Big Data has established itself as a game-changer that can transform business operations. Further, the application of big data analytics is expected to completely change the way we use and consume data. As a large number of businesses are sitting on a massive wealth of data that was collected from multiple sources, there’s no denying that it can be a revolutionary tool that can transform specific business functions. However, it can only make a difference and provide some value if you know how to harness and analyze an enormous amount of data and which data should be analyzed as a first priority.
The data that exists come from multiple sources like financial data, point-of-sale information, mobile app data, digital and social data, and research data. But making sense of all this is not an easy task. As a result, the full potential of big data has yet to be realized.
For now, harnessing all this information is a massive labor-intensive task. At the same time, some misconceptions about big data still exist. Let’s take a look at the top five big data misconceptions.
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1. Big Data will always lead to revolutionary changes
By itself, big data isn’t actionable even after a data scientist has been able to extract some valuable information. This useful technology can only incorporate the next steps to help the user gain insights from the data to make improvements.
So if you’re going to engage in big data analytics, you also have to develop processes where someone can take the valuable insights and implement it. If you don’t do this, the outcome will just be information, not solid business intelligence.
2. Big Data always provides the right answers
Even if you’re using next-generation predictive analytics that combine, measure, and harness data from multiple sources, it won’t necessarily mean that you’re anywhere closer to the truth.
Sometimes you can even end up with conflicting evidence. As a result, accuracy will heavily depend on human judgment to iron out the discrepancies.
3. Big Data is always inherently valuable
Data by itself has no inherent value. To extract value, you must sort, process, and distribute the data. This is the reason why data scientists are a hot commodity these days.
What these data scientists do is cull through vast amounts of data to determine what’s valuable. Once they do that, they write algorithms to extract the valuable information.
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It all starts out with a hypothesis that will be used to guide the search. This approach will help to isolate useful predictors and eliminate impertinent information. So it’s really analytics that provides value, not the data by itself.
4. Big Data is best suited for Big Business
You hear this common misconception all the time. But the truth is, big data technologies aren’t prohibitively expensive. So it’s not just for Fortune 500 companies, it can be used by much smaller businesses, too.
Big data technologies are geared toward almost all industries as most companies produce a massive amount of data. So regardless of whether a business is large or small, there’s a good chance that they’re sitting on some data that can be useful.
5. Bigger is always better
Although it’s big data that’s getting all the attention these days, little data can be just as effective. Like with everything else, it’s about quality versus quantity.
Even if you’re holding on an enormous amount of data, it might not provide any value. It’s the quality of the information collected that matters.
Further, when you’re dealing with a vast amount of data, it has to be first sorted and organized to fit within analytical parameters. Little data will be a lot easier to deal with as it’s usually cleaner, more easily controlled, and unique.
Although most of us may never understand the algorithms that make big data analytics possible, it’s not as complicated as most people think. In fact, we use it every day without even thinking about it.
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