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What is the most valuable asset a business has?
There are a number of potential answers such as its staff, location, technology etc… but for most businesses, one of them is ‘data’.
Most businesses, no matter what industry they’re in passively collect data that, when analysed can provide deep insight. More and more businesses are getting smarter about how they collect and manage their data, and quite rightly are giving analyses a prominent place in their decision-making processes.
But becoming a data-driven or even efficient business isn’t easy. There are plenty of pitfalls that can turn data into a liability and even result in decisions being made without the facts being known.
Here are five rules for data-driven businesses looking to avoid those pitfalls.
1) Too much data is harmful
More and more businesses have recognised the importance of collecting data and analysing it to drive important decisions. That’s a good thing. But many can make the mistake of collecting every bit of data they can find. This is not only distracting; it can reduce the quality of data-driven decisions because those decisions are only as sound as the analysis they’re based on. When too much data is collected, there’s a greater likelihood that the wrong analysis will be performed.
2) KPI metrics derived from data should be tied to goals
Sure, knowing, for instance, what your company spends, on average, acquiring each new customer is a good thing to know. But how much should you be spending acquiring each new customer? Chances are that’s a lot more important, which is why, in many if not most cases, metrics should be associated with goals.
3) Context helps
Context is your friend, so use it when setting goals. Using the customer acquisition example: what is the average cost of customer acquisition in your market? Are you above or below that? Are you getting what you want from your activity? What reduction in customer acquisition costs would boost income by 10%?
By adding context to your equation, you can make sure that the goals you’ve tied to KPI metrics are meaningful to your business.
4) What happened then, will not necessarily happen now
Data is inherently limited to yesterday and today – therefore applying predictive analytics which applied yesterday to today’s data – does not necessarily mean the future will follow the same historical performance. No matter how sophisticated your data is or how you have performed previously, predictions are simply educated guess work.
Data-driven business that use data to make educated decisions; should not naively believe that data is a crystal ball.
5) Don’t dismiss the qualitative
Hard data is wonderful, but if you’re only paying attention to the hard data, you’re missing out on a huge part of the big picture. How do your customers relate to your products and services? What is most important to your stakeholders? These are questions that can help guide a business down the right path, but the most important aspects of the answers to these kinds of questions won’t always be provided by numbers that can be crunched.
Tags: Data