Big Data Market Review

The sheer volume of big data continues to grow as corporations and consumers depend on mobile devices, streaming, the Internet of Things (IoT), and other technologies that collect and use massive quantities of data. 

And with the growth of big data, the market is seeing a steady increase of tools and capabilities for storing and analyzing it. 

See below to learn about the top tools in the big data market and how enterprises are benefiting from the increased insights borne from big data.

What’s Happening in the Big Data Market

• Big data
• Big data market
• Growing 3 Vs of big data
• Benefits of big data
• Big data use cases
• Big data solution providers

Big data

“Big data” was first coined in the 1990s when technology experts began to recognize the quickly expanding pools of data in enterprises as well as the growing problem of processing and applying that data with existing technology.

Big data has grown across industries throughout the 21st century, and with that growth has come the development of different big data tools to handle the data.

Big data informs several business decisions and operations, but is especially helpful in the following categories:

• Customer Analytics
• Operational Analytics
• Fraud Detection
• Compliance
• Data Warehouse Optimization

Read next: The Pros and Cons of Edge Computing

Big data market

The global big data market reached $208 billion in 2020 and is projected for a steady compound annual growth rate of 10%, reaching $450 billion by 2026, according to Expert Market Research. 

The growth is mostly attributed to a growing desire to make all business data actionable in a competitive marketplace, with the growth of IoT devices contributing to the expansion of big data solutions.

Although North America is leading the market in big data software adoption and general strategy, China is the fastest-growing country in this predicted time period, with Australia, India, and South Korea also increasing their big data investments at a healthy rate.

Growing 3 Vs of big data

In order for software to be classified as appropriate for big data management, it must meet the criteria of the “3 Vs”: variety, volume, and velocity:

• Variety: The tool is able to work with a variety of data types, whether structured, unstructured, quantitative, or qualitative.

• Volume: Big data consists of terabytes and petabytes of data to manage. Big data software must have the capacity to store/analyze higher volumes of data than traditional data tools as a result.

• Velocity: Especially in a fast-developing IoT marketplace, big data solutions have to be able to work with data quickly and in real-time in order to produce desired results.

More on data variety: Structured vs. Unstructured Data

More on data variety: Qualitative vs. Quantitative Data

Benefits of big data

Understanding big data provides huge benefits to organizations that take the time to set up, uncover and analyze their data. 

Some of the top benefits that companies realize when they tap into their big data potential include:

• A better understanding of customer behavior at various stages based on large amounts of data that might have previously gone unanalyzed. This information is particularly helpful in the entertainment, e-commerce, and tourism industries.
• For organizations that want to simplify their network security and operations practices, big data makes it possible to develop AIOps and automate different network functions, such as application performance management and network monitoring. This frees up time for network administrators to spend on more strategic projects.
• In industries, such as finance, banking, government, healthcare, and others with frequent and large transactions, big data analytics improves fraud detection, risk management, and general compliance.

Big data use cases

Customers across industries use big data tools to make sense of their customer and product data at scale. 

They frequently rely on the big data analytics in these tools, but perhaps more importantly, they use these tools for the data visualizations and reports that make big data digestible for non-data professionals:

“Tableau is integrating with other software applications, as well as banking institutions. As such, you can log in to Tableau and see all your accounting data in one place, rather than bouncing back and forth between different platforms and spreadsheets. Tableau also has easy to understand and beautifully-designed reports. You can tell that there’s a lot of technology going on behind the scenes to generate these reports, but nothing on the user-facing side is complex or overwhelming.” –User review of Tableau at TechnologyAdvice

“We have been using [Hitachi Vantara’s] Pentaho Business Analytics for the past 3 years in our department due to its best services for data visualization and data analytics. It is open and easily added to any platform. It provides such an easy UI that non-technical persons can get the use and analysis results. Easily configurable and deployed at our organization.” –Software manager, manufacturing industry, software review at Gartner Peer Insights

Big data solution providers

Some of the top big data solutions in the market are:

• Amazon Web Services: AWS’s solutions for big data include cloud storage, databases, data warehousing, analytics, and machine learning services.
• Hitachi Vantara: This lineup features big data storage, DataOps, IoT services, and big data analytics.
• Tableau: The Salesforce-acquired tool offers big data analytics, business intelligence, and data visualizations
• Cloudera: This big data platform offers a Hadoop distribution, plus data science and analytics tools.
• Microsoft Azure: The cloud platform offers storage, big data analytics, machine learning, data warehousing, and data lakes.
• IBM: IBM’s big data solutions include cloud services, database management, data warehousing, analytics, and machine learning.
• Oracle: The Oracle suite of big data solutions includes cloud-based and on-premises database management, data integration, and analytics.
• Splunk: This offering primarily focuses on analytics for log and security data.
• Talend: The solution features a set of big data integration tools.
• RapidMiner: The data science platform includes data mining, predictive analytics, and machine learning solutions

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Learn more about what’s happening in big data here.