5 Reasons Most Companies' Big Data Projects Will Fail

Business.com / Technology / Last Modified: February 22, 2017

The majority of big data projects fail, but yours doesn't have to. Here's what to avoid—and how to make sure big data works for you.

As much as businesses want to achieve new goals and reach new levels through big data projects, the sad truth is that most of those projects will fail.

While surveys show differing 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.

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. Though big data projects are prone to failure, much of the time it’s because companies aren’t aware of the major pitfalls that often accompany them.

Recognizing these reasons for failure can help businesses steer clear of the most common mistakes and succeed where so many others have failed.

very few organizations succeed in big data implementation
5 Crucial Considerations for Big Data Adoption

1. Not Identifying Business Objectives

Most companies have one idea in mind: use big data because it’s good for business. That’s a good start, but it’s easy to see why this mindset can lead a big data project down the wrong path to a dead end. Using big data for the sake of using big data is a surefire ticket to nowhere. Instead, businesses need to identify a clear business objective that they are hoping to achieve.

They shouldn’t be focused on how they’re going to solve a problem, but rather, they need to understand why they’re trying to solve it. By stating the business objectives of a big data project at the start, companies make sure the project has a clear direction and a clear goal they can strive for.

Related Article: Hadoop, SQL and ACID, Oh My: Big Data Demystified

2. Lacking Big Data Skills

Big data is useful, but when businesses don’t have the expertise on hand to use it properly, projects likely won’t have much success. Finding the right big data talent can be a formidable challenge. The big data talent gap is wide, with the demand for the right expertise largely outweighing the supply. The gap isn’t likely to be closed any time soon, so companies will have to work hard to find employees with the 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 a project.

3. Relying Too Much on the Data

It may sound counterintuitive, but a big data project that relies solely on the numbers is likely to fail. Big data, strangely enough, is about so much more than the data. If it were only about numbers, algorithms would be all that’s needed to succeed. Big data projects, however, need the right human touch -- a certain amount of emotion -- to reach its goals. The human touch helps in asking the right questions and understanding the implications of what certain findings might mean as part of a larger picture. This approach is especially important when working with customer interaction data.

4. Failing to Convince Executives

The biggest decision makers in a company are usually the ones that fail to grasp the true impact big data can have. They need to be convinced that a big data project will benefit a business in many significant ways. Since big data projects can take up a lot of time, resources, and money, if the executives aren’t on board, the project will have little chance of success. This point relates to the first one, in that one of the best ways to win over a business executive is to convince him or her of the business objectives that will be reached through a project.

Related Article: Big Data is Redefining the Supply Chain: Putting Business Intelligence to Work

5. Planning Poorly

As the saying goes, failing to plan is planning to fail. This is especially true for big data projects. These types of projects are always evolving. Sometimes a small scale project can turn into a large one. Simple problems can turn into complex issues. Without the right planning, these changes can spell doom for a big data project. Companies need to plan ahead before starting a project, preparing for multiple contingencies and possibilities.

With all the big data use cases out there, big data projects can be utilized in various ways. At the same time, failure is all too common. With these points in mind, companies can better manage their projects, ensuring that success is the rule rather than the exception.

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