Analytics projects fail all the time, resulting in lost opportunities and wasted resources. There are a number of factors that contribute to that failure and not all of them are under our control. However, many of them are and as data engineers we can help to keep our projects on the path to success. Eugene Khazin is the CEO of PrimeTSR where he is tasked with rescuing floundering analytics efforts and ensuring that they provide value to the business. In this episode he reflects on the ways that data projects can be structured to provide a higher probability of success and utility, how data engineers can get throughout the project lifecycle, and how to salvage a failed project so that some value can be gained from the effort.
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- Your host is Tobias Macey and today I’m interviewing Eugene Khazin about the leading causes for failure in analytics projects
- How did you get involved in the area of data management?
- The term "analytics" has grown to mean many different things to different people, so can you start by sharing your definition of what is in scope for an "analytics project" for the purposes of this discussion?
- What are the criteria that you and your customers use to determine the success or failure of a project?
- I was recently speaking with someone who quoted a Gartner report stating an estimated failure rate of ~80% for analytics projects. Has your experience reflected this reality, and what have you found to be the leading causes of failure in your experience at PrimeTSR?
- As data engineers, what strategies can we pursue to increase the success rate of the projects that we work on?
- What are the contributing factors that are beyond our control, which we can help identify and surface early in the lifecycle of a project?
- In the event of a failed project, what are the lessons that we can learn and fold into our future work?
- How can we salvage a project and derive some value from the efforts that we have put into it?
- What are some useful signals to identify when a project is on the road to failure, and steps that can be taken to rescue it?
- What advice do you have for data engineers to help them be more active and effective in the lifecycle of an analytics project?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Prime TSR
- Descriptive, Predictive, and Prescriptive Analytics
- Azure Data Factory
- Azure Data Warehouse
- SSIS (SQL Server Integration Services)