Data and Pervasive Intelligence

In last week’s blog I talked about the concept of pervasive intelligence—of embedding intelligence capabilities that go beyond traditional analytics into every facet of your organization. The goal is to make the organization itself intelligent, to entrench digitally-enabled decision-making into the organizational DNA.

When you make intelligence pervasive throughout the organization, it changes the way you interact with your customers, the way you operate, and the way you deploy IT capabilities. However, all of these changes involve one fundamental element: data. Your organizational data–how reliable it is, how valuable it is to you, and how you use it—will largely determine how successful you are at achieving pervasive intelligence.

Make data reliable

You’re only as good as your data. It’s a hackneyed phrase, but it’s true. Reliable data—i.e., data that is clean, consistent, and accurate—is foundational to any effort to embed intelligence and make it pervasive. And though it’s not a glamorous topic, data governance is how you get, and maintain, reliable data.

Governance doesn’t just happen, though. It must be an enterprise effort, preferably led by a chief data officer who’s tasked with leading the effort to develop and enforce enterprise-wide data governance policies and procedures and with making every business unit, department, and work team, accountable for maintaining data standards.

Concentrate on valuable data

Not all data is created equal. The deluge of data that inundates you on a daily basis can make it hard to determine which data is valuable, and which is simply noise. What’s more, data that’s noise today can be valuable tomorrow when markets fluctuate, customers develop new wants and needs, and competitive conditions change.

My point here is not to give a lecture on finding the value of data; that’s for another post. Instead it’s to remind you that whatever data you choose to analyze, and the applications you create to analyze that data, must provide value to you—value that far exceeds their cost. Those applications must provide you with information that enables faster, better decision-making that help you achieve game-changing outcomes that drive costs out and increase your bottom line.

Use data to become intelligent

As I’ve said before, analytics is table stakes. Most companies of any size have fairly robust analytics capabilities. Companies that compete successfully in the future will not simply “use” analytics. They will transform their business with intelligence.

Analytics can tell you what happened, and why it happened. Advanced analytics can tell you what might happen, given well-defined parameters. However, AI technologies can imbue your information systems and decision-making with almost-human-like learning capabilities.

To achieve pervasive intelligence, these AI technologies are a must-have. Technologies such as machine learning (ML) can help you make sense of the data you have and make the best use of it by providing you with real intelligence and the ability to learn from your environment and make increasingly-accurate predictions about the future.

Tying it together—and going further

In the quest to make intelligence pervasive, you need reliable data, applications built from data that provides value, and new technologies that enable you to use data in novel ways to gain near-human-like learning ability. But how do all those things tie together? How do they help you embed intelligence into the fiber of your business?

It’s simple. Reliable data provides access to accurate, consistent information. Valuable data provides the ability to get answers to the most pressing questions you have—answers that will help you compete and thrive. Finally, data used in novel ways helps you better predict changes in your environment and become proactive in order to meet those changes and make the most of the opportunities they present.

But there’s more. To go further, and make intelligence pervasive, you have to make a commitment to avoid complacency—to avoid ever thinking that you’re done, that you can rest on your accomplishments. Instead, you must make a commitment to continuing excellence in maintaining your data standards; to continually evaluate your information systems, vis-à-vis the value you receive from them; and to invest in new technologies that will enable you to look ahead and own your future.

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