Companies take on innovative, analytics-driven projects for two main reasons: 1) to generate organic revenue growth, and 2) to gain a competitive advantage. There are many side benefits, but the ultimate driver of why companies use data to make better decisions can generally be rooted in one or both of these goals. And while this is a worthy ambition, there is always risk in planning a project that has never been done before, especially when it is a complex or groundbreaking initiative.
When developing an analytics-based solution, it is vital to keep the approach to problem-solving flexible and iterative in order to maximize the likelihood of success and user adoption.
But why do analytics projects fail as often as they do? Project management professionals generally attribute the failure to missing the mark on one or more of the following: schedule, resources, and scope.
Complex projects leveraging advanced analytics, big data, and PhD-level mathematics are even more subject to changes in scope due to the complexity and innovative nature of the proposed solution. In order to account for this, project leaders must focus on the structural planning elements and allocate time for iterating on the initial vision as progress is made. While there should be a strong vision locked in at the beginning of a project, project leaders must also be willing to change and re-think the execution of that vision as the project moves along and nuances are discovered. The concept of the captain going down with his ship is romantic in maritime literature, but not something most modern day professionals aspire to.
Think of it like building a house. It’s vital to have a clear blueprint from the beginning, with details on the purpose of each room which help to frame the house. Yet, the architect must leave room for adjustments and individual elements that can be added, such as windows, doors, flooring, lighting, and appliances that work best together. This will ensure the home layout is practical while still fulfilling the buyer’s vision.
Planning multiple iterations of design, prototype, and feedback within an analytics module is critical to ensure the solution can keep up with current trends and market developments. If too much is planned out in advance, there is risk of executing towards a less refined vision, and of potentially building something that is inhabitable, but not very desirable, and certainly not at a premium position in the market. However, too much ambiguity in the initial design introduces major risk of creating something that doesn’t make sense, e.g., putting two bathrooms next to each other to save on plumbing expenses.
Though some industries move at a faster pace than others (think high tech startup vs. a publishing company), project leaders in all industries and professions face the same challenges when designing an analytically-driven solution. While it may take finesse and experience to achieve, striking the right balance of clear upfront vision, combined with iterative development, market testing, and deployment is a requirement to beat the odds in the analytics game and generate meaningful organic revenue growth and competitive advantage.