Why most of the big data projects fail?

It’s not that easy to convince decision makers when it comes to big data projects. Reason- The high investment made on the big data project might not always yield favorable results.

While survey shows different numbers, they all agree that the majority of big data projects aren’t successful. This can be a frustrating fact for companies that are eager to put their big data to good use and to those who are dealing with it. After all, big data is everywhere these days, so not being able to use it correctly can derail companies attempts to push their organizations forward.

Can you imagine why big data projects are often prone to failures? Its because companies aren’t aware of the pitfalls that often accompany them.

If one can recognize the following reasons leading to failure at right time, then they can clearly steer their way of the most common mistakes and succeed where so many others have failed.

Not identifying business objectives– Most companies are going with a mind set that they are using big data because it has proved as good for other businesses and so would like to follow the same trend. Well, this can prove as a good start, but its easy to see why this mindset can lead to a big data project down the wrong path to a dead end.

Instead, of blindly following the trend, businesses need to identify a clear business objective that they are hoping to achieve. The focus must not be solving a problem, but rather, they need to understand why they are trying to solve it. By knowing the business objective of big data project at the start of the project, companies can make sure the project has a clear direction and a clear goal they can strive for.

Lacking big data skills- A project meets success path only when right people handle it. Therefore, big data projects prove successful only when experts are employed to handle these projects. However, finding the right big data talent can be a formidable challenge. As of now, the big data talent gap is wide, with the demand for the right expertise largely outweighing the supply. And the bad news is that the talent gap is wide and is not going to close anytime soon. Thus, companies will have to put in a lot of energy to find employees with skills to make a big data project work. It’s also important to note that the talent involved isn’t always technical; there are many other skills that can contribute to the success of the project.

Getting data and relying on it- Often we see that big data projects that rely solely on numbers are likely to fail. That’s because, if it were only about numbers, algorithms would be all that’s needed to success. However, that’s not the case because it needs the right human touch. A human touch helps in asking the right questions and understanding the implications of what certain findings might mean as part of a bigger picture. This procedure is extremely important while working with customer interaction data.

Lack of confidence among executives- The biggest decision makers in a company are usually the ones that fail to grasp the true impact of big data. Often it is found in these projects that executives loose confidence in the project midway. That’s because a lot of time, resource and most importantly money flow is needed to drive the project towards success. Here, all these points boil down to relate to first point- convince the business executive about the business objective that will be reached through this project.

Planning poorly- Big data projects need proper planning as these type of projects are always evolving. Sometimes a small scale project can turn into a large one. Simple problems can turn into complex issues. Therefore, companies need to plan ahead before starting a project, preparing for multiple contingencies and possibilities.

To make your big data project successful, approach DNF Corporation.


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