Redefining “The Last Mile” in Analytics

Why is “the last mile” always last? And why is it even called that? In every company, the people who make the vast majority of daily decisions are the people in the field — the people making tactical, impactful decisions on an hourly basis. But in tech terms, the point at which these users interact with analytics is thought of as the “last mile.” While I’ll use the term “the last mile” in this blog, I’d like to propose that we use the term “the last mile of analytics” to illustrate the importance of this strategically critical thought process.

The last mile should be your first priority

It’s always bothered me that, in today’s customer-centric world, we’re still using the words “last mile.” This makes it sound like the point at which analytics has become embedded into all workflows and processes throughout the company and successfully used is an afterthought, when nothing could be further from the truth. Your strategy for bridging that “last mile” should really be driving your project, because, to be blunt, your analytics project is a failure if it’s not successfully deployed to and used by your front-line employees on a regular basis. And by regular, I mean daily, if not hourly.

What has been termed the last mile is truly an integral part of any analytics project, but I find that people often overlook planning because they don’t understand how strategic that effort needs to be from the get-go. Planning can be expensive or time-consuming if the project’s already underway, but companies need to take the time to consider exactly how analytics can be used for a certain process and made intuitive for the user to use.

The last mile of analytics can make or break your analytics project

I recently read a McKinsey report that outlined nine key drivers of company success when it comes to analytics. Those drivers are considered best practices that enable these companies to break away from the rest of their competitors when it comes to using analytics to dramatically improve a company’s performance. While these drivers cover a fairly wide range of successful strategies to improve analytics and overall company performance, the last mile of analytics was, understandably, a major fixture in their report.

McKinsey found that nearly 90% of these organizations that are significantly outperforming peers are devoting more than half of their analytics budgets to bridging the last mile. It’s not considered an afterthought, as the last item on a company’s project checklist. It’s treated as the point where users and the company as a whole are ultimately able to put the value of the data they’ve collected to work.

Bridging the last mile of analytics means finding the right place for insights

Without considering the last mile of analytics, analytics investments can go to waste. Spending time and resources getting a robust platform up and running means nothing if those algorithms and capabilities aren’t brought into business processes where they’re actually useful. All of those powerful capabilities need to be embedded in every process for every user — and it also needs to be simple and intuitive for users to take advantage of them.

I’ve been talking a lot about the idea that, for analytics to become pervasive and for the last mile to be bridged, the user’s analytics experience has to be seamless. Unfortunately, the user experience is often treated as an afterthought but it should be shaping your entire analytics project. For users to actually use analytics, it has to be simple and intuitive to use. Any associated insights and recommendations presented in the user’s typical workflow so there’s no sense of disruption.

At the end of the day, companies who overlook the importance of the last mile of analytics will find their projects failing and their performance suffering. Either analytics won’t be deployed where it should be, or user adoption will suffer due to poor user experience. Companies should be putting the last mile — or maybe we should start calling it “the first mile” — at the top of their priority list for every analytics project.