Bernardo M. Villegas
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Better Decisions From Big Data

          For those of us who are several generations behind the millennials, it is quite a challenge to continue learning so that we can keep up with the dramatic changes affecting business and the economy, especially as regards digitalization.  I am glad I am affiliated as a Visiting Professor of one of the best business schools in the world today, the IESE Business School in Barcelona, Spain.  In my regular trips to Barcelona in order to participate in some executive education programs at IESE, there is always new knowledge that I am able to acquire both as an economics professor and as a business consultant.  In my most recent visit last September 17 to 26, 2017, I obtained very valuable insights from IESE professors on how to get the most out of data analysis.  IESE has some of the world’s leadings professors in Managerial Decision Sciences who teach executives from all over the world on how to make better decisions with the use of mathematical and statistical tools as applied to data analysis.  Special attention was given in the sessions I attended to big data.

         The expression “big data” refers to large volumes of information that cannot be processed with the use of ordinary data analysis systems.  As explained in a paper by three IESE professors of managerial decision sciences, big data do not only imply size, but other characteristics which are described by experts using the three V’s:  Volume, Velocity and Variety.   As regards volume, ordinarily, we talk about megabytes and gigabytes.  A gig is a thousand megs.  With big data, however, the orders of magnitudes are much higher.  We can reach terabytes (a thousand gigs) or even exabytes (a billion gigs). Big data involves large volumes of data that are difficult to manage with conventional software on a personal computer.  Velocity-wise, in some applications of big data, there is continuous generation of data which have to be processed in real time, or within a shrinking window of time.  This is what we call streaming data.  Finally, as regards variety, the data are often not structured.  Normal databases are made up of collection of tables, with rows and columns, mostly filled with numbers.  In contrast, big data are found in unstructured data, which can be numerical, textual (such as comments on Facebook, Twitter or a company blog), or multimedia (like photos and songs).  They can come from many different sources and can be saved in different formats (cvs, jpeg, pdf, and so on).

         The pioneers in the use of big data were giant corporations like Google, Amazon. and Netflix.  Big data analysis, however, has expanded to organizations of all types and shapes, resulting in improved processes and innovative features.  For example, two of the industries that use big data most are banking and phone companies.  Banks employ customer data that they collect through credit card purchases and account transactions in order to decide which products to offer each customer.  Phone companies use data on phone usage to predict which customers are most likely to change companies and then plan a custom marketing campaign to try to convince them to stay.   Another source of an infinite amount of data is a company’s website.  Each time someone visits a site and clicks, there is a record of what time the person enters the site, for how long, when that person leaves, what page he or she came from, and so on.  It is possible for the company to design experiments to verify where to position the information it most wants visitors to see.  The results of this analysis can be used to maximize visitor retention.

         One of the most fertile sectors in which to apply big data analysis is the booming retail sector.  Anyone who is familiar with the Sara brand of fashion goods would realize how retail has been reinvented over the last decade through the innovative practices of Inditex, the Spanish business establishment that implemented fast fashion ideas that drive frequent changes and quick-response production and distribution for retailers of fashion goods.  Thanks to the use of big data analysis by Inditex, the retail landscape has become quite turbulent (some of our local fashion enterprises have been adversely affected), leading to uncertain demand patterns.  To adapt, retailers are investing in technology to monitor rapidly changing trends.  This provides stores with large amounts of data—big data—that can reveal helpful and actionable information, which can be used to make better decisions regarding stocking, merchandising and product promotions.

         Big data analysis has made obsolete previous techniques of consumer surveying.  There are now more sophisticated methods for tracking individual customer behaviors, as Professor Victor Martinez de Albeniz of IESE wrote in a paper entitled “Five Tips on Big Data Optimization for Retailers.”  On the Internet, cookies allow firms to know customer demographics as well as interests from past click history.  When it comes to physical stores, loyalty cards can track past purchase behavior.    These systems require that retailers record individual actions.  However, in retail categories where conversion is low, such as in fashion, these systems are not very useful, because they are unable to track customer visits without purchase.  There are alternatives, however.  One possible option is to track customer smartphones, via Wi-Fi, which allows a retailer to know when and how long a customer visited, and whether he or she made a purchase.  This data provides some sort of customer relationship management system for all customers, across stores, which can then be matched with credit card or other types of data.

         Fast food chains can also benefit from big data analysis.  They can monitor the number of people who are waiting for be served.  When the line is long, screens would advertise products that are quick to cook and serve.  When there is no line, they advertise products that take longer to cook but have a higher profit margin.  These examples illustrate that big data analysis can have a significant impact on the bottom line through strategies related to pricing, customer service, benchmarking, customer retention and marketing.  In most cases, there is no need to collect new data; the data that already exist is sufficient.   For example, a bank has data on account transactions or credit card purchases of each of its clients.  A supermarket knows what kinds of products are bought by customers possessing loyalty card.  A company has data on its employees, its customers, its products and its supply chain.  Other supplementary data can be extracted with relative ease and minimum cost from social networking sites or public information.

         It must also be pointed out that big data analysis can also benefit relatively small and medium-scale enterprises.  With the creation of customer digital platforms, mobile apps, small businesses can easily find themselves working with big data on orders and customer behaviors.  Operational data from sensors and automated processes bring even more data.  For all firms, leveraging big data and analytics is about understanding their customer and operation in detail.  Marketing efforts have been the major users of big data and analytics.  We can discern a trend, however, where we see many firms, especially digital firms, being built on the strategy of creating new data and using it to disrupt markets.  We foresee that strategy and operations will increasingly be important users of big data and analytics, too.  For comments, my email address is