
• SAP Datasphere: Best For Data Warehouse Templates
• Snowflake: Best For Cloud Data Warehouses
• Key Features Of Data Warehouse Providers And Solutions
• How To Choose What Data Warehouse Provider Best For You
• Frequently Asked Questions (FAQ)
• Bottom Line: Data Warehouse Providers And Solutions

Top Data Warehouse Providers and Solutions

The top seven providers all offer feature-rich data warehousing plans at varying prices. A business’s specific needs will determine which is right for them. When selecting a provider, consider the use cases and costs for each as outlined below.

Data Warehouse Providers And Solutions Comparison Table

Amazon Redshift: Best For Deployment Options

With Amazon’s entry into the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment. Redshift deploys with Software as a Service (SaaS), cloud, and web-based solutions.

Pricing

Amazon Redshift has a pricing page where users can sign up for a trial period, request a quote, or calculate costs based on needs. Pricing starts at $0.25 an hour and can be configured using various models based on usage.

Features

• Spectrum Feature: This feature allows organizations to directly connect with data stores in the AWS S3 cloud data storage service, reducing startup time and cost.
• Strong Performance: The performance benefits companies from AWS infrastructure and large parallel processing data warehouse architecture for distributed queries and data analysis.
• Integration With AWS Glue: AWS Glue makes it easy to write or autogenerate Extract, Transform, and Load (ETL) scripts in addition to testing and running them.

See all Redshift features at https://aws.amazon.com/redshift/features.

Pros

• Parallel processing capabilities
• Contains network isolation security
• Good documentation

Cons

• Expensive
• Poorly designed user interface
• Unable to restrict duplicate records

For more on AWS: AWS Data Portfolio Review

Google BigQuery: Best For Serverless Technology

Google BigQuery is a reasonable choice for users looking to use standard SQL queries to analyze large data sets in the cloud. It is a serverless enterprise data warehouse that uses cloud, scale, Machine Learning (ML)/Artificial Intelligence (AI), and Business Intelligence (BI).

Pricing

Google BigQuery’s pricing page contains specific information about pay-as-you-go plans and longer-term (one to three year) commitments. The provider offers multiple versions of the platform, including Enterprise Edition and Enterprise Plus Edition. The Standard Edition is a pay-as-you-go plan starting at $0.04 per slot hour and the Enterprise Edition has different plans to help a company find its cloud data warehouse.

Features

• Serverless Technology: Using serverless technology, Google handles the functions of a fully managed cloud service, data warehouse setup, and resource provisioning.
• Logical Data Warehousing Capabilities: BigQuery lets users connect with other data sources, including databases and spreadsheets to analyze data.
• Integration With BigQuery ML: With BigQuery ML machine learning, workloads can be trained on data in a data warehouse.

See all BigQuery features at https://cloud.google.com/bigquery.

Pros

• Works with Google Cloud
• Full SQL query support
• Efficient management of data

Cons

• No user support
• Difficult for beginners in data warehouses
• Difficult user interface

For more information on Google: Google Data Portfolio Review

IBM Db2 Warehouse: Best For Analytic Workloads

IBM Db2 Warehouse is a strong option for organizations handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Pricing

IBM offers a free trial for IBM Db2 Warehouse and provides a pricing page where users can ask for a quote and estimate the cost. For the flex one plan, the pricing is $1.23 per instance-hour, $0.99 per VPC-hour, and $850 per a service endpoint dedicated connectivity.

For more information, go to IBM’s pricing page.

Features

• Helpful Integration: IBM Db2 Warehouse integrates an in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
• Netezza Technology: Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities.
• Cloud Deployment And On-Premises: Deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

See all Db2 Warehouse features at https://www.ibm.com/products/db2/warehouse.

Pros

• Includes in-memory columnar database
• Cloud deployment options
• Configuration flexibility

Cons

• Expensive
• Limited references online
• Limited buffer pool commands

For more on IBM: IBM: Hybrid Cloud Portfolio Review

Azure Synapse Analytics: Best For Code-Free Offerings

Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, is well suited for organizations of any size looking for an easy on-ramp into cloud-based data warehouse technology, thanks to its integration with Microsoft SQL Server.

Pricing

Azure Synapse Analytics’s pricing page allows customers to request a quote or explore pricing options. For tier one, Azure offers 5,000 units for $4,700; tier two offers 10,000 units for $9,200. For other tier options, refer to the pricing page.

Features

• Dynamic Data Masking (DDM): Azure Synapse Analytics provides a granular level of security control, enabling sensitive data to be hidden on the fly as queries are made.
• Azure Integration: Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly, SQL Server for a database.
• Parallel Processing: In contrast to simply running SQL Server on-premises, Microsoft has built on a massively parallel processing architecture that can enable users to run over a hundred concurrent queries.

See more Azure Synapse Analytics features at https://learn.microsoft.com/en-us/azure/synapse-analytics/whats-new.

Pros

• Easy integration
• Some code-free offerings
• Strong data distribution

Cons

• Difficult logging metrics
• Limited diagramming tools
• Limited documentation

For more on Microsoft Azure: Microsoft Azure: Cloud Portfolio Review

Oracle Autonomous Data Warehouse: Best For Integration

For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud including the benefits of data marts, data warehouses, data lakes, and data lakehouses.

Pricing

Oracle’s Autonomous Data Warehouse’s main page offers pricing information as well as a cost estimator for users. The bottom price for Oracle Autonomous Data Warehouse shared and dedicated infrastructures is $0.25 per unit.

Features

• Works With Cloud And Hardware: A key differentiator for Oracle is that it runs the Autonomous Data Warehouse in an optimized cloud service with Oracle’s Exadata hardware systems, which has been purpose-built for the Oracle database.
• Easy Collaboration: The service integrates a web-based notebook and reporting services to share data analysis and enable easy collaboration.
• Strong Integration: While Oracle’s namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.

See more features at https://www.oracle.com/autonomous-database/autonomous-data-warehouse/.

Pros

• Migration support for other database services
• Purpose-built hardware
• Fast query performance

Cons

• No on-premises solutions
• Limited data connection
• Complicated setup

For more on Oracle: Oracle Data Portfolio Review

SAP Datasphere: Best For Templates

Thanks to the pre-built templates it offers, SAP’s Datasphere might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse. SAP Datasphere allows data professionals to deliver scalable access to business data.

Pricing

SAP Datasphere’s pricing page lists a free tier and range of flexible pricing options based on needs. The price for capacity datasphere units is $1.06 per unit.

Features

• SAP’s HANA (High-performance Analytic Appliance): The cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
• Pre-Built Business Templates: Templates can help solve common data warehouse and analytics use cases for specific industries and lines of business.
• Integration with SAP Applications: SAP Datasphere integration means easier access to on-premises as well as cloud data sets.

See more features including a product demo at https://www.sap.com/products/technology-platform/datasphere.html.

Pros

• Inventory controls
• Extract data from multiple sources
• Strategic solutions

Cons

• Difficult for beginners
• Difficult integration
• Limited visual analytics

For more on SAP: SAP Data Portfolio Review

Snowflake: Best For Data Warehouse In The Cloud

Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities. Snowflake aims to bring development to data, help companies govern data for users, and work globally and cross-cloud.

Pricing

Snowflake’s pricing page links to a quote page and offers a 30-day free trial with $400 of free usage.

Features

• Database Engine: Snowflake’s columnar database engine capability can handle both structured and semi-structured data, such as JSON and XML.
• Cloud Provider Of Choice: Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
• Virtual Data Warehouse: The system creates what Snowflake refers to as a virtual data warehouse, where different workloads share the same data but can run independently.

See more features at https://www.snowflake.com/en/.

Pros

• SQL-based queries for analytics
• Support for JSON and XML
• Integration with AWS, Azure, and GCP

Cons

• Limited data visualization
• Unable to create dynamic SQL
• Difficult documentation

For more information on Snowflake: Snowflake and the Enterprise Data Platform

Key Features of Data Warehouse Providers and Solutions

Cloud data warehouses typically include a database or pointers to a collection of databases where the production data is collected. Many modern cloud data warehouses also include some form of integrated query engine that enables users to search and analyze the data and assist with data mining.

Other key features to look for in a cloud data warehouse setup:

• Integration or API Libraries
• Data Quality and Compliance Tools
• ETL Tools
• Data Access Tools/Database Searchability
• SQL and NoSQL Data Capabilities

For more features and benefits: Top 10 Benefits of Data Warehousing: Is It Right for You?

How To Choose Which Data Warehouse Provider is Best for You

When looking to choose a cloud data warehouse service, there are several criteria to consider.

Existing Cloud Deployments. Each of the major public cloud providers has its data warehouse that provides integration with existing resources, which could make deployment and usage easier for cloud data warehouse users.

Ability to Migrate Data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage Options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services can provide lower-cost options.

Bottom Line: Data Warehousing Providers and Solutions

When considering providers and solutions of data warehousing, it’s important to weigh features and cost against your company’s primary goals, including deployment and analytic needs and cloud services.

While each provider and solution offers a variety of features, identifying a company’s own use case can help better evaluate them against a company’s needs.

For more information: 15 Best Data Warehouse Software & Tools

Big Data security is routed through a circuitous path, and in theory could be vulnerable at more than one point. 

Navigating Big Data Security & Trends

Two of the biggest trends in the world of big data stand somewhat in opposition to each other: the proliferation of big data that informs smart technology, and also the growing movement for consumers to own and decide how their personal data is being used.

Technologies like IoT, artificial intelligence, machine learning, and even customer relationship management (CRM) databases collect terabytes of data that contain highly sensitive personal information. This personal form of big data is valuable for enterprises that want to better cater their products and services to their audience, but it also means that all companies and third-party vendors are held responsible for the ethical use and management of personal data.

As big data and its enterprise use cases continue to grow, most organizations work hard to comply with consumer data laws and regulations, but their security holes leave data vulnerable to breach. Take a look at some of the top trends happening in the big data world, the important security points that many companies are missing, and some tips for getting big data security right:

Update your cloud and distributed security infrastructure

Big data growth has caused many companies to move toward cloud and data fabric infrastructures that allow for more data storage scalability. The problem? Cloud security is often established based on legacy security principles, and as a result, cloud security features are misconfigured and open to attack.

For a company to navigate this requires speaking with cloud and storage vendors about their products, whether a security solution is embedded, and if they or a third-party partner recommend any additional security resources. 

Set mobile device management policies and procedures

IoT and other mobile devices are some of the greatest sources and receivers of big data, but they also offer several security vulnerabilities since so many of these technologies are owned and used for personal life. Set strict policies for how employees can engage with corporate data on personal devices, and be sure to set additional layers of security in order to manage which devices can access sensitive data.

Provide data security training and best practices

Most often, big data is compromised as the result of a successful phishing attack or other personalized attack targeted at an unknowing employee. Train your employees on typical socially engineered attacks and what they look like, and again, set up several layers of authentication security to limit who can access sensitive data storage.

For more big data trends: Big Data Trends and The Future of Big Data

Benefits Of Big Data Security

With the benefits of customer retention, risk identification, business innovation, cost, and efficiency, a big data security system can be of value to companies everywhere. 

Here are key benefits of big data security:

• Customer Retention: With big data security, a company can observe many data patterns, which allows them to better fit their products and services with their clients needs. 
• Risk Identification: Because of big data security, a company can use big data tools to identify risks in their infrastructure, helping companies create a risk management solution.
• Business Innovation: Big data security can help companies update their tools and help transfer products into new secure systems. This innovation can improve business processes, marketing techniques, customer service, and company productivity.
• Cost Optimization: Big data security technologies can reduce customer costs by efficiently storing, processing, and analyzing large volumes of data. Big data security tools also will calculate how the product will benefit the company, so companies can pick a company that is better for their infrastructure.

For more information on data management: 5 Top Data Management Predictions

Challenges of Big Data Security

There are several challenges to securing big data that can compromise its security. Keep in mind that these challenges are by no means limited to on-premise big data platforms. They also pertain to the cloud. When you host your big data platform in the cloud, take nothing for granted. Work closely with your provider to overcome these same challenges with strong security service level agreements.

Here are the key challenges to big data security:

• Newer technologies can be vulnerable: Advanced analytic tools for unstructured big data and nonrelational databases (NoSQL) are examples of newer big data technologies in active development. It can be difficult for security software and processes to protect these new toolsets.
• Variable impact: Mature security tools effectively protect data ingress and storage. However, they may not have the same impact on data output from multiple analytics tools to multiple locations.
• Access without permission: Big data administrators may decide to mine data without permission or notification. Whether the motivation is curiosity or criminal profit, your security tools need to monitor and alert on suspicious access no matter where it comes from.
• Beyond routine audits: The sheer size of a big data installation, terabytes to petabytes large, is too big for routine security audits. And because most big data platforms are cluster-based, this introduces multiple vulnerabilities across multiple nodes and servers.
• Requires constant updates: If the big data owner does not regularly update security for the environment, they are at risk of data loss and exposure.

Big Data Security Technologies

None of these big data security tools are new, from encryption to user access control. What is new is their scalability and the ability to secure multiple types of data in different stages.

• Encryption: Your encryption tools need to secure data in transit and at rest, and they need to do it across massive data volumes. Encryption also needs to operate on many different types of data, both user- and machine-generated. Encryption tools also need to work with different analytics toolsets and their output data, and on common big data storage formats including relational database management systems (RDBMS), non-relational databases like NoSQL, and specialized filesystems such as Hadoop Distributed File System (HDFS).
• Centralized Key Management: Centralized key management has been a security best practice for many years. It applies just as strongly in big data environments, especially those with wide geographical distribution. Best practices include policy-driven automation, logging, on-demand key delivery, and abstracting key management from key usage.
• User Access Control: User access control may be the most basic network security tool, but many companies practice minimal control because the management overhead can be so high. This is dangerous enough at the network level and can be disastrous for the big data platform. Strong user access control requires a policy-based approach that automates access based on user and role-based settings. Policy-driven automation manages complex user control levels, such as multiple administrator settings that protect the big data platform against inside attacks.
• Intrusion Detection and Prevention: Intrusion detection and prevention systems are security workhorses. This does not make them any less valuable to the big data platform. Big data’s value and distributed architecture lend themselves to intrusion attempts. IPS enables security admins to protect the big data platform from intrusion, and should an intrusion succeed, IDS quarantines the intrusion before it does significant damage.
• Physical Security: Don’t ignore physical security. Build it in when you deploy your big data platform in your own data center or carefully do due diligence around your cloud provider’s data center security. Physical security systems can deny data center access to strangers or to staff members who have no business being in sensitive areas. Video surveillance and security logs will do the same.

Also read: How Big Data is Used: Business Case Studies

Implementing Big Data Security

Whether you’re just getting started with big data management and are looking for initial big data security solutions, or you are a longtime big data user and need updated security, here are a few tips for big data security implementation:

• Manage and train internal users well: As alluded to before, accidental security mistakes by employees offer one of the most frequently used security vulnerabilities to malicious actors. Train your employees on security and credential management best practices, establish and have all users sign mobile and company device policies, and offer only minimum-necessary data source access to each user based on their role.
• Plan regular security monitoring and audits: Especially in larger companies where big data and software grows on a near-daily basis, it’s important to regularly assess how the network and data landscape changes over time. Several network monitoring tools and third-party services are offered on the market, giving your security staff real-time visibility into unusual activity and users. Regular security audits also give your team the opportunity to assess bigger-picture issues before they become true security problems.
• Talk to a trusted big data company: Big data storage, analytics, and managed services providers usually offer some form of security or partner with a third-party organization that does. The platform that you use might not have all of the specific features that your industry or particular use cases require, so talk to your providers about your security concerns, regulatory requirements, and big data use cases so they can customize their services to what you need.

More on security implementation: Top 10 Ways to Prevent Cyber Attacks

Who Is Responsible For Big Data Security?

A big data deployment crosses multiple business units. IT, database administrators, programmers, quality testers, InfoSec, compliance officers, and business units are all responsible in some way for the big data deployment. Who is responsible for securing big data?

The answer is everyone. IT and InfoSec are responsible for policies, procedures, and security software that effectively protect the big data deployment against malware and unauthorized user access. Compliance officers must work closely with this team to protect compliance, such as automatically stripping credit card numbers from results sent to a quality control team. DBAs should work closely with IT and InfoSec to safeguard their databases.

Finally, end-users are just as responsible for protecting company data. Ironically, even though many companies use their big data platform to detect intrusion anomalies, that big data platform is just as vulnerable to malware and intrusion as any stored data. One of the simplest ways for attackers to infiltrate networks, including big data platforms, is a simple email. Although most users will know to delete the usual awkward attempts from Nigerian princes and fake FedEx shipments, some phishing attacks are extremely sophisticated. When an admin is administering security for the company big data platform, never ignore the power of a lowly email.

Secure your big data platform from high threats and low, and it will serve your business well for many years.

Read next: Top 10 Cybersecurity Threats

Big Data Security Companies

Digital security is a huge field with thousands of vendors. Big data security is a considerably smaller sector given its high technical challenges and scalability requirements. However, big data owners are willing and able to spend money to secure valuable employments, and vendors are responding. Below are a few representative big data security companies.

Snowflake

Snowflake’s team of data experts believe that data security should be natively built into all data management systems, rather than added on as an afterthought. Snowflake’s Data Cloud includes comprehensive data security features like data masking and end-to-end encryption for data in transit and at rest. They also offer accessible support to their users, allowing them to submit reports that Snowflake and their partner, HackerOne, can analyze while running their private bug program.

Teradata

Teradata is a top provider of database and analytics software, but they’re also a major proponent and provider of cloud data security solutions. Their managed service, called Cloud Data Security As-a-Service, offers regular third-party audits to prepare for data regulatory committee audits. They also offer features such as data encryption in transit and at rest, database user role management, storage device decommissioning, cloud security monitoring, and a two-tiered cloud security defense plan.

Cloudera

Cloudera’s primary strategy for big data security is to consolidate security management through their shared data experience (SDX), or to manage security and policies from a unified standpoint across all workloads. This means that even as tools and most frequently used workloads change over time, policy and security updates can still be managed centrally without siloes. Among their security solutions, Cloudera provides unified authentication and authorization, end-to-end visibility for audits, security solutions, data policy-specific solutions, and several forms of encryption.   

IBM

IBM’s data security portfolio focuses on multiple environments, global data regulations, and simple solutions so that users can easily manage their data sources and security updates after deployment. Some of the main areas that IBM pays attention to for data security include hybrid cloud security management, embedded policy and regulation management, and secure open source analytics management. 

Oracle

Oracle is one of the largest database hosts and providers in the big data market, but they also offer several top-tier security tools to their customers. Their security solutions focus on the following categories: security assessment, data protection and access control, and auditing and monitoring They also extend platform-specific security support for two of their most popular solutions, Autonomous Database and Exadata.

Hear from a Big Data Exec at Teradata: Ask an Executive: Data Analytics in Business

Bottom Line: Big Data Security

If a company uses well chosen big data security tools, these tools will serve the business well for many years, enabling it to secure its big data platform from threats of all kinds. 

The cloud networking market has made it easier for companies to use intent-based networking, business intelligence (BI), configuration management, and services such as software-defined, cloud, edge, and networking solutions.

For more network trends: Top Network Segmentation Trends

Top 5 Cloud Networking Trends

1. Enterprise Network Strategy In The User’s Home

Changing workforce expectations have led many companies to a more globally distributed remote workforce – a trend that also rises with the cloud. 

As a result, enterprise networking infrastructure now has to support users in their homes.

Drit Suljoti, co-founder and CTO of Catchpoint, a digital experience monitoring platform provider, explained that consumer-grade networking technology does not always offer the levels of support and visibility necessary for remote work, which is increasingly becoming a problem.

“Organizations across the board have experienced the frustrations and performance volatility that can result from consumer-grade WiFi, VPN clients, and increased dependence on the internet from the employee’s wider household,” Suljoti said. “At the ground level, how can IT support desks ensure they have the necessary visibility into the daily digital life of their remote employees? 

“These mission-critical teams need the ability to understand the digital performance of an individual’s device, network, and applications, and the third-party providers they rely on. This is even more essential when employees are working remotely, without on-site support to troubleshoot performance issues.”

Bob Friday, VP and CTO of Mist, Juniper’s artificial intelligence (AI)-driven enterprise business, believes that many companies are starting to respond to this remote work shift by increasing networking security and monitoring their employees’ remote work environments.

“[A] major shift is in how enterprise-level networking trends are becoming increasingly important for personal users as well,” Friday said. “Whether you’re an executive at a company or you work in a profession that puts you into contact with sensitive information, the continued normalization of remote and hybrid work environments means that enterprise-grade networking and security will move into the home networking space.

“To ensure end-to-end network visibility, reliability, and security, we can expect enterprise-grade networking solutions to begin permeating remote and hybrid workforces, as enterprise IT teams take an even sharper look at their network edge.”

2. Networking With Remote AI Support

Users and enterprise devices often need technical support that was normally provided in the office. As remote work – again, supported by the cloud – continues to become a standard approach, many companies are adopting AI solutions to assist with customer experience (CX) and support requirements of the network.

“More help is needed in managing this critical infrastructure, which is why AI has become a necessity for network management,” said Friday. “Enterprises and technology providers have already adopted AI assistants in their networking support teams. Cloud AI has enabled a new tech support model, one that has created the volume and quality of data necessary to train AI technologies. 

“This AIOps model has led to incredible progress. At present, AI can answer up to 70% of support tickets with the same effectiveness as a domain expert. Eventually, this AIOps technology will move all the way to the end-user. 

“And like the average human employee, AI has the ability to learn and improve over time, thus providing a better customer experience consistently and proactively. But unlike the average human employee, that skill and expertise is not lost when they retire or quit. The more that AI is used as part of the IT help desk, the more the technology can improve its answers and, ultimately, the end-user experience.”

3. The Growth Of Intent-Based Networking (IBN)

Networking technology continues to grow more sophisticated. Particularly with the more widespread use of software-defined networking (SDN), intent-based networking is being used more in enterprise networks that want additional business intelligence (BI), configuration management, and other features embedded in their networks. All of these feature are part of the growing sophistication of cloud technology. 

Eric McGee, senior network engineer at TRG Datacenters, a data center vendor, explained why IBN is helpful to network administrators who want to better understand and manage their networks.

“One important networking technology trend that network engineers need to take note of is the emergence of intent-based networking,” McGee said. “The main role of IBN is to capture business intent and apply these insights across the network, ensuring that network administration is aligned with business intent. In other words, the IBN framework will receive an intent from the business and translate it, or encode it into the configuration of the network, resulting in the desired changes. Now, the network infrastructure is aligned with the business’s current needs.

“IBN also enables the automation of network administrative tasks involved, such as the configuration of networks, mitigation of risks, as well as the reporting and solving of network issues. Implementing IBN as a form of network administration makes the process of creating, managing, implementing, and monitoring network policies easier, simpler, and less labor-intensive. A lot of the manual effort put into traditional configuration management is made redundant when IBN is implemented.”

4. Holistic Networking Offerings

Traditional networking solutions typically need a variety of hardware and software components to work properly. 

However, as networks continue to evolve their software-defined, cloud, edge, and solutions, many networking vendors are offering more holistic networking packages to manage every aspect of the network.

Patrick MeLampy, Juniper Fellow at Juniper Networks, a top global networking company, believes that enterprise client-to-cloud connectivity is one of the biggest drivers behind more unified networking packages.

“I’d have to say that there are a few key networking trends that are gaining steam,” MeLampy said. “Enterprise client-to-cloud connectivity service offerings will take off. This means we’ll see Wi-Fi, wired, routing, and security capabilities pulled together, all in one simple offering, making it more efficient and effective for teams to manage ever-expanding networks.”

For more on cloud networking: The Cloud Networking Market

5. Managing Network Data With Different Ops Methodologies

With more software- and cloud-based networking solutions used across the board, several companies are looking into new ways to manage and read their networking data.

Richard Larkin, manager of North America sales engineering at NetBrain, a next-gen network operations company, believes that the knowledge and approach of different ops teams are particularly applicable to new ways of automating network data management. 

“The days of managing networks with SNMP polling and traps as well as Syslog data are almost over,” Larkin said. “Many enterprises still leverage these telemetry sources, but it’s not enough. We need a more comprehensive solution harvesting data, from API, CLI, packet, netflow, and other sources, to get the complete picture as well as visibility into SD-WAN, SDN, cloud, and SaaS offerings.

“A trend that I am seeing is the blending and blurring of lines between NetOps, SecOps, and DevOps. With networks becoming more software-defined and cloud-based, organizations are trying to fill the gap of the traditional network monitoring data (SNMP, Syslog, etc.) with homegrown solutions using Python, Ansible, and other coding. What would be interesting is if there was an easier way to codify the knowledge of the NetOps teams that required minimal coding and can be produced in minutes, not hours, days, and weeks.”

For more on networking management: The Network Management Market

The Future Of Cloud Networking

With the vitality in cloud networking for businesses, these trends above will further develop in the future, offering more opportunities for the growing market. From automation and network efficiency, businesses will see more benefits than ever.

Looking ahead, the future developments in cloud networking may include:

• Networking automation: Using network automation will help a company with a variety of tasks, including configuring, provisioning, managing, and testing network devices.
• Network-as-a-Service (NaaS): NaaS is a cloud model that allows users to control their network and attain the performance they expect from it without having to own, build, or maintain their infrastructure.
• 5G Cellular: 5G, the latest cellular update, allows a new network designed to connect virtually, including machines, devices, and more.
• Wi-Fi 6: Wi-Fi 6 is the new release for Wi-Fi network protocol that can be faster than its predecessors due to more focus on traffic and other technologies.
• Network Efficiency: With improved network scalability in the next couple of years, traffic will be aggregated for IP and Ethernet platforms. 
• Universal Networks: In the future, networking will have the ability to add new protocols and functions for better service. This can include services such as Ethernet services, mobile services, and more.

Along with the listed predictions and processes, more technologies are developing in networking, including AI, ML, the cloud, edge computing, Internet of Things (IoT), and more as they continue to play an increasingly important role in the future of networking. 

Bottom Line: Top Cloud Networking Trends

With remote training becoming a necessity in businesses, networking can help manage workers at home with a network strategy and remote AI support – a trend that leverages cloud networking to a great extent. 

Companies can use tools such as software-defined networking (SDN), intent-based networking, business intelligence (BI), and configuration management through their networking infrastructure.

Networking used to be based on hardware-defined networking, increasingly also offers services such as software-defined, cloud, edge, and networking solutions. 

For more information: Top 10 Enterprise Networking Companies

Manufacturers are jumping on board the IoT trend and have begun adding Internet connectivity to a host of products. A quick Internet search turns up countless of “smart” or “connected” products.

Many of them are consumer IoT devices in the home automation space, but the industrial Internet of Things device market is also picking up steam. Additionally, there are many companies offering components and boards for makers and inventors interested in creating their own IoT devices.

Table of Contents, Top IoT Devices:

• Home Automation IoT Devices
• Industrial IoT Devices
  
• Health and Fitness IoT Devices
• Miscellaneous IoT Devices
• Development Boards IoT Devices
• Virtual Reality and AR IoT Devices
• Bottom Line: The Importance of IoT Devices

For more information on IoT: Top 7 IoT Analytics Platforms

IoT Devices – About The List

For this IoT devices list, we focused on IoT devices that are particularly popular, interesting, or otherwise noteworthy. Please note that this is not a ranking; devices are arranged into categories and then alphabetically within each category. 

Top 85 IoT Devices

IoT devices can help not only businesses but consumers as well. From home automation devices to virtual reality, IoT can improve how people work and live. Here are the top 85 IoT devices:

Home Automation IoT Devices

Home automation IoT devices are gaining in popularity among consumers, due to the help they offer for a user’s home. From being able to order groceries online to managing smart homes, IoT devices can contribute to any home.

Amazon Dash Buttons

Amazon offers an assortment of buttons that will order additional supplies of commonly used household products directly from Amazon.com. There are buttons for toilet paper, goldfish crackers, soap, laundry detergent, trash bags, cleaners, Gatorade, soup, razors, beauty products, baby formula, and much more.

Amazon Echo

This smart home hub is essentially a speaker that can listen to users and respond to commands. It can play music, answer questions, read audiobooks, deliver traffic and weather reports, control lights and thermostats, order pizza, order an Uber, and much more.

Amazon Fire TV Stick

With the latest Fire Stick model that includes 4K streaming with Alexa voice remote, the Amazon Fire TV Stick is a streaming solution that meets many other household needs. Whether a user is ordering more detergent through their Amazon Prime shopping cart or pulling up live home security feeds on the TV, the Alexa-enabled Fire Stick gives a user access to more than their favorite TV shows and movies.

Awair

A good option for people with asthma or allergies, Awair is an air-quality sensor that can send smartphone alerts and recommendations for improving the indoor air quality. It also has programs for improving sleep and productivity.

Belkin WeMo

Belkin’s WeMo is a complete line of home IoT devices that include smart switches, cameras, lights, an air purifier, a heater, a slow cooker, a humidifier, and more. The company has partnerships with several other firms that allow end users to control a lot of different devices with one smartphone app.

Bitdefender BOX

This cybersecurity hardware is designed to unify and protect all of a user’s IoT devices, regardless of their native level of security and antivirus protections. The BOX application enables a user to set custom security settings for each of their devices, create profiles and identify strange devices in their network, and catch a snapshot of overall network activity at any given time.

Canary

This all-in-one home security system captures video and audio and sends alerts to a user’s smartphone. It automatically knows when they are home or away (no need to enter a security code), and a user can also view the live video feed from their phone.

Chamberlain MyQ

A user doesn’t have to buy a new garage door opener to control it with their smartphone. Chamberlain MyQ products allow a user to control their existing garage door with their iPhone or Android device.

Cinder

Cinder’s website describes this product as “a cross between sous vide, the high-end slow-cooking water bath method used by restaurant chefs, and the George Foreman grill.” It’s a countertop grill that connects to a user’s smartphone to make cooking fast and easy.

Eve

This line of home automation products works with Apple HomeKit to allow users to monitor indoor air, outdoor weather, energy consumption, and whether windows and doors are open or closed. The same company also offers a line of smart lighting products that can be controlled with Android or iOS devices.

GE Smart Appliances

GE makes quite a few different types of connected appliances, including wall ovens, ranges, refrigerators, dishwashers, washers and dryers, water heaters, and air conditioners. Through GE’s WiFi Connect service and apps, consumers can control the appliances or receive alerts.

Honeywell Smart House Products

Honeywell also offers a huge lineup of products related to home automation and security. It includes thermostats, GPS asset tracking, locks, lighting, video surveillance, and more.

June Intelligent Oven

This countertop oven aims to be “more efficient than your conventional oven, more precise than your toaster oven, and way more intelligent than either.” It includes digital core temperature probes, a built-in digital scale, and a camera with a food recognition engine. The company is accepting pre-orders, and products are scheduled to begin shipping before the end of the year.

LG SmartThinQ

LG divides its SmartThinQ line of connected appliances into categories for the kitchen (ranges and refrigerators), living (washers, dryers, robotic vacuums, and air conditioners), and safety (robot vacuum doubles as a safety monitor with a video feed). They all integrate with the company’s smartphone app.

Nanit Plus Baby Monitor

Nanit Plus maintains the features of traditional baby monitors while adding several smart features to track a baby’s sleep patterns. The device includes sleep stats to track metrics like sleep onset, time asleep, visits, and overall sleep efficiency, helping parents to sleep coach with real insights. Another personalized feature is Memories, which gathers photos and videos in a sleep time capsule over time. This device works when the internet goes down and can be paired with other IoT devices, such as Amazon Echo.

Nespresso Prodigio

With this connected coffee maker, a user can use their smartphone to schedule coffee brewing, order supplies and receive maintenance alerts. The machine heats up quickly and uses single-serve coffee capsules.

Nest

Nest is best known for its Internet-connected thermostat, but it also makes smoke and carbon monoxide detectors and cameras. Its products also integrate with IoT home automation products from a variety of other vendors.

Neurio

With Neurio, homeowners can track their energy usage (or energy production if they have solar panels installed) from their smartphones. It takes a little work to install the device onto an electrical panel, but it can help users identify opportunities for energy savings in their homes.

Philips Hue

Philips offers a complete line of connected lighting products. It includes dimmers, light strips, switches, controllers, and more.

Piper

Piper incorporates both home security and a home automation hub. It has a motion sensor and video camera for security, and the smartphone app allows a user to control lighting and appliances. For added security, it can also integrate with door or window sensors.

Ring Doorbell

The Ring Doorbell doubles as a door answering device and an additional form of home security. With its built-in video camera, Ring senses and records motion near a door, allowing a user to communicate with their visitor in real-time or review the most important security footage later.

Roost

Roost’s most unique product is its connected battery. Designed for use in smoke detectors, this battery tells the user when it needs to be changed so that they don’t get those annoying chirps at three in the morning. The company also makes smart smoke alarms and water leak/freeze detectors.

Samsung SmartThings

Samsung’s SmartThings Line includes smart outlets, hubs, motion sensors, multipurpose sensors, arrival sensors, water leak sensors, and more. The company also sells a complete home monitoring kit that makes it easy to get started with home automation.

Schlage

Long known for its deadbolts and door knobs, Schlage is getting ready for the IoT era with two lines of smart home locks: Schlage Sense is a Bluetooth-enabled smart deadbolt that integrates with iOS devices, and Schlage Connect is a similar smart locking system that integrates with alarm and security systems.

Sleep Number 360 Smart Bed

This smart mattress was designed to automatically adjust to a person’s sleep patterns and to give them real-time data on the quality of their sleep. The 360 Smart Bed automatically adjusts temperature microclimates, firmness, position, and other features based on their movements and sleep cycle. With features like SleepIQ and Partner Snore technology, the 360 Smart Bed application also allows the user to track and improve their sleep health over time.

Sonos

Designed for music lovers, Sonos is a smart speaker system that they can install in a single room or throughout a house. Use the free smartphone app to control the music playing on the speakers—a user can even play different music in each room.

Whirlpool Smart Appliances

For now, Whirlpool’s only smart appliances are washers and dryers, but its website seems to suggest that it has plans to offer more appliances that can connect to smartphones and tablets. Interestingly, the washer and dryer can also connect to the Nest thermostat to help consumers save money on their energy bills.

Wink

Wink is a smart home hub that connects a lot of other IoT products from companies like GE, Nest, Philips, and Schlage. Control lighting, thermostat, door locks, appliances, blinds, and more from a single console.

For more on consumer IoT: IoT in Smart Cities

Industrial IoT Devices

Industrial IoT is made up of devices, applications, and networking equipment made to collect, monitor, and analyze data from industrial processes. Every industrial business can benefit from these devices. 

Aptomar

Safety is always a concern in the oil and gas industry. Aptomar makes IoT sensors and systems for detecting spills and increasing safety. The company also offers services for monitoring oil and gas facilities.

ATrack trackers

Focused on the transportation and logistics industries, ATrack offers GPS tracking for monitoring assets and vehicles. It supplies its technology to a variety of other manufacturers and tracking services.

Centrak

With Centrak’s low-energy Bluetooth beacons, companies can track the locations of employees, assets, customers, patients, and more in real-time. The service is focused primarily on the healthcare, retail, and manufacturing industries.

Bosch

Bosch has set its sights on becoming a leader in the IoT space. It manufactures sensors that go into other IoT devices as well as some smart home appliances. It also offers a complete cloud platform for building IoT applications.

CargoSense

The CargoSense solution includes sensors that can be included with product shipments to track temperature, humidity, shock, light, tilt, and pressure every five minutes. That data is tracked by an integrated analytics system that allows manufacturers and logistics companies to see what is happening with shipments at every point in the delivery process.

DorsaVi ViSafe

These wearable sensors track how employees are moving. The goal is to improve safety and reduce risk by helping prevent injuries. The company also offers similar technology that can be used by healthcare providers to help assess injuries and recommend therapies.

Filament

Filament makes industrial sensors with long-range wireless capabilities. It offers two products—the Tap and the Patch—that can be used to monitor environments and transmit data back to the network.

GridConnect

This company sells a host of sensors, probes, modules, adaptors, convertors, and networking tools that companies can use to connect their factories, warehouses, and other facilities to the Internet of Things. The company also makes a line of home automation devices under the ConnectSense brand name.

Impinj

Impinj claims to offer “the most comprehensive and widely adopted RAIN RFID platform.” It offers tag chips, gateways, readers, antennas, and software for retailers, healthcare, and other markets.

Rethink Robotics

Rethink doesn’t just make robots—they make smart, collaborative robots that can work together and learn, accomplishing precise tasks in manufacturing and testing facilities. A user can either purchase one of their pre-built robots with names like Sawyer and Baxter, or a user can work with the company to build a custom bot for their purposes.

RoboCV

A user can think of RoboCV as a robot forklift. Designed for warehouses, it can move pallets and boxes from place to place without human intervention. Users can control and monitor the vehicle from a centralized panel, and they can also integrate it with external IT systems if they choose.

Samsara Sensors

This startup makes industrial IoT sensors for fleet telematics, energy monitoring, cold chain monitoring, asset monitoring, and other purposes. The sensors transmit data to its cloud-hosted software where it can be monitored and analyzed.

Tachyus Sensors

Tachyus makes IoT solutions for the oil and gas industry. Their products allow producers to measure what is happening with their oil and gas extraction processes and then analyze and optimize those processes to maximize output.

Wzzard Wireless Sensors

Made by a company called Advantech B+B SmartWorx, this line of industrial sensors can track liquid or air temperature, current, liquid flow, vibration, and levels for various types of equipment and tanks. The platform also includes a gateway for collecting and transmitting data from the sensor network.

Xerafy

This company makes RFID tags and other technology for asset tracking in a wide variety of industries. Their tiny tags are small enough to be attached to medical supplies and surgical tools, and they are rugged enough for environments like oil and gas exploration.

Healthcare And Fitness IoT Devices

Healthcare and fitness IoT devices offer many new opportunities for healthcare and fitness professionals to monitor patients, as well as help experts research for recovery. 

AdhereTech

AdhereTech makes smart, wireless pill bottles that help ensure that patients are taking their medication. They are currently being used for research studies, but their use will likely expand to the general population.

Apple Watch

Perhaps one of the most popular forms of personal IoT, the Apple Watch has been adopted by many users for its versatile communication and health-related functions. Apple users can use an Apple Watch to call or text, get directions via Siri, listen to music, track their heart rate and daily activity, and even notify EMS during a personal emergency. Apple Watches pair with other Apple devices like iPhones, but users don’t have to have their phone nearby to use Apple Watch features.

Babylon Health

Babylon Health created an app to help patients have remote appointments and consultations with either a virtual or real doctor. This includes text and video appointments that are AI-powered chatbot that is meant to help patients find correct care.

Biotricity Bioflux

Available by prescription, Bioflux is an ECG monitoring device that allows physicians to keep track of their cardiac patients 24 hours a day. The full solution includes the device, analytics software, and monitoring service that can contact patients and healthcare providers when patients are in distress.

Breathometer Mint

Breathe into the Mint device, and it will tell how effectively a user is brushing their teeth. (Yes, essentially, this is an IoT device that tells a user if they have bad breath). It gives a grade and tracks their progress toward better oral hygiene.

Fitbit

Fitbit leads the market for wearable fitness and health trackers. Devices are available in a wide variety of styles and colors, and they can help consumers track progress toward their fitness goals.

Garmin Forerunner

This line of fitness trackers is focused on people who consider themselves to be athletes. It tracks their heart rate, pace, and times, and it includes tools for a wide variety of sports.

Genoox

Genoox is a cloud-based system that uses genomic patient data and converts it into clear medical data. Genoox records patients’ data so medical professionals can analyze all of the records to find the best treatment for a patient.

Helix

Helix works to help patients have new opportunities to have remote patient monitoring. With the devices Helix provides, medical professionals can provide personalized care without having to leave their houses. 

Karius

Karius is an IoT healthcare service that only needs a blood sample to detect over 1000 pathogens that can detect infections. This can help clinicians and patients alike by running tests to save time and prevent further medical problems.

Peloton

Peloton is a home-based bike workout with built-in classes, instruction, and social engagement opportunities with other users. The Peloton IoT is designed to help a user track their heart rate, resistance, and other fitness metrics in real-time, and their Peloton Bike can pair with other IoT devices, such as Apple Watches so that a user’s activity is accurately tracked across their fitness devices. Although it is an at-home workout, a Peloton device allows a user to build a community and communicate with other riders around the world.

Tempo Studio 

Tempo Studio combines features of both a gym membership and a home workout, with all weights and workout equipment included in the membership subscription-based home set. Through a central “studio” that uses AI to give personalized coaching and weight recommendations for each user, as well as a growing library of live and pre-recorded classes, the program is tailored to provide a smart touch to a home workout.

Withings Blood Pressure Monitor

Extremely easy to use, this blood pressure monitor slips over an arm, takes a user’s blood pressure readings, and sends the results to their smartphone where they are tracked over time. The same company also makes other health-related IoT products like fitness sensors, oxygen sensors, baby monitors, scales, thermometers, and more.

Miscellaneous IoT Devices

Not all IoT devices fall under one industry or category. These devices are equally as necessary and can be used for gardening, fishing, security, and pet ownership.

Benjilock TSA Fingerprint Padlock

The Benjilock TSA Fingerprint Padlock uses a user’s fingerprint to unlock any tool or suitcase a user locks. This helps with security and convenience, so whatever it is locked to is protected.

Click and Grow

If users think gardening was a low-tech activity, meet Click and Grow, the smart indoor garden. The company claims it helps balance oxygen, water, and nutritional ingredients to help plants grow better and faster.

Deeper

Designed for fishermen, Deeper is a portable fish finder that transmits sonar readings to a smartphone. Simply attach Deeper to the line and cast it into the water. Then check the phone to see the water depth and temperature, bottom contours, and where the fish are hiding.

Foobot

Foobot is an IoT device that works to avoid energy waste, make sure indoor conditions are better, and keep the company up to date.

Furbo Dog Camera

Furbo was created to address all dog owner anxieties when a user’s pet is home alone. This IoT can alert an owner when their dog is barking or howling, send photos and video clips of their dog’s activities to their phone via the cloud, and even toss them treats when an owner is away from them. Although the system primarily focuses on dog activity, it can also notify a user of home emergencies related to smoke or CO alarms.

Mimic GO

Mimic GO is a small, portable device that can attach to anything from a gym bag to a dorm room door. Anywhere that someone wants to track unwanted movement or potential theft, Mimic GO sends immediate notification of any disruption. Activate a Mimic GO device through a smartphone app, and it will notify a user if movement or other environmental changes are detected nearby. A user can also set up the device to sound an alarm and ward off unwanted visitors.

Particle Photon 2

Particle Photon 2 is a developing module that contains both a microcontroller and Wi-Fi networking. Different from the original device, it now can support large applications.

Philips Hue Go

Philips Hue Go is a portable smart light to be used anywhere, either attached to a lighting system or on its own. Philips Hue Go is a multi-color light that can be used anywhere.

Theatro

Designed for the retail and hospitality industries, Theatro makes a wearable WiFi-based communication device designed to improve worker productivity. It weighs just an ounce and a half and helps hourly workers stay in touch with each other and receive quick answers to questions.

Whistle

Have a dog who likes to run? Whistle is an Internet-connected collar that tracks a pet’s location and activity level. It can also monitor a pet’s health trends, making it a little bit like a Fitbit for dogs.

For more: Internet of Things (IoT) Security Market

Development Boards IoT Devices

Development boards ‍IoT devices are both portable and flexible for developers. These small computers can help hobbyists, developers, and students alike.

Arduino

Arduino sells a variety of IoT development boards and related accessories, many of them based on open source hardware designs. They offer special collections for kids and home hobbyists as well as more serious products for inventors and developers.

BeagleBoard

This organization offers credit-card-sized computers under the BeagleBone brand name. Most of the boards can run Linux or Android, and they are based on open source specifications.

Cypress IoT Products

Cypress recently purchased Broadcom’s IoT product portfolio. It includes Bluetooth sensors and smart tags, microcontrollers, and system on chips (SoC), as well as the WICED IoT Platform for developing IoT applications.

Discovery STM32MP157C Crypto Board

The Discovery STM32MP157C Crypto Board can leverage its microprocessors to allow users to develop applications using STM32 MPU OpenSTLinux Distribution software.

Flutter

Aimed at hobbyists, students, and engineers, Flutter modules incorporate ARM processors, long-range wireless capabilities, built-in battery charging, and an integrated security chip. The organization says its products are “an ideal choice for robotics, wireless sensor networks, consumer electronics, and educational platforms.”

Microduino

Microduino boards and related modules are about an inch square and stackable. Basic modules start at low cost, and the company also offers kits for building robots, quadcopters, weather stations, and more.

NVIDIA Jetson Nano

NVIDIA Jetson Nano Developer Kit is a computer that allows users to run multiple networks in applications like image classification, object detection, segmentation, and speech processing. The NVIDIA Jetson Nano runs in as little as five watts.

Onion Omega2

The Onion Omega2 Linux Compute Modules have been designed for applications that require connectivity and computing. The Onion Omega2 package features a CPU, memory, flash storage, and a WiFi radio. It is an advanced IoT tool.

OpenMote

This firm offers open source IoT hardware, including computing modules, interface boards, and battery modules. Pre-built devices are somewhat more expensive than many other open source boards, with the OpenMote-CC2538 computing module retailing for 90.00€.

Raspberry Pi

Undoubtedly the best-known of all the IoT development boards, the Raspberry Pi is a complete computer the size of a credit card. The third-generation model includes a 1.2GHz 64-bit quad-core ARMv8 CPU, Bluetooth, 1 GB RAM, 4 USB ports, an Ethernet port, a Micro SD card slot, and much more. It costs around £30.00, and purchasing is available through a network of distributors.

SODAQ

Short for “solar-powered data acquisition,” SODAQ offers Arduino-compatible boards, modules, and sensors that are powered by the sun. The company also develops custom IoT solutions for clients.

UDOO

This company offers boards based on open source hardware designs. Its primary products are the introductory-level Neo ($49.90), the more powerful Quad/Dual ($135), and the x86, which is being funded through Kickstarter.

For more information on IoT sensors: 5 Internet of Things (IoT) Sensor Trends

Virtual Reality (VR) And Augmented Reality (AR) IoT Devices

Interacting with the world through VR and AR is one of the top focuses on entertainment and design work alike. Made for both consumers and professionals, VR and AR IoT devices can help connect people.

Here are the top seven VR and AR IoT devices:

AVEGANT

Light engines, such as AVEGANT, are at the heart of what powers every augmented reality product. Avegant offers edge technologies that allow a user to have compact and manufacturable AR light engines.

Google Cardboard

A surprisingly low-tech approach to virtual reality, Google Cardboard requires users to slot an Android smartphone into a cardboard (or plastic) viewer that they can make themselves or buy. For nostalgic toy lovers, there’s also a viewer that looks like a Mattel View-Master.

Microsoft HoloLens

Designed primarily for enterprise use, Microsoft HoloLens is an augmented reality device that can assist with design work, communication, training, and more.

Oculus Rift

Facebook’s Oculus Rift device offers consumers a virtual reality experience for playing games, watching movies, and other entertainment experiences. Devices start at $399, and to use them buyers will need a compatible PC.

RealD 3D

RealD is one of the well-known VR IoT tools. RealD aims to have the perfect visual experience for their users that works on many devices. As a premier visual technology, RealD designs and licenses contain technologies that allow a premium viewing experience in the theater, at home, and on personal devices.

Samsung Gear VR

Powered by Oculus technology, Samsung’s VR headset integrates with its Galaxy line of smartphones. Key features include a Super AMOLED display, wide field of view, precise head tracking, and low latency.

TPCAST

TPCAST Wireless Adaptor, a Vive product, allows users to install it on their own. With an antenna array and large bandwidth, it ensures a great display experience for users that is the same as that with the cable. Vive users now can enjoy a VR experience freely.

Bottom Line: The Importance of Internet of Things Devices

The growing Internet of Things sector benefits home automation, industrial, healthcare, development boards, and more. Many companies offer IoT devices to help improve the lives of users.

IoT devices help connect the business and consumer worlds to help exchange data among many industries. IoT has become a necessary tool to help with company decision-making, monitoring data, and automating home and business processes. As IoT grows, it will play an increasing role in business and consumer commerce. 

For more: It’s Time to Embrace Intelligent IoT

Indeed, the Internet of Things has become a vital part of modern technology with its ability to scale, learn, and connect. IoT business analytics helps companies keep up data from both the current system and historic trends. 

IoT analytics platforms have become necessary in all industries to improve their organization’s market strategy. From suppliers like AWS to Oracle, the leading IoT analytics vendors in the list of leaders below are helping companies grow. 

For more information, also see: What is Big Data Analysis

Table of Contents

• IoT Analytics Platforms: Comparison Chart
• Top IoT Analytics Platforms
• AWS IoT Analytics: Best for Automation
  
• Microsoft Azure IoT: Best for All Industries
• IBM Watson IoT Platform Best for Flexibility
• ThingSpeak: Best for Beginners
• Oracle IoT Cloud Services: Best for Cloud Services
• Datadog: Best for Monitoring
• Cisco IoT: Best for Industrial Businesses
• IoT Analytics Key Feature
• How to Choose an IoT Analytics Platform
• FAQ: IoT Analytics Platforms
• Bottom Line: Top IoT Analytics Platforms

IoT Analytic Platform Comparison Table

For more information, also see: Top Data Analytics Tools 

Top 7 IoT Analytics Platforms

There are many excellent IoT analytics platforms, but these are the top seven platforms that all have unique and helpful features for businesses in need of IoT analytics tools:

AWS IoT Analytics: Best For Automation

Amazon Web Services (AWS) provides IoT services and solutions to connect and manage the company’s devices. AWS IoT Analytics helps companies analyze a large amount of IoT data. As a managed service that provides advanced data analysis for IoT devices, it will help companies collect, process, and store data. 

AWS IoT Analytics uses automation to help companies process the difficult steps that are required for IoT devices. Companies are then able to then analyze IoT data by running queries to help create, copy, delete, or change data.

Pricing:

AWS gives customers the ability to start for free, request a quote, or use their pricing calculator based on what the company wants.

Features:

• Only Collects Data A Company Wants: AWS IoT validates data to be defined by a company’s needs including specific ways to process, transform, and improve data.
• Different Processes: AWS IoT Analytics offers different processes including the ability to filter, transform, improve, and reprocess what a company needs.
• Time-Series Analysis: AWS IoT Analytics helps a company see how their devices are changing over time and pays attention to any problems to fix them.

Pros:

• Easy to deploy and configure.
• Great Predictive analysis.
• Scalable.

Cons:

• Expensive tool.
• Lack of guidelines.

Microsoft Azure IoT: Best For All Industries

Microsoft Azure IoT works to develop industry cloud solutions that vary for each customer, adding a platform with edge-to-cloud technologies. Their IoT solution includes security, privacy, and compliance built-in. It also has the ability to connect, monitor, automate, build, deploy, and update any models and devices.

Pricing:

Similar to AWS, Microsoft Azure gives customers the ability to start for free, request a quote, or use their pricing calculator based on what the company wants.

Features:

• Three IoT Products: Microsoft Azure IoT offers three tools to cover all IoT needs. This includes Azure IoT Hub, Azure Digital Twins, and Azure IoT Central.
• Single Control Plane: Azure IoT organizes all IoT devices and applications into a single view to help a company automate, monitor, and troubleshoot.
• Serves Multiple Industries: Azure IoT has a presence in many industries, including manufacturing, energy, healthcare, retail, and transportation.

Pros:

• Easy integration.
• Secured communication on devices.
• Positive scalability.

Cons:

• Expensive.
• Documentation can be confusing.

For more on IoT: 5 Internet of Things (IoT) Edge Computing Trends

IBM Watson IoT Platform: Best For Flexibility

IBM Watson IoT Platform is a top IoT platform due to its flexibility in working with a company’s needs. Their Analytics Service creates calculations on the data in the system as often as a company would like, typically every five minutes. If the automated functions do not match all requirements, a company has the ability to build their own custom code, making the IBM Watson IoT Platform responsive to company needs.

Pricing:

IBM Watson IoT Platform offers two ways to receive pricing. A company can get started for free or book a meeting with sales representatives for specific pricing.

Features:

• MQTT and HTTP Connection: IBM IoT tools allow a customer to connect to the IBM cloud by using MQTT and HTTP connections.
• Real-Time APIs: IBM allows customers to connect their applications to secure APIs connected to data feeds in their devices.
• Helpful Analytics: IBM IoT tools and IBM Cloud can create analytic applications for all of the company’s own servers.

Pros:

• Great centralized dashboard.
• Easy integration.
• Flexible for customers.

Cons:

• Needs better training.

ThingSpeak: Best For Beginners

ThingSpeak is an IoT analytics platform that allows customers to form a cluster or group of data, visualize, and analyze live data in the cloud. ThingSpeak provides automatic visualizations of data posted on a company’s devices to ThingSpeak. It also allows customers to perform online analysis and processing of the data as it comes in. It can also be used for IoT systems that require analytics.

Pricing:

Pricing for ThingSpeak can be found on their pricing page with categories such as Standard, Academic, Student, and Home.

Features:

• Communication On Different Platforms: ThingSpeak will automatically use data to communicate with third-party service providers.
• No Need For Servers Or Web Software: ThingSpeak allows customers to prototype and build IoT systems without any new servers or web software.
• MATLAB: ThingSpeak will often use MATLAB to help a customer understand their IoT data.

Pros:

• Instant visualizations.
• Event alerts.
• Easy integration.

Cons:

• Limited support.
• Not built for experts.

Oracle IoT Cloud Service: Best For Cloud Service

Oracle Internet of Things (IoT) Cloud Service is a managed Platform as a Service (PaaS) which is a cloud-based tool that helps a company make better business decisions and strategies. Oracle IoT Cloud allows a company to connect their devices to the cloud, analyze their data from devices quickly, and integrate data with applications, web services, or with other Oracle Cloud Services.

Pricing:

Oracle Cloud has a pricing page that offers free tiers on some devices, and other ways to reach out to sales.

Features:

• Many Device Connection Options: Oracle IoT Cloud Service provides device connection options to make it simple to connect many kinds of devices, such as JavaScript, Java, Android, C POSIX, and iOS as well as REST APIs.
• Uses Predictive Analytics: Oracle IoT Cloud Service uses predictive analytics, so users can predict events and outcomes based on their data.
• Uses Forecasting: Oracle IoT Cloud Service offers users a look into their potential future trends based on their data.

Pros:

• Simple deployment.
• Thorough documentation.
• Helpful support.

Cons:

• Needs more integration.

For more on IoT in cloud: The IoT Cloud Market

Datadog: Best For Monitoring

Datadog’s IoT tool provides IoT monitoring from their customer’s devices and gives the ability to aggregate metrics. They also offer the ability to monitor IoT software performance, device hardware metrics, application logs, network performance data, and more. They aim to give companies the ability to have a comprehensive view of their devices and can troubleshoot particular regions of their systems. 

Pricing:

Datadog offers the ability to start for free and a pricing page to see every product and solution Datadog offers.

Features:

• Alerting for IoT Devices: IoT operators build alerts that are triggered when a problem has sustained or widespread device failures, using ML algorithms. 
• Analyze All IoT Data From Every Device: Datadog’s IoT tool analyzes all of the IoT devices and data to provide visibility and actionable notifications.
• Monitor Performance: Every device containing IoT data will be monitored to ensure that the system is working properly, and in a manner that works for the company unique needs.

Pros:

• Easy to look at metric history.
• Great network mapping.
• Helpful notifications.

Cons:

• Difficult for beginners.
• Needs better documentation.

Cisco IoT: Best For Industrial Businesses

Cisco IoT is a platform that provides IoT-based solutions based on exactly what a company needs. Their tool aims to make business tools smarter such as smart lighting, smart locks for security, self-regulating HVAC, and systems that adapt automatically. Cisco IoT seems to be aimed mostly at industrial businesses who need an IoT tool customized to their industry. 

Pricing:

For pricing, go to the how to buy page, where a customer can request more information.

Features:

• Operational Resiliency: Cisco IoT platform can improve safety by reducing employee work with IoT automation. Cisco can also monitor, manage, and help with equipment and processes.
• Protection From Security Threats: Cisco IoT gives companies visibility in security measures in case they are detected in the system.
• Bridge Between IT and Operations: IT and operations within a company can get support from IoT solutions with any line of business.

Pros:

• Great network monitoring.
• Improves uptime in routers and connected devices.
• Strong visibility.

Cons:

• Needs more language tools.

IoT Key Features

When a company is searching for the right platform for their unique needs, it is important to keep in mind the five key features of IoT analytics that should be supplied by any platform:

Analytics 

All of the IoT analytics platforms have analysis tools, but it is important to look for a platform that offers the style and level of analytics that fits best with its business. A highly specific fit with analyzing data is one of the most important parts of an IoT platform. Without it, IoT would not be as strong as a platform.

Connectivity

Connection is a vital feature for IoT platforms. Without a secure and well monitored connection to company devices, IoT can lose effectiveness. The platform should be able to connect to a company’s specific devices, so communication between company systems is always easy and efficient. 

Security

Important company and customer data needs to be protected in all applications, but for IoT, it is necessary to have high amounts of security. With the amount of data that IoT applications hold, if security is breached, it can be a huge problem for all businesses. Most IoT platforms have security measures, but it is necessary to have strong security that interoperates with a given company’s infrastructure. 

Intelligence

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) should be included in IoT applications. Implementing these tools is now necessary, and as the technology grows, will become an ever larges part of all IoT platforms.

Scalability

As a company’s data grows, an IoT platform needs to be able to scale larger with it. It is essential for an IoT platform to monitor all data, new and old. Patterns within the system cannot be determined without all data being visible and included.

For more on security in IoT: Internet of Things (IoT) Security Market

How To Choose An IoT Analytics Platform

Businesses need a scalable, intelligent, connected, and secure analytics platform to monitor and mine company data. Choosing a platform may be based on industry, business size, or level of professionalism. 

Here are questions to ask while deciding on an IoT analytics platform:

• What is the company budget?
• Which platform works best for the amount of company data?
• Does the platform integrate with current applications?
• How much automation and documentation does the platform need?
• What is best for the company’s industry?

Companies can expand their questions by reading reviews to see other companies’ experiences. Reading the summary of each platform will help make a decision easier for customers in need of an application as well.

For more information, also see: The Data Analytics Job Market 

Frequently Asked Questions (FAQ)

• What is IoT data analytics?

IoT data analytics is a tool that helps businesses collect and analyze their data.

• What is the focus of the IoT analytics platform?

The platforms should focus on scalable, intelligent, connected, and secure analytics for an IoT network. 

• What are the top four types of IoT analytics?

Descriptive analytics, diagnostic analytics, predictive analysis, and prescriptive analytics.

Bottom Line: Top IoT Analytics Platforms

Many large technology companies have some sort of IoT application. The top providers create unique and highly powerful IoT analytics applications for businesses, based on size, industry, and professional levels. 

As the IoT industry grows, the need for strong applications grows. The top seven providers listed here are suitable for many business needs.

For more information on IoT software: Best IoT Platforms & Software

Efficiency, documentation, a reliable infrastructure, constant monitoring, and communication are five of the most important practices when it comes to data science.

See more data science: 6 Top Data Science Predictions

5 Data Science Best Practices

There are many data science best practices, but the five below can be considered five of the most important data science practices:

1. Ensure Data Science Project Efficiency

When looking at data science practices, one of the most important is to ensure project efficiency for customers and companies alike. There are multiple ways to ensure that data science projects give value to the company:

• Stakeholder/Employee Engagement.
• Identify Companies’ Objectives.
• Modeling Efforts.

Stakeholder/Employee Engagement

Engagement begins with identifying the stakeholders and employees that may work on a company’s data science project. Both groups should run data science tools often to see frequent updates, so they will notice unusual behavior.

Identify Company Objectives

Whoever is working on the data science project needs to understand why the project is happening, and see what parts of the company need to be changed for improvements.

Modeling Efforts

Data science models are vital to any data science project. Depending on what a business needs, a company can find the best data science model for their metrics.

Engagement, identifying objectives, and needed modeling efforts can ensure efficiency in data science projects. And can also provide overall benefit for the company and customers’ infrastructure.

For data science trends: Data Science & Analytics Predictions, Trends, & Forecasts

2. Document Data Science Results

Collecting and keeping track of data science results is necessary to perform correctly. It gives workers the ability to see what stays the same and what changes, whether positive or negative.

Before a professional documents the project, it is important to ask questions:

• Who will read the documentation?
• What is needed from this documentation?
• How should the documentation be written?

These questions can help professionals know how to write the documents for a company to understand it more effectively. This can also reduce unnecessary results and help grow the infrastructure needs.

For more information, also see: Top Data Analytics Tools 

3. Create a Reliable Data Science Infrastructure

Data science is based on the infrastructure of the model in which a company needs to choose wisely to have the right performance, integrity, accuracy, and scalability a company needs. Reliability in both the data science field and a company’s infrastructure is vital to a successful system.

There are multiple key needs for a company to pick the right infrastructure:

• Easy to fix infrastructure: A company may want their tool to provide logs of any errors to be sent back to them to see what the issues entail. Companies also want a system that can track errors, classify them, and group them to make the errors easy to fix.
• Scalability options: The infrastructure a company chooses needs to have flexibility with data transferring, processing speed and power, quick file transfers, and the ability to grow and change the data in workflows.
• Security in the infrastructure: Cybersecurity is still one of the top needs for companies and their data. Develop an infrastructure that can help find errors whether through the authorization of employees or prioritizing the security levels.
• Easy to automate and connect: Automation saves a company time and money, and data science can benefit an infrastructure with automation as well. The infrastructure should also connect automatically with server providers, databases, and essential machines.

It is also recommended that the model should be aligned with the needs above. There are many companies that offer data science solutions, including:

• Deloitte
• Dice
• AWS
• Microsoft
• Accenture

For data scientist opportunities: Best Companies Hiring Data Scientists

4. Monitor Data Science Structure

When a company deploys data science in their infrastructure, they need to shift their attention to monitoring what system metrics, error rates, traffic volume, and app loading times that will be part of their infrastructure.

Learning these factors about the data science infrastructure will help a company create reports for their stakeholders and other needed leaders so any problems can be solved early on. The reports will also help a company see if their systems are working properly.

To avoid any challenges while setting up a data science infrastructure, some questions need to be answered:

• Who is responsible for the data science models in the infrastructure?
• Can the monitoring process help track performance?
• Is there a way to check production?
• What is the plan if the system stops working well?
• How can a company ensure further security measures are helping?

Once a business is sure about the answers to the questions, it is important to monitor the data science model as much as possible. Keeping up with metrics, error rates, and traffic is vital to keep a business running.

For more information, also see: The Data Analytics Job Market 

5. Communicate Within the Company

Communication is vital when it comes to data science. Tech experts will learn more easily than non-technical employees. Senior leadership, customers, or even other departments in the company need to understand what a report means for the company.

Explaining key concepts, and what exactly is needed and what is not, is a necessary skill. As the data science field grows, it is important to keep every part of the company on the same page.

Three main points on communicating include:

• Understanding how to explain in non-technical terms.
• Giving complete clarity in all necessary information.
• Getting to the point quickly.

If the information is clear, there is less of a chance to explain it again. Once the information is communicated, a company can ensure they have what they need.

For data science tool suggestions: Best Data Science Software And Tools

How To Apply Data Science Practices In Your Business

Applying data science practices is a must to use the tools to a business’s advantage. Ensuring projects efficiently, documenting reports, reliable data science in infrastructure, monitoring, and communicating throughout the company offers the best result.

Data science best practices help businesses make faster decisions and through the best practices, a company will find major benefits:

Business Planning With Reporting And Documentation

Reporting every result from evaluations can help a business make better decisions. Other tools may help with decision-making, but data science is known to give fast answers for a business.

Performance Tracking With Monitoring And Communication

Awareness of results that are monitored will help a company see what changes need to be made when it comes to employee and performance tracking. This not only uses monitoring, but communication to help improve company performance.

Process Automation With Efficiency And Reliability

Time is often wasted in business when employees are responsible for repetitive tasks. Efficient automation can benefit a company by adding efficiency through these tasks. When a company has a reliable infrastructure, automation can become very easy yet still track company information.

For more information, also see: What is Big Data Analysis

Bottom Line: Best Practices for Data Science

Working with data science is vital, especially as the industry grows. Efficiency, documentation, infrastructure, constant monitoring, and communication are five of the most important practices when it comes to data science.

If a company uses these data science best practices, it will in most cases offer significant competitive advantage.

• Data analytics mines big data sets to uncover specific insights and trends, usually with the goal of competitive business advantage.
• Data science, in contrast, focuses on the larger picture of data, and involves creating new models and systems to build an overall portrait of a given data universe.

In essence, data science takes a “larger view” than data analytics. But both data methodologies involve interacting with big data repositories to gain important insights.

For more information, also see: What is Big Data Analysis

Key Differences Between Data Analytics and Data Science

Data Analytic vs. Data Science: Micro and Macro 

As noted, while data analytics and data science and are closely related, they both perform separate tasks. Some more detail:

Data Analytics

Data analytics analyzes defined data sets to give actionable insights for a company’s business decisions. The process extracts, organizes, and analyzes data to transform raw data into actionable information. Once the data is analyzed, professionals can find suggestions and recommendations for a company’s next steps.

Data analytics is a form of business intelligence that helps companies remain competitive in today’s data-driven market sectors.

For more on data analytics: Best Data Analysis Methods

Data Science

Data science is the process of assembling data stores, conceptualizing data frameworks, and building all-encompassing models to drive the deep analysis of data.

Data science uses technologies that include statistics, machine learning, and artificial intelligence to build models from huge data sets. It helps businesses answer deeper questions about trends and data flow, often allowing a company to make business forecasts with the results.

Given the complexity of data science, it’s no surprise that the technology and tools that drive this process are constantly – and rapidly – evolving, as they are with data analytics.

For more on data science: Data Science Market Trends

Data Analytics vs. Data Science: Benefits

Both data analytics and data science are essential disciplines for companies seeking to find maximum benefit from their data repositories. Among the benefits:

Data Analytics

• Improve decision-making: Data analytics can help guide business decisions by offering specific suggestions about what might happen if there are changes within the business. Data analytics also offers advice on how a business might react to changes.
• Streamline operations: Data analytics has the potential to gather and analyze a company’s data to find where current production is slowing and improve efficiency by helping a company predict future delays.
• Mitigate risks: Data analytics can help companies see and understand their risks. Data analytics can help take preventative measures as well.

Data Science

• Discover unknown patterns: Data science can find overall patterns within a company’s collection of data that can potentially benefit them. Analyzing these larger, systemic models can help a business understand their workflow better, which can support major business changes.
• Company innovation: With data science, a company can find foundational problems that they previously did not fully realize. This deep insight benefits may benefit the company at several different levels of operation.
• Real-time optimization: The larger vision offered by data science enables businesses to react to change quickly –  an overall systemic view offers great guidance.

For more information: Data Science & Analytics Predictions, Trends, & Forecasts

Data Analytics vs. Data Science: Disadvantages

While both data analytics and data science have great benefits for any business, they have disadvantages as well:

Data Analytics

• Lack of communication within teams: Team members and executives may not have the expertise to provide much granular insight into their data, despite their control over it. Without a data analyst, a company could miss information from different teams.
• Low quality of data: Decisions for a company can be negatively affected if low-quality data or data that has not been fully prepped is involved in the process.
• Privacy concerns: Similar to data science, there are problems with privacy while using data analytics. If a company or professional does not govern sensitive information in a compliant manner, the data can be compromised.

Data Science

• Domain knowledge required: Using data science requires a company or staffer to have significant knowledge about data science as it grows and changes, which means that companies must allot budget for hiring and training qualified professionals.
• Unexpected results: Occasionally, data science processes cannot incorporate or mine data that is considered “arbitrary” data, meaning data this is not recognized by the system for any reason. Because a data scientist may not know which data is recognized, data problems could go under the radar.
• Data privacy: As with data analytics, if data is treated without careful standards, the large datasets are more susceptible to cybersecurity privacy problems.

Data Analytics vs. Data Science: Tools

Companies need to select the optimum tools to use data analytics and data science most  effectively. See below for examples of some leading tools:

Data Analytics

Here are the top six data analytics tools and what they can do for a business:

• Tableau: Collects and combines multiple data inputs and offers a dashboard display with visual data mining.
• Microsoft Power BI: AI and ML functionality, powering the augmented analytics, and image analytics.
• Qlik: AI and ML, easy deep data skills, and data mining.
• ThoughtSpot: Search-based query interface, augmented analytics, and comparative analysis to anomaly detection.
• Sisense: Cloud-native infrastructure, great scalability, container technology, caching engine, and augmented data prep features.
• TIBCO: Streaming analytics, data mining, augmented analytics, and natural language user interface.

Data Science

Here are the top six data science tools and what they can do for a business:

• Alteryx: Powerful analytics, data science, and process automation.
• H2O.Ai: End-to-end data science platform and artificial intelligence.
• IBM Watson Studio: Building, managing, and deploying data models with an AI-centric approach.
• KNIME Analytics Platform: Big data and predictive analytics.
• Microsoft’s Azure Machine-Learning Studio: Low-code and no-code framework for developing, training, and deploying data models.
• SAS Visual Analytics: Analytics visualizations, composite AI, MLOps, and decision intelligence.

Which Data Tool is Best For Your Business?

When researching which data analytics and data sciences tools to buy, it is important to understand that data analytics and data science work in combination with one another – meaning that more than one software tool may be needed to create the optimum data strategy.

Given that data science and data analytics are unique fields that have major differences, the tools that best serve these different technologies will be different – yet they ideally will interoperate with one another. This is a crucial point: each business should select the best tool for both disciplines, but as they research, they must seek for a commonality between the two advanced data tools.

In some cases this means buying both data solutions from one vendor, but this isn’t necessary. It also works to buy “best of breed” from two different – competing – vendors. Just make sure to do an extensive trial run with both applications working in concert, to ensure that the combination creates the ideal result.

Bottom Line: Data Analytics vs. Data Science

Data science and data analytics are separate disciplines but are both are crucially important to businesses.

For businesses looking to increase their understanding of data and how it can help their organizations, data analytics and data science play a contrasting and complimentary role. They are different – but they are both essential.

Therefore, business must understand the differing roles of data analytics and data science, and be prepared to select tools for each discipline that work well in combination.

IoT professionals looking to advance their careers must prove they have the necessary knowledge and abilities and a certificate can help grow a person’s knowledge.

Table of Content:

• Top 8 Internet of Things Certifications
  • CCC Internet Of Things Foundation Certification: Best For Cloud IoT
  • CertNexus Certified Internet Of Things Practitioner: Best For Vendor-Neutral Learning
  • Microsoft Certified Azure IoT Developer: Best For Azure Users
  • Arcitura Certified IoT Architect: Best For Beginners
  • Global Tech Council Certified IoT Expert: Best For Programmers
  • AWS Internet Of Things Foundation Series: Best For Price
  • Stanford Internet Of Things Graduate Certificate: Best For Experts
  • hIOTron’s End-To-End IoT Certification Course: Best For Job Hunting
• Why Should You Get An IoT Certification?
• Bottom Line: Internet of Things Certifications

For more on IoT platforms: Best IoT Platforms & Software

Top 8 Internet of Things Certifications

IoT certifications can provide that proof that a student has the education in IoT for future jobs or improvement with how a company uses IoT.

Here are eight that could help workers impress employers:

1. CCC Internet Of Things Foundation Certification: Best For Cloud IoT

The Cloud Credential Council (CCC) offers one of the most comprehensive, vendor-neutral IoT certifications. The Internet of Things Foundation (IoTF) certification covers six learning modules, including IoT security and governance, architecture, and business use cases. According to the CCC, ideal participants include software engineers, system administrators, and IT architects.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Define concepts and terminologies of IoT.
• Examine new devices and interfaces that are driving IoT growth.
• Relate to business perspectives of IoT (advantages of early adoption of IoT technologies).
• Predict the implications of IoT for your business.
• Examine the role of enabling technologies for IoT, such as cloud computing and Big Data.
• Identify security and governance issues with IoT.
• Examine future growth opportunities of IoT in the coming years.

Requirements

This course has no prerequisites, but participants should have a firm grasp of cloud-related concepts and terms.

Duration, Location, And Cost

Length of exam: 60 minutes, 25 questions.
Location: Webcam-proctored online only.
Cost: $349 (Study materials and voucher for exam).

For more on IoT Cloud: Internet of Things (IoT) Cloud Trends

2. CertNexus Certified Internet Of Things Practitioner: Best For Vendor-Neutral Learning

Another comprehensive, vendor-neutral certification is CertNexus’s Certified Internet of Things Practitioner. This course covers six topics, from constructing and programming IoT devices to processing data and identifying real-world use cases. It stands out because it’s accredited under the ANSI/ISO/IEC 17024 standard, a requirement for many government projects.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Foundational knowledge.
• Implement IoT systems.
• Design IoT systems.
• Manage an IoT ecosystem.

Requirements

There are no prerequisites, but participants can take a readiness assessment to see if they have the recommended baseline skills and knowledge.

Duration, Location, And Cost

Length of exam: Two hours, 100 questions.
Location: In person at Pearson VUE test centers or online via Pearson OnVUE.
Cost: Exam $250, self-study $450, in-person classes up to $1,500.

3. Microsoft Certified Azure IoT Developer: Best for Azure Users

IoT professionals looking for vendor-specific options should consider Microsoft’s Certified Azure IoT Developer certification. It equips participants to develop, deploy and manage Azure IoT Edge applications. It focuses mainly on programming and implementation, ideal for workers who lead Azure-specific IoT teams.

Skills Acquired

The certification teaches many skills based on Azure IoT.

This includes:

• Set up the Azure IoT Hub solution infrastructure.
• Provision and manage devices.
• Implement IoT Edge.
• Implement business integration.
• Process and manage data.
• Monitor, troubleshoot, and optimize IoT solutions.
• Implement security.

Requirements

Candidates must be able to program in at least one Azure IoT SDK-supported language and understand device types and services.

Duration, Location, And Cost

Length of exam: ~Two hours.
Location: Proctored online (contact for more details).
Cost: Between $2,000-3,000; exam $165.

4. Arcitura Certified IoT Architect: Best For Beginners

Arcitura’s Certified IoT Architect certification includes three IoT courses, covering skills in IoT architecture, radio protocols, telemetry, and real-world use cases. After learning about these concepts in the first two courses, applicants will apply them in lab exercises in the third. Participants can take the exam without completing the coursework but may be unprepared if they skip it.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• Introduction of Internet of Things (IoT) concepts.
• Terminology and common models.
• IoT technology architecture and solution design.
• IoT communication protocols.
• Telemetry messaging.
• IoT architecture layers.

Requirements

There are no requirements for the certification.

Duration, Location, And Cost

Length of exam: 110 minutes.
Location: On-site Pearson VUE test centers.
Cost: $249.

5. Global Tech Council Certified IoT Expert: Best for Programmers

IoT professionals seeking a more flexible option may find the Global Tech Council’s Certified IoT Expert course appealing. The entirely self-guided course lasts eight hours in total, and lifetime access means applicants can take it at whatever pace they choose. By the end, participants will learn skills in IoT architecture, protocols, cloud and smart grid applications, Arduino and Raspberry Pi, and more.

Skills Acquired

The certification can teach many skills in IoT from software to key components.

This includes:

• IoT Key Components.
• IoT Layer Architecture.
• IoT Middleware.
• Communication and data link protocol.
• Layer protocols.
• IoT Cloud.
• Fog, Edge, and Grid Computing.
• IoT-aided Smart Grid System.
• Introduction to Arduino.
• Raspberry Pi Models.

Requirements

There are no formal prerequisites, but applicants should have basic programming and app development skills.

Duration, Location, And Cost

Length of exam: N/A.
Location: Online.
Cost: $199.

6. AWS Internet Of Things Foundation Series: Best For Price

Amazon Web Services (AWS) is one of the most popular networking service providers globally, so IoT professionals can gain much from understanding it. Consequently, working through AWS’s Internet of Things Foundation Series is an excellent choice for any IoT worker. Professionals can point toward the course as evidence they have experience in AWS IoT applications.

Skills Acquired

The AWS class can teach many skills in IoT.

This includes:

• Telemetry.
• IoT command and control.
• Fleet management.
• Predictive maintenance.

Requirements

Participants should likely have baseline IoT technical knowledge.

Duration, Location, And Cost

Length of class: 9.5 hours.
Location: On the AWS website.
Cost: Free.

For more on IoT: Internet of Things (IoT) Use Cases

7. Stanford Internet Of Things Graduate Certificate: Best For Experts

Another certification that stands out from the others is Stanford University’s Internet of Things Graduate Certificate. This is a graduate school-level program covering four non-credit online courses, and participants can pick from a list of 15. Applicants can show IoT experience from a leading engineering school after receiving a B or higher in the program. Specific takeaways will vary by course, but participants will generally learn about underlying IoT technologies, circuit design, web applications, security, and emerging tech.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• IoT technologies.
• Circuit design.
• Web applications.
• IoT security.
• Emerging tech.

Requirements

This certificate requires a bachelor’s degree with a GPA of at least 3.0 and advanced knowledge of programming languages.

Duration, Location, And Cost

Length of exam: Three-year course; exam N/A.
Location: Online.
Cost: $16,800-$21,000.

8. hIOTron’s End-To-End IoT Certification Course: Best For Job Hunting

hIOTron’s End-To-End IoT Certification Course is a certification that allows users to teach monitoring, analyzing, and IoT experience. Users will be certified by the course, ensuring that a user has a complete understanding of core IoT needs. This also includes IoT frameworks and architecture with practice for users.

Skills Acquired

The certification can teach many skills based on the path a student decides to use.

This includes:

• IoT device communication.
• IoT industry uses.
• Learn to build the first End-To-End IOT product using Rasp-berry pi devices.
• Hands-on practicals with IoT Gateway.
• Set up MQTT Broker and Node server.
• End-To-End IoT applications.

Requirements

There are no requirements for the certification.

Duration, Location, And Cost

Length of exam: N/A
Location: Online and classroom.
Cost: Upon request.

For more information on the IoT job market: 5 Trends in the Internet of Things (IoT) Job Market

Why Should You Get An IoT Certification?

IoT certifications can help a user demonstrate their understanding of IoT, such as architecture, management, and security. IoT may have not been included in a university course due to the technology being new for many developers. Understanding IoT helps a company’s employees as well as tech experts looking for a job.

Many jobs require at least baseline knowledge of IoT. Some jobs include:

• Data analyst (IoT).
• IoT developer.
• Chief developer.
• IoT application developer.
• Engineering IoT field application engineer.

Bottom Line: Internet of Things Certifications

IoT is a growing industry that is becoming more relevant in the tech field. Certification can help a user to advance, find a great career, and help with further education.

IoT certifications can seem very difficult, however, finding the best one can be easy as the topic grows and changes.

For more on IoT: The Internet of Things (IoT) Software Market

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential. 

Big Data Trends: Table of Contents

• Real Time Analytics
  
• Stronger Reliance on Cloud Storage
  
• Ethical Customer Data Collection
• AI/ML Powered Automation
• Big Data in Different Industries
• Challenges in Big Data
• Bottom Line: Big Data Trends

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

• Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
• Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data. 
• Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage. 
• Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

• Amazon Web Services S3
• Microsoft Azure Data Lake
• Google Cloud Storage
• Oracle Cloud
• IBM Cloud
• Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes. 

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection 

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media. 

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. 

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. 

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. 

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers. 

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries 

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes. 

Real Estate And Property Management 

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume. 

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows. 

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.