How is it that data can generate different answers to the same strategic business question? How can we be confident in the findings we gather and insights we glean if there can be such variation in the outcomes? Isn’t the beauty of data its objectivity?

Assorted business analytics have become both the cornerstone and the future of business decision-making, generating incredible opportunity. At the same time, data doesn’t seem to be making business decisions easier (more astute) for many companies. In many cases, it’s actually making business decisions more convoluted.

Over the next three weeks we’ll explore the use of business analytics – including Sales, Marketing and Communications data – to answer these questions and more. We’ll also provide a deeper look into W2O Group’s approach to analytics, how we think, what we do and how we do it, which helps us deliver the most actionable and relevant solutions for our clients.

First up….

Data (Alone) is Not the Answer

We all know that there is more data available now than ever before, ushering in what Alan Murray calls a “new era of business enlightenment.” At the same time, McKinsey reported last year that only one percent of data is turned into useful information. What’s causing the disconnect? Why isn’t more actionable insight a result of having more data that’s more easily accessible?

For us, the answer can be found in understanding and appreciating data for what it is (and isn’t). In the broadest sense, data is a resource and tool. It helps us get to the answer, or the best course of action given a variety of variables. As our title indicates, it’s not (unfortunately) the solution itself. Being a tool, it’s only as effective as the person using it – specifically as strong as his/her:

  • Clarity around the objective
  • Understanding of business and research context
  • Ability to identify the right type(s) of data to speak to what’s driving the inquiry

The Objective. No meaningful analysis comes from broad, unfocused exploration. We must be clear on what we’re looking for and open-minded to learning something new, and unexpected. Have a hypothesis. Have focus on what we want to learn about. Understand how learning about that topic will help us take next steps. If it doesn’t, omit it. It will only distract.

Sample objectives: How are our customers interacting with our brand online? Who is our next generation of customers? What are their interests? Are we relevant to our consumers in China the same way we are in England?

The Context. This includes anything that impacts why our objective is important – the urgency and business imperatives. It can be product challenges, opportunities, competitors, policies/regulations, economic health, customer demand, and the list goes on and on. It also considers where “the ask” is coming from internally; Marketing’s interests in customer segmentation, for example, will be different from Communications’ or Operations.’ Understood before analysis kicks-off, context validates the question we’re asking and our objective. Context applied after data is collected gives us insight behind our numbers – why they might be the way they are.

Sample context: Employees expressed frustration in June and July. That’s when we reorganized business units. In August, frustrations lowered AND referrals increased. The open-forums we conducted and manager trainings we implemented seem to be making a positive impact on employee perceptions.

The Right Data. Just because data is available, doesn’t mean it will help us answer our objective. Good analytics is about customized research design that gathers only the information that will help us answer our question. Similarly, it’s also about looking beyond one type of data source and integrating information from a variety of relevant data points as determined by our objective. Identifying the right data reinforces why data is only as good as the people leading the analysis – our understanding of the objective, context and information needed to make sense of and explore a situation.

Sample data: Web analytics, social, search, and traditional market research

As Intel’s Chuck Hemann reminded us at SXSW last Spring, it’s the people leading the analysis who make all the difference in a world of ever-maturing data, technology and automation software. The skill and strategic thinking of these people help explain why (and how) the same data sets can produce different results. Data is not black and white; it’s not purely objective. It is a piece of problem solving that requires an innately curious team of people with tremendous customer, cultural and business acumen. Next week, we’ll take a deeper look at what our team is doing with clients to help them solve their most pressing business questions

This blog series was authored was by Meriel McCaffery, Corporate & Strategy Senior Manager and Abigail Rethore, Corporate & Strategy Group Director. It was made in Los Angeles, Austin, New York, and London with experience and insight from our colleagues Lucas Galan, Head of Analytics Productization; Meredith Owen, Analytics Director; Kelley Sternhagen, Analytics Director; and Paul Dyer, President of Analytics and Insights. Connect with them to learn more!