June 21, 2019
If John Oliver of “Last Week Tonight” did a segment on data analytics, he might open it by saying, “Data analytics, that topic you discuss incessantly that involves two things you don’t understand—data and analytics.” Okay, maybe that’s unfair, but it does seem that we talk about data analytics in business without always knowing quite what it means or how to use it.
Arriving at a Meaningful Definition of Data Analytics in the Business World
We all have a general sense of what data analytics is about. The practice involves studying data, such as financial figures, and trying to attain insights that might be useful for our businesses. The marketing engines of the tech world do not help with any further clarity, though. They’re endlessly ginning up vague, hyperactive platitudes about “Actionable Insights!”, “Predictive Analytics!” and all manner of useless fluff.
One way to gain a more useful understanding of data analytics is to place it into a specific business context. For example, let’s say you want to run an analytical process on your sales data. You could visualize the data in a bar chart. That’s a very simple analytics task. It would show whether your sales are trending up or down.
From there, you could add Key Performance Indicators (KPIs), which are your sales goals. The chart could then reveal if you are hitting your KPIs. Thus, the analytical process is starting to tell you something about your business. That is what data analytics is actually about: It’s a process that uses data to help you learn new things about what you’re doing, and what you might want to be doing.
Being Proactive Vs. Reactive with Data Analytics
How do you implement data analytics so it will create meaningful insights for your specific business? Each company will have its own unique answer to that question. In general, though the best practice is to take a proactive, not reactive approach to the analytics process.
Being proactive means structuring your data analytics to serve ongoing business objectives. It creates tangible, business-facing actions that arise from data-driven insights. For example, you can use data analytics tools to create a business dashboard. The dashboard might display, on a dynamic, ever-changing basis, how your results are matching up to your KPIs.
A sales dashboard, for instance, could show sales results versus plan on a region-by-region basis. With common tooling available today, you can program the dashboard (without coding) to show underperforming regions in a yellow-to-red spectrum of severity. You could then overlay marketing spend by region to reveal if under-investment in marketing correlates with poor sales performance. If that’s the case, then you would have a data-driven reason to increase marketing spend. Data drives action. This is the essence of proactive data analytics.
You could also build a dashboard that showed financial and operations data in real time. There are an infinite number of configurations for such a display, but your unique business will suggest a setup that makes sense for you. For example, if you run a trucking business, you could visualize data on truck locations, miles driven, on-time delivery and driver overtime pay. This data visualization might highlight problematic routes or delivery customers. With such knowledge, you could then remediate the problem. In this case, data drives physical change in the business. That’s another way of being proactive with data analytics.
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