In 2017, Gartner interviewed companies that had implemented a big data project and found that most of these initiatives were not successful. In fact, they uncovered a startling statistic: 85% of big data projects fail. At Incite, we’re frequently brought in to clean up analytics implementations that have gone off the rails, and I can tell you from our direct experience that the situation hasn’t really improved since that study.

Identifying the core issue

The Gartner study revealed that there are several factors that cause analytics implementations to miss the mark. These include technology integration problems, management resistance, internal politics, insufficient expertise, and governance issues. If you look closely, it’s easy to find a common thread in many of these causes: the human element.

Understanding human-centric causes

Human challenges in analytics are best addressed with understanding and empathy. When rolling out big analytics projects, it’s easy to focus on the tough technical problems because data architectures, analytics toolkits, and storage needs are the obvious stuff. However, this data-centric approach means it’s all too easy to overlook the people who are involved in and impacted by the problem. It’s crucial to listen to and work with those affected in order to properly resolve issues and have the new solution stick.

Let’s look at several ways that human resistance can manifest itself.

Real-world manifestations of human resistance

• Fear of redundancy. People who are currently doing a manual process such as filling out spreadsheets or generating reports can be fearful of a new solution that will negate their contribution. The key is to redirect their focus on opportunities for tackling new challenges, allowing them to become even stronger contributors. Offer targeted training programs if feasible.

• Loss of specialization. There may be employees who specialize in extracting important data and on whom the company currently depends. They may therefore feel threatened as their unique skills become less relevant. Transitioning them to new roles where they can share their expertise (such as a training or mentorship role) can help mitigate this.

• Lack of trust. For people who rely on their company’s data to make important decisions, it may be difficult to trust information from a new system. Sometimes this manifests itself in risk intolerance, a fear of automation, or a loss of control. These stakeholders need to be reassured that the new analytics system will result in better outcomes. A gradual phase-in period that demonstrates the system’s reliability can help build the necessary confidence – a pilot project in one department, for example.
• Lack of inclusion. People who are not directly involved in the development of a new system may feel left out, which can lead to disaffected and detached employees who aren’t vested in the solution’s success. The way to avoid this is to foster a sense of ownership through inclusive strategies and active involvement – for instance, a cross-functional team with representatives from each department. This strategy will also result in a much more robust solution that services a broader population.

Inclusive strategies that foster a sense of ownership and commitment are critical to the success of analytics projects.

Embracing empathy in business

The idea of prioritizing empathy may seem too sentimental for some business leaders. However, we have found that genuine care and understanding can significantly enhance employee engagement and create a more successful analytics implementation.

Remember, the key to unlocking the full potential of your analytics lies not just in the data, but in the hearts and minds of those who work with it.