How AI is Changing the Enterprise Data Management Space | Tips & Tricks

Whether it’s helping enterprises to more efficiently identify and serve customer needs, eliminate manual workflows, or discover actionable business insights, artificial intelligence (AI) has the power to transform business operations. But leveraging the power of AI first requires organizations to take a closer look at their approach to enterprise data management.

AI can automate the task of analyzing vast amounts of data, transforming raw information into actionable business intelligence. Take IBM’s Watson as an example: the cloud application applies complex analytics to big data, simplifying predictions and correlations into user-friendly insights. But if organizations want to reap the rewards of this emerging technology, they must first rethink how they store and process data, so that every document and piece of information they collect can be used to fuel better business results.

Read on to discover how AI is changing the enterprise data management space and why prioritizing the quality and accessibility of organizational data is more important than ever before.

AI: Powered by Data

It’s hard to overhype the potential of AI. And while many organizations have deployed AI for various functions already, the fact that we have yet to reach wide implementation means that we don’t yet know the full enterprise potential of artificial intelligence.

But AI itself is merely a vehicle; as we consider the power of these automated insights to transform operations, it’s important to understand what fuels AI so that organizations can best position themselves to take advantage of these innovations. That’s where enterprise data management comes in. At the most basic level, AI is powered by a steady stream of information. While the complexity of a task determines the amount of data required, in any scenario, it would seem that the more data, the better the results will be. This is only half true, however. A high quantity of data can provide more analytical information, but the quality of that data plays a crucial role.

Garbage In, Garbage Out

When it comes to utilizing data to fuel AI insights, not all information is equally useful. Think about how much data the average Fortune 500 company generates in a year, and all of the forms in which this data might exist. Data exists in ECM, CRM, and ERP solutions, in contracts, emails, and purchase orders, on spreadsheets, and in countless formats and locations. While the term “data lake” refers to a storage repository that holds vast amounts of data in its raw form, most of these lakes could more accurately be described as swamps, with a mess of information that cannot be readily utilized.

Meanwhile, the old adage “garbage in, garbage out” has never been truer than in the relationship between data and artificial intelligence. If the quality of insights gleaned through AI hinges upon deep stores of quality information, it follows that if much of that data is unorganized and exists in formats that can’t be readily ingested by analytics application—or if redundancies and errors are rampant—the data won’t drive useful insights.

AI requires that these vast stores of data are searchable and exist in a common format. Similarly, in order to reap the full benefits of AI, enterprises require powerful automated data management tools that will allow them to convert data into useful formats. Examples of these tools include:

• Enterprise Optical Character Recognition (OCR) technology, which transforms image-based documents into searchable PDF assets. Scanned documents are typically captured as images, meaning their contents cannot be readily searched or analyzed. But OCR converts scanned pages into text, unlocking the data within.
• Progressive Classification technology, which automates the document classification process and reduces ROT (Redundant, Obsolete, Trivial) data. Progressive Classification eliminates the manual work using advanced document conversion, clustering, and rules-based workflows to process massive volumes of unstructured data that exist across multiple lines of business. The process groups similar documents to enable easier processing.
• Other data-enrichment tools that aid enterprises in automatically identifying documents of interest and converting them into formats that can be readily processed.

Unstructured Data: A Vast, Untapped Resource

IDG estimates that there will be 163 zettabytes of data in the world by 2025—and 80 percent of that data will be unstructured. Unstructured data can include image files, nested and threaded emails, paper documents, and documents that exist in outdated formats. A lack of visibility between business lines can also contribute to high degrees of redundant information.

With all of that unstructured data, most organizations are likely sitting on huge, untapped pools of AI fuel. The challenge is getting all of that information into useful, structured formats from which data can be efficiently extracted. And while data currently being collected is one valuable resource for powering AI, enterprises are also likely sitting on another vast, unutilized resource: years of historical data. Unlike new data, this legacy information can yield insights that transcend a specific moment in time, revealing patterns or trends, put outlying information in context, and yield more accurate predictions.

But the analysis of historical information opens up a new challenge within enterprise data management as old records are by nature likely to be unstructured: they may or may not be digitized and likely exist in formats which cannot be readily digested and analyzed. Though manually converting such data would be an onerous task, the analysis of historical documents is yet another area where automated data management can provide enterprises with a significant edge, yielding vast insights that real-time data alone cannot match.

Wrap Up

Though AI has the potential to transform business operations, strong enterprise data management is a vital precursor to success. Before enterprises can derive the benefits of machine learning, they must first invest in creating the high-quality data fuel that will drive powerful business insights. By adopting robust data-enrichment tools, enterprises can transform vast volumes of unconsolidated data into readily accessible, process-ready PDF/A assets that support big data analytics, workflow automation, and information governance and compliance initiatives.