Imagine this all-too-common data analysis scenario:
Your company has recently pulled the trigger on a data project. Excitement is in the air. You’re finally doing something about all the data you’ve been collecting. You’re getting answers that will make your organization stronger. You’re going to leave the competition in the dust. Then your first reports and visualizations arrive. And they are, indeed, packed with information. For example, maybe you pinpoint a bottleneck in the supply chain. Maybe a visualization instantly shows a big spike in customer complaints. Perhaps you notice a drop in over-the-phone sales, despite more customer calls overall. And maybe you see a strange dip in admin productivity.
Now, all of these insights are useful, of course. And it’s not something you could have easily understood by looking at endless spreadsheet rows and columns. But something is still missing.
The importance of context in data analysis
If one of your vendors is suddenly taking longer to deliver materials, you need to understand why before taking any action. You need to know why customers are complaining before you can know what to do about it. You need to determine the reasons sales are slow and productivity is down, before you can decide if new training programs should be rolled out or if new managers should be hired.
So, as powerful as data analytics and visualizations are, they’re just two of the essential three elements required for real insight. The missing piece is context. Without the bigger picture, you may take the wrong action on your data. Which, of course, misses the whole point of the data analysis initiative.
The big takeaway is this: Data all by itself – even data accompanied by stunning visualizations – isn’t going to do the whole job. You’ve got to take the 10,000-foot view. There’s a bottleneck in the supply chain because a hurricane has backed up containers at the destination port. As a result, customers are complaining because their deliveries haven’t arrived. Phone sales and admin productivity are down because employees are spending a lot of time on customer care. This is an overly simple example, but the point remains. Seeing the big picture means you avoid short-sighted and misinformed decisions.
Once the full problem is explained and you understand the whys, then you can take impactful action. That’s when you can truly harness the full power of data analysis. If you’d like some help getting the big-picture overview, check out Insight Snapshot. It’s an easy way to see your data in context, quickly and affordably.
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