The following is adapted from Guaranteed Analytics.
Building a carefully crafted analytics roadmap is essential to the success of your system, especially if you are starting analytics from scratch. When you take the time up front to set priorities, ask thoughtful questions, and build in enough time to reach your goals, you have a much greater chance of deriving true value from your analytics, which is typically monetizing your data somehow.
Using time fencing and design edits are two key elements of analytics that your business can start using today to make your analytics program effective. You’ll stop missing deadlines or having projects that go on forever. You’ll also make the entire process more user-friendly for employees.
Eager to learn more? Here are some quick tips to get you started.
It’s not just enough to allocate appropriate time for your analytics tasks. You need to set realistic deadlines and delivery expectations. This not only helps keep your analytics program on track, but it also shows the program works and can generate value for your business.
Make your efforts time-sensitive by setting and sticking to deadlines. Don’t keep extending deadlines or making excuses. This is what is known as time fencing.
If you don’t have 100 percent of your data, information, or conclusions by the agreed-upon date, it’s better to turn in 90 percent of it than to change the timeline.
The beauty of time fencing is it builds in accountability. It also helps manage expectations. If a team is waiting for information from another group, they can’t demand it ahead of schedule, nor can the first team deliver it whenever they get around to it.
“Design edits” are an innovative and efficient way to build a successful analytics roadmap. This means beginning with a prototype and making small changes to it bit by bit, rather than starting from scratch.
Imagine you’re designing a new automobile. How would you deal with being handed a lump of clay and being told to create a car out of that raw material? Most people would be so stuck that they’d make no progress whatsoever.
But if you’re given the rough design of a car, you can more easily make changes and work it into something unique that meets your needs. Move the parking brake, improve the dash—it’s all easier to do when they’re roughly in place already.
Likewise, you can’t hand knowledge workers the task of creating an analytics system. They use analytics programs; they don’t design them.
If you create an analytics prototype with a small group and then invite user participation and feedback, your chances of success will be greater, and you won’t be wasting human effort (which is time and money) on reinventing the proverbial wheel. You’ll be able to get your program up and running five to 10 times faster this way and save a lot of negative feelings and frustration.
Time fencing and design edits sound like major concepts—and they are—but you can use elements of them today to launch or improve your analytics system. If you’re never reaching your deadlines, time fencing is for your team. It takes a few hours to create a timeline with milestones you can stick to.
If you’re getting your program off the ground, think carefully about who is shaping the initial system, versus who is coming in later to tweak it and find new ways to make it useful. Analytics is always best when it not only brings value but does it in a streamlined, efficient manner. If you’re monetizing data, doesn’t it make sense to start reaping the profits as early in the game as possible?
For more advice on how to make your analytics system more effective, 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.