If Data is The New Oil, Here’s How To Refine It: Enterprise Information Management

Background Image
The Exela Blog

If Data is The New Oil, Here’s How To Refine It: Enterprise Information Management

Share This
by Lauren Cahn

Back in 2006, the mathematician, Clive Humby, made the observation, “Data is the new oil[1],” thus spawning a seemingly endless debate, which should have been considered settled when the Economist observed in 2017 that data has overtaken oil as the world’s most valuable resource.[2] But what Humby said next has never been disputed. Like oil, he pointed out, data must be refined in order to be made useful. Specifically, Humby was referring to data “analysis,” although it seems unlikely he meant to exclude the other forms of refinement that together comprise what we now know as “Enterprise Information Management” (EIM).  

Here’s a round-up of some of those “refinements” that can help turn your data into the Digital Age-equivalent of “black gold”:

Data unification

Data comes in many forms, but most falls into the categories of structured versus unstructured data. Structured data is highly organized into discrete fields and is easily recognized, digested, searched, and otherwise utilized by machines. Unstructured data, which refers to information that doesn’t exist in machine-ready form, is pretty much everything else (think: photos, handwriting, social media, the contents of emails). Unifying data that currently exists in multiple forms is critical to EIM. One way we accomplish this at Exela is through cognitive automation, including RPA (robotic process automation), which helps reduce error and otherwise streamlines the process, and which we invite you to read about here in the context of billing and receivables, here in the context of legal discovery, and here in the context of compliance.

Data centralization

Data silos are separate sets of data that aren’t integrated enterprise-wide. Sometimes the result of legacy IT systems or pre-existing corporate culture predating the adoption of EIM, silos are a common obstacle to effective data management, resulting in internal inconsistencies, redundancies, and other inefficiencies. Although limited segmentation of information may be called for in certain circumstances, enabling a common information source for the enterprise is a far more useful starting point. For example, a “single source of truth” can be critical to accurate and effective financial reporting. In sales and marketing, that “single source” can mean the difference between your customers receiving the same communication once versus multiple times.

Federated Search

In those cases where a central data hub is either not possible, not desirable, or not required, it may nevertheless make good sense to adopt federated search capabilities for easy information retrieval. Federated search allows for information in disparate systems to be pulled via a single query. Here’s an example of how and why federated search can streamline your company’s compliance with KYC (Know Your Customer) regulations.

Data analytics

Analytics and modeling engines can extract deep insights from your data sets, enabling you to discover patterns, identify correlating factors, and utilize predictive modeling to anticipate future trends. Imagine, for example, a hospital emergency room that used patient-traffic analytics to predict which times of the week, and which times of day, require heavier staffing. Or what if your information were “assetized” to the point where you received automated triggers on an integrated dashboard to notify you that it’s time to make an equipment lease payment or time to file a required regulatory disclosure.

Data visualization

Even the most insightful data analysis loses its worth if the critical information uncovered can’t be displayed in a way that people can understand. Integrated visualization tools are an aspect of EIM that permit users to easily create intuitive charts, reports, and other comparative visualizations.

Quality control

What’s the value of data that isn’t accurate? Rhetorical question, of course. A comprehensive EIM program requires quality controls to sustain data fidelity and usability. As with data unification and other aspects of EIM, the use of cognitive automation can reduce error and streamline the process.

Ultimately, there’s a harsh truth at work here, which is that just like digital transformation, implementing EIM isn’t really an option so much as a mandate. But like digital transformation, implementing EIM need not overwhelm your business’s resources, particularly if you enlist the help of the right technology partner—one who’s experienced at “refining” data for enterprises of your size in your industry and is committed to guiding through the process of defining and executing your overall data strategy.

Stay tuned for future installments in our EIM series, including Best Practices for Implementing EIM.

 

[1]https://enterprisersproject.com/article/2019/7/data-science-data-can-be-toxic
[2]https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
Share This