Too often, organizations fail in the last mile of the analytics process and never secure the … [+]
Being married to an avid marathoner and triathlete, I have had the opportunity to observe multiple race finishes over the years. It’s an incredible experience to see athletes push through the final mile of a race when they’re often physically, mentally, and emotionally exhausted. When I asked my wife about her experiences with the “last mile,” she confirmed it was often the most grueling part of each race but also the most exhilarating and rewarding. It culminates weeks and months of planning, training, and conditioning.
Similarly, in the field of analytics, organizations make a considerable upfront investment in their data initiatives. After data is collected, it typically must be aggregated and prepared before any analysis or modeling can occur. It’s estimated these data collection and preparation activities consume up to 80% of the time and effort. In the analytics process, the “last mile” represents the final stage in which insights are translated into changes or outcomes that drive value. If you’re fortunate enough to reach this final stage in the analytics process, not crossing the finish line will mean all of the preceding work was for naught—no medal, no glory. And yet, many organizations stumble and fall in the last stretch—never achieving the expected return on their data investments.
Companies pass through multiple stages in the Analytics Process before reaching the last mile of the … [+]
After passing through the data collection, preparation, exploration and modeling phases in the analytics process, too many organizations assume everything will magically come together at the end. In the 2019 NewVantage Partners survey, 77.1% of companies found business adoption of data initiatives was a major challenge. Executives who participated in this survey noted people and process (95%) represented the bulk of the challenge—not technology. The last mile is where the emphasis pivots from technology to people. At this stage, the data is inserted into everyday processes and placed at people’s fingertips to help influence decision making. Unfortunately, like an athlete that has expended too much energy on the initial phases of the race, companies frequently misjudge the full distance and then fail to get their data initiatives across the finish line.
Recently, a technology startup ran into a last mile problem. Its professional services team wanted to provide deeper client insights to its customer success team to assist with their core activities. The professional services team introduced a new process and tool to capture richer data on client engagements. The customer success team recommended embedding this information directly into an account management application they used on a daily basis. However, due to limited technical resources, they could only manage to put the information into a standalone dashboard. Although the new dashboard contained valuable information, it wasn’t a part of the customer success team’s workflow . . . so it wasn’t adopted. Eventually, when the professional services team saw the data wasn’t being used, they stopped collecting and sharing the client information. In this case, as in many others, a potentially beneficial data initiative with good intentions unraveled in the last mile.
Two key steps to finishing strong in the last mile
First, part of the last mile problem can be attributed to how organizations approach the analytics process. Most companies start with the data and then try to determine where and how it can be utilized. Instead, you should prioritize the last mile of the analytics journey and work backward. In an excellent McKinsey article “Breaking away: The secrets to scaling analytics,” authors Peter Bisson, Bryce Hall, Brian McCarthy and Khaled Rifai noted, “[Companies] should start by identifying the decision-making processes they could improve to generate additional value in the context of the company’s business strategy and then work backward to determine what type of data insights are required to influence these decisions and how the company can supply them.” By beginning with the end in mind, organizations are better prepared and focused to finish strong in the last mile.
Second, in the last mile, user adoption can make or break the success of your data initiatives. In order to finish strong, you need to ensure people are running downhill, not uphill. While it’s not always feasible to do so in actual races (as my wife reminds me), your analytics solution should make it easier, not harder, for people to make better decisions. If the end users haven’t informed your design approach, you’re not prepared for the last mile. You need to look beyond just the data or technology to other human-centered elements such as training, communication, usability, data visualization, workflow, incentives and accountability. As Bain & Company’s Chris Brahm stated, “When you’re designing the analytics, involving the customer of those analytics in that process, you think about the context into which those analytics are being put. Whatever incentive changes, training changes, how the analytic is presented, the visualization of it, the simplicity of it, et cetera—all those things are contemplated, and you prepare for adoption in the last mile.” If you take a holistic approach to the last mile, you’re more likely to complete the race.
Libby Dykes (my Ironman wife) completing the last yards of the Indian Wells, CA Half-Ironman event … [+]
A 80-90% failure rate for data-related projects shows we collectively haven’t yet figured out how to run the entire analytics race. While many data initiatives start strong, very few finish strong. Rather than having these initiatives fall apart in the last mile, you’ll want to begin with the end in mind. If you can pinpoint the specific value-generating decisions to be optimized upfront, you can establish a much clearer path to success. In addition, you must explore how the data will ultimately be applied by the end users and how it can be integrated into their existing workflows, embedded in their existing applications, tailored to their existing capabilities, and so on. The more they feel like they’re running downhill, rather than up a steep incline, the more likely your data initiatives will be embraced and adopted.