Are We Really Heeding Our Data?

Everyone understands the value of data, at least everyone currently trying to extract value from it. It promises to aid decision making by adding objective facts to our subjective assumptions. Data shows us things as they really are, which is exactly why it can’t be ignored. So, why are so many of us ignoring it?

A recent survey from McKinsey highlights this issue. Data is revolutionizing the decision-making process, yet 72 percent of the executives surveyed believe bad decisions are just as common as good ones. Despite all the insights at our disposal, we don’t appear to be getting better at choosing right over wrong.

The reason should be quite obvious – we are not leveraging data as effectively as we believe. Maybe you are looking up data yet not factoring it into your decision making. Or perhaps you’re simply avoiding analytics, choosing to rely on intuition for most of your decision making. Everyone wants to use data to the fullest – be it leveraging sales analytics or even big data! The simple fact is that most companies don’t. But that doesn’t mean they can’t.

Heeding your data takes more than a commitment. It takes a conscientious effort from everyone on your team throughout every decision-making process. Following these four steps ensures you’re using every resource available to act as wisely as possible.

Step One – State Your Purpose

Everything you do has a purpose – lowering costs, increasing revenue, etc. It aims to solve a problem in order to provide a benefit, and both of those must be clearly defined. Insights from sales analytics is great at helping you focus your objectives while trying them to specific indicators and metrics. For instance, if you want to lower costs, how low do you want them to go, and where will cuts have the biggest impact? Most projects have an implied purpose, but data brings it into stark relief. That way, everyone involved understands exactly what you’re trying to do.

Step Two – Questions Your Assumptions

This is where things often go awry. Decision makers assume they understand the situation. A combination of past precedent and gut feeling turn decision making into a kind of guessing game. People either dismiss the need for analytics. Or, worse, they seek out data that merely confirms what they already believe. It’s the exact opposite of smart decision-making, yet it’s tempting to operate on instinct. The better strategy is to question anything, trust nothing, and confirm everything. You have data at your disposal that reveals the truth. Above all else, let data provide your guidance and justification.

Step Three – Develop Key Business Questions

At this point you should have defined a purpose and agreed on shared assumptions. The final step is to develop key business questions (KBQs). These are the questions you need to ask to follow through with your purpose. For instance, if you want to lower costs, ask where the biggest sources of waste exist? Alternately, ask what your most efficient and cost-effective process currently are? The goal is to get all the information you need to act before you actually put plans in action. You are trying to systematically eliminate unknowns so that every choice is certain. Quantity is more important that quality when it comes to KBQs, so create a list, of questions, then begin methodically hunting down answers.

Human nature is not all to blame for poor decision making. Sometimes the tools we use to manage data are part of the problem too. If those tools are too confusing or complex to use, people will just avoid them. Understandably, they would rather push forward then spend extra effort just to prove themselves wrong. Your processes are important and deserve attention and improvement. But none of that will matter unless you have the right tools too.

Niels Bosch
About the Author

Niels is the founder of http://Amongtech.com. He writes about technology, gadgets, tech news, and more. Contact Niels by email at [email protected]

Leave a Reply

Your email address will not be published. Required fields are marked *