Big data is much more than a trend that´s being studied in the innovation departments of cutting-edge companies. Some multinationals have already changed forever, thanks to the strategic use of data.

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With big data now on everyone's lips, the term runs the risk of becoming trivialized amid all the headlines. Nonetheless, it's making a real stir: the use of large flows of data in management has become common, not just in specific business areas (for example, digital audience measurement, social networks or the state of inventories) but also in companies of all kinds that have made big data the foundation of their business.

Three of these companies - Under Armour, John Deere and Walmart, all of them from the United States - were analyzed in a recent conference entitled 'Big Data: the Challenges of the Present and the Opportunities of the Future,' hosted by the IDC consulting firm. Often the most difficult thing is to have the strategic vision required to make such a change, and these three brands are a good example of how a company re-invents itself.

John Deere is no longer a tractor company

John Deere is a company with a 179-year history. For 174 of those years, it did basically the same thing: it sold tractors and other work vehicles. But in 2012, John Deere decided that it had to change direction, due in part to the growing competition from low-cost rivals. At that time, it began to place sensors in its tractors and process the information they collected, combining it with historical data about the weather, the state of the soil, or crop yield.

In parallel, the company developed the myjohndeere.com website and the 'Mobile Farm Manager ' application, so that farmers could have quick and easy access to all that big data. Both on the website and in the application, farmers can consult recent and historical data on their machines and their land, so they can plan their work and see the results.

The next step came in 2013, when John Deere opened its platform to third parties so that they could collaborate with their own applications and software, and the volume and richness of the data. They achieved three objectives at once: boosting the sale of their products; defending themselves against the likely entrance of technological competitors, known as AgTech (Agriculture and Technology) firms; and opening the door to monetizing all the data they collected, apart from the value that big data has in the day-to-day business with their customers. In addition, by tying the consumer to an entire management platform, John Deere could have the luxury of not competing on price, and could maintain suitable profit margins.

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As reported in an article in the Harvard Business School's Digital Initiative Digest, John Deere - a company that last year had more than $26.6 billion in sales and a net profit of $1.524 billion - is a perfect case study of a company focused on a product, that transforms itself into a company centered on a platform. This was a key step in its adaptation to the digital economy.

At Walmart, news and data both travel fast

The American hypermarket chain Walmart, which has 20,000 stores in 28 countries, has centered its big data strategy on the speed of processing of that data.

At its Arkansas headquarters, Walmart has created what it calls the data café, a work center focused on the analytic management of data that proceeds from more than 200 sources, both internal and external.

Walmart hypermarkets use big data to reduce the time for making marketing decisions.

But the jewel in this crown is its capacity to quickly process more than 40 Petabytes of data (enough to store 542 years of high-resolution video), that correspond to recent transactions.

All this data is processed, manipulated and visualized, with a clear objective: to reduce the time needed to make correct business decisions. What used to take weeks is now done in minutes.

As Naveen Peddamail, data scientist at Walmart, told Forbes magazine: 'If you can't get insights until you've analyzed your sales for a week or a month, then you've lost sales within that time. If you can cut down that time…to 20 or 30 minutes…That's the real value of what we have built with the data café.'

During the conference on big data held recently in Madrid, Arturo Gutiérrez, a consultant at SAP, gave a conclusive example of the good use of big data at Walmart. The first sales data that are produced on the U.S. East Coast on Black Friday are used for making decisions before the opening of the stores on the West Coast, leveraging the time zone differences.

Under Armour flaunts its data, not its running shoes

In April, 2015, when the sports wear retailerUnder Armour presented its first-quarter results for that year, its share price dropped on Wall Street. The results were good, with a more than 20% increase in sales for the twentieth consecutive quarter, but the market frowned on the 13% drop in net profit, which was dragged down by a series of acquisitions of fitness and lifestyle apps.

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Two and a half years later, these acquisitions are making sense. The company spent $710 million to buy three applications: MyFitnessPal, MapMyFitness and Endomondo, so as to create a community of sports and health addicts. This meant giving a strategic shift to the company, one that went beyond the mere sales of sports products.

Thanks to the 200 million people registered on its applications (according to data from the beginning of this year), Under Armour gathers an enormous amount of data about who trains, where they train, with whom they train, and even at what time they train and what exercises they do. The company knows who its customers are and even what they eat and how much they weigh - thanks to MyFitnessPal - so it can communicate with them in a unique fashion. This information is useful for more than marketing; it´s also used to design products. The company´s long-term commitment is clear: connectivity and big data.

BBVA - Banco Bilbao Vizcaya Argentaria SA published this content on 23 October 2017 and is solely responsible for the information contained herein.
Distributed by Public, unedited and unaltered, on 23 October 2017 00:36:03 UTC.

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