The following is adapted from Guaranteed Analytics.
Creating an analytics system is a wise move for nearly every business. But it’s easy to make mistakes when you’re first starting out that can derail your program before it ever gets going.
“Shiny ball syndrome” has become a popular expression for becoming easily distracted. The result is a string of unfinished projects or projects that last months longer than they should.
How do you put an end to shiny ball syndrome in your analytics system? By building roadmaps, you won’t be directionless and carried by whims when you embark on making something from your data. Try using the three key philosophies below to stay on track in your analytics setup.
Shiny Ball Syndrome in Analytics
Imagine you throw a piece of bread in a koi pond. Immediately, 10 fish dash over and start eating the bread. But toss another piece in a few feet away, and half of those fish will jump to that meal, abandoning the first slice of bread. Throw in another piece, and you can watch the process repeat itself.
That’s shiny ball syndrome, and humans are just as guilty of it as animals are. In analytics it shows up as being entranced by a new data tool or piece of technology, only to become distracted a month later by some new buzzword or computer program.
We think these novelties are going to make analytics easier or more fun. But they become another boondoggle that keeps you from reaching your goals. Try using the following ideas instead.
You Can’t Eat an Elephant in One Bite
To create an analytics roadmap that works, you first have to realize you can’t eat an elephant in one bite, as the proverb says. Too often, businesses are overly ambitious in their analytics goals, trying to do everything at once, when they’ve never even had an analytics system before. Instead, you need to break projects into manageable chunks, even microtasks, which allows you to see progress and evaluate as you go.
The corollary to the “you can’t eat an elephant in one bite” rule is that everything takes longer than you expect it will. Therefore, you need to build in time buffers, so you stay on track without disappointment or conflicts. And don’t let shiny ball distractions suck up even more of your precious time.
Crawl Before You Walk
Many companies are also overly optimistic about how fast they can scale their analytics. Sure, analytics done right lets you eliminate many labor barriers and time factors, but you still need to crawl before you can walk and walk before you can run. And we all know crawling and those early walking steps can involve some tumbles and get back on your feet (remember everything takes extra time, so budget for it).
Before you rush out and start collecting huge volumes of data without even knowing how you plan to use it, you want to set value-driven goals and some questions you want answered with your data. For a simple example, say you want to know how many blue shirts, your top-selling product, you sold this year. Why is this important?
As a raw number, your blue shirt sales are probably meaningless. But imagine you discover your sales take a dramatic rise every June. There’s a story in the data that can give you even greater insight, like that Father’s Day or graduations, are driving sales. Knowing that, you could plan to sell other blue merchandise or similarly colored shirts around that time of year.
That sounds pretty basic, but being content with the basics at first and focusing on information you have to have (versus nice to have) will keep you on track until you get the hang of it and can slowly grow your analytics. Understanding your flagship blue shirt sales is vital before you can go off collecting data about a potential new product or an item that makes up a tiny segment of your market.
Planning and Prioritizing
Obviously, deciding what questions you need to be answered and where you will get your initial data takes some planning. You can brainstorm with your team to put together a list of top issues to tackle with analytics, so you have some sense of priority and a calendar to accompany your list.
At this point, many companies make the mistake of making everything an absolute top priority. If everything is an “A” priority, then in reality, there are no defined priorities. You need to go back and look at your company culture and business management to figure out why everything seems so urgent. A basic tenet to keep in mind is that analytics is never a firefight but a strategic tool to be used over time.
If you define quarterly phases with an 18- to 36-month roadmap, set specific and realistic goals, and evaluate your progress to make adjustments, you’ll be able to keep finding value in even your earliest analytics attempts. Participants will stay engaged, and you’ll be able to stick to your roadmap and expand on it for the future.
There will be less temptation to become sidetracked by shiny balls when you define your analytics path first, like setting a GPS map in your car and not stopping at every rest stop along the way. You’ll have fewer unfinished projects and missed deadlines, and you’ll finally get the value from your analytics that led you to start your program in the first place.
For more advice on how to create an analytics roadmap, you can find Guaranteed Analytics on Amazon.
Jim Rushton began his career in analytics working with some of the biggest consulting companies in the world, including Accenture, Deloitte Consulting, and IBM Global Services. Jim then moved to an executive position with Verizon, where he oversaw the company’s customer and marketing information. Leveraging his experience across corporate America, he helped found Armeta Analytics, and in the past decade, his team has helped dozens of Fortune 1000 companies learn how to monetize their data.