3 Myths of Big Data Busted

In simple terms, Big Data is the mix of structured and unstructured data. Today, several businesses all around the world are exploiting Big Data to get useful information. Many revolutionary advances are being made every day by enterprises and research institutes for being able to combine and access a vast amount of data at their fingertips. In spite of all advances made in the field of Big Data, a lot of mystery and hype is being associated with it. As a result, several myths have emerged about Big Data.

Let’s understand and debunk the top three Big Data myths.

1. Only Big Businesses can Implement Big Data

Big Data analytics can help businesses to gain a better understanding of their current market position and conditions. For example, a company can analyse its customers’ buying patterns, and thereby find out the kind of products that are sold the most. Thus, Big Data analytics can help in determining what products it should produce more according to this trend.

Big businesses can afford to spend an ample amount of capital on data analytics processes and tools. They also have an entire team of data analysts for analyzing large amounts of data. It is a common myth that smaller businesses are not able to invest in these advanced analytics tools, thus they are not able to harness the benefits of Big Data.

But this myth is completely false. Smaller businesses can certainly get started with inexpensive tools. Several established giants such as SAS, Microsoft, IBM are offering affordable, cloud-based data analytics services that can help small businesses to get started with Big Data analytics. Using these services, small businesses can successfully mine Big Data. By cross-referencing a company’s internal information-pricing histories with customer traffic patterns, along with combining them with multiple outside sources, smaller businesses can thus increase revenue by understanding customers’ behaviour. This would also help in reducing costs by eliminating inefficiencies, thereby strengthening customer relationship by anticipating clients’ needs. Therefore, even smaller businesses can provide enriched service offerings with better knowledge of their customers, and also give their employees new tools to perform their jobs better.

2. Big Data will Predict Future. 

There is a difference between analytics and prediction. All Big Data analytics tools collect and analyses data that comes entirely from the past. We have not yet reached the point in data science where we can collect data points and values from the future. That is why, we can only analyse what has happened in the past and try to draw visualizations and insights between previous actions of the company and their consequences.

Many organizations try to obtain the futuristic predictions out of a mass of data. This is a bad idea, as the future is always changing. You must always remember that past performance cannot guarantee future results. Thus, to gain benefits from Big Data analytics, you should use it optimize and enhance what’s currently stands true for your enterprise. This means that, you should be able to find useful trends that can help you in gaining profits in current market conditions.

Big Data can certainly provide you with loads of insights using which you can interpret about the future. However, you should remember that these insights are based on past data and their interpretation will be the result of data originally selected and the questions that were initially asked. Thus, Big Data tools cannot give accurate future predictions and can result in several miscalculations.

3. Big Data is Equal to More Data

Huge data volume or size is just a characteristic of Big Data. Nowadays data is coming in faster than ever, thus it is important to identify what data sets are relevant for analysis, so that companies can process it quickly. If you want up-to-date and relevant market trends, then you cannot just dump all the data on analytics tools. Remember quantity does not always bring quality. Larger data sets can also lead to potential oversights. This means than rather than giving you a simple and accurate insights, analysis of these huge datasets can give you a more complicated solution. Therefore, organizations must look beyond the size of data and build a model that can sift through the data and identify what is genuinely useful for analysis.

Myths commonly tend to build up around anything that is huge and transformational, and Big Data is no exception. Therefore, it is up to businesses to adopt an iterative approach while implementing Big Data and use its tools to gain best competitive advantage. 

What do you think about Big Data and its uses ? Let us know in the comments below.

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