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Why is Data Analysis So Important?

Why is Data Analysis So Important?

By Mark McGready, Data Insights Advisor, NAED

Believe it or not, we’re in December. Of 2021. The Coronavirus era has had a strange effect of both feeling like it’s lasted forever and yet time is flying by. As a father of two young kids, I’m used to time distortions. With a three-year-old, days can feel like weeks, and yet years can feel like weeks. Chalk that up to the human experience.

Nevertheless, here we are in December. Buying season and budget season. It’s where we reflect on this year and what might come next. What to do in 2022?

Much as we’d hoped we’d be out of the woods on Coronavirus, it’s still there in the shadows threatening to jump out and scare us. It feels like the stories we’ve read about the Spanish flu seem to be repeating now. It infected nearly 500 million people worldwide, killed nearly 700,000 Americans, closed businesses, made mask-wearing the norm, and took three years to finally start retreating. We could be looking at one more year living with Covid before life returns to the new normal. As always, history has a lot to teach us.

And that gets us back to this column—history does have a lot to teach us. And that’s really maybe the best way to think about Data Analytics.

Reporting is the “what” of data. What’re my sales? What’s up and what’s down? What’s my strongest product line? What regions are doing better than others?

Data Analytics is the “why”. Why are my sales where they are? Why is this product line strong and this product line weak? Why are some regions doing better than others?

Going back to the kids, “what” is a pretty easy question. What’s the moon made of? What’s the world’s biggest country? What am I getting for Christmas?

“Why” is far harder. Try explaining why we have a moon to a four-year-old. Or why they can’t get a Playstation 5. However, “why” is the most popular question, and that instinct that kids have for why a thing is what it is gets to the heart of why Data Analysis needs to be a cornerstone of your business assets.

“Why” is an explanation of something, therefore Data Analysis is an explanation of something. It’s the presentation of your business in a manner that provides patterns and insights on behaviors and decisions that had expected or unexpected consequences. It’s essentially your history retold with the benefit of hindsight. You can learn tremendous things from it, and if you’re lucky, you can avoid repeating the history of things gone wrong and repeat the history of things that went right.

The trouble with this is that data analysis is always something that’s talked about and encouraged, but it’s really very hard to put into practice. If it were easy we’d all have large investments in data analytics teams, and every decision would be made based on the absolute truths revealed in data. But that’s not the case, and in fact, data analytics still has a tenuous relationship with business decisions in most circumstances. Experience and gut instinct still have heavier weightings in most circumstances. And that’s not to say that’s wrong. But we have to get to the why. Why is Data Analytics so difficult? And if it’s really that difficult, why is it still important?

We work in a predictably unpredictable industry. In many ways, we have a fairly stable reliable industry. We don’t have many new competitors entering our space each year, our mix of products remains largely the same every year, the sales in a market can be relatively stable from one year to the next. We have some unstable forces coming and going, beyond economic conditions we have acquisitions and moving personnel and manufacturer management teams that can shake things up now and then, but in the same way that we see the same faces again and again at various conferences, we have the same basic business from one year to the next. We don’t have that many new products each year, and most of what we sell has a near-infinite shelf life. Compared to other industries, things are relatively unexciting.

Any unpredictability to our financial success is something we can typically foresee – supply issues, personnel changes, economic challenges. This reliability has removed the urgency for data analytics to tell us what’s going on and what radical changes we need to be making. We feel we kind of know already what’s around the corner. Beyond this, we know that for individual customers business ebbs and flows. While our overall business might be up a few percent or down a few percent, individual customers easily swing in high double digits from one year to the next. It’s the nature of how they buy. Needs rise and needs fall, jobs come and go, only a small part of our business is predictable and reliable.

That’s one of the places where to start with data analysis – with the predictable. Data analysis is really all about finding patterns and having reliable models. You only study history if you think circumstances are repeatable. And those don’t apply to every aspect of our business. There’s no point in studying the eating habits of hunter-gatherers when we have groceries on-demand. We don’t need to learn the best time of year to hunt wooly mammoths. So rather than looking for the universal answer, you first understand where data analysis can be applied. Sales analysis for example might be limited to stock sales only, order management analysis to only larger customers, inventory analysis only to significant product lines, profitability analysis to more controllable customer relationships. It’s going to be different for each company. Perhaps that’s one of the reasons why data analysis hasn’t taken hold as strongly as it should have.

Another aspect of data analysis limitations is primarily looking beyond the basics like sales revenues. Examining other easily measurable factors, like product line diversity consumption, or sales frequencies, or sales repeatability can give you a better understanding of the “why” of your business. For example, why do I have to sell so many non-stock items? A quick run of your numbers might indicate that you sell a significant amount of 1-2 transaction items that are purchased by a tiny part of your customer base. Hence there’s not enough predictable demand to keep a regular stock, and so you’re selling them non-stock. Add up how much of your revenue looks like this and you can see how significant the issue is. From there you can decide if those items only sell infrequently because they’re not marketed well, or not kept in inventory, or not understood by your salespeople or indeed the customers, or if ultimately is a rare sale. And if that’s to be the case, are you making enough on that transaction to cover all incremental costs associated with non-stock transactions, or are you selling to larger customers who have enough price pressure that service and support are more important than profitability?

Lots of data analysis is basically going down a rabbit hole to get to a truth, much like studying history or answering why you can’t have Oreo cookies for dinner. You need an expert in truth-finding who can quickly get to the reality, articulate it in an easily understood manner, and make recommendations on what to do. It takes some time to acquire that skill set, but it’s the heart of data analysis. It’s not about the data, it’s about the communication of the data.

So with that said, why go to all this trouble? Why, this close to the end of the year, is data analysis as important or more important than in the past? Well, it gets back to history again. Without the “why” you don’t understand the consequences, and so you’ll keep doing what you’re doing. Things that might just be realities of doing business could end up being something you could easily correct with just the right approach. Without this, you could continue making business mistakes that are easily correctable – like getting the right mix of stock, allocating resources to the right customers, training in the right products, getting a handle on the predictable and patternable aspects of your business. Cornerstones of your operations that could improve significantly with just the right adjustments.

So, as we close out another year that most of us are glad to see the back of, and with a little caution going into 2022, I’d challenge everyone to reflect on their business and ask a “why”. Why is this the way it is? Can we change it, how much can we change it, and what would it mean to our overall business? If you’re stuck, ask a four-year-old for ideas. So long as you don’t have to promise them a Playstation 5 in exchange.

Reach out directly to Mark McGready with questions or for more ideas at mmcgready@naed.org.

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