How to Increase the Success Rate of Analytics Projects? – Experts View | InsightsNow

Written by Maavrus

October 17, 2022

Analytics Project

Video Transcript

Hi. From my own experience, from studying various industry researches, and Dipstick surveys, it generally found that around ten to 15% of analytics projects deliver on business expectations, while the vast majority tend to fail.

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What are the Factors that can Increase the chances of Success Rate of an Analytics Project?

So we’ll look at a few factors which when focused upon, could increase the chances of success.

Number one, it’s imperative to have a very clear definition of the problem statement or the opportunity that we are trying to address.

Number two, before starting the analytics project, it is necessary that you have a clear discussion with the business stakeholders and anyone else who could be involved in the eventual project delivery to ensure that you have a very clear understanding of the constraints these could be market-related, related to business competition, data systems, people, etc. But also discuss with these stakeholders to have some amount of clarity on their hypothesis in terms of the various factors that could possibly impact the outcome. Realize that the other guys were closest to the business and the customer and their input are worth its reaching goal.

Number three the whole analytics project when you’re delivering it has to be a collaborative journey where there is a constant discussion with the business stakeholders to ensure that there is a validation of your insights or your analysis so far so that they can help you course correct in case the insight that you are working on do not connect with the realities on the ground. So please make it a very collaborative and iterative journey.

Number four is to ensure that before you get onto a project there is a very clear commitment in terms of business sponsorship and investment support, assuming that we are able to provide practical actionable insights or recommendations. Because if you do not have a mechanism to deliver your recommendation, the project, and the analysis is no good.

Last but not least, it is important to ensure that you look at the quality and veracity of data before you get into the project. So you have to look at data for its completeness, for its wholesale ness, for missing data points, for inconsistency patterns, and ensure that that is very clearly assessed before we get into the project, otherwise, garbage in is garbage out.


So in summary, have a very clear problem statement, define the constraints and hypothesis, interface with the stakeholders, ensure that the whole antics project is delivered as an Iterative process, look for upfront stakeholders, business sponsorship, and commitment and ensure that the data quality is assessed fully before you jump into the budget. Hope these insights are helpful. Thank you.

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