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Saturday 23 June 2018
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Why Do Big Data Integration Projects Fail? How Do I Prevent It From Happening?

Why Do Big Data Integration Projects Fail? How Do I Prevent It From Happening?

Close to 85% of big data projects fail — but they don’t have to. A transition to big data is important for many organizations, but it can be sabotaged internally through a lack of preparation and understanding. Not only do organizations need to properly understand the benefits of big data, but they need to be properly equipped to complete the transition in an efficient way.

Encountering resistance from management

Though management often spurs an organization into big data integration, management can also be big data’s greatest enemy. Upper management has a tendency to be resistant to adjustments in business processes, and many executives prefer trusting their gut rather than their data. This is even more likely when executives don’t understand the new data that they’re being presented. When executives don’t get on board with creating a data-driven, analytics-based company culture, it’s difficult for it to filter down to the rest of the organization.

Executives need to see the value of data integration if they are going to be completely on-board. That isn’t just the value to the organization, but also the value in their own day-to-day tasks. Once they see the benefits, they will be encouraged to advocate the new technology.

Integrating big data with existing business processes

The very nature of big data analysis may require that an organization modify its existing business processes from the ground up. However, many organizations aren’t willing to do this; instead, they want their existing processes to remain largely unmodified. This can result in “Frankenstein’s monster” business processes, which are ill-suited to meeting the organization’s goals.

To truly embrace big data, organizations have to be willing to revamp and modify their business processes from the beginning. Without this, big data will only introduce complications and have limited utility.

Moving too fast with big data integration

A major reason why big data integrations fail is that companies begin in a way that is overly ambitious. A company may want to transition the entirety of their organization towards big data, through a combination of software development and modified business processes. Transitioning in this way makes sense for a business that wants to take full advantage of big data right away, but it seldom works that way. Instead, it leads to an overwhelmed organization that can’t properly adjust to the shift.

By starting small and eventually leading to a full transition, an organization is able to better adjust to each step-by-step change. Organizations can grow their big data adoption incrementally and organically, adjusting as they need to, and scaling at their own pace.

The recipe for a successful big data transition

Companies have to be extremely conscientious when planning their big data efforts. Most importantly, they need to keep results in mind. What is the organization hoping to provide to itself through a data transition? Is it switching to big data for a specific reason, or is it simply switching because it knows that big data is the way to the future?

Ultimately, organizations often find that their integration projects fail because they are ill-prepared to take advantage of the new technology. By adopting vastly new data analysis processes — and grafting them roughly over existing organizational procedures — a business can become overwhelmed with its new technology and fail to adequately adopt it.

Businesses that are able to take their transition slowly and learn from each step are more likely to be able to successfully complete the transition — especially if they have the support of upper management and their software development team.




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