Why Big Data Projects Fail?

A lot of companies don’t know how to use data and what can it provide to their organization. They are stuck in many misunderstandings including assuming that a data researcher will amazingly fix all their problems. That’s why they usually fail to use data as it can be. There are few typical reasons for big data project failings, consisting of:

Management resistance: Despite what data has hidden in it, most of the managers and decision makers are not ready to accept that they need to change their existing process. They trust their already existing talent and procedures. They think that the real-world insights are difficult to analyze and to find a result.
Incorrect selection: Companies either start with an extremely ambitious project that they’re not yet all set to deal with, or they try to resolve big data problems using traditional data technologies. In either case, failing is the common result.
Irrelevant Questions: Data science is a complex blend of domain knowledge (the deep understanding of financial, retail, or an other market); math and stats know-how; and programming skills. Many organizations work with data scientists who might be math and programming geniuses but who do not have one of the most vital part: domain knowledge. It’s best to look for data scientists from within, as learning Hadoop is less complicated compared to learning business.
Lack of Skills: This one is closely pertaining to “asking the wrong questions. Many of the big data projects delay or fail due to the inadequate skills of those involved. Often the individuals entailed come from IT– as well as those are not most qualified to ask the right questions of the data.
Problems beyond big data technology: Analyzing data is just one component of a big data project. Being able to access and process the data is critical, but that can be obstructed by such things as network issues, training of personnel, and lot more.
Inadequate Enterprise Strategy: Big data projects are successful when they’re not actually isolated “projects” at all and how a company uses its data. The problem is aggravated if different groups worth cloud or other strategic issues compared to big data.
Big data silos: Big data vendors are fond of talking about “data lakes” and “data centers” but the reality is that many businesses try to build the matching of data puddles, with sharp boundaries between the advertising data puddle, the production data puddle, and more. Big data is more valuable to an organization if the walls between groups come down and their data flows together. Politics or policies often prevent this dedication.
Problem avoidance: Sometimes we know or assume the data needs us to take action that we don’t really intend to do, like the pharmaceutical industry not running sentiment analysis because it wishes to avoid the succeeding legal obligation to report adverse side results to the U.S. Food and Drug Administration.

Throughout this list, one thing is common: As long as we can focus on data, people keep getting in the way. As much as we might intend to be ruled by data, folks eventually rule the big data projects, including making the initial choices about which data to collect and save, and which questions to ask of it.

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