Data Scientists - Why they are on the rise

Online content is produced and consumed so rapidly that sorting through all that data can feel like sifting through sand in the Sahara. Identifying individual data points is like finding a grain of sand in a desert. Although daunting, great potential lies in the wealth of information available. Big data is transforming business, and we have just begun to tap into its potential.

Big Data: Turning Information Into Insights

“There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days” –Eric Schmidt, Executive Chairman at Google

In an age where we are inundated with unfathomable amounts of data, businesses need to invest in analytics, or risk missing out on key information. The goal of big data analytics is to sift through the wealth of data to find information that can be translated into knowledge and practice. 

 

Why is big data so important?

“The goal is to turn data into information, and information into insight” – Carly Fiorina, former chief executive of Hewlett-Packard Company

Big data is changing the game. There is no shortage of information available and the speed at which content is produced and consumed is rapidly increasing. As Carly Fiorina suggests, we need to learn to pull out pertinent information and transform information into action. In business, this information can be used to identify cost-saving measures, improve time management, identify market gaps, and improve decision-making.

 

Case Study: Big Data Analytics at Wal-Mart

Let’s look at Wal-Mart, whose annual revenue in 2016 was over 480 billion. Wal-Mart’s C-level management collects data on where and when items are sold across all 11,528 stores worldwide – that’s a lot of data! Wal-Mart’s Chief Information Officer, Linda Dillman, wanted to track which items sold in the wake of natural disasters, like hurricanes and earthquakes.

You can imagine a traditional business analytics approach would anticipate an increased demand for emergency supplies like flashlights and bottled water, with sales increasing as storms approach. The data agreed – there was a notable spike in sales of emergency supplies at Wal-Mart stores across the East Coast of the United States in the wake of Hurricane Sandy, one of the most devastating hurricanes in U.S. history.

But analysis of the data collected from previous natural disasters showed something traditional business analysts would likely have missed – sales of strawberry flavoured Pop Tarts skyrocketed too. In light of this, Wal-Mart stores across the East Coast increased the stock of strawberry Pop Tarts as Hurricane Sandy approached and, sure enough, sales went through the roof.

This example highlights the predictive abilities of big data and an industry shift whereby managers and executives are able to predict sales trends and anticipate consumer needs before even the consumer can.

 

Fast Data

It’s not just the volume of data that has increased; its velocity has increased too. By the time data is processed, organised, and stored, it’s outdated. Today, data needs to be collected and stored in real-time in order for management to recognise trends as they’re happening – not after the fact. Big data is changing the database landscape too; businesses are moving from data warehouses to data lakes. Think of data warehouses like bottled water, where the raw material is gathered, processed, packaged, and stored. Whereas a data lake is a repository where the data is dumped in its more natural state. This way, all the data is stored in one central location for data analysts to manipulate and draw insights in real-time as the information flows in.

 

Conclusion

Big data is not just a buzzword; it should be a central part of your marketing strategy. The insights drawn from the effective collection and storage of information can improve production, increase profit margins, and eliminate inefficiencies, transforming any business into a successful enterprise. So take this opportunity to learn how to pull pertinent information from data, turn that information into insights, and use those insights to grow your business.

 

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