Do you really need Big Data?

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Big Data is fairly one of the trendiest word of this year, and it’s not a causality. Big Data is said to be the holy grail of business marketing and the track where every company should lead to.

For the non familiarized with the term, Big Data refers to a massive set of data that spans four dimensions of volume, velocity, variety and value.

Big Data is a massive volume of data that can not be processed or analyzed with traditional tools

Big Data is more than just the size of the data, it is the tool that help us find insights and answer questions to make our business more profitable, costumer-oriented and agile. And so it is true that we require huge amounts of data to extract value and predict tendencies, it is not the size of the data that is the key factor in our business, but having the right data.

big data or right data?

In most cases having the Right data is actually Big data, but instead of focus on the vast amount of data we can collect, we should focus on the data that derives value.

So what is Right data?

Right data is the exact data that we need to get the job done. Having more data that we actually need can lead to unnecessary expenses and even fake or false metrics.

In a recent interview, Thoryn Stephens, the Chief digital officer at American Apparel recalled an example of how big data can actually distract us from underlying problems or unexploited potential: “This company was renowned as the darling of Silicon Beach” -he pointed. The emerging startup had all the “flashy metrics”: over 2 million Facebook followers, 10 million members and millions of customers. But if one looked “under the hood”, said Stephens, the actual buyers were a “sliver of a percent” of the presumed customers.

Stephens assessed the Realized Customer Value (RCV) for the customers (he calculated their margin of contribution and acquisition cost) and realized many of the customers had negative numbers. “This was because these users were serial exchanges and returners”, he explained. “They would buy our product and cyclically wear it once or twice and send it back. There were literally thousands of them”. The information his analysis revealed were crucial, as it was affecting the business. With this information, the company could take action and applied it in its return and exchange policy.

It’s not “are you collecting data?”, it’s “are you collecting the right data, and reporting upon the right data, and driving insights from that?” –Thoryn Stephens

Uber is one of the companies that successfully implemented the right data strategy. It captures immense amounts of data, but to get the things done, they realized that they were using more information that they needed. They stopped using a biological detection algorithm for scanning for human-shaped figures with their arms outspread (someone asking for a ride) and asked the right question: Who needs a taxi service? The amount of data used dropped significantly which improved the business.

How to identify the Right data?

Keeping with the right data varies across industries, sometimes it will be small data or actually big data. But to get the right data for the job, you should at least know answer the following three questions:

What is to be done?

You must know where do you want to go before take action. Performing unnecessary work reflects directly in business profit and competitivity.

What actions leads to waste in your business?

You should identify what action or decision drives waste in your business, remember “waste makes for opportunity”. Anything that does not represent value is a source of waste.

What you should do to eliminate waste?

Automate decisions can reduce wasted effort and resources. Most of the companies are moving towards automate repetitive and operational decisions by implementing algorithms that machines tend to perform better than people.

Conclusion

Companies can benefit from big data but for most of them, big data imply waste of resources and effort in cleaning, format, integrate and store data, and also distraction from underlying problems or insights. Instead of focusing on the amount of data you can collect, you should focus on the actual value you get from it.