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Return on Investment: Turbocharging analytics project adoption

Three critical mindsets – business case, entrepreneur’s and adoption – close the gap between big data investment and production.

Shashank DubeyBy Shashank Dubey

Analytics has gone mainstream in large enterprises. Walking around the office halls, you hear talk of tensor flow, DNN, artificial intelligence (AI) and machine learning (ML), but there is still a large gap between expected and realized ROI from analytics projects.

While you might think technology is the hardest part of an enterprise analytics project, most don’t fail due to inefficient neural networks or bad machine-learning models. They fail because of people.

Research shows that the worldwide revenues for big data and business analytics will grow to more than $203 billion in 2020, with a compound annual growth rate of 11.7 percent. A Gartner survey shows that only 30 percent of organizations have invested in big data, of which only 8 percent have made it into production. That gap indicates that projects are stopped short of delivering their potential.

To close the gap, we suggest enterprises consider a three-point mindset framework consisting of: 1) business case mindset, 2) entrepreneur’s mindset, and 3) adoption mindset.

Business case mindset. Recently, a leading e-commerce marketplace approached us, looking for help reducing customer churn. But not all churn is bad; what it really needed was to retain its profitable customers.

“What the business wants versus what the business needs” is a common refrain in large enterprises. In this case, the business wants to reduce churn, but it needs to retain profitable customers. As data scientists, we are forever ready to pounce at a problem and solve it to completion. But if we don’t take the time to evaluate the business case, we could be off solving the wrong problem. This calls for three perspectives: proof of concept, proof of value and proof of implementation.

Proof of concept takes on the lens of a data scientist. We need to examine constraints of data, algorithms and engineering. The question being explored here is whether the problem is solvable given the finite resources at our disposal, including time.

Proof of value takes on the lens of a finance controller. The problem here is being evaluated from the perspective of financial ROI, with the question, “Is the problem worth solving?”

Proof of implementation takes on the lens of an engineer. The big question being addressed: “Is the solution implementable?”

Most of us use one or two of the above lenses but rarely all three – and they are all critical. This is the first phase of the project, which we call conceptualization – the “thinking” phase. The next phase of the project is the “doing” phase. This is where the potential discovered in the thinking phase needs to become real. Here you need an entrepreneur’s mindset.

Proof of implementation takes on the lens of an engineer. The big question to address: Is the solution implementable? Photo Courtesy of

Proof of implementation takes on the lens of an engineer. The big question to address: Is the solution implementable? Photo Courtesy of

Entrepreneur’s mindset. Imagine your project is to build a car for a client who has never seen one before. The client is going to drive the car you build. She is understandably anxious and needs to be involved in the build process. There are two ways you can execute (assuming you know how to build cars) – you can start from scratch or you can start with a prototype.

In the first approach, you may start with showcasing a chassis and then in stages add tires, steering mechanism, engine, etc. This is how cars get built on a shop floor.

In the second approach, you start with showcasing a prototype. This prototype looks like a real car except that none of its components work. You gradually keep on replacing the dummy components with real ones, evolving the overall design on the go. This is how successful products (and companies) get built. We need this entrepreneur’s mindset to execute analytics projects.

Entrepreneurs also distinguish themselves by prioritizing outcomes over tasks. They seldom land in “operation successful, but patient dead” situations. Here, the outcome mindset warrants the need for effective stakeholder synergies.

But before we get there we need to ask ourselves: Who is the ultimate stakeholder? In most enterprises, there are many proximate stakeholders: analytics leaders, company executives, IT group, etc. However, the ultimate stakeholder – the frontline manager – is often discounted. Ideally, your frontline managers must be the loudest voice in key conversations. But in reality, in most cases they don’t even have a seat at the table. Effective synergy among analytics, executives, IT and frontline managers is the cornerstone of outcome mindset.

The next step is the adoption phase.

Adoption mindset. This last lap is critical. A shiny toy that does the job doesn’t guarantee frontline adoption. Simplicity, scale and integration will need to triangulate here.

Simplicity must be approached from the end user’s perspective. Your analytics solution should make the end user decision-making simpler and faster, not just more accurate. The solution must also be scalable enough to seep across frontline managers who can leverage the solution when and where they want. And finally, the solution must be one that can seamlessly integrate into legacy systems, with minimal resistance and orientation. Nobody wants another app.

In today’s world, analytics is not a luxury; it is basic hygiene. But analytics is not just the work of numbers. Humans are ultimately responsible for the uptake. Keeping the three mindsets in mind – business case, entrepreneur’s and adoption – will help ensure you can convert analytics projects into profitable ROI.

Shashank Dubey is the co-founder and head of analytics at Tredence, an analytics services and solutions company. With more than 13 years of research and consulting experience in applied mathematics and analytics, he has provided analytics consulting across multiple industries – retail, telecom, technology, online marketplace, airline and healthcare – and for clients including Facebook, eBay and Dell.

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