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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4. The analytics project won’t scale

Organizations often get too fixated on the first version and don’t plan enough for scaling. Even the most successful products need several iterations and constant tweaking to get the solution right. Most projects don’t plan this runway to let their solutions take off. They don’t set aside a budget to improve on continual user feedback. They miss setting this expectation with users.

How you can address it: While building your first version, plan for a broader vision and roadmap. Explicitly set aside a budget, resources, and expectations for rapid revisions. Avoid letting teams getting too wedded to the initial solution and keep them willing to make major changes. Plan periodic upgrades to keep the user interest levels high and avoid a loss of momentum.

Ed Catmull, co-founder of Pixar Studios, famously said our most daring ideas are ugly babies: "They are truly ugly: awkward and unformed, vulnerable and incomplete. They need nurturing – in the form of time and patience – in order to grow.” Having a strong vision at the start helps, too. 

5. The analytics project lacks executive buy-in

Even when all of the above are taken care of, initiatives will fall flat if they lack an executive mandate. Change is not easy and the natural human tendency is to resist it. In addition, organizations often have conflicting priorities. This makes new initiatives highly vulnerable in their early stages. If not nurtured carefully, transformation projects stand very little chance of success.

How can you address it: Innovations must be led from the top to see the true benefits. Executives must present a vision for the future and rally people towards it. You need to push firmly to abandon old habits, at times, with unpopular calls to avoid a relapse. Make sure to onboard leaders at the next levels who can champion the initiative and act as change agents.

According to a survey by Deloitte, executive sponsorship is vital to organizational change. Companies with the CEO as the lead champion are 77 percent more likely to exceed their business goals significantly.

Analytics project lessons learned

At Gramener, we learned our lessons from the project with the telco churn model that failed to get adopted. When the next opportunity arose, this time with a global conglomerate, we started the data science initiative differently.

We found that their top business challenge was to make better decisions in commodity purchases and sales. Working with their target users, we prioritized the features based on what was impactful, urgent, and feasible. We decided to forecast commodity prices, and we built a minimum viable prototype that used explainable time-series models for forecasting.

Early feedback showed that what users really wanted was the direction of movement, not the exact price forecast. We changed the solution by picking a different class of simple models. It was actively marketed to all user segments, with iterative improvements.

We channeled executive firepower to build momentum for the initiative and promote usage. In production, the solution led to a savings potential of $22 million for one of their largest commodities. This time, we didn’t have to watch our efforts go down the drain, and the celebrations were shared by all. 

[ How can automation free up more staff time for innovation? Get the free e-book: Managing IT with Automation. ] 


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