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AI lives on data. The more data that can be accessed, and the more accurate and contextual that data is, the better the results will be.

The problem is that the amount of data currently being generated by the global digital footprint is so great 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 achieve a meaningful effect on AI-controlled processes.

AI helps AI

Because of this, many companies are turning to AI to filter 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, with the global load skyrocketing from “just” 1.45 petabytes in 2016 to 14.6 petabytes today. Since data is generated in the data center, in the cloud, at the edge and on networked devices around the world, it can be assumed that this upward trend will continue in the future.

In this environment, any organization that is not making full use of data is literally throwing money out of the window. For the future, the question is not whether AI should be integrated into data management solutions, but how.

AI brings unique capabilities to every step of the data management process, not only because of its ability to search through huge volumes in search of salient bits and bytes, but also because it can adapt to changing environments and changing data flows. According to David Mariani, founder and Chief Technology Officer at AtScale, AI can, for example, automate key functions such as matching, tagging, join and annotation, especially in the area of ​​data preparation. From there, it is able to verify data quality and improve integrity before scanning volumes 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, with medical research bearing much of the burden. So it’s no wonder that clinical research organizations (CROs) are at the forefront of AI-driven data management, according to Anju Life Sciences Software. On the one hand, it is important that data sets are not overlooked or simply discarded, as this can distort the results of extremely important research results.

Machine learning has already proven itself in the optimization of data collection and management and often maintains the validity of data records that would normally be rejected due to entry errors or incorrect documentation. This in turn leads to better insight into the results of the experimental effort and leads to a higher ROI for the entire process.

Master the data

Still, many companies are just getting their new Master Data Management (MDM) suites up and running, so it is unlikely that they will be replacing them with new intelligent versions anytime soon. Fortunately, you don’t have to. According to Open Logic Systems, new classes of intelligent MDM boosters are emerging that will enable companies to integrate AI with existing platforms to support everything from data creation and analysis to process automation, rule enforcement and workflow integration. Many of these tasks are trivial and repetitive, leaving data managers free for higher-level analysis and interpretation.

This trend towards using AI to manage the data it needs for other tasks in the digital enterprise will transform the way data scientists and other knowledge workers work. Employees are no longer tasked with the work they are doing now, but instead focus on monitoring the results of AI-driven processes and then making changes when they deviate from defined goals.

Most importantly, AI-driven data management will dramatically accelerate the pace of business. Data is king in the digital universe, and kings don’t like to wait.


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