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 and here in the context of legal discovery

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

The end game of Enterprise Information Management (EIM) is refining raw information into a valuable enterprise asset. What makes information/data valuable is a function of:

• The nature of the information/data in question
• The ways the proprietors of that information/data can envision using it
• The technology available
• The insight, talent, and skill of those charged with executing EIM strategy.

Thus, while it’s common to think of EIM as tech-driven, the most successful EIM strategies are actually far more business-driven than one might expect. Here’s a look at best practices planning and executing an EIM strategy:

Approach it from a pain-point perspective

Perhaps your marketing department is mired in data inconsistencies and duplications, making communications-driven campaigns unwieldy and inefficient. Perhaps the rapidly evolving set of regulations and standards applicable to your business means your compliance program is straining to aim at a moving target. Perhaps you’re a provider, and the six different payers with whom you’re under contract have six different sets of forms and requirements. If you’re thinking about adopting EIM, you probably already have in mind some of the challenges you believe can be addressed through better information management. Such challenges can create concrete business cases you can present to potential executive sponsors and which can form a foundation for assessing the success of your strategy.

Get your stakeholders involved from the get-go

Involving your stakeholders in brainstorming and strategic discussions is, itself, a low-tech form of information management. That’s because EIM is best positioned as a way of supporting critical business activities. Where do your identified pain points interfere with the key business priorities of your stakeholders? How can they be addressed through information management?

Assess your baseline

What’s your baseline level of information management? What are the systems you’re currently using? Who are the vendor(s)? How long are the contractual relationships meant to run? What’s your organization’s approach to information governance?

Secure executive sponsorship

One or more strong executive sponsors can be helpful in securing the necessary funding and support for an EIM initiative. You’ll want to marshal your allies right at the outset, keeping them informed and on-board and available to manage points of friction.

Cultivate enterprise awareness

Congratulations, you understand the need for EIM. But not everyone does. At least not yet. Communicating the need for EIM, the challenges it addresses, unrealized value of information as an asset will go a long way toward readying the people in your organization for the changes envisioned. Overall, your organization needs to be “ready” for EIM, and here are some factors to consider in assessing that readiness.

Adopt a phase-in approach to implementation

You may have a global/big-picture vision of what your organization can achieve as a result of implementing comprehensive EIM, and while EIM is, at its core, an enterprise-wide strategy, it can only be accomplished in manageable bites. But your overall plan should be global and take into account how each phase may affect the next one and the big picture overall. For more specific guidance, each of the factors identified here are equally applicable to EIM as they are to digital transformation in general.

Come up with objective standards for measuring success

The best argument for moving from the first phase to the second and the second to the third, and so on, is results. Ideally, you’ll come up with a way of objectively measuring those results before you begin EIM implementation.

Start with a quick-and-easy win

Starting with a project that’s relatively quick to implement and evaluate, and that’s likely to be a win, will go a long way toward cultivating the continued support and cooperation of your sponsors and stakeholders.

Choose the right partner

EIM implementation requires more than a technology vendor and more than a service provider. EIM implementation requires a strategic partner, one that’s experienced in all aspects of comprehensive EIM planning and implantation. Ideally, your EIM partner will have experience in EIM planning and implementation within your particular industry, as well as in working with customers around the same size as your organization.

As a global leader in business process optimization, Exela works with over 4,000 customers in more than 50 countries and in numerous industries, including banking, finance, healthcare, legal, manufacturing, the public sector. Although we count over 60% of the Fortune® 100 among our customer roster, we work with organizations of all size and on projects of all imaginable scale. We’d welcome the opportunity to talk to you about your organization’s specific challenges and needs.

Information is in abundance, and it’s only becoming more so. The availability of information presents opportunities to create new value and optimize experience for both customers and employees. But leveraging information as an asset presents challenges and risks, including:

• The sheer volume at which data, and especially unstructured (data not easily parsed by basic algorithms) is entering organizations, which volume is only accelerating
• Inconsistent data sources
• Data duplication
• Data inaccuracy, including data that was, at one time, accurate, but no longer is
• Data content inconsistencies
• Data security
• Data privacy
• Compliance with global regulations and industry standards.

The good news is these challenges and risks can be met via “Enterprise Information Management” (EIM), which is also known as “Enterprise Data Management” and “Master Data Management.” EIM is an aspect of Information Governance (IG), which is also known as Data Governance and refers to a business’s overarching policy for handling all information in any form, received from any source or generated by the enterprise, with the end game being the optimization of that information (i.e. maximizing value while mitigating risk associated with the information). You can learn more about IG here.

How is EIM different from IG?

IG addresses an enterprise’s overarching policies and strategies with regard to information. EIM addresses how those policies and strategies are carried out within the enterprise.

So what is EIM?

EIM is an integrative discipline for structuring, describing, and governing information assets across organizational and technological boundaries to improve efficiency, promote transparency and enable business insight. In other words, it’s not any one thing, and it can’t be achieved through the “plugging in” of any one solution or platform. EIM may be best understood as the following four pillars that support the structure of your business:

• Enterprise Content Management (ECM), which is a system for managing information flows across a business, from ingestion to archiving and disposition. Included within ECM is content digitization. Content digitization addresses, among other things, the challenge presented by inconsistent data sources and unstructured data. Also included within ECM is content organization and storage, which establishes rules for storing, sharing, securing, and culling information for use in business analytics and process automation. Exela’s ECM solutions leverage artificial intelligence (AI) and machine learning (ML), among other tools, for establishment and carrying out of such rules.
• Business Process Management (BPM), which is a system for routing your content-managed information into day-to-day processes, working across information siloes while maintaining data privacy as required by government regulation and business standards. The end goal of BPM is optimizing business efficiency while reducing enterprise risk. Exela’s BPM solutions make use of AI and ML, among other tools, to optimize employee experience at both the front-office and the back.
• Customer Experience Management (CEM), which sets up a process for culling your managed content to track and gain insight into customer interactions, make predictions, and optimize each part of the customer experience journey, from business generation to end-user-experience. Powered by AI and ML, among other tools, Exela’s CEM solutions help businesses to optimize customer experience, including by identifying and addressing customer needs and maximizing reach.
• Business Intelligence (BI), which refers to turning information into actionable insight leading to better decision-making and then making that insight work for you through consolidation, visualization, alerts, and search capabilities. Exela’s BI solutions include AI-powered information management and consolidation/centralization of search capabilities.

We’ll be discussing more about EIM and how best to implement EIM in future blogs so stay tuned to the Exela Blog. In the meantime, don’t miss this insightful thought leadership article from Exela’s President, Suresh Yannamani on how healthcare organizations can use EIM to prevent fraud