4 Reasons Data Analytics Projects Fail
We explain the four most common reasons data analytics projects aren't successful and how to avoid these mistakes.
- By Nohyun Myung
- November 1, 2021
Digital transformation projects, regardless of scope or business discipline, are by nature challenging -- they are designed to challenge the way an organization does things. They also challenge the organization to commit sufficient teams and resources, as well as corporate will, to the project. Organizations struggle if the project is not viewed as essential to the enterprise's competitiveness and future profitability.
These dynamics are especially applicable to data analytics projects. Without a focused commitment to pushing the envelope in terms of what the organization wants to achieve, teams won't be motivated to step in, get to work, and achieve results.
There is no shortage of data analytics opportunities that can transform an enterprise as long as the projects are implemented effectively. However, many of these initiatives fall victim to the same set of pitfalls. Here are four of the top reasons such projects fail, along with ways to avoid them.
Pitfall #1: Lack of a comprehensive plan
At the onset of a new project, organizations tend to focus immediately on the data, the tools, the algorithms, and the models long before they concern themselves with the fundamentals of the project. It's critical to articulate your project's value to your organization upfront.
Tie the project back to the benefits for the sponsoring department, the area of the business, or to the larger enterprise. Be concise, focused and, where possible, visionary. Is this a potential revenue-generating opportunity? Does it remove risk from some important process? Does it enable a greenfield capability or some other important opportunity? Whatever the value is, communicate it in clear terms.
Planning, however, doesn't stop there. Defining success criteria is also important. Set benchmarks for completion (such as timelines and end dates) and include overall cost projections for your project; break down costs for human resources as well as for tools, data integration, and algorithms required. Determining and communicating these parameters is the single most important step when beginning a data analytics project.
Pitfall #2: Lack of quick outcomes
When teams work on a major project without achieving results for an extended period of time, it drastically decreases the chances of success. Teams are rarely going to get it right on the first go, so get to outcomes quickly.
Fail fast or get to the next plateau. Be willing to acknowledge quickly whenever something, whether it's a model, an algorithm, technology, data, or even the team itself, didn't work. Then reassess, determine the next iteration, and move forward.
Pitfall #3: Poor or infrequent presentations
Even if planned effectively and executed well, a data analytics project can be severely hindered when stakeholders, especially senior executives, aren't briefed about the value created. Too often an implementation team (understandably) focuses excessively on the difficult behind-the-scenes work -- the nuances and iterations -- yet doesn't communicate the business results.
The question is not only what to present but how. Frame the benefits in terms of your enterprise's business strategy; this is the language managers and C-suite executives understand. Return to the original plan; talk about revenue, profit, business intelligence, and competitive advantage.
Furthermore, focus on telling your story visually and interactively. Encourage people to interact with the tool you've produced. Help them see the value in the data itself, rather than simply viewing code or spreadsheets. Hands-on visual interaction is personal, engaging -- and memorable.
Pitfall #4: Lack of talent
Make sure your implementation team has the expertise it needs. One organization I know successfully completed its digital technology initiative because it built an entire division around the project -- 130 people, pulled from existing teams and departments. Their fulltime responsibility during the project was its completion.
Most companies don't have those kinds of resources. Nonetheless, success on a data analytics project requires focused, concerted effort -- and that includes assembling a talented team devoted to its realization. Data analytics may be a truly exciting technology field, but the human factor will be the biggest component of its success.
Nohyun Myung is vice president of global solution engineering and custom success for OmniSci. In his career, Myung has played critical roles in leading teams in pursuit of seeing the adoption of emerging technologies applied to some of the most challenging business problems across telecommunications, automotive, financial services, transportation and logistics, retail, and utilities markets. Myung has extensive experience as a technologist, strategic executive, and board advisor spanning 20 years in the data, analytics, and high-growth technology space. You can reach the author via email or LinkedIn.