Data, and our fascination with it, has been around for a long, long time. Early examples of information documentation are found in the Caves of Altamira, pyramids around the world and in leftovers from prehistory. But now the data is coming at us with volumes and pace so furious it is truly difficult to comprehend. Organizations big and small are struggling with data governance, data strategy, data analytics, data security, data gathering, data warehousing, data extracting, data monetization, and trying to adjust to an environment that is changing far more rapidly than a given organization’s ability to adapt to it. Large organizations have cultures that are designed to move slowly and protect policies, practices and procedures, but these cultures and practices are not serving them well in our data-driven age.
This is a problem and not an insignificant one.
We have all read about the growth in the data world, but here are a few more fun examples of our rapidly changing environment:
There are now more mobile phones ~7.2 billion than there are people
2.4 billion worldwide internet users and about half of them are in Asia
2.7 billion ”Likes” on Facebook each day
60+ billion Wordpress sites
Today’s environment demands that an organization either uses data to achieve business process improvement or, become a cadaver on the littered highway of dead companies. Each day we generate 2,500,000,000,000,000,000 bytes of data. This is roughly equivalent to all of the words in all of our known languages. We generate this much data every day, but at an increasing rate. Data volume is growing at a whopping rate four times faster than the global economy.
"90% of today’s available data was generated in the past 24 months."
Much of this information gets analyzed. But how and for what purposes? Most of the work revolves around consumer behavior and associated sales and marketing programs. Let’s take a quick stroll down memory lane for some important context.
This business of Big Data was formerly known as The Information Industry. Sears Roebuck and Company started today’s data business at least insofar as marketing and analytics are concerned. Sears, now staving off rigor mortis, will soon die because they were too big to bail on outdated practices and adapt to the fast changing data-driven landscape in retail, (more on this below). Sears, however, was truly grand in its day with far reaching influences that are still vital in today’s market.
Sears published and printed a catalog of consumer goods and pioneered the direct response industry. The Sears Catalog has been studied more than the Bible, the Koran and the Torah combined. Every inch of page-use was analyzed. Every response scrutinized. Fonts and colors were studied. Every address evaluated. As computers become the main tool of the direct marketing trade, lists became the logical byproduct of “catalog selling” or selling direct to the end-user without a brick and mortar presence. Sears’ practices foreshadowed today’s Amazon Prime World. As Amazon knows better than most organizations, “the way positive reinforcement [reward] is carried out is more important than the amount,” B.F. Skinner.
Lists became the first, and still may be best, example of “information exhaust” - lists of responders, lists of lapsed buyers, lists of recent buyers, frequent buyers and big spenders were developed with increasing precision and with a much greater frequency than previously imaginable. The cornerstone of today’s RFM predictive statistical models – the frequency of purchasing, the recency of purchasing and the monetary value of purchasing - came from Sears. And with these lists came targeting. What took several years in the print-mail-analyze-reprint-remail-and-so-on-world is achieved today in minutes.
The Web has become the key driver of data - a giant data delivery and data gathering laboratory and the world’s most effective Skinner Box. It’s all about clicking and getting the reward. B.F. Skinner is either rolling over in his grave or laughing his ass off. The advertising and marketing industries, along with every online merchant, owe Sears (and B.F.) a large debt.
Like moths to light, w are attracted to Big Data and analytics for primarily one reason – money. We study data to either help sell more stuff or to help optimize costs associated with human behavior or production. Ninety percent of our fascination with Big Data - metadata, unstructured data, transactional data, purchase data, scanned data, social media data, set-top data, photo-tagging data, listening preferences data - is driven by the pursuit of one thing; improving the targeting and language of advertising messages.
Data is the new currency. Forget the USD, forget Bitcoin, forget the Euro or the Renminbi – it’s data, bro.
Modern data mania began in the 1960’s. During this time, the social sciences were very popular academic majors. Studying psychology, philosophy, archeology, sociology, and anthropology not only taught critical thinking skills, but also ushered in the important data gathering techniques used to evaluate consumer interests, attitudes, opinions, and behaviors. Sociologists and social and industrial psychologists developed these tools. Later these techniques were co-opted by direct marketers.
Social and industrial psychologists developed interviewing methods, questionnaire design, scaling methods such as the 5 or 7-point Likert Scale, bibliometrics (this is how Amazon initially learned about us), advanced statistical methods such as Factor Analysis, Cluster Analysis, Multidimensional Scaling and Principal Components Analysis. The list goes on. Very importantly, it was the critical thinking skills and the training to be critical thinkers that enabled these techniques to be productive.
Critical thinking skills are the mission critical ingredient, the essential atomic particle in data analytics and the world of data in general. Because, if you cannot question context, question reasonableness, question purpose and question the nature of questions being asked, you or your organization will make big, perhaps fatal mistakes. This is a certainty. Either find critical thinking talent or stay far away from the data business. The science underlying “data science” is much more than having a flare for arithmetic. You need to think, challenge and be relentlessly curious. Therefore, logically, since businesses are necessarily becoming data oriented, if not data-centric, either hire or cultivate critical thinkers. If not, you may end up lying down with Sears.
Trends also suggest that 90% of data generated from this point foreword will be unstructured data – tweets, photos, consumer purchase history, customer service call logs, and so forth. If this does not emphasize the need for critical thinking, nothing will.
As Mettacite pursues its work in assisting organizations with a variety of projects involving data, product development, analytics, organizational design, and talent assessment, we are stunned at how shallow some of the thought pools are. We do not see enough critical thinking skills in today’s workforce and it is indeed a serious issue.
At Mettacite, we do not see enough
critical thinking skills in today’s workforce and
it is a serious issue.
So what is a business to do? Only a very few businesses are in the data business per se, the rest have to run the business they are in – making automobiles, making shampoo, making cheeseburgers, making money in one fashion or another. While they were building their businesses, the information industry grew up and around them. The Data world has penetrated and often upended every aspect of corporate activity. Large organizations are now shackled to heavy, slow moving and overly vertical organization structures that are incapable of reacting to the light speed pace of the information age. The irony in this environment is that the larger the organization, the greater the risk it faces in not adapting to the information age and exploiting the promise of Big Data. Hiring a Chief Data Officer will not solve the problem.
Look at the charts below:
Chart A shows enterprise size by headcount and the ability to act, react or otherwise be nimble in the information age on a 9-point scale, 9 being very nimble and adaptive.
Clearly, the larger the organization, the more risk it takes on and the lower the likelihood that a data-driven culture will emerge in the C-Suite. At the other end of the scale we have a hacker who can wreak data havoc on any organization.