5 reasons analytics projects fail

5 reasons analytics projects fail

Many analytics projects pass a pilot test with flying colors but fail to earn wide adoption. Here are five common culprits that doom projects – and advice for tackling them

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Some years ago, at Gramener, we built a customer churn modeling solution for one of the largest global telecom operators. The machine learning solution predicted which of their customers would leave, one month before they stopped usage. In test pilots, the solution helped reduce customer churn by more than 56 percent compared to the earlier process.

We were amazed at the impressive results and stellar accuracy. But the celebrations were a bit premature, for the solution was never used. Despite a sound solution and successful pilot, months of our painstaking effort went down the drain.

Sound familiar? Unfortunately, this is far too common with technology transformations and data science initiatives. What causes these failures in adoption? How can you successfully navigate the necessary change management?

[ Could AI solve that problem? Get real-world lessons learned from CIOs in the new HBR Analytic Services report, An Executive’s Guide to Real-World AI. ]

Here are the top five challenges your analytics projects will face, and how to tackle them.

1. The analytics project doesn’t solve a business problem

A Gartner report says that 80 percent of data science projects will fail. Most initiatives don’t deliver business benefits because they solve the wrong problem. The problem with these pilots is that most of them are too technology-focused, quite like science fair projects. They are not driven by the challenges organizations face, and hence end up not being useful for the business.

Never start data analytics initiatives by talking about data or analytics.

How you can address it: Never start data analytics initiatives by talking about data or analytics. Brainstorm to identify all the roadblocks for your business objectives. Prioritize them based on three factors: business impact, urgency, and feasibility. Pick the top-ranking business problems from this list and carve out a solution using data science. 

Your data science journey is like a 30-hour marathon hike. Pick the wrong target on the map, and all your heroics will be in vain.

2. The analytics project doesn’t match the user’s workflow 

Teams often get over-ambitious and build a solution for everyone. If you aspire to satisfy everyone’s wants, you will meet no one’s needs. Another common mistake is to build on behalf of the end users without talking to them. If you miss understanding the user’s context or their natural workflow, your solution will stick out like a sore thumb.

How you can address it: Start by defining who the users are – and who they aren’t. Conduct interviews, build personas, and understand the scenarios of usage. Be ruthless in prioritization to trim the asks. Design the solution into their natural workflow. Ensure that the model results are explainable and talk to the user’s needs. Yes, you’ve heard this before, but it’s missed too often in practice.

Take the case of Grammarly, the popular grammar-checking tool. While the model suggestions are accurate, its biggest win is in designing a solution that reduces friction for the user. The spell-checks work in-line, across apps, and they continuously learn from user feedback. Now, imagine if you must copy and paste the text into a separate interface to do your spell checks. Would you use it?

3. The analytics project falls short on marketing

If your project addresses a burning need for defined users, then they need to know about it. Even the best of solutions need a sales push. People often think that if you build it, the users will come. But projects die a slow death without carefully planned marketing efforts. Organizations make the mistake of leaving the internal marketing efforts to teams that build the solutions.

How you can address it: Define your go-to-market approach for every internal project. Plan launches by the executives, organize roadshows, and run internal campaigns. Engage users through gamification and hand out cool giveaways to spread the message. Accept the help of professional marketers. Track your adoption metrics and celebrate small wins. Quantify return on investment to show the value and help secure future budgets.

A good example is the launch of a TV audience analytics solution by a client, one of the largest media companies in the world. Starting with the senior leadership rollout, a national roadshow was organized with champions covering every regional team. This led to stellar adoption with continued usage.

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Ganes Kesari is an entrepreneur, AI thought leader, writer, and international speaker. He co-founded Gramener, a data science company, where he heads Analytics and AI Labs. For close to 10 years, he has been advising CXOs of large enterprises on data science.

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