The era of big data, machine learning, and artificial intelligence (AI) is here. As a result, business leadership has deep data streams to harvest and analyze, in search of how to run their organizations, create better products, expand markets, and increase sales.
Make Sure Data and Analytics Follow Business Strategies
There are caveats in the utilization of big data, however. Data capture and analysis can be so abundant, and such a state-of-the-art pursuit, that teams may fall prey to pursuing and analyzing data for its own sake. It’s relatively common in technology news for data teams to be entranced enough by data that they may lose sight of the larger picture, unless top leadership has a clear plan for what they want to be analyzed, and it’s firmly aligned with business strategy needs.
Your team always needs to be able to answer “data for what?” when the question is posed. Make sure you not only provide the answer but also construct their plans to align with the goals.
…but make sure your team aligns its utilization with business goals.
Data Can Find Out What, But Not Why
In addition, business observers ranging from the Harvard Business Review to the Kellogg School of Management at Northwestern stress both the promise and perils of data streams. Big data and sophisticated analytics are wonderful at telling business leadership the answer to what questions. What demographic purchases your product, or what products’ sales are increasing (or falling) – answered and answered easily.
But these tools are far less good at answering the why. Questions about motivations, preferences, and so on very likely take different tools to unearth.
A recent Harvard Business Review article pointed out that organizational leaders interested in big data and analytics should not forsake other tools, such as focus groups, small group interviews, and observation. These are all well-known qualitative tools. If you want to know why 35- to 45-year-olds are purchasing 401(k)s but 50- to 65-year-olds aren’t, big data is unlikely to tell you (although it could pinpoint any economic or other factors that define these groups other than age).
With qualitative methods, you may learn, for example, that the purchase of 401(k)s is being driven by a widespread desire among 35- to 45-year-olds to retire early. The 50- to 65-year-olds have realized that they are highly unlikely to be able to retire early, and have thus scaled back on their 401(k) contributions.
So effective use of big data requires knowing the business strategy behind its deployment and realizing it can tell you what but not why.
Because of this, big data is often utilized best for predictive maintenance. As the HBR points out, oilfields and railroads, for example, can utilize predictive applications of big data crunching to make sure that equipment is maintained properly, eliminating the likelihood of breakdowns or unforeseen maintenance events.
Big data has ushered in a new world in terms of what, perhaps, but not why. Why questions still require qualitative methods to answer.