Four Mistakes in the Life of an Analytics Project

• Data Analytics

Data, analytics and artificial intelligence (AI) are fast becoming mission-critical elements across industries to ensure businesses continue to remain competitive. Most businesses have been investing heavily to pursue their analytics agenda, including partnerships with analytics outsourcing vendors. However, the success rates of these projects have been quite disturbing. While there are several reasons for failure, here are the top four mistakes companies make that you should be wary of while starting on any analytics initiative:

Mistake #1: Solving the wrong problem!

This usually tends to happen when the solution solves the wrong problem. When the problem is not closely tied to challenges faced by the organization, the solution is not useful for the business.

The key to success in analytics is to think about the problem first. Just because a solution worked for your competitor doesn’t mean that you need to implement it as well. Focus on the challenges that your business stakeholders are facing and drill-down to identify insights gaps. Successful analytics programs are the ones that start with a clear problem definition and expected outcome and find ways to empower business users with insights that will make their lives easier.

Mistake #2: Lack of leadership involvement

The lack of leadership involvement and effective program planning are other significant contributors to project failures. Chris Chapo, SVP of Data and Analytics at Gap said,

Chapo goes on to say that right leadership support is essential to create the conditions for success.

Building a data-driven culture requires active coaching across the organization, starting from the CEO. Senior leaders can provide perspective about the organization’s vision and goals and how they align with a proposed analytics solution. They can explain why an analytics solution is needed and how it can help improve the life of the end-user and help the business. Will it help save time? Make more money? Or will it help accelerate the decision-making process?

Mistake #3: Lack of collaboration

Most organizations run analytics projects in silos and lack of collaboration and communication between business leaders is another common mistake.

Fostering collaboration between business leaders can help them learn from each other’s mistakes or identify areas where they can work towards mutually beneficial outcomes. Even within a business function, sometimes the most important stakeholder, the end-user, gets left out of the analytics goal-setting discussions. Involving the end-user early into the project can make them more comfortable with an upcoming change. Early adopters can also help with word-of-mouth marketing of the solution.

Mistake #4: Lack of empathy for the end-user

Another reason behind analytics implementation failures is that most businesses fail at adoption. A New Vantage survey reported that 77% of businesses surveyed said that “business adoption” of big data and AI initiatives continues to represent a big challenge for business. That means three-fourths of the software being built globally is apparently collecting dust. Ambitious analytics projects are planned without spending time to understand a typical day in the end-user’s life and what she needs to make her more effective in her role.

It’s also important to design analytics dashboards tailored to role. Before designing the dashboards, put yourself in the shoes of the end-user. Which metrics are most important to him on a regular day? Are these metrics easy for him to find and monitor? In short, ask yourself if the solution makes his life a little easier than it was before.
Have you encountered any of these mistakes? What is the path forward? One may arrive at the erroneous conclusion that there is just no way to fully succeed at data. This is far from the case. In my next article about, analytics roadmap, I will share a framework we have found useful in creating analytics strategies that actually deliver value.

What are some other mistakes that cause failure? How do you think we can avoid them? Feel free to comment or share your experience with analytics projects in the comments section.

Citations

1. https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/

2. https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/