All data is good, particularly when it’s relevant and accurate. The greater amount of data AI Systems can access and the more precise and contextual that data is, the better the results will be.
The difficulty is that the current volumes of data being generated by the worldwide digital footprint are so vast that it would take millions, if not billions, of data scientists to crunch it all — and it still wouldn’t happen fast enough to make a real difference.
AI technologies are used to assist AI.
With all of this, it’s no surprise that many businesses are turning to AI in order to assist clean the data required for AI to operate correctly.
According to Dell’s Global Data Protection Index for 2021, the typical company now manages ten times as much data as it did five years ago, with worldwide load soaring from 1.45 petabytes in 2016 to 14.6 petabytes today. We may expect this upward trend to continue well into the future, since data is generated in the datacenter, cloud, edge, and on connected devices all around the world.
In today’s world, any business that doesn’t use data to the fullest extent is literally squandering money. So, going forward, the question isn’t whether AI should be integrated into data management solutions; it’s how.
AI’s differentiators come into play at each step of the data management process, not just because it has the ability to sift through large quantities of data for important bits and bytes, but also because it can adapt to changing scenarios and shifting data flows. AI can take on many of the heavy lifting in terms of data preparation, according to David Mariani, founder and chief technology officer at Atscale.
AI can automate critical activities like matching, tagging, connecting, and annotating just in the area of data prep. It’s great at checking data quality and increasing integrity before scanning volumes to find trends and patterns that would otherwise go unnoticed. This is especially beneficial when the data is unstructured.
Health care is one of the data-intensive sectors, with medical research accounting for a significant portion of the load. It’s no surprise that clinical research organizations are at the forefront of using AI to manage data, according to Anju Life Sciences Software. For starters, it’s critical that data sets aren’t overlooked or simply discarded since doing so might distort important study findings.
Machine learning is becoming increasingly useful in reducing data collection and management costs, as it helps to preserve the legitimacy of data sets that would otherwise be lost due on to failed collection or faulty documentation. As a result, the whole process gets better insight into trial outcomes and generates enhanced ROI.
Working with data effectively
Even as smart MDM versions become increasingly popular, many businesses are still getting their new master data management (MDM) systems up and running, making it unlikely they’ll be replaced with even smarter versions any time soon. Fortunately, this isn’t necessary.
New classes of intelligent MDM boosters are entering the channel, according to Open Logic Systems, allowing organizations to integrate AI into existing platforms to support data production and analysis, process automation, rules enforcement, and workflow connectivity. Tasks that are time-consuming and monotonous are generally eliminated. This frees up data managers’ time for more in-depth analysis and interpretation.
People working in the data science and other knowledge roles will have their jobs change as artificial intelligence is used to manage the information it needs to execute other functions in the digital business. Data scientists and other knowledge workers will no longer be required to perform the duties they do presently, but rather they’ll be charged with monitoring AI-driven processes and making adjustments if goals are not met.
In other words, data management driven by AI may greatly enhance business activities. Data is vital in the digital world, and monarchs dislike to wait.
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