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

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. 

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.

A database job uses several data skills, including how to perform queries, SQL statements, create databases, and run stored procedures on DB2, Oracle, Microsoft SQL Server, and Netezza databases. Database certifications can give a user the ability to use those skills and learn more.

Top 10 Database Certifications: Table of Contents

• Microsoft DP-300 Exam: Administering Relational Databases On Microsoft Azure: Best For Cloud
  
• Google Cloud’s Professional Data Engineer: Best For Machine Learning
  
• MongoDB Database Administrator: Best For Beginners
• Meta Database Engineer Professional Certificate: Best For Applications
  
• Oracle Certified Professional, Oracle Database 19c: Data Guard Administrator Certification: Best For Administrators
• EnterpriseDB’s Postgres Certification Program: Best For Postgres
• IBM Certified Administrator – Db2 12 for z/OS: Best For Advanced Users
• SAP Certified Application Associate, Reporting, Modeling, And Data Acquisition With SAP BW/4HANA 2.X: Best For SAP HANA
• Teradata Vantage Administrator Exam: Best For SQL
• Certified Data Professional (CDP): Best For Variety
  
• Why Should I get a DBA Certification?
  
• Bottom Line: DBA Certifications
  

See more: Database Market

10 Top Database Certifications

These are 10 of the top database certifications you can earn right now, along with the different skills they signal to a recruiter or interviewer:

1. Microsoft DP-300 Exam: Administering Relational Databases On Microsoft Azure: Best For Cloud

The Microsoft DP-300 exam is a baseline accreditation designed to teach the fundamentals of database knowledge. This course is intended to train students in using Microsoft’s cloud computing service Azure, ending in the Microsoft DP-300 exam.

Check out this practice exam!

Skills Acquired

The Microsoft DP-300 Exam: Administering Relational Databases On Microsoft Azure will not only teach about Microsoft Azure. It will also include how to:

• Plan and implement resources.
• Implement a safe database environment.
• Monitor, configure, and optimize database resources.
• Configure and manage automation of tasks.
• Plan and configure a high availability and disaster recovery (HA/DR) environment.

Requirements

There are no prerequisites for the DP-300 exam, but the Azure Fundamentals certification does recommend a user have baseline knowledge that will make the exam easier.

Duration, Location, And Cost

Length of exam: Two hours, 40-60 questions.

Location: The exam can be taken at a remote location or a testing center.

Cost: $165.

Practice tests are also available on Microsoft’s website.

2. Google Cloud’s Professional Data Engineer: Best For Machine Learning

Google Cloud’s Professional Data Engineers allow users to make data-driven decisions by collecting, transforming, and publishing data. With Google Cloud’s Professional Data Engineer certification, a user should be able to leverage, deploy, and continuously train pre-existing machine learning models.

Skills Acquired

The Google Cloud’s certification will teach many skills to whoever completes it, including:

• Design data processing systems.
• Ensure solution quality.
• Operationalize machine learning models.
• Build and operationalize data processing systems.

Requirements

There are no prerequisites, but Google Cloud recommends experience: three years of industry experience and one year of using Google Cloud.

Duration, Location, And Cost

Length of exam: Two hours.

Location: A user can take the online-proctored exam from a remote location or take the onsite-proctored exam at a testing center.

Cost: $200.

Google Cloud’s Professional Data Engineer has a certification guide as well.

3. MongoDB Database Administrator: Best For Beginners

MongoDB is often the go-to database choice for teams that need a NoSQL or non-relational database. It’s not the most commonly used database, but it’s employed frequently enough that database administrators may want a MongoDB certification at some point in their careers.

MongoDB offers two platform-specific certifications, MongoDB Developer and MongoDB Database Administrator, which will be the most relevant for database professionals.

Skills Acquired

The certification will teach many skills to beginners in the field. This includes:

• Covering fundamentals in server and database administration.
• Basic JavaScript.
• Data system programming.
• Database software development.
• Shows the unique advantages of MongoDB. 

Requirements

The Database Administrator course has no prerequisites.

Duration, Location, And Cost

Length of exam: 90 minutes, 60 questions.

Location: A user can take the online-proctored exam.

Cost: $150.

Check out this practice exam!

4. Meta Database Engineer Professional Certificate: Best For Applications

Meta Database Engineer Professional Certification will give students the key skills required to create and manage databases, as well as industry-standard programming languages and software such as SQL, Python, and Django used for supporting outstanding websites and apps like Facebook, Instagram, and more.

Skills Acquired

The Meta certification will teach many skills to whoever completes it, including:

• Demonstrating proficiency in SQL syntax.
• Learn to interact with a database.
• Create databases.
• Learn how to add, manage, and optimize databases.
• Write database-driven applications in Python.
• Develop knowledge of advanced data modeling concepts.

Requirements

There are no requirements.

Duration, Location, And Cost

Length of exam: Self-paced.

Location: A user can take the exam online.

Cost: From $39.99 a month.

5. Oracle Certified Professional, Oracle Database 19c: Data Guard Administrator Certification: Best For Administrators

The Oracle Certified Professional, Oracle Database 19c: Data Guard Administrator certification is for database systems administrators with responsibility for disaster recovery. The certification challenges students on their knowledge and pushes their limits of database skills and increases value in the marketplace.

Skills Acquired

The certification will teach many skills to database administrators. This includes:

• Knowledge of Oracle Data Guard concepts.
• Database configuration.
• Database management.
• Database optimization and monitoring.
• Data protection.
• Disaster recovery. 

Requirements

The student will need to complete one of several Oracle Database courses. 4 to 5 years of Database Administration with 2 to 3 years of Data Guard.

Duration, Location, And Cost

Length of exam: Two hours.

Location: The exam can be taken online or at a Pearson VUE testing center.

Cost: $245.

Check out this practice exam!

For more information: Guide to Database Management

6. EnterpriseDB’s Postgres Certification Program: Best For Postgres

Since Postgres is open source, there are no certificates available directly from the PostgreSQL development team. If a user wants a Postgres certificate, the only option will be a third-party certification program offered by EnterpriseDB.

Skills Acquired

The certification will teach many Postgres skills to whoever completes it. This includes:

• Basics of database administration.
• The specifics of working with Postgres. 
• Benchmarking and replication. 
• Management of high-availability systems.

Requirements

There are no prerequisites for the associate certificate. The professional certification requires the completion of the associate exam.

Duration, Location, And Cost

Length of exam: One hour.

Location: The exam is conducted online.

Cost: $200.

7. IBM Certified Administrator – Db2 12 for z/OS: Best For Advanced Users

An IBM Certified Administrator on IBM Db2 12 for z/OS is the lead database administrator for the Db2 product on the z/OS operating system. When a user completes the certification, this individual is capable of performing intermediate to advanced tasks related to database design and implementation.

Skills Acquired

The certification will teach many skills to advanced users. This includes:

• Database design and implementation.
• Operation and recovery.
• Security and auditing.
• Installation and migration.
• Additional database functionality.

Requirements

It is required for a user to have passed Exam C1000-078: IBM Db2 12 for z/OS Administrator.

Duration, Location, And Cost

Length of exam: 90 minutes, 60 questions.

Location: The exam is conducted online.

Cost: ~$200.

For more: 5 Trends in the Database Job Market

8. SAP Certified Application Associate, Reporting, Modeling, And Data Acquisition With SAP BW/4HANA 2.X: Best For SAP HANA

Certification from SAP can be valuable for professionals working with SAP HANA. The company offers a wide variety of credentials, but only two are directly relevant to SAP HANA.

Skills Acquired

The SAP Certified Application Associate certification will teach the following skills to whoever completes it: 

• Database modeling.
• Data acquisition.
• Query design with SAP HANA.

Requirements

There are no prerequisites for the exam.

Duration, Location, And Cost

Length of exam: 180 minutes, 80 questions.

Location: The exam is conducted online.

Cost: $227 for one attempt; $568 for six attempts.

Check out this practice exam!

9. Teradata Vantage Administrator Exam: Best For SQL

Do you want to learn more about Teradata Vantage and its features, like its advanced SQL engine? Teradata offers a course and exam for DBAs that will teach a user more about Vantage and measure their knowledge.

Skills Acquired

The certification will teach many skills to whoever completes it, including advanced skills in SQL, and the following:

• Advanced SQL Engine.
• Database administration.
• Performance management.
• Security and system logging.
• Workload management.

Requirements

There are no prerequisites for the course or exam. However, Teradata recommends one to three years of hands-on experience with Vantage.

Duration, Location, And Cost

Length of exam: 130 minutes.

Location: The test can be taken remotely or at a Pearson VUE testing location.

Cost: $249.

Check out this practice exam!

10. Certified Data Professional (CDP): Best For Variety 

The CDP is an update of the Certified Data Management Professional (CDMP). This applicable credential from The Institute for the Certification of Computing Professionals (ICCP) allows you to specialize in an area of interest within the data field. 

Skills Acquired

The CDP certification will teach many skills to whoever completes it, including:

• Business analytics.
• Data governance.
• Data integration.
• Data management.
• Data and information quality.
• Data warehousing.
• IT management.

Requirements

Depending on the level of certification a user wants, a user may need to have from 0-5 years of experience in the field.

Duration, Location, And Cost

Length of exam: ~90 minutes.

Location: The test can be taken remotely or at a Pearson VUE testing location.

Cost: From $250-4,500, for more information see here.

For more information: Database Trends

Why Should You Get A DBA Certification?

Database professionals are in high demand right now, particularly those who are familiar with multiple databases. One or more certifications can help database administrators (DBAs) or students demonstrate their experience and stand out from other candidates.

When employers see a DBA certification, the employer might consider a candidate for the following job titles:

• Database manager.
• Data analyst.
• Data scientist.
• Information security analyst.
• Database administrator. 
• Data modeler.
• Software engineer.
• Data specialist.
• Computer programmer.
• Computer systems analyst.
• SQL developer.
• Information systems manager.
• Database developer.
• Computer network architect.

Getting a DBA certification can help a candidate’s resume and the experience they have had. With any job above, an employee can also help a company with their expertise in their field.

For more on job opportunities: Top Companies Hiring for Database Jobs

Bottom Line: Database Certifications

A database certification will show both current and future employers that a worker possesses specific skills, knowledge, and experience. A DBA certification is a great add-on to a resume, and the database skills will help employers find the right candidate.

With a DBA certification, a user can learn more and increase their database skills for a future or current employer.

Most modern data catalog tools rely heavily on artificial intelligence (AI) and machine learning (ML) capabilities. Often ML provides a score that shows how reliable data is. ML in data catalogs can also provide other types of recommendations and enable some basic analytics.

For more information, also see: Data Management Platforms

Table of Contents

• Data Catalog Software Comparison Chart
  
• Alation: Best for Behavioral Intelligence
  
• Alex Solutions: Best for Metadata Management
  
• Collibra: Best for Cloud Products
  
• Data.World: Best for Understanding Company Data
• Erwin: Best for Data Modeling
  
• Google Cloud Data Catalog: Best for Data Security
  
• Lumada Data Catalog: Best for Lineage Analysis
  
• Infogix Data360 Analyze: Best for Automation
  
• Informatica: Best AI Capabilities
  
• IBM: Best for Flexibility
  
• Data Catalog Software Key Features
  
• How to Choose Data Catalog Software
  
• Should You Use Data Catalog Software
  
• Bottom Line: Data Catalog Software
  

Data Catalog Software Comparison Chart

Best Data Catalog Software

A data catalog software automates the discovery of data sources throughout an enterprise’s systems. It has capabilities to organize that data, show the relationships among different pieces of data, enables search, and tracks data lineage, which is where the data originated.

See below for the top 10 data catalog software:

Alation: Best For Behavioral Intelligence

A pure-play data governance and data catalog vendor, Alation is a leader in the data catalog industry. Key features of the Alation Data Catalog include behavioral intelligence, seamless collaboration, guided navigation, data governance capabilities, and connections to popular big data and BI tools, as well as APIs and an Open Connector SDK. It also offers tailored solutions for finance, healthcare, insurance, manufacturing, retail, and technology companies. In addition, it has a large partner ecosystem that includes systems integrators, resellers, and complementary technology vendors.

Pricing:

Pricing is available on request. The company offers a weekly live demo, as well as the opportunity to request a demo.

Features:

• Behavioral intelligence: The tool learns a user’s use of data to give businesses more efficiency.
• Open Connector SDK: The tool allows the data catalog software to connect to any source that doesn’t currently have a pre-built connector.
• Guided navigation: The tool gives developers recommendations, flags, and policies to help users and businesses.

Pros:

• Good machine learning capabilities. 
• Strong collaboration capabilities.
• Early pioneers of data catalog technology.

Cons:

• The tool can be very expensive.

Alex Solutions: Best for Metadata Management

Australia-based Alex Solutions describes its product as a metadata management solution that incorporates both data catalog and data governance capabilities. Alex offers a data catalog, business glossary, policy-driven data quality, intelligent tagging, technology-agnostic metadata scanners, and workflow capabilities. Its metadata management capabilities are useful for data inventory, enrichment, usage analysis, sensitivity detection, data lineage support, risk management, and more. Its ML capabilities are highly advanced, and it has an intuitive interface.

Pricing:

Demos and pricing are available on request.

Features:

• Business glossary: The business glossary simplifies the enterprise by putting all definitions, policies, metrics, rules, processes, and workflows.
• Technology-agnostic metadata scanners: policy-driven data quality combined with data lineage, data profiling, and machine learning-based intelligent tagging.
• Sensitivity detection: Alex Solutions’s product uses cybersecurity techniques to keep a user’s data safe.

Pros:

• Broad range of capabilities.
• Easy to deploy and use.
• Strong lineage profiling.

Cons:

• Needs better training for business users.

For more on metadata: Top Metadata Management Tools

Collibra: Best for Cloud Products

Collibra aims to make data meaningful with its Data Intelligence Cloud, Platform, Data Catalog, Data Governance, Data Lineage, and Data Privacy products. Collibra’s Data Catalog product includes wide-ranging native connectivity, ML-powered automation, data scoring, and embedded data governance abilities. Data catalog capabilities are also included in the company’s flagship Data Intelligence Cloud.

Pricing:

Pricing is available on request. Collibra offers a free trial as well.

Features:

• Scalable: A user can use programs that integrate data intelligence cloud platforms such as data catalog, governance, lineage, quality, and privacy capabilities into one.
• Secure cloud product: Collibra provides support for identity and access management, encryption, and network vulnerability testing, to ensure safe security techniques.
• Flexible connection: Collibra’s product can deploy on-premises, or in cloud environments to meet a company’s needs.

Pros:

• Strong data intelligence capabilities and graph technology.
• Good for large enterprises.
• Strong ecosystem of third partners and peer-support user groups.

Cons:

• Complex interface.

Data.World: Best for Understanding Company Data

Like many of the other vendors included in this list, Data.World is a pure-play vendor focused on data catalog capabilities. A cloud-native product, Data.World offers contextual data cataloging that includes metadata, dashboards, analysis, code, docs, project management, and social collaboration capabilities. It also incorporates knowledge graph technology and provides real-time integration capabilities. In addition, the company follows agile development processes, continually releasing updates and feature improvements.

Pricing:

Data.World offers a free demo for their customers.

Features:

• Understanding company data: Data.world’s knowledge-graph-powered data catalog offers a consistent, enterprise-wide understanding.
• Strong data governance: Offers more accurate business insights with Agile Data Governance, which enables organizations to curate well-informed data products.
• Scalability: A company needs a data catalog to adapt, change, and be able to represent all the different parts of their business.

Pros:

• Upfront pricing.
• Easy to use interface.
• Public benefit corporation devoted to providing social benefits, including providing free access to many datasets, supporting data journalism, and making education and community resources freely available.

Cons:

• Not as many third-party partners and integrations.

Erwin: Best For Data Modeling

Erwin focuses on products for the Enterprise Data Governance Experience (EDGE), including business process modeling, enterprise architecture, data modeling, data catalog, and data literacy. Erwin offers Data Catalog (DC) as a standalone product or as part of its Data Intelligence suite. Benefits of Erwin DC include a centralized data governance framework, a metadata-driven approach, accelerated project delivery, increased data quality, regulatory compliance, and accurate analytics. It includes a metadata manager, mapping manager, reference data manager, lifecycle manager, business data profiling, and data connectors.

Pricing:

For Erwin’s Data Intelligence and Data Catalog products, you will need to contact a representative. A free trial is available.

Features:

• Centralized data governance framework: The framework offers accurate analytics, data literacy, and more.
• Data modeling: The tool helps a company discover, compare, and use models to migrate data to new database management systems and platforms.
• Enterprise Architecture Solutions: The solutions provide one source of truth about the enterprise and how it operates.

Pros:

• Broad range of data governance capabilities.
• Great data modeling.
• Strong ecosystem of customers, partners, and resellers.

Cons:

• Expensive.

For more on data modeling: Types of Data Models & Examples: What Is a Data Model?

Google Cloud Data Catalog: Best for Data Security

Part of Google Cloud’s Dataplex, Google Cloud Data Catalog is a fully managed cloud service with data discovery and metadata management capabilities. Key features of the service include serverless architecture, metadata as a service, a central catalog, search and discovery, schematized metadata, cloud DLP integration, on-prem connectors, cloud identity, and access management (IAM) integration and governance capabilities. It offers a faceted-search interface, metadata syncing and tagging, easy scalability, and integration with cloud data loss prevention (DLP) and other Google Cloud services.

Pricing:

Pricing is available on their website. 

Features: 

• Technical and business metadata: Google Cloud Data Catalog supports data-driven decision-making and accelerates insight time by enriching data.
• Unified view: Users can gain a unified view to reducing time while searching for the right data.
• Cloud Data Loss Prevention (DLP): Data Catalog can use the Cloud Data Loss Prevention (DLP) scan to identify sensitive data directly within Data Catalog.

Pros:

• Integration.
• Scalability.
• Affordability.

Cons:

• Difficult to estimate the total cost as needed.

For more on Google Cloud’s Dataplex: Google Cloud Launches Unified Data Platform with Analytics Hub, Dataplex and Datastream

Lumada Data Catalog: Best for Lineage Analysis

Hitachi Vantara’s Lumada Data Catalog offers very advanced machine learning and behavioral intelligence capabilities. It promises faster data tagging and includes features like AI-driven discovery, end-to-end data lineage, self-service data access, sensitive data management, and cross-functional collaboration.

Pricing:

Pricing is available on request. Hitachi Vantara offers a “Try Hands On Experience.”

Features:

• AI-driven discovery: Lumada Data Catalog offers data fingerprinting to automate the discovery and classification of structured, semi and unstructured data.
• Business rules: The tool allows users to validate data against business rules to assess conformity to business policies.
• End-to-end lineage analysis: The tool allows companies to find hidden lineage to trace data back to original sources. 

Pros:

• Advanced ML and behavioral intelligence features.
• Helpful lineage analysis capabilities.
• Interface is user-friendly.

Cons:

• Not many connectors to third-party applications.

For more on data analytics: 5 Ways Brands Underutilize Data Analytics

Infogix Data360 Analyze: Best for Automation

Infogix Data360 Analyze, now part of Precisley’s Data360 portfolio, includes data catalog, data governance, data quality, and data analytics capabilities. Key data catalog features in Data360 Analyze include automated metadata management, machine learning-based search and discovery, smart business glossary, data lineage, impact analysis, and more. It integrates with the other Precisely Data360 products, and the company also offers professional services, training, and support.

Pricing:

A demo and pricing are available on request.

Features:

• Data Transformation: The products help companies prepare, cleanse and blend data to create data sets for analysis.
• Data Integration: This lets users acquire data in multiple formats and combine, reconcile, and restructure data.
• Automation: The product can operationalize multiple steps needed to process data based on a variety of triggers.

Pros:

• Wide range of data intelligence capabilities.
• Helps organizations quantify the value of their business data and manage data assets.
• Easy to use.

Cons:

• Could use better documentation.

Informatica: Best AI Capabilities

One of the most well-known data catalog vendors, Informatica offers an Intelligent Data Platform that incorporates a wide range of cloud-based enterprise data management products. Informatica’s Enterprise Data Catalog provides enterprise-wide data discovery capabilities that make use of AI technology. It provides a holistic view of data within its business context. Key features include AI-powered automation, data provisioning, end-to-end data lineage, integrated data quality capabilities, and collaboration abilities.

Pricing:

Pricing is available on request.

Features:

• Catalogs all data: Users can use AI-powered automation to discover, inventory, and organize their data assets.
• Unified view: A unified view adds rich context to a user’s data by giving a single view of enterprise metadata.
• Data into insights: A business can find and prepare data by using AI/ML applications for insights.

Pros:

• Data Catalog service is helpful for enterprises.
• Great metadata intelligence engine.
• Scalable.

Cons:

• Expensive for some companies.

IBM Watson Knowledge Catalog: Best for Flexibility

IBM Watson Knowledge Catalog can be deployed on the IBM Cloud or a private cloud through IBM Cloud Pak for Data. Noteworthy features include intelligent discovery recommendations, an end-to-end catalog, automated data governance, data lineage, quality scores, and self-service insights. It also includes data quality, collaboration, and compliance capabilities.

Pricing:

For pricing, go to the IBM Cloud pricing page. There are two options for a Free Trial and booking a consultation on IBM Watson Knowledge Catalog’s main page.

Features:

Operationalized quality: A company can track lineage and quality scores across all data, AI models, and notebooks.

End-to-end catalog: The tools can organize, define, and manage enterprise data to provide the right context and drive value.

Global search: The global search bar is available 24/7, no matter where users are in the navigation or what content they are working on.

Pros:

• Integrates well with other IBM products.
• Cloud Pak for Data deployment option is good for large, complex ecosystems.
• Upfront pricing.

Cons:

• Deployment can be difficult.

Data Catalog Software Key Features

When it comes to data catalog software, a company needs to know what features they require. Some vendors and tools will provide exactly what a company needs, some will not. 

There are specific features to look for in tools:

• Understanding of data through context: A data catalog software needs to provide documentation or detailed descriptions of data, so users and companies grasp a better understanding of how data is linked to the business.
• Increased operational efficiency: A data catalog should create a division of labor between users and IT professionals. Data catalog software should access and analyze data faster to allow users and IT professionals to have more time for different tasks.
• Reduced risk: A business should have confidence that they are working with data they are authorized to use for a purpose, in compliance with regulations.
• Greater success with data management initiatives: A data catalog software can help find, access, prepare, and trust data, so BI initiatives and big data projects will be successful.
• Better data and better analysis: Data professionals can respond rapidly to problems, challenges, and opportunities with analysis and answers.

How To Select Data Catalog Software

If you are in the market for data catalog software, keep these tips in mind:

Think about who will use your data catalog software.

 Data scientists have very different needs than chief data officers (CDOs), who have very different needs than business analysts and chief financial officers (CFOs). When selecting a tool, make sure that the software or service is designed to meet the needs of your users.

Consider your deployment needs. 

Many data catalog tools are available as a cloud-based service, but that isn’t always the best option if you have unique security or compliance needs, or if your data resides in a wide range of cloud and on-premise locations.

Make sure it will support your workflows. 

Your data catalog software will need to integrate with the other software you use for your data lake, and it will need to fit in with your current processes. If you purchase a tool that will require you to make huge changes in the way you conduct day-to-day activities, you may find that it gets limited use or provides limited value.

Ask for a demo and detailed pricing. 

Some vendors offer upfront pricing, but many do not. Conduct a thorough total cost of ownership (TCO) analysis to make sure that you are comparing apples to apples when evaluating your options.

Should You Use Data Catalog Software?

A data catalog software helps data professionals collect, organize, access, and enrich metadata to support data discovery and governance. It is recommended that all companies have strong data catalog software.

Data catalogs are vital due to how they allow users to access useful data and help users collaborate and maintain business data definitions.

Data catalogs are useful to all businesses, and it is recommended that all companies have some sort of data catalog software to help stay organized and keep companies safe.

Bottom Line: Data Catalog Software

Every company can find a data catalog software that fits their requirements, from industry needs to how a tool integrates with their data.

As more data catalog software is created, these software companies are some of the top data catalog providers on the market.

These days nearly every enterprise wants to have predictive analytics capabilities to better understand their future possibilities. This enterprise expectation corresponds with a growing interest in Big Data and artificial intelligence solutions – both of which support predictive analytics. 

For more information, also see: Data Management Platforms

Table of Contents

• Top 7 Predictive Analytics Tools
  
• IBM SPSS Statistics: Best for Dashboard Capabilities
  
• SAS Advanced Analytics: Best for Variety
  
• SAP Predictive Analytics: Best for ERP Data
  
• TIBCO Data Science/Statistica: Best for Collaboration
• Oracle Data Science: Best for Cloud Management
  
• Q Research: Best for Market Research
  
• H20: Best for Data Science Professionals
  
• Predictive Analytics Key Features
  
• How to Choose a Predictive Analytics Tool
  
• Should You Use Predictive Analytics?
  
• Bottom Line: Predictive Analytics Tools
  

Predictive Analytics: Vendor Comparison Chart

Top 7 Predictive Analytics Tools

Here is a list of eight predictive analytics software solutions worth considering as the company begins their selection process:

For more information, also see: Top Data Warehouse Tools

IBM SPSS Statistics: Best For Dashboard Capabilities

IBM SPSS Statistics is a popular predictive analytics tool. It offers a user-friendly interface and a strong set of features including the SPSS modeler, which provides advanced statistical procedures, helps ensure precision, and provides positive decision-making. All of the analytics lifecycle features are included, such as data preparation and management to analysis and reporting.

Pricing:

IBM provides multiple payment options, including a subscription plan, term licenses, and academic plans. IBM also offers a free trial to allow users to try it before they buy it.

Features:

• Linear Elastic Net Regression: Estimates linear regression models for dependent variables with one or more independent variables.
• Custom Tables: Gives customization to customers’ data sets.
• Bootstrapping: Approximation of sampling distributions by an estimator by resampling the original data set.

Pros:

• Easy statistical analysis of large data for beginners.
• Improves efficiency with coding.
• Interactive dashboard.

Cons: 

• Expensive software.

SAS Advanced Analytics: Best For Variety 

SAS is a leader in various analytics markets and offers an incredibly long list of different predictive analytics and other advanced analytics products. That list is so long that it might be difficult to figure out which tool(s) a company needs for their historical data management purposes. With so many different tools available, chances are good that SAS has exactly what a company needs.

Pricing:

SAS Advanced Analytics does not have pricing on their website. They have contact sheets in case a company needs to request demos, free trials, and price quotes.

Features:

• Quality-tested algorithms: Updates to reflect the latest statistical methodologies to analyze the past, present, and predict the future.
• Diverse areas with one environment: Provides analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis.
• Customization: Create task-specific graphics for effective interpretation and communication of results.

Pros:

• Great for data exploration.
• Processes large data sets.
• Develop ML algorithms.

Cons: 

• Difficult learning process.

SAP Predictive Analytics: Best For ERP Data

If a company will primarily be using their predictive analytics solution to analyze data that resides in SAP software or the SAP Analytics Cloud, such as their ERP data, SAP Predictive Analytics might be a good fit. The company has quite a few different options available when it comes to features. Whoever is using the system from business analysts to data scientists, the tool allows a company to create, operationalize, and monitor the predictive models. It has advanced machine learning and security features for its predictive models.

Pricing:

SAP Predictive Analytics does not have exact pricing for the product. SAP has a store for their products, and customers can call sales for more information.

Features:

• Rapid insight: Creates insights with 1,000’s and 10,000’s variables with no expert intervention.
• Discover hidden insights: Build predictive models and discover hidden relationships in the data, which can help make predictions about future events.
• Automated analytics: Allowing data scientists to focus on the work they need without the tedious tasks.

Pros:

• Great integration with SAP Lumira.
• Convenient diagram.
• Different databases can be connected.

Cons: 

• Complicated for novices.

TIBCO Data Science/Statistica: Best For Collaboration

TIBCO Data Science/Statistica puts the emphasis on usability, with a lot of collaboration and workflow features built into the tool to make business intelligence possible across an organization. This makes it a good choice for a company if they expect lesser-trained staff will use the tool. It also integrates with a wide range of other analytics tools, making it easy to extend its capabilities. This is also the only tool on the list that emphasizes its IoT/embedded capabilities.

Pricing:

Like many analytics tools, TIBCO Data Science/Statistica does not have prices listed online. They do have a free trial page, and for more information, a company can contact sales.

Features:

• Faster insights: The tool can easily go through large amounts of data to find and access the information needed to quickly pull the most relevant insights.
• Streamlined data access: A company can access all of their data from an environment to simplify complex data sources and prepare them for analytics.
• Faster data prep: Prep and combine data faster using enhanced joins and unions, and gain performance improvements for data sources.

Pros:

• Provides reliable results.
• Quick at calculating.
• The point-and-click process for neural networks is helpful.

Cons: 

• Default analytic models are difficult.

Oracle Cloud Infrastructure (OCI) Data Science: Best For Cloud Management

Oracle Cloud Infrastructure (OCI) Data Science is a cloud-based big data and machine learning platform that includes predictive analytics capabilities. OCI Data Science is an analytics platform for teams of data scientists to build, train, deploy, and manage machine learning models using Python, and open source tools. It takes models into production and keeps them healthy with MLOps capabilities, model deployments, and model monitoring.

Pricing:

Oracle provides a pricing page for their analytics tool. The tool also has a free lab option for potential customers.

Features:

• Flexible data access: Data scientists can access and use any data source in any cloud or on-premises. This provides more potential data features that lead to better models.
• Data labeling: With data labeling, data scientists can assemble data, create and browse data sets, and apply labels.
• Model explanation: Automated model explanation helps data scientists understand the behavior of the model and provides guides on what caused the model’s predictions.

Pros:

• Works quickly.
• Built-in APIs.
• Wide range of solutions.

Cons: 

• Setup can be complicated.

For more on ML: Key Machine Learning (ML) Trends

Q Research: Best For Market Research

Q Research focuses on one sector: market research. If a company needs a predictive analytics solution only for market research and marketing analytics, this application has all the capabilities a business could ever want. This highly automated platform streamlines the predictive analytics process so that users can spend less time running the tool and more time strategizing for the next marketing campaign.

Pricing:

Q Research has a pricing page that offers a standard license and a transferable license. Q Research also offers a free trial and the ability to book a demo.

Features:

• Automates work: Everything that can be automated is automated, from formatting data, statistical testing, and generating tables, to updating analyses and reproducing reports.
• DIY advanced analysis and visualization: Automates all the steps that can take people years of training to learn.
• Create reports to share insight-filled stories: Visualize information to show patterns that matter and design reports with integration.

Pros:

• Good prices.
• Ease-of-Use.
• Ability to expand skill sets.

Cons: 

• Updates occasionally can interfere.

H2O: Best For Data Science Professionals

If a company is interested in an open-source predictive analytics tool with data mining features, put H2O at the top of the list. It offers fast performance, affordability, advanced capabilities, and extreme flexibility. The dashboard for H2O offers a veritable smorgasbord of actionable insights. However, this tool is more for the expert data science crowd than for citizen data scientists. 

Pricing:

Open source versions are free. H2O also gives customers the opportunity to request a demo.

Features:

• Automatic scoring pipelines: H2O’s AI provides scoring pipelines that can be deployed to production for interpreted models.
• Automatic feature engineering: The tool aims to help with the problem of feature creation by automatically building new features from a data set.
• The flexibility of data and deployment: H2O’s analytics tool gives flexibility on how and where the company’s applications run.

Pros:

• Great analytical and prediction tool.
• Open source tool.
• Well documented for training.

Cons: 

• Needs containerization facilities.

For more on data mining: What Is Data Mining? Types & Examples

Predictive Analytics Features

Predictive analytics enables companies to actively monitor their data systems, detecting trends for decision-making. Organizations use predictive analytics to get forecasts and provide additional insight. 

Here are some predictive analytics features:

• Reduce cyberattack risk: Predictive analytics can detect unusual activity to reduce reaction time and any cybersecurity breaches.
• Customer segmentation: By organizing a customer base into groups, predictive analytics can make decisions based on customer groups and industry. 
• Organizational improvement: Companies can use predictive analytics models to keep track of their inventory, manage their resources, and operate efficiently.
• Automates work: Everything that can be automated is automated, from formatting data, statistical testing, and generating tables, to updating analyses and reproducing reports.

How To Choose A Predictive Analytics Tool

How does a company choose predictive analytics tools with predictive models that fit their needs? Experts suggest that organizations begin the selection process by defining their exact data analytics and data source needs. Asking the following questions may help:

Who within the organization will be using the predictive analytics tools? 

Will the company have dedicated data scientists who will be running the software and interpreting the predictive analytics model? Or does the company need a solution designed for regular business users and “citizen data scientists” to understand the predictive models? 

The company may also require a tool that can meet the needs of both groups for a well-rounded business intelligence approach. Some tools offer automation and artificial intelligence or machine learning support that simplifies analytics for all kinds of users.

What will be the initial use case for your predictive analytics? 

Is the company planning to use predictive analytics for market research? Fraud detection? Supply forecasting? Machine learning? Predictive maintenance? Data mining? Depending on the company’s industry and their immediate needs, some use cases will make more sense for some companies rather than others. 

The company may also need to determine if classification or regression models are a better strategic fit for the organization. Defining which use case needs to be solved most immediately will determine what features and capabilities the company needs in a predictive analytics software platform.

How will the company’s predictive analytics needs change over time? 

Many organizations start with a predictive data analytics use case that can pay off in a short amount of time, but they hope to expand their use of predictive analytics to other business goals over time. Make sure the company can choose a tool that can meet their current and future outcome expectations.

What other tools does your predictive analytics solution need to support or integrate with? 

If the company will be pulling historical data from a CRM or ERP into their chosen analytics platform, it might make sense to choose an analytics solution designed to support the existing software. Or if the company is already using open source big data software like Spark, the company might need a different tool.

What is your preferred deployment model? 

If the company’s data or other applications leverage cloud computing, they might prefer a cloud-based solution for their predictive analytics techniques. But if most of the company’s data is on-premises, it might make more sense to choose a tool they can deploy on their own servers.

What is the company budget? 

Some vendors offer upfront pricing on predictive analytics tools, but most will make the company speak with someone on the sales team in order to get a quote. The company will likely need to do quite a bit of legwork to figure out the actual cost.

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

Should You Use Predictive Analytics?

Many companies say they believe predictive analytics will be important to their future data analysis success. However, fewer companies have put predictive analytics software into production or fully integrated the software into their business models.

Part of the problem may be the difficulties involved in choosing the right technology for an organization’s needs. Rushing to make a purchase in order to keep up with the data analytics trends is a sure way to end up with an expensive tool that disappoints expectations.

After going through the questions that help organizations begin the selection process, they can define their exact data analytics and data source needs.

Bottom Line: Predictive Analytics Tools

Predictive analytics is valuable to every company. Predictive models are used for reducing cyberattack risks, managing resources, organizing data, managing equipment maintenance, and finding the best solutions possible for a company. 

Many tools can be helpful for predictive analytics, but a company must research and consider many variables to know the best choice for their needs.

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