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5 Ways Big Data Projects Can Fail

Big data initiatives are large in both size and scope. Even though the majority of these projects begin with lofty objectives, only a small number of them are successful. The overwhelming majority of these initiatives fail. More than 85% of big data projects fail. Despite technological advancements and advanced applications, little has changed.

According to big data experts, enterprises face challenges in adopting big data and AI efforts. These days, nearly every organized company is attempting to launch Machine Learning or Artificial Intelligence projects. They intend to put these projects into production, but their efforts will be in vain. They are still struggling to derive value from these ventures.

Here are five things that can go wrong with big data projects:

  1. Incorrect integration

A variety of technical issues cause big data projects to fail. Incorrect integration is one of the most serious of these issues. Most of the time, businesses combine contaminated data from multiple sources to obtain critical insights. Connecting to isolated, older systems is difficult. The cost of integration is much higher than the cost of the program. As a result, one of the most difficult challenges to overcome is basic integration.

Nothing extraordinary will occur if you connect every data source. The outcome will be nil. The segregated data itself is one of the most serious aspects of the problem. When you place data in a shared setting, it can be difficult to figure out what the values mean. Knowledge graph layers are required to allow robots to interpret the data mapped beneath. You are left with a useless data swamp if you do not have this data. Because you would have to spend money on security to prevent future data breaches, poor integration implies that big data will be a financial burden for your company.

  1. Misalignment of technical reality

Almost always, technical abilities fall short of business expectations. Corporations want technology to be integrated for them to perform specific tasks. AI and machine learning, on the other hand, have limited capabilities. Failure results from not knowing what the project is capable of. Before beginning work on a project, you should be aware of its capabilities.

  1. Inflexible project architectures

Most businesses have all of the resources, skills, talent, and infrastructure they require. Nonetheless, they are unable to develop a successful big data project. What is the cause of this? This happens when the project architecture is inflexible and rigid from the start. Furthermore, some businesses wait to establish a unified architecture from the start, rather than developing it gradually as the project progresses.

Even if the project isn’t finished and you haven’t created a perfect model, you can still gain a lot of commercial value. Even if you only have a small amount of data to work with, you can use ML to mitigate risks.

  1. Setting unattainable objectives

Businesses frequently have unrealistic expectations of the technology that is about to be integrated into their operations. Some of these assumptions are unrealistic and will be difficult to meet. As a result of these assumptions, big data projects fail spectacularly. Corporate leaders should set reasonable goals when working on big data projects.

  1. Production procedure

This is one of the most common causes of big data project failure. It makes no difference how much money you put into a project if it is never put into production. Experts build ML models. Nonetheless, they are left for months with nothing happening. The majority of IT businesses lack the tools required to build an environment capable of handling an ML model. They lack qualified personnel with the necessary knowledge to manage these models.

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