While it’s true that data science initiatives can deliver numerous benefits to enterprises, it’s important to remember that these projects are not fail-safe. When they don’t turn out as expected, companies are left with wasted time, money, and resources. Worse, flawed data science initiatives can cause leadership to lose faith in future projects.
In light of these factors, it’s critical to be cognizant of common data science project failures and take steps to avoid them. A recent CIO article by Bob Violino outlines a number of reasons data science projects may flounder, including:
Poor Data Quality
If a company uses unclean data for data science projects, the models will be skewed, the outputs will be off, and the insights will do nothing to move the business forward. In other words, embarking on a data science initiative without ensuring data quality is a complete waste of time. Particularly for large enterprises, addressing data quality often involves taking stock of the entire infrastructure and addressing incompatibility issues with legacy systems.
Lack of Clarity on the Problem at Hand
If team members don’t understand the business problem they are attempting to solve, it’s unlikely that the data science project will succeed. It’s important to allocate time at the outset to define the problem and its scope and to identify the correct sources for the data needed to solve the problem.
Lack of Relevant Data
As the above suggests, if you don’t have the right data needed for your particular issue, then your project is essentially doomed from the start. Many companies operate under the assumption that large data will lead to great insights, but this is not typically the case. Rather, smart, tailored datasets tend to be more effective in producing robust, generalizable models.
Lack of Data Transparency
Another common cause of data project failures is a lack of data transparency. After all, if people don’t trust the model or understand the solution, it’s unlikely that they will buy into the results. The experts with whom Violino spoke stressed that data leaders must be able to “show the math,” communicating where the data originates from and what they did to calculate the models in terms that non-technical stakeholders can easily understand.
Absence of an Executive Champion
To ensure that data science projects receive sufficient resources and support, they need to have a champion in the C-suite. Ideally, this should be the CIO, who, in addition to advocating for data science investments, should also be responsible for ensuring the company is getting the most out of the information it captures.For more on these and other common causes of data science project failures, you can read the full CIO article.
For more on getting the most out of your data science investments, check out this previous APEX of Innovation post.