In the race to be data driven, many organizations undertaking transformation projects find themselves in the middle of the pack at the finish line, instead of at or near the front. Especially with GIS projects, companies are disappointed when their supposedly best-of-breed hardware stack doesn’t unleash the full potential of their data or their people.
Perhaps surprisingly, there are common issues that most organizations deal with when implementing major GIS analytics projects. Here are three of the top situations to avoid, along with solutions that can ensure the success of your project:
1. Prepare a comprehensive plan.
It’s remarkable how many organizations set out on a GIS implementation without a clear, tightly defined goal. They tend to focus immediately on the data, tools, algorithms and models, long before they concern themselves with the fundamentals of the project.
It’s critical to articulate the value of the project upfront to the enterprise. Be concise, focused and, where possible, visionary. Is this a potential revenue-generating opportunity? Does it de-risk some current process? Does it enable a greenfield capability or other important new function? Whatever the value is, communicate it in clear and inspiring terms.
But don’t stop there; define the success criteria. What are the timelines for completion? What is the organization willing to invest in financial and human resources? What are the tools, data and algorithms that can be applied? Defining these parameters is a critical step, and one that shouldn’t be ignored.
2. Fail fast or succeed quickly.
A famous quote from Klaus Schwab, founder and executive chairman of the World Economic forum, summarizes the world of technology today: “In the new world, it’s not the big fish which eats the small fish; it’s the fast fish which eats the slow fish.” When teams don’t show results on an implementation project after months and months of work, it drastically decreases their overall chances of success.
The overriding approach should be to either fail fast or rise to the next plateau. Be willing to quickly acknowledge that something — whether it’s a model, an algorithm, the technology, the data or the team — didn’t work. Then reassess, reevaluate, figure out what the next iteration is going to be, and move forward.
3. Present the outcomes.
Even when planned and executed well, a geospatial data project can be severely hindered when stakeholders, including senior executives, haven’t been briefed at the conclusion about the value created.
Too often the implementation team focuses on the behind-the-scenes work, the nuances and iterations, and doesn’t communicate the business results. Yes, it’s impressive to visualize billions of data points on a map as a showcase, but not necessarily valuable. Instead, humans need additional context. Show the opportunity for meaning. Billions of tweens plotted on a GIS map will look like a big blob, but if it’s enriched with zip code or census tract data, practical and usable insights will be revealed.
Frame the discussion in terms of organizational strategy. Circle back to the original plan and talk about increased intelligence, greater competitive advantage, major ROI or another desired outcome. Don’t forget to tell the story visually and interactively; encourage people to interact with the tool themselves. Let them see the value in the data itself, rather than looking at code or spreadsheets. Hands-on, visual interaction is human, personal and memorable.
One final point: From the outset, make sure the implementation team is adequately equipped in terms of talent and support. Success on a GIS analytics project requires a focused, concerted effort — and that includes assembling a talented team devoted to its realization.
Just as important, the team will struggle if there isn’t a personal commitment from top managers as to what the organization wishes to achieve. In a race, every team needs a strong sponsor. Make sure your team has the backing it needs and chances are, your GIS project will finish a winner.