The enterprise world has unlimited data collation opportunities, but interestingly, many analytics programs still fail. Why does this happen, especially when firms have spent so much time and money on them.
According to “Our Top Data and Analytics Predicts for 2019” by Gartner, almost 85% of data analytics programs fail. While some believe this is due to their size and complexity, the reality is quite different. Failures are caused by the execution of technology, not by the technology itself.
A data analytics project may fail for a variety of reasons. Some of them are listed below.
The wave of data analytics has captivated the interest of everyone, from industry executives to subject matter specialists. Everyone wants to get the most out of this technology, so they collect all of the company’s data. However, this data must be properly placed in data modeling and analytical environments, such as a Data Lake or Data Warehouse. Data silos cause conflicts, and a lack of data quality, data governance, and integrity can sabotage success.
No detailed plan in place
Organizations tend to focus on the data, algorithms, tools, and models long before worrying about the project’s fundamentals at the start of a new project. It’s vital to highlight the project’s worth to the company right away.
The project leads need to connect the project to the benefits for the sponsoring department, the business unit, or the entire company, in a succinct, focused, and visionary manner, if they believe in the feasibility of the project. The questions to be answered are- is this a viable source of income, is it removing risk from a critical process, and finally, Is it possible to build a greenfield capacity or take advantage of another significant opportunity? There needs to be a clear communication of the value in clear words, whatever it is.
However, planning does not end there. It’s also crucial to define success criteria. There need to be set completion benchmarks (such as timeframes and finish dates) which include total project cost forecasts; break down expenses for human resources, data integration, tools, and algorithms required. The single most critical step in starting a data analytics project is pinpointing and communicating these parameters.
Lack of communication
When working on data analytics or data science models, it’s critical to have a simple and straightforward communication route. Stakeholders must communicate with data scientists to understand how things function. In many cases, a lack of engagement is to blame for a failure to grasp the problem.
Inadequate management oversight
Big data projects are successful when they are not “isolated projects,” but rather are at the heart of how a firm intends to use its data. Organizations frequently make the mistake of prioritizing different strategic priorities and ideologies over data. For data execution, top-level leadership needs to have a crystal clear picture and prepare ahead.
Choosing the wrong KPIs
Instead of the other way around, let the company drive robust analytics projects. No matter how large the empire or how many difficulties a company faces, it must focus on issues that directly affect day-to-day operations; this necessitates focusing on the correct KPIs.