5 Reasons Behind The Failure of Most Big Data Projects

Big data projects are not only big in size but also their scope. Although most of these projects start with a high ambition, only a few succeed. The majority of these projects fail. Over 85% of big data projects end up in failure. Even after the advent of technology and advanced apps, not much has changed.

According to Big data experts, it is a challenge for businesses to adopt big data and AI initiatives. Almost every established organization today is trying to launch Artificial Intelligence or Machine Learning projects. They are planning to move these projects into the production process but to no avail. It is still a challenge for them to gain value from these projects. No matter how carefully planned the big data project is, it doesn’t get off the ground. To succeed with big data projects, you need to identify the reasons behind the failure. This will provide organizations a better chance at success.

That’s what we are going to cover in this article. So without further ado, let’s get to the details.

5 Reasons why big data projects fail

1. Improper integration

Various technological problems cause big data projects to fail. One of the most important of these problems is improper integration. Most of the time to get the required insights, companies tend to integrate soiled data from several sources. It is not easy to build a connection to siloed, legacy systems. The cost of integration is much higher than the cost of the software. This makes simple integration one of the biggest problems to overcome.

Nothing magical will happen if you link every data source. The outcomes will be zero. One of the biggest parts of the problem is the siloed data itself. When you put data into a common environment, it is hard to figure out what the values mean. Knowledge graph layers are needed to enable machines to interpret the data mapped underneath. Without this information, you only have a data swamp that is of no use to you. Since you would have to invest in security to stop any potential data breaches, improper integration means big data will only be a burden on your company‘s finances.

2. Business assumptions and technical reality misalignment

Most of the time, the technical capabilities don’t come up to business expectations. Organizations want the technology to be integrated to have unique functions. However, the capabilities of Artificial Intelligence and Machine Learning are limited. Being clueless about what the project is capable of doing, results in its failure. You need to be aware of the capabilities of the project before developing it.

3. Rigid project architectures

From resources to skills, talent to infrastructure, most companies have it all. Yet they fail to implement a successful big data project. Why does that happen? This happens when the project architecture is inflexible and too rigid from the beginning. Moreover, certain companies wait to achieve a seamless model from the beginning rather than incrementally improving it as the project progresses.

Even if the project isn’t complete and you haven’t achieved the perfect model, it is still possible to acquire considerable business value. Even if you have a subset of data to work with, you can implement machine learning to reduce the associated risks.

4. Setting unachievable goals

Sometimes, businesses set high expectations from the technology that’s about to be integrated into their systems. Some of these expectations are unrealistic and cannot be met. These expectations cause big data projects to fail miserably. Business leaders should set realistic goals while working on big data projects.

5. Models are taken into the production process

This is one of the biggest reasons why most big data projects fail. No matter how much you invest in a big data project, it is of no use if you don’t move it into production. Machine Learning models are created by experts. However, they are left for months without anything happening. In the majority of the cases, IT companies are not equipped with the tool required to create an environment that handles an ML model. They don’t have skilled specialists with the expertise to handle these models.

4 Ways to make a big data project successful

Artificial Intelligence is groundbreaking technology. However, to leverage it into your systems, you need to create an environment where these models would work. Following are some of the best ways to make a big data project successful.

1. Careful planning

Several companies invest in AI projects just so they could say they are using this technology. To make a big data project successful they need the right infrastructure to handle the technology. They need to hire the skills and resources required to support this innovative technology.

Apart from that businesses also need to ensure that the AI projects launched to serve a purpose for their business. There are three goals you should plan to achieve with your AI project including:

• An increase in the bottom line of your business
• Reduction in the operational cost
• Improvement in employee or customer experience

2. Identify the problem

Without proper planning, it is hard to make a big data project successful. It is very important to identify the problems your business is facing before finding an AI solution that works. Before building a project you must understand the value it will produce. You need to ensure whether or not you have set realistic goals for your business and you have the right environment to take these projects into the production process.

You must also estimate the budget required to build a big data project. Size the price before investing in building a big data project.

3. Keep partners engaged throughout the process

Companies need to engage all partners from the beginning and throughout the development process. Provide them access to the project through the entirety to make sure changes are made on time. Don’t wait until the project is finished and massive changes are required.

Assess the level of analytic, data, technical, and business expertise required to take the project idea to the production level. Without a proper assessment, your project will fail.

4. Keep track of failures

Failures are inevitable when it comes to business continuity. Make sure you are failing forward. This means you are keeping a track of what went wrong and doing your best to avoid these problems in the future.

If your big data project has failed, you should identify what went wrong and why that happened. Document your failure to update your process so that you don’t end up making these mistakes over and over again.

Wrapping up!

With more organizations adopting Artificial Intelligence in their business processes, it is time for them to create environments where big data projects work. You need to maximize resources to leverage big data into your IT operations for your company’s growth.