AI can soon manage its own data

AI lives on data. The more data it can get, and the more accurate and contextual it is, the better the results.

The problem is that the amount of data currently being generated by the global fingerprint is so huge that it would take literally millions, if not billions of data scientists to process it all, and it still wouldn’t go fast enough to perform a task has a significant impact on AI-controlled processes.

AI helping AI

It is for this reason that many companies are turning to AI to eliminate the data that AI needs to function properly.

According to Dell’s Global Data Protection Index 2021, the average company now manages ten times more data than five years ago, and the global load has increased from “only” 1.45 petabytes in 2016 to 14.6 petabytes today. For data centers, cloud, edge and networked devices around the world, it can be assumed that this upward trend will continue in the future.

In this environment, any company that doesn’t make the most of its data is literally throwing money out of the window. Therefore, in the future the question will no longer be whether AI should be integrated into data management solutions, but how.

AI provides unique capabilities for every step of the data management process, not only by the ability to search through vast amounts of featured bits and bytes, but also by the way it can adapt to changing environments and data flows. According to David Mariani, founder and CTO of AtScale, for example, AI can only automate key functions such as matching, tagging, join and annotating in the area of ​​data preparation. Improve quality and integrity prior to volume scanning to identify trends and patterns that would otherwise go unnoticed. All of this is especially useful when the data is unstructured.

One of the most data-intensive industries is healthcare, where medical research is a good part of the burden. So it’s no wonder that clinical research organizations (CROs) are at the forefront of AI-supported data management, according to Anju Life Sciences Software. It is important that records are not overlooked or simply discarded as this can distort the results of a very important investigation.

bMachine Learning has already proven itself in optimizing data collection and management and often preserves the validity of data records that would normally be rejected due to entry errors or incorrect documentation, which in turn leads to a better understanding of the test results of trial efforts and leads to a higher return on investment for the entire process.

Mastering the data

Still, many companies are just getting their new Master Data Management (MDM) suites up and running, so they are unlikely to be replacing them with smart new versions anytime soon. Fortunately, you don’t have to. New classes of intelligent MDM drivers are coming into the pipeline that will enable companies to incorporate artificial intelligence into existing platforms to support everything from data creation and analysis to process automation, rule application, and workflow integration – many these tasks are trivial and repetitive giving data managers time for extensive analysis and interpretation.

This trend towards implementing AI to manage the data you need for other tasks in the digital enterprise will change the way data scientists and other knowledge workers work. People will no longer be tasked with doing the work they do now and instead will focus to monitor the results of AI-controlled processes and to make changes in the event of deviations from defined goals.

But above all, AI-supported data management will accelerate the pace of business dramatically. Data is king in the digital universe, and kings don’t like to wait.

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