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Bob Gourley

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Data Algebra: What is it and Why Does it Matter?

Businesses all over the world have seen the trending movement toward big data, and if they’re not already on board, they’re looking to join the fun soon. Big data analytics brings a lot to the table as it helps companies discover new insights on how to run their organizations, how to reach customers, and in what areas they should be focusing their attention. This can be seen in the explosion of the big data industry, which is predicted to hit $125 billion this year. With big data comes complications, however. After all, businesses are suddenly collecting and analyzing vast mountains of data -- information that can be incredibly difficult to organize, not to mention the inefficiency that comes with it. As with most problems in the business world, solutions are usually close behind, and a better way to manage big data analytics may soon come to the forefront in the name of data algebra.

Data algebra plays into the goals that many companies wish to achieve with big data. Those goals include wanting to process, analyze, manage, and search for data in the most efficient and least costly way possible. This was always a challenge using big data analytics techniques. Some tools and platforms, like Hadoop or Apache Spark, are known for their difficulty and complex setup. Newcomers often struggle, and even if they understand what to do, the whole process can be time-consuming. Data algebra aims to make the process easier, or at the very least something that won’t take up an entire day.

While the inner workings of data algebra, as described by data analytics platform provider Algebraix Data, can be complicated, the basic concept revolves around the idea that all big data can be represented algebraically. Data algebra essentially uses mathematical set theory to take data tasks and describe them in ways that are understood and processed quickly and efficiently when dealing with analytical systems. It’s basically a specialized analytical approach that takes a lot of the busy work out of doing big data analytics.

This approach results in a number of benefits for companies. The biggest one is the optimization of the analytics process. Depending on the size of the data sets, queries could often take hours to complete, with new queries needing to be run any time new data was added to the set. Companies that adopt data algebra can significantly cut down on the amount of time needed to perform these queries. This is because data algebra resolves requests for data, then stores those requests inside a specialized catalog. When queries are performed on the same data sets using the same specifications, analytics platforms need only reuse the same parameters while also adding in any new information from the last time the query was run. Reusing in this manner makes for speedier performance and more sophisticated optimization. In addition, data algebra also bypasses the costly process of ETL -- or extract, transform, load -- which also reduces the number of errors organizations may encounter.

The overall effect of using data algebra is more efficient big data analytics, which is exactly what businesses are looking for. While the actual process may seem complicated, the uses are many, especially for data tasks that are repetitive. Those can be seen in the gaming world, security features (a particularly important item in today’s business environment), and especially for the Internet of Things (IoT). In fact, the IoT is expanding at a tremendous pace as sensors get embedded into more items and used in cities than ever before. If data algebra becomes a more regular capability of these items and the companies behind them, the long task of analytics can be seriously shortened. This would also solve some of the problems many are finding when trying to implement the IoT.

It’s important to note that data algebra is still a new concept. Even though Algebraix Data has been around for a number of years, the company has only recently started to reveal how it all works. Time will tell if the idea catches on, particularly among the major players in the big data vendor market. With so much money at stake, it would be surprising if data algebra didn’t become a popular process used by organizations of all types.

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Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com