8 Reasons Big Data Projects Fail - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Data Management // Big Data Analytics
09:06 AM
Matt Asay
Matt Asay
Connect Directly

8 Reasons Big Data Projects Fail

Most companies remain on the big data sidelines too long, then fail. An iterative, start-small approach can help you avoid common pitfalls.
1 of 2

1 of 2
Comment  | 
Print  | 
Newest First  |  Oldest First  |  Threaded View
User Rank: Ninja
8/15/2014 | 12:22:04 PM
Knowing questions to ask
Great overview.  According to a recent IDG SAS survey, knowing which questions to ask and interpreting meaningful insights are two tasks very few organizations feel capable of accomplishing.  There needs to be more education around making use of data. At the same time, organizations need to learn to start small, celebrate successes and build on. 


Peter Fretty
User Rank: Apprentice
8/11/2014 | 7:46:15 PM
Nothing beats the basics.  Such as an intimate understanding of the data.  Other basics exist.  Such as Gartner's research is more than a year old.  Ancient in othr words. Gartner's news release did not clarify if they had 720 respondents, or if they sampled 720. No dicussion of response rate. They say, "survey of 720 Gartner Research Circle members"--which doesn't actually sound like a sample at all.  Given the lack of basic information it is hard to tell if the small year to year differences may or may not be meaningful.  What Gartner's study says to me is that despite the hype, nothing much changed year to year.
User Rank: Moderator
8/10/2014 | 1:28:29 PM
Re: Just Spot on!
I agree with the test and learn stage. The analytics of big data is a crucial element within further understanding the big data process and to create strategies. With so much data available for the enterprise, the right test and learn applied with appropriate metrics is key.
User Rank: Author
8/10/2014 | 1:10:32 AM
Just Spot on!
This is actually 100 percent spot on. The word "big data" has already become a byword along with "context" and yet only a few people really understand them. The herd mentality certainly hits a soft spot to me, as well, because that's how I see most of these companies do: just because everybody is doing it, they think they have to do it too. I am not saying only a few have to do big data, but again, it's about understanding the reason why it should be done in the first place. With understanding of the vision and/or the goal comes the proper planning that helps identify the right process, people, and technology for big data. 
D. Henschen
D. Henschen,
User Rank: Author
8/7/2014 | 9:59:43 AM
Pleased to have Matt Asay Contribute
I've enjoyed Matt Asay's writing on big data for some time. I'm really please that he offered this contribution to InformationWeek. Practical, real-world advice for an enterprise audience is our stock in trade, so this column is a perfect fit. I particularly like (and echo) the advice to rely on your existing staff to experiment with big data approaches to known problems.
User Rank: Author
8/7/2014 | 9:49:56 AM
Big data talent
"Sicular is right when she advises that it's best to look for data scientists from within, as "learning Hadoop is easier than learning the business." Interesting to hear an expert like Matt state this so succintly. We hear the same from some forward-looking CIOs. Are you listening, hiring managers?
Enterprise Guide to Edge Computing
Cathleen Gagne, Managing Editor, InformationWeek,  10/15/2019
Rethinking IT: Tech Investments that Drive Business Growth
Jessica Davis, Senior Editor, Enterprise Apps,  10/3/2019
IT Careers: 12 Job Skills in Demand for 2020
Cynthia Harvey, Freelance Journalist, InformationWeek,  10/1/2019
White Papers
Register for InformationWeek Newsletters
Current Issue
Getting Started With Emerging Technologies
Looking to help your enterprise IT team ease the stress of putting new/emerging technologies such as AI, machine learning and IoT to work for their organizations? There are a few ways to get off on the right foot. In this report we share some expert advice on how to approach some of these seemingly daunting tech challenges.
Flash Poll