in the health care industry calculated a 49X economic impact (they need to look harder to find that missing 1X).
History has shown that the most significant technology innovations are ones that drive economic change. From the printing press to interchangeable parts to the microprocessor, these technology innovations have provided an unprecedented opportunity for the more agile and more nimble organizations to disrupt existing markets and establish new value creation processes.
Big data possesses that same economic potential whether it be to create smart cities, improve the quality of medical care, improve educational effectiveness, reduce poverty, improve safety, reduce risks, or even cure cancer. And for many organizations, the first question that needs to be asked about big data is:
How effective is my organization at leveraging new sources of data and advanced analytics to uncover new customer, product, and operational insights that can be used to differentiate our customer engagement, optimize key business processes, and uncover new monetization opportunities?
Big data is nothing new, especially if you view it from the proper perspective. While the popular big data discussions are around “disruptive” technology innovations like Hadoop and Spark, the real discussion should be about the economic impact of big data. New technologies don't disrupt business models; it's what organizations do with these new technologies that disrupts business models and enables new ones. Let's review an example of one such economic-driven business transformation: the steam engine.
The steam engine enabled urbanization, industrialization, and the conquering of new territories. It literally shrank distance and time by reducing the time required to move people and goods from one side of a continent to the other. The steam engine enabled people to leave low-paying agricultural jobs and move into cities for higher-paying manufacturing and clerical jobs that led to a higher standard of living.
For example, cities such as London shot up in terms of population. In 1801, before the advent of George Stephenson's Rocket steam engine, London had 1.1 million residents. After the invention, the population of London more than doubled to 2.7 million residents by 1851. London transformed the nucleus of society from small tight-knit communities where textile production and agriculture were prevalent into big cities with a variety of jobs. The steam locomotive provided quicker transportation and more jobs, which in turn brought more people into the cities and drastically changed the job market. By 1861, only 2.4 percent of London's population was employed in agriculture, while 49.4 percent were in the manufacturing or transportation business. The steam locomotive was a major turning point in history as it transformed society from largely rural and agricultural into urban and industrial.2
Table 1.1 shows other historical lessons that demonstrate how technology innovation created economic-driven business opportunities.
Table 1.1 Exploiting Technology Innovation to Create Economic-Driven Business Opportunities
This brings us back to big data. All of these innovations share the same lesson: it wasn't the technology that was disruptive; it was how organizations leveraged the technology to disrupt existing business models and enabled new ones.
Critical Importance of “Thinking Differently”
Organizations have been taught by technology vendors, press, and analysts to think faster, cheaper, and smaller, but they have not been taught to “think differently.” The inability to think differently is causing organizational alignment and business adoption problems with respect to the big data opportunity. Organizations must throw out much of their conventional data, analytics, and organizational thinking in order to get the maximum value out of big data. Let's introduce some key areas for thinking differently that will be covered throughout this book.
Don't Think Big Data Technology, Think Business Transformation
Many organizations are infatuated with the technical innovations surrounding big data and the three Vs of data: volume, variety, and velocity. But starting with a technology focus can quickly turn your big data initiative into a science experiment. You don't want to be a solution in search of a problem.
Instead, focus on the four Ms of big data: Make Me More Money (or if you are a non-profit organization, maybe that's Make Me More Efficient). Start your big data initiative with a business-first approach. Identify and focus on addressing the organization's key business initiatives, that is, what the organization is trying to accomplish from a business perspective over the next 9 to 12 months (e.g., reduce supply chain costs, improve supplier quality and reliability, reduce hospital-acquired infections, improve student performance). Break down or decompose this business initiative into the supporting decisions, questions, metrics, data, analytics, and technology necessary to support the targeted business initiative.
CROSS-REFERENCE
This book begins by covering the Big Data Business Model Maturity Index in Chapter 2. The Big Data Business Model Maturity Index helps organizations address the key question:
How effective is our organization at leveraging data and analytics to power our key business processes and uncover new monetization opportunities?The maturity index provides a guide or road map with specific recommendations to help organizations advance up the maturity index. Chapter 3 introduces the big data strategy document. The big data strategy document provides a framework for helping organizations identify where and how to start their big data journey from a business perspective.
Don't Think Business Intelligence, Think Data Science
Data science is different from Business Intelligence (BI). Resist the advice to try to make these two different disciplines the same. For example:
• Business Intelligence focuses on reporting what happened (descriptive analytics). Data science focuses on predicting what is likely to happen (predictive analytics) and then recommending what actions to take (prescriptive analytics).
• Business Intelligence operates with schema on load in which you have to pre-build the data schema before you can load the data to generate your BI queries and reports. Data science deals with schema on query in which the data scientists custom design the data schema based on the hypothesis they want to test or the prediction that they want to make.
Organizations that try to “extend” their Business Intelligence capabilities to encompass big data will fail. That's like stating that you're going to the moon, then climbing a tree and declaring that you are closer. Unfortunately, you can't get to the moon from the top of a tree. Data science is a new discipline that offers compelling, business-differentiating capabilities, especially when coupled with Business Intelligence.
CROSS-REFERENCE
Chapter 5 (“Differences Between Business Intelligence and Data Science”) discusses the differences between Business Intelligence and data science and how data science can complement your Business Intelligence organization. Chapter 6 (“Data Science 101”) reviews several different analytic algorithms that your data science team might use and discusses the business situations in which the different algorithms might be most appropriate.
Don't Think Data Warehouse, Think Data Lake
In the world of big data, Hadoop and HDFS is a game changer; it is fundamentally changing the way organizations think about storing, managing, and analyzing data. And I don't mean Hadoop as yet another data source for your data warehouse. I'm talking about Hadoop and HDFS as the foundation for your data and analytics environments – to take advantage of the massively parallel processing, cheap scale-out data architecture that can run hundreds, thousands, or even tens of thousands of Hadoop nodes.
We are witnessing the dawn of the age of