Making Data Analytics Work for You – Instead of the Other Way Around

The authors of a recent article by our friends at McKinsey stress that data, and data analytics, are means to an end, and not an end in themselves. Specifically, they exist to inform better decisions and drive improved performance. They suggest, therefore, that organizations pursue purpose-driven data.

Ask the Right Question

It all begins by asking the right question. With limited time and resources, the more well framed the question the better. Here I’d like to offer an excerpt:

In the real world of hard constraints on funds and time, analytic exercises rarely pay off for vaguer questions such as “what patterns do the data points show?” One large financial company erred by embarking on just that sort of open-ended exercise: it sought to collect as much data as possible and then see what turned up. When findings emerged that were marginally interesting but monetarily insignificant, the team refocused. With strong C-suite support, it first defined a clear purpose statement aimed at reducing time in product development and then assigned a specific unit of measure to that purpose, focused on the rate of customer adoption. A sharper focus helped the company introduce successful products for two market segments.

In another case, a company spent years developing a data lake and making sure the data was pristine. While they were busy doing that their competition ate their lunch.

Combine Sources of Information

The author’s note that combining sources of information can make insights much sharper. That means of course the ability to integrate with different systems and harmonize the data. A great example can be found in this Puma Case Study.

The authors make a compelling argument including this observation:

Insights often live at the boundaries. Just as considering soft data can reveal new insights, combining one’s sources of information can make those insights sharper still. Too often, organizations drill down on a single data set in isolation but fail to consider what different data sets convey in conjunction. For example, HR may have thorough employee-performance data; operations, comprehensive information about specific assets; and finance, pages of backup behind a P&L. Examining each cache of information carefully is certainly useful. But additional untapped value may be nestled in the gullies among separate data sets.

A key requirement of enabling technology has to include the ability to connect to multiple systems; and if needed, the ability to harmonize, map and massage that data right inside the system.

It's a Team Sport

Data sitting in a system with only a handful of people using it delivers substantially less value than a system that is embraced by the business. The User Experience (UX) may sound technical, but it will ultimately determine how widely the system is accepted and how extensively it is used. The challenge is that different user communities (think Finance, Marketing, Operations, Human Resources) will have different needs and different ideas about “what easy to use” actually means. So flexibility in delivering the right look and feel for multiple groups becomes a critical requirement.

Drawing your users in—and tapping the capabilities of different individuals across your organization to do so—is essential. Analytics is a team sport.

What’s the payoff?

The payoff depends on the value of the decision. For example, basket analysis for a retailer or restaurant chain can reveal what items are often ordered together. Consumer behavior can be altered by offering the right promotions that please the customer and improve profitability.

The authors describe another example -- a CPG company that established a goal of improving the margins on one of its leading cereal brands. Since they were in a very competitive market they found it hard to increase pricing, so they looked to ways to improve productivity and reduce Cost of Goods Sold (COGS). They documented process flow maps for entire manufacturing process, breaking it down into sequential increments and then, with advanced analytics, scrutinized each of them to see where it could unlock value. In this case, the answer was found in the baking process. By adjusting the baking temperature by a small amount, they found the product taste better and also made production less expensive. The savings dropped right to the bottom line and they met their margin improvement goal.

Another example? The authors point to a large steel manufacturer that rationalized an end-to-end system linking demand planning and forecasting, procurement, and inventory management. By making small improvements throughout the value chain they realized savings approaching 50 percent—hundreds of millions of dollars in all.

The Puma Case Study referenced above provides another detailed example.

We’ve covered some of the highlights of McKinsey’s insightful article here, but there is more (including how to institutionalize and embed data analytics in the business) and I would urge you to read the full article here.

With special thanks to the authors and contributors:

Helen Mayhew, COO QuantumBlack, a McKinsey affiliate based in London

Tamim Saleh, Senior Partner McKinsey

Simon Williams, Co-Founder and Director of QuantumBlack

and Nicolaus Henke, Chairman of QuantumBlack for his contributions to this article