SOMETIMES, BIG DATA CAN MISLEAD IN TAKING BUSINESS DECISIONS

2Apr - by aiuniverse - 0 - In Big Data

Source – https://www.analyticsinsight.net/

Most of the big data analytics we perform centers around what we expect it to be

“Why is the product unsuccessful? Did we not plan it after going through big data analytics?” asked a company executive. This is not the first company or the first time such confusion has happened. If we take a close look around, most of the data analytics we perform centers around the concept we expect them to be, leading to massive setbacks. Yes, it is true. Even though we praise big data for being the accelerator of every decision, we can’t deny the fact that it can be misleading at times.

Big data is more than just structured and unstructured data. It is seen as a base ingredient of all decision-making processes. For the past two decades, ever since mobile phones came into existence and technology evolved exponentially, It became a critical part of every business operation. Big data in business is a very common substance that executives and employees used to get insights into their market performance. Technology experts are all praises about data, with many touting it as the best thing that has happened to humankind. But the truth is a little twisted. When data is used correctly, it opens the door to double or triple-fold the revenue in minimum time. Unfortunately, it can also be misleading, draining the company’s efforts to go down the gutter. A report by Blazent, an IT intelligence company unravels many findings of big data disadvantages. It shows that around 42% of executives state that misuse of data can impair revenues and 39% said this can be deteriorating for correct decision-making. Henceforth, this article takes you through how big data is misused and what can be done to patch the gap.

Drawing an example from political endeavor

Political circle, especially, presidential elections were heavily relying on big data outcomes. Of course, curiosity didn’t let us be silent. It almost became a custom to know the result through pre-poll analysis. But if we look back at the records, they were not always right. Most recently, the 2016 election that gave Hillary Clinton a 90% chance of victory ended up making Donald Trump the President of the United States. This could either be because of a crack in the data or the data itself was faulty. The big data analytics clearly depicts the fact that human nature as of yet, cannot be reduced to a series of ones and zeros.

Moving on to big data in business and its disadvantages 

Businesses are increasingly relying on big data today. Starting from making simple decisions on marketing and promotion to big ones like where to invest and how to gain more revenues, literally, everything revolves around data. Unfortunately, business executives are unaware that technological innovation is a double-edged sword. If it is not used for good intent, It can wreak havoc.

Datasets are huge and are spread across many disparate locations and diverse forms. Henceforth, business organizations are unaware of whether the data is clean, accurate, manageable, and usable. Besides, some of the data are also manually entered into the system, prompting human errors. While such mismatched data are processed together, it leads to serious negatives and misleading outcomes. However, companies, unaware of the datasets condition take the result as everything and proceed with it.

Businesses are increasingly relying on algorithms to sort company issues. Brian Bergstein of MIT Technology Review suggests that the growing reliance on big data in business is creating a corporate bubble of overconfidence. But why are algorithms unreliable? Even though algorithms are computer-based, they have their own form of risk since they are ‘created by people and they contain interferences and assumptions coded in.’ These coded-in values shape the outputs like computer-generated predictions, recommendations, and simulations.

Finally, one of the biggest setbacks of big data analytics is people’s perceptions. While company executives have a perception on certain products or product developments, the consumers’ viewpoint might vary. But this goes unnoticed when companies focus on delivering their viewpoint to customers without addressing their concerns. Organizations design questions that they want to ask. It is solely on the executive’s perception of what clients needed to answer. They weren’t reflecting on what clients wanted to express. As a result, business takes the wrong path in the name of following big data insights.

What can be done?

Listen to customers. It is the only option to keep away misconceptions. Even though engaging with customers and having a face-to-face or virtual conversation may not be as exciting as compiling big data answers, they reflect on people’s thoughts. When we ask random questions and let them talk, they talk their hearts out and say things that might build the stairs for the organization’s success. For example, Toyota and Adobe are two such companies that go for people’s view than big data decision-making.

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