Where Big Data Projects Fail by Bernard Marr.
From the post:
Over the past 6 months I have seen the number of big data projects go up significantly and most of the companies I work with are planning to increase their Big Data activities even further over the next 12 months. Many of these initiatives come with high expectations but big data projects are far from fool-proof. In fact, I predict that half of all big data projects will fail to deliver against their expectations.
Failure can happen for many reasons, however there are a few glaring dangers that will cause any big data project to crash and burn. Based on my experience working with companies and organizations of all shapes and sizes, I know these errors are all too frequent. One thing they have in common is they are all caused by a lack of adequate planning.
…
(emphasis added)
To whet your appetite for the examples Marr uses, here are the main problems he identifies:
- Not starting with clear business objectives
- Not making a good business case
- Management Failure
- Poor communication
- Not having the right skills for the job
Marr’s post should be mandatory reading at the start of every proposed big data project. And after reading it, the project team should prepare a detailed statement of the business objectives and the business case, along with how it will be determined the business objectives will be measured.
Or to put it differently, no big data project should start without the ability to judge its success or failure.