How To Implement An Analytics Solution For A Business Problem?

How To Implement An Analytics Solution For A Business Problem?

How To Implement An Analytics Solution For A Business Problem?

During a panel discussion in Gartner Business Intelligence and Analytics Summit early this year in Barcelona, vendors estimated:
70% of Analytics projects fail to meet expectations

While the exact number is difficult to find and would vary significantly from organization to organization, poor implementation of analytics projects is definitely a huge challenge for Analytics leaders across the globe.
For an industry based on scientific and logical thinking, these kind of failure rates are appalling. More surprisingly, analytics community has not learnt from its mistakes and continues to see poor implementation project after project.

This post aims to bring out some of the common mistakes people make while solving problems through analytics and suggest a few ways to minimize these mistakes.
Common reasons for failure of Analytics projects due to analysts

Most of the analyst related failures arise because analysts have a tendency to assume similarity across projects. A predictive model which works wonders for one company can lead to disasters in other (if copied blindly). Similarly, each Organization has its own way of storing and dealing with data. Same status code could have different definition.  Each and every project should be thought through and planned independently. One solution fit all approach doesn’t work in analytics. Following are the top 3 errors which I think cause most of the failures:

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Not planning for and spending time to understand business process: An analyst needs to spend a lot of time to make sure he understands the business processes and gaps before he can start working on an analytics solution. More often than not, process improvements might give you a better solution than applying analytics to a process you are not clear about. Ironically, people do not budget the required time to be spent on understanding business processes in their project planning. Have provided some good practices towards the second half of the article.

Unclear and non-specific requirement capturing: This reason stands out for the amount of failures it causes. If you are preparing the requirements document and are not clear about any aspect of the problem, ask out! If you do not clarify things now, you will come across surprises which will de-rail your project from timely completion. In one of the project for implementing a BI tool across an MNC (buying more than hundreds of licenses), the analyst spent less than a day, in total across all the departments and created the BRD (Business Requirement Document) on basis of which entire project pricing was arrived at. End result of the project – Shelved after putting in more than 9 months of effort of 5 member team.

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Half baked thoughts on implementation: You may create the best of predictive models, but without a clear implementation in mind, these models are useless. A common mistake while building predictive models is to include variables which can not be controlled. For example, you find out that people who are Self Employed are more profitable and incentivize your Sales teams to bring more of these customers. What are the chances that people who were mentioning occupation as Professional or Salaried would start calling themselves Self employed? How do you avoid this to fool your model?

Common reasons for failure of Analytics projects due to stakeholders
Limited buy in from customers or stakeholders across levels: You need to ensure a complete buy in from a customer in order to make sure that your project stands a chance to achieve success. If the stakeholders are not convinced about the idea, it will reflect across as poor business understanding of analysts, analysts not being aware of strategic changes impacting their project etc. which will ultimately lead to project failure.

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