This post is part of an ongoing series that explores how organizations can use data and analytics to drive performance outcomes.
A client recently told us they were considering a large-scale analytics solution. They excitedly described all the new capabilities they will get, such as real-time reporting, integration of data across multiple platforms and parallel crunching of high-volume data. After probing a bit, we found that while the client was clearly excited about the program's features and technological capabilities, they hadn't yet identified the problem they were trying to solve. Moreover, they had no plan for what they would actually do with the data and insights.
Given the time and expense associated with implementing a large-scale solution, it's worth considering whether building small programs around specific problems and then scaling up is the right approach for your organization. Starting small provides some real benefits if done correctly, because the organization can find its analytical footing and learn what it needs from a technology, data, management and talent standpoint to drive business impact. Moreover, a small application on a specific problem allows an organization to better understand the ROI of its analytics approach before scaling up to a larger solution.
Here are some questions you should ask when trying to decide between going small or big with your analytic solutions:
Does your organization effectively use existing data and insights? Making productive use of data requires a host of different talents and skills. These include performing sophisticated analyses, of course, but also managing data storage and integration, translating business problems into analytics, and reporting actionable insights in an easy-to-digest format. If your organization does not have these skills now, it will not suddenly learn them just because you invest in an analytics solution.
What is the organization doing with the insights it has already produced? An organization that ignores data-driven insights isn't going to stop just because the insights start arriving in real-time. If you aren't already data-driven, starting with a smaller project can win support, demonstrate business value and encourage taking action on insights before investing in a larger solutions.
What specific business problems would the solution solve today? Estimate the business value for the known problems the solution would likely help solve, and see if it justifies the investment. The safe bets are those in which the ROI is high based only on the known problems. Be explicit when making the list of problems your organization is seeking to solve, because it informs system requirements, implementation, staffing and cost. If the list has few problems -- or mostly low impact problems -- it's time to consider knocking off the problems individually with smaller-scale solutions.
Do you know how the analytics program will solve your business problems? Realistically, most leaders can't recite the math, but they should still have a firm grasp on the predicted impact of the solution and how it fits into their organization's analytics strategy. Realizing the value of analytics requires a lot of moving parts besides technology, though it can be tempting to believe that the new solution will somehow put all the pieces together. If you are clear on who the solution will serve, in exactly what ways, and what specifically will be done with the results, you're in good shape. On the other hand, if the solution sounds a bit like magic to you, then you should consider getting your feet wet with smaller applications first.
Who is going to use the new solution? Even the best analytical teams can be overwhelmed if they are asked to master a new solution while continuing their existing projects. The larger the solution, the larger the disruption you could see. If your organization isn't ready to add specialized staff, it may not realize the full benefits of the new solution. In this case, the organization may be better off adding smaller programs incrementally, especially as the ROI becomes more apparent.
A final thought to consider is whether your organization understands that any solution you buy or build will never improve your profits by itself. Profit increases when talented analysts find business-relevant insights and when energetic, data-driven cultures act on those insights to seize opportunities. Without those pieces in place, buying a solution and expecting an analytics strategy to emerge is much like buying a hammer and hoping a house will appear. In both cases, there is still a lot of work to be done before seeing the results you want.