As the Frost and Sullivan study concluded, industry experts are expecting big data to bridge the gap between IT and business more and more because of the following factors:

         -Data is experiencing exponential growth.  To address this challenge, meaningful data discovery holds the keys. It is important to businesses in framing future business decisions.  It is, therefore, critical to transform huge volumes of complex and high-velocity (real-time) data into business insights.

         -Since the value of data is increasing, organizations are consuming data as a service and monetizing their own data to identify new opportunities and create new revenue streams.

         -The ability to analyze trends, accurately predict/forecast and enable quicker/faster business decisions improves overall efficiency.

         Although China is increasingly challenging the US as the leader in the Fourth Industrial Revolution, there is no question that it was the American economy that ushered in the area of Big Data and Data Analytics.  In the book “Thank You For Being Late,” Thomas Friedman wrote that AT &T was one of the pioneers in data analytics.  Its chief strategy officer, John Donovan, once remarked that this U.S. telecom enterprise was turning more and more “digital exhaust into digital fuel” and generating and applying the insights faster and faster.  In retailing and advertising, the American department store owner John Wanamaker once famously quipped: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”  Today, that problem can be effectively solved.  Barely six years ago, Latanya Sweeney, the then chief technology officer for the US Federal Trade Commission , explained how sensing and software are transforming retail:  “What a lot of people may not realize is that in order for your phone to make a connection on the Internet, it’s constantly sending out a unique number that’s embedded in that phone, called the MAC address, to say, ‘Hey, any Wi-Fi’s out there?’ And by using these constant probe requests by the phone looking for Wi-Fi’s, you could actually track where that phone has been, how often that phone comes there, down to a few feet.’“ 

         As will soon be done in the Philippines by such big retailers as SM, Robinson, Mercury Drug, Seven Eleven, Market Market and many others, retailers can use this information generated by millions of phones to see what displays a customer has lingered over in their stores  and which ones  tempted him/her to make a purchase, leading these stores to adjust displays regularly during the day.  But that is not all.  Big data can now allow retailers to track who drove by which billboard and then shopped in one of their stores.  Just consider the following report that appeared in The Boston Globe on May 19, 2016:

         Now the nation’s largest billboard company, Clear Channel Outdoor Inc., is bringing customized pop-up ads to the interstate.  Its Radar program, up and running in Boston and 10 other US cities, uses data AT&T Inc. collects on 130 million cellular subscribers, and from two other companies, PlaceIQInc. and Placed Inc. which use phone apps to track the comings and goings of millions more. …Clear Channel knows what kinds of people are driving past one of their billboards at 6:30 p.m. on a Friday—how many Dunkin’ Donuts regulars, for example, or have been to three Red Sox games so far this year…It can then precisely target ads to them. It is just a matter of time when a politician like Manila Mayor Isko Moreno Domagoso can have access to similar information about how many people passing billboards on EDSA have seen his handsome face in various advertisements and what are some of their buying preferences.  Whether or not such information can help him run for some national elective position in 2022 and beyond is, of course, another matter.

         Generation Z Philippine youth (those born after 2000) can consider the global demand for specialists in data analytics and Big Data in addition to the domestic market.  According to the Frost and Sullivan study, worldwide spending on BDA was expected to be US$ 121 billion in 2015 and likely to grow by roughly 50% in the succeeding five years.  Predictably, large enterprises are expected to drive three-quarters of overall growth during the forecast period.  The SME (small and medium-scale enterprise) sector remains largely untapped owing to cost barriers.   Open source technologies such as Apache Spark are experiencing wider uptake due to the faster process speed and flexibility these tools offer to customized large-scale big data deployments.  Use-cases of big data in businesses are widespread, most commonly in customer analytics, operational analysis as well as fraud and compliance.  North America continues to dominate but China and other Asia Pacific economies are fast catching up. 

         As mentioned above, data is expected to grow over the next few years exponentially. Huge volumes of unstructured data are currently generated on a daily basis. Especially notable are the huge volumes of unstructured data that are currently generated on a daily basis.  As I discussed in a previous article, the advent of Hadoop enabled analysts to make use of unstructured data.  Before Hadoop, most big companies paid little attention to unstructured data.  Most of them relied on Oracle SQL, a computer language that came out of IBM in the 1970s.  SQL stood for Structured Query Language.  In a structured database, the software tells you what each of piece of data is.  For example, in a bank system, it tells you “this is a check,” “this is a transaction,” “this is a balance.”  They are so structured that the software can easily find your latest check deposit.  Unstructured data was anything that you could not query with SQL because they were lumped together without any rhyme or reason as long as they could be digitized and stored.  Hadoop enabled data analysts to search all unstructured data and find the patterns.  The ability to sift mountains of unstructured data, without knowing what you are looking at, and be able to query it and get answers back and identify patterns was a profound breakthrough.

         The explosion of data can be significantly attributed to the increasing digital footprint.  Despite our relatively low per capita income compared with most of our East Asian neighbors, Filipinos have reached above-average usages of smartphones, tablets, the internet, social media, and many other forms of engagement that have become part of the end user’s daily life.   End user interactions with the digital world yield huge amounts of data, thus fueling the drive to find meaning to the data for descriptive, prescriptive or predictive purposes.  This convergence of technology and consumer behavior sharpens the desire for operational efficiency in business and other organizations. BDA provides real-time insights that enable quick decision-making for generating more business value, thus improving overall efficiency.   The use of predictive analytics has improved the ability of decision makers to identify new opportunities as well as accurately forecast trends that would impact the business significantly.  Finally, data available from customer interactions could help in making better pricing decisions.  Prices can now be tailored to individual customers.  BDA could also be useful in examining customer feedback on a regular basis, such as feedback and suggestions on social media websites.  Such feedback can lead to improved products and services more quickly than any traditional methods in use.  For comments, my email address is bernardo.villegas@uap.asia.