AI will soon oversee its own data management

The problem is that the amount of data currently being generated by the global digital footprint is so vast that it would take literally millions if not millions of data scientists to analyze it – and it still wouldn’t be fast enough to make a significant impact. on AI-powered processes.

AI in service of AI
This is why many organizations are turning to AI to help clean up the data needed for AI to function properly.

According to the Dell Global Data Protection Index 2021, the average enterprise now handles ten times more data than it did five years ago, with the total load dropping from “only” 1.45 petabytes in 2016 to 14.6 petabytes today. With data being generated in the data center, cloud, edge and connected devices around the world, we can expect this upward trend to continue in the future as well.

In this environment, any organization that is not leveraging data to its full potential is literally throwing money out the window. Therefore, in the future, the question is not whether to integrate AI 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 sift through massive volumes for key bits and bytes, but also because of the way it can. Adapt to the changing environment and changing data flow. For example, AI can automate key tasks such as matching, tagging, joining and annotation in the field of data preparation alone, according to David Mariani, founder and CTO of Atscale.

From there, it is able to check 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.

Healthcare is one of the most data-intensive industries, with medical research driving a fair share of the load. According to Anju Life Sciences Software, it is no surprise that clinical research organizations (CROs) are at the forefront of AI-powered data management. On the one hand, it is important that datasets are not overlooked or simply discarded, as this can skew the results of extremely important research.

Machine learning is already proving its worth in optimizing data collection and management, often preserving the validity of datasets that are usually discarded due to collection errors or faulty documentation. This, in turn, leads to a better understanding of the results of testing efforts and generates a better return on investment for the entire process.

master the data
Yet many organizations are starting to implement and operate their new Master Data Management (MDM) suites, so they are unlikely to be replaced with smart new versions anytime soon. Luckily, they don’t have to. According to Open Logic Systems, new classes of smart MDM boosters are coming to the channel, giving organizations the ability to integrate AI into 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, freeing up time for data managers for high levels of analysis and interpretation.

This trend of deploying AI to manage the data needed to perform other tasks in the digital enterprise will change the nature of work for data scientists and other knowledge workers. People will no longer be responsible for the work they are doing now and will instead focus on monitoring the results of AI-powered processes and then making changes when they deviate from set goals.

However, more than anything, AI-powered data management will dramatically speed up the pace of business. Data reigns supreme in the digital world, and kings don’t like to wait.

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