5 reasons why most big data analytics projects fail

5 reasons why most big data analytics projects fail

Steer clear of failure to deliver by keeping an eye out for these issues.

It’s been a few years now since enterprises started talking of interpreting big data, getting actionable insights into customer preferences and putting in place personalised customer engagement programs for existing and prospective customers. But not many of these projects seem to have delivered the objectives the enterprises sought to achieve when they first embarked on them. Marketers continue to spam ‘potential’ customers on email and social media. Messages from some of the best-known brands are more often misdirected and end up as spam in the inboxes of people who are probably least interested in their offerings.

According to data gathered by an Internet security site, 45 per cent of all email in 2018 was spam. While there is no data available for misdirected messages or spam on social media, it appears that many enterprises are yet to get their big data analytics act together. It’s clear from the way many companies advertise and try to do community building on social media sites that they are yet to learn to mine and understand the data they get on customers on these sites and formulate strategies that enable meaningful engagements.

There are many reasons why big data analytics has not lived up to its hype and delivered what enterprises have expected to achieve. Here are some of the reasons.

Data quality
How good is your data? Have you streamlined and connected all your data sources? Have you eliminated overlap and do your BI systems have access to clean data? If your answer is no to any of these questions, you are probably not ready for a big data analytics project.

Have you connected the data silos?
Your data could reside on multiple systems and databases. But are they connected? Do they talk to one another? If they don’t you’ll invariably have data quality issues and your big data project is headed for disaster.

Are your processes geared up for big data analytics?
Your existing processes may not support the kind of requirements you have while executing a big data analytics projects. Do you have the buy-in to tweak or re-engineer the processes to ensure that you have the right organisational ecosystem to execute the project?

Competencies
The success of any big data analytics project depends on the people executing it. Do you have people with the right competencies? In this case you require people who understand working with databases, analytics software, and have statistical and econometrics skills.

Change management
This becomes a huge challenge when you are implementing a big data analytics projects. You’ll need to get the C-suite comfortable with going through dashboards and getting high level views generated from analytics. You might have functional heads who are threatened by the way analytics can change their fiefdoms. You could also have employees who need to culturally adapt themselves to the way your enterprise functions in the changed environment.

To conclude, big data analytics has great potential to transform the way your enterprise functions and engages with customers. But unless you transform legacy and overcome these obstacles, it’s hard to achieve your business goals in a digitally transformed world.

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