Information Competency: Some Data Just Matters More
This is the second of three collaborative posts (Part I) in which David Lyle, Rob Karel and I take an introductory look at the next-generation Integration Competency Center that we’re calling Enterprise Data Competency (EDC). Our first post discussed integration competency, and the need for agility despite escalating complexity. We also considered that most businesses don’t realize that their largest system is the often ad-hoc collection of technologies and processes used to connect information. You might not call it an enterprise integration system, but you have one, and it’s probably suffering.
With this post, we’d like to address information competency. If integration competency is the ability to connect disparate streams of data throughout your organization, information competency is the ability to manage the meaning and context—and therefore the business value—of that data. Data is hard to question, but businesses can argue forever over what it’s saying.
The simplest example might be a client in the insurance industry who shared that the first half hour of every executive meeting was spent arguing over three different reports showing claims paid in the past month, all with different results. You’d think if there was one metric every insurer would get right, it’s the one showing how much money is going out the door. But it turned out that three different systems were reporting three distinct data points regarding claims:
- One system was counting claims by looking at the payment authorization date on the claim.
- A second report counted claims based on the date the check was cut.
- A third report registered the date the check was cashed.
The problem wasn’t the data—every report was current and accurate. The problem was that the company had not agreed on which report represented their source of truth—and more importantly lacked sufficient data governance processes and best practices to ensure everyone could agree on how the business wanted to define a “paid claim” in the first place. You can see similar problems across many industries. You may assume your company has a shared definition for a “customer,” but that word probably means different things to your sales, service and legal departments. For example, is it a business customer or a consumer? If a parent carries the contract and pays the bill on a cell phone family plan, are the children on the plan also customers?
Information competency is not a technological concern, but a business one. Put simply, it’s clarity on how data delivers value to the organization. The business owns the processes and decisions dependent on data, so this is not an IT challenge alone; the business must accept accountability to ensure success.
The art of understanding
That insurance example would appear to have a clear-cut answer: Just pick one of the three available definitions and metrics around paid claims, and move forward. But overall information competency is not so easily attained.
Information competency requires new behaviors, skill sets and measurements. Industries such as telecommunications, financial services, healthcare, consumer products, and media & entertainment are all harnessing digital technologies to attract and retain new customers. No industry, or enterprise, can afford to be on the sidelines of the shift to digital. And every industry is going to have to grapple not only with the science of the data, but the art of deriving meaning and value from it.
Informatica helps organizations transform the business into a data-centric operating model to compete in the digital marketplace. We rely on the ICC and EDC models because we realize that it’s hard to serve the enterprise’s data needs when, rather than a holistic approach, we’re constantly chasing individual goals with isolated initiatives. Many think of data as something that feeds today’s initiative, rather than as part of a wider ecosystem. The systems or solutions that support CRM or a data warehouse or sales operations function seem tangible and concrete, whereas the idea that “all our data should be integrated” can sound hopelessly vague.
The holistic enchilada
But no system in an organization, especially in this new digital world, exists on its own. The only way to do effective, business-driven analytics, and to be able to respond quickly to new ideas and opportunities, is to approach your data infrastructure as an ecosystem, a broader collection of systems that are tuned to connect, cleanse, master, govern, secure and share data. Being data centric means not obsessing over your ordering or manufacturing system. Instead, think about the data associated with an order, a customer, a product and the critical business processes that capture, update and consume that data. From there, we can determine which systems can help automate the process and deliver data as needed. It’s a more abstract notion, and therefore a harder one for organizations to adopt, but the distinction is important.
How do you get there? The most useful way to approach it may be the most clichéd: the alignment of people, process and technology. And it’s worth noting that those are listed in order of importance. The tools you use, and the features they offer, have potential, and the processes you instill can promise efficiencies, but anyone who’s led even a minor business transformation knows that if your people don’t embrace the tools and adopt the processes, failure is all but certain.
Instead, it’s important to look at the people required to get the job done. Define clear job descriptions, roles and responsibilities. Train and recruit effectively to ensure the right skills are in place. And ensure an attractive development path exists for those willing and able to dedicate their careers to ensuring your data assets deliver optimal value. Then you can look at the tools and processes that facilitate the goals of the organization. It’s a journey much further into process reengineering than you might otherwise expect.
Getting started on your business value-centric data journey
The first steps toward information competency involves realizing that not every process, business opportunity or piece of data is equally important. Organizations must identify the ones that deliver the greatest value, and that present the greatest challenges. Juxtaposing the low hanging fruit against the most meaningful priorities helps you pick the initial targets. From there, small successes mount to build momentum.
Our next post will tackle transformational competency—perhaps the central competency of digital success. Until then, you’ll find more on information competency in our Just Enough Data Governance eBook and the GovernYourData.com website.