Big Data misuse can break your business

Correct use of the Big Data analytics and ML algorithms helps boost the customer satisfaction, secure the bottom line and increase the ROI. Quite opposite, the Big Data misuse results will be awful.

Blazent, an IT data intelligence company, has published a research on the state of Big Data quality back in 2016, which yielded some interesting insights on the perceived outcomes on the misuse of Big Data by enterprise business, conducted amongst top-level business executives.

For example, while 80% of enterprise C-suite managers acknowledge using Big Data analytics helps them find new profit opportunities. In addition, 50% of respondents said such approach helps increase the revenues and 50% of the executives assure this lowers the costs. We have already told about 8 real-life business success stories based on Big Data, highlighting the incredible results that emerge when the Big Data analysis is used correctly.

On the other hand, 42% of executives state that misuse of Big Data can impair revenues and 39% said this can be deteriorating for correct decision-making. To say even more, while 40% of business executives are sure their Big Data mining projects are successful, nearly 95% are aware that incorrect results of such endeavors can be devastating for the business.

Thus said, there indeed were the cases when misusing Big Data analytics in some way has brought some negative consequences for the businesses. We list them below.

One huge medical company was determined to adopt Big Data as quickly as possible. This has led to the insufficient planning of the Big Data relevancy checks and curation procedures, largely leading to “collect everything, analyze later” approach. As a result, their Big Data analysis reports were mostly more than 200 pages long, which flooded the readers with textual details and efficiently lead to the reports being absolutely useless.

However, after reshaping their data stream curation strategies to track only the influential data and opting for correct Big Data visualization tools, the company ended up with 20 pages of infographics that were easy to comprehend and draw decisions upon.

Many businesses are using on-prem Business Intelligence systems for quite a while already. However, as opting for Big Data analytics becomes mainstream, multiple companies endeavor to adopt the top-of-the-line tech and practices ASAP. Unfortunately, this can lead to certain damage, as more than 50% of underplanned data migration projects result in certain disarray and require much more time and effort to be put into order. Thus said, careful planning and taking a slow approach to Big Data migration allows saving much money and effort in the end.

A large online retailer decided to move to leveraging Big Data analysis without a help of experienced contractors. They ended up in horrible disarray and had to pause their customer-facing systems and processes in order to fix the errors. No need to say that prolonged website maintenance has led to significant customer frustration, not to mention reputational losses and missed revenues.

Big Data analytics is a complex business process that should have many input and output points. When some company departments consider it to be their internal business that should not bother anyone else, including their colleagues from other departments — the business as a whole might suffer dire consequences.

One online electronics vendor met a situation when their Marketing department representatives thought of themselves as “major money bringers” and the rest of the departments somewhat inferior to them.