4 Things big data can't do for your business

4 Things big data can't do for your business

Faith Ocampo
January 5, 2017

businessman under red umbrella during digital number rain

The big data craze isn’t likely to quiet down anytime soon. It’s continuing to build a positive reputation for itself, as it helps organizations solve business-related problems in innovative ways.

Across industries, entrepreneurs see data—coupled with the ability to understand it—as a means of fully understanding their customers and business processes. Indeed, plenty of companies are already reaping massive rewards of data utilization, and many are intending to follow suit. Last year, an estimated 48% of companies have invested in data analytics, and this number is expected to expand to 75% this 2017.
We should acknowledge, however, that there are things data can’t do for enterprises. Otherwise, we’d end up relying too much on its capabilities, in turn blinding us to its weaknesses.
Here are some things big data won’t be able to do for you.
 

1.     Predict the future.

fortune teller businessman looking into crystal ball

Whenever you’re working with data, remember that these bits of information are based from events or cases that have already happened. In short, they’d give you insights about the recent past. Analyzing and interpreting these data will thus allow you to anticipate what’s likely to happen within a given time frame, but your predictions will never be 100% accurate.

So when you make decisions based on your data, always allow room for uncertainty and inaccuracies. This means coming up with flexible, rather than rigid, strategies and backup plans. Soliciting insights and ideas from your fellow business leaders would also help you design more holistic business strategies.

 

2.     Come up with creative solutions.

miniature businessman thinking

This one’s a rather controversial topic. Some experts argue that if data analytics tools are run by sophisticated enough algorithms, they’d be able to generate creative ideas or recommend solutions to existing business problems—whether it’s about customer service, marketing, and organizational communication.

No one can deny, however, that it would take tons of effort before we can get there. Until that happens, brands would still be relying on their brightest employees for innovative ideas and solutions.

Needless to say, you can’t expect great ideas to automatically surface after analyzing big data sets. After you’ve subjected your data to layers upon layers of analysis, you need the human mind to make sense of the preliminary interpretations, make wise data-driven decisions, and ensure that plans are effectively implemented.

 

3.     Understand itself.

scared bald businessman crawling into hole in wall with analytics

Most entrepreneurs, especially those new to data management, mistakenly think it’s a simple and easy endeavor. Awed by the potential of data to revolutionize business processes, they tend to overlook the prerequisites of successful data utilization.

The truth is, it requires more than statisticians and data analytics tools to be able to extract powerful insights out of heaps of information. Depending on what you’ll be analyzing, you need data scientists and experts from multiple disciplines, such as social relationships, marketing, psychology and human behavior, and others. You also need a highly skilled IT team to help you optimize your tools and build features that suit your purposes.

 

4.     Make sense of people’s emotions.

faceless man surrounded by faces on wall

Because more and more business owners aim to build a customer-centric brand, most of their big data projects are geared to understand their target market. This includes people’s purchasing habits and motivations, preferences, and expectations. Having access to this information will allow companies to enhance customer service and build better relationships with their clients.

The thing is, understanding the customer entails identifying and making sense of their emotions, and this is something that often falls outside the realm of data analytics.

Take speech analytics for example. Tools with voice recognition capabilities can detect happiness based on a person’s pitch or tone of voice, but these are merely indicators or clues into one’s real emotions. To make up for this gap, you may consider conducting one-on-one interviews and surveys among your target market.

 
 

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