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.
Alluxio provides an open source unified data orchestration layer for hybrid and multi-cloud environments, making data accessible wherever data computation and processing is done. By seamlessly pulling data from underlying data silos, Alluxio unlocks the value of data and allows for modern data-intensive workloads to become truly elastic and flexible for the cloud.
Want a free Alluxio t-shirt? Sign up below and we’ll send one to you!
strongDM enables you to easily manage and audit access to databases and servers. Leading organizations including Hearst, SoFi, and Peloton rely on strongDM to eliminate the manual-heavy work required to onboard, offboard, and audit staff’s access to everything. Simplify your access control strategy today and schedule a demo to see how much easier your life can be.
Do you want to try out some of the tools and applications that you heard about on the Data Engineering Podcast? Do you have some ETL jobs that need somewhere to run? Check out Linode at dataengineeringpodcast.com/linode or use the code dataengineering2019 and get a $20 credit (that’s 4 months free!) to try out their fast and reliable Linux virtual servers. They’ve got lightning fast networking and SSD servers with plenty of power and storage to run whatever you want to experiment on.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
- Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems.
- Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- 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)