Three Steps to Becoming Data Profitable

Big Data is out and the businesses that continue to win won’t just be data-driven. They’ll be data profitable.
3 Steps to Becoming Data Profitable 1

If you read our last two blogs The big promise, and big lie, of Big Data and What is Data Capitalization Management you know that Big Data is out and the businesses that continue to win won’t just be data-driven. They’ll be data profitable.

Specifically, there are three steps on this path to becoming a “data profitable” organization.

Going from What to Why

Dashboards — the most prominent form of data management within business over the past two decades — exist to provide a macro understanding of the business via data visualization. At their most helpful, the dashboard can inform you of existing or emerging challenges. If sales are down in the southwest, it’s fairly easy to see that in your dashboard.

In other words, dashboards tell you what is happening. Understanding why it’s happening is more complicated.

But it’s this why that matters most.

Understanding the why is when challenges can be addressed head on and opportunities uncovered. Most organizations (indeed most leaders) struggle with getting from the what to the why. And using dashboards to get to that level of granularity usually involves several challenges including:

  • The insights are surface-level — meaning actually capitalizing on them is difficult at best;
  • You need to know the exact question to ask — if you don’t have the question you want answered formed with extreme precision and the data structured to answer that exact question, the insights revealed won’t be actionable;
  • Getting to the root cause requires a lot of filtering — once you’ve identified an issue, you need to filter down to increasing levels of granularity to understand the root causes with no guarantee of success.

Using the DCM process, the focus goes from diagnosing what is happening in the business to capitalizing on why. This essentially moves the business units from collectively working against high-level goals to executing on granular, often nuanced, insights. The goal is to put these insights at the team’s fingertips rather than requiring deep dives and pulling on data science or analytics resources for lengthy diagnostic cycles.

This isn’t to say dashboards aren’t useful. It’s recognizing what they’re useful for, and that to compete effectively in today’s market requires your business teams to have access to a more granular layer of insights.

Turning Complexity Into a Competitive Advantage

Complexity is often seen as the enemy of progress when it comes to analytics. Traditional BI and even AI often can’t handle the inherent complexity of the data a business collects. So tradeoffs are made.

The amount of your data left on the cutting room floor could actually lead to new, fresh, and surprising insights.

To understand why, it’s helpful to think of our knowledge of the business in three spheres of increasingly growing sizes.

  1. The first and smallest sphere represents what we know. This is the knowledge of the business we understand today.
  2. The second sphere, which is larger, represents what we don’t know but have a well-formed hypothesis about. In other words, we don’t fully understand the problem or opportunity, but we’ve got theories we can test.
  3. The last sphere, and by far the largest, represents what we don’t know and don’t even have an hypothesis about. Put succinctly, this falls into the category of “we don’t know what we don’t know.”

Most organizations only probe the first two spheres. Tools like BI are available to help visualize the first sphere. Analytics and data science experiments are good for probing the second sphere. But most only scratch the surface — at best — of the third sphere.

Yet, it’s only when the business is able to analyze data across all three spheres that it is able to fully transform their data from a liability to an asset. When the full picture across all spheres is revealed, the business secures a competitive advantage. Not only that, it’s a proprietary competitive advantage as attributes within the data, particularly the complex data, often is only knowable to the specific business.

In a DCM business, complexity is embraced rather than simplified.

Moving from Insights to Opportunities

This is the last mile of analytics. Frustratingly, even when insights are painstakingly revealed many (some would argue most) never get acted on.  After all that hard work of finding the insight, it simply sits in a backlog and becomes irrelevant to our future progress.

In the DCM process, moving from the insight to an actual opportunity is as important as finding the insights themselves. Data profitable organizations ensure there’s a clear workflow for prioritizing insights based on their monetary impact, moving them into action, and tracking the realized return over time.

Businesses do not get rewarded in the market based on how many insights they successfully identify; they are rewarded for the opportunities they find, act on and successfully execute. The organizations that outperform their competitors are the ones that expertly manage opportunities unearthed in the data.

The Rise of DCM

Research from analyst firm Gartner predicts that we will soon see the convergence of Analytics/BI, data science and AI. And that this convergence will allow companies to be more agile and will transform the analytics ecosystem.

We believe that convergence is already happening.

We call this Data Capitalization Management. Put succinctly, when AI and human intelligence are working together harmoniously, it is seen in the efficiency that the business transforms its data into provable ROI — not in questions answered or theories probed, but in capital created by the business through its data analysis.

If you’re ready to learn more about Data Capitalization Management, click here.

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