Big data projects don’t fail because one particular reason, rather, it’s a combination of factors that eventually derail big data implementations.
Sometimes failure can be attributed to any number of factors from lack of expertise, poor strategy, inattention to details, capacities to the accelerated evolution of the digital economy.
The scarcity of experts within this space also heavily contributes to the failure of big data projects.
As a result, there are a lot of novices within the space that follow a trial-by-fire model to try and achieve business goals (and that’s just a really bad idea!).
What contributes to the failure of big data projects?
The primary reason why big data projects fail is directly attributed to its heavy dependence on the right expertise. Having the right team of experts who are able to develop a viable strategy is essentially the first step to delivering real business value. However, as demand far outpaces supply, putting together a team of big data experts will be a significant challenge.
Once you have managed to get top talent onboard, it’s also important for them to first identify the right problems that demand a big data solution. Furthermore, they should actively question the data and try to understand the different nuances of analytical models which are applied to big data.
The lack of financial resources is also a big contributor to failure. I mean let’s face it, not everyone has the same resources that are accessed by the likes of Facebook and Google!
What’s more, big data projects should never be started without specific cost reduction and revenue enhancement goals. This means, if the project isn’t going to make an impact on either expense savings or revenue generation, there’s actually no point in pursuing it (and will be viewed as a failure by business leaders).
There are also a lot of misconceptions that often plague various industries that ensure that big data projects fail. For example, some folks in management believe that just setting up a big data analytics platform is an end in itself.
How do you prevent big data projects from failing?
It’s important to lead by example from the beginning and has all the stakeholders engaged from day one. By encouraging transparency and regularly communicating how data is being processed and analyzed to derive real business value, you can start changing the culture within the company.
With sufficient financial backing, you can explore if your IT infrastructure is actually capable of handling the enormous amount of data that will be generated. By focusing on how the project will scale, you can take steps to appropriately optimize the company’s existing infrastructure.
For example, tools like enterprise-class capacity optimization, capacity visualization, and predictive modeling will help enterprises forecast future usage and better predict infrastructure growth.
To have a real chance at being successful, the big data environment should also be able to seamlessly interface with other data sources and enterprise applications. This will enable the organization to successfully accelerate insights.
But to achieve this, you have to efficiently manage workflows on the network, end-to-end. You also have to manage workflow schedules to ensure that the analytics team gets to view it in a timely manner. Bad timing can be a significant pain point, so this should also be addressed to ensure big data success.
Finally, keeping the data secure will be paramount to the project’s success. The consequences of a security breach can significantly damage brand image, so you must take adequate steps to avoid it.
If you look at the industry as a whole, enterprises that are successful in keeping their big data secure have a holistic view of how enterprise applications are connected to big data infrastructure.
This is critical going forward because when you take steps to manage access to sensitive data, encrypt it in transit, and protect it where it's stored, it will go a long way to ensure that all your efforts weren’t in vain.
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