Introduction to Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics
- Raw Data, the New Oil
- Top 10 Business Questions for Analytics
- Vital Lessons Learned
- Linking Analytics to Business
- Conclusion
This chapter is from the book
This chapter is from the book
This chapter is from the book
Raw Data, the New Oil
“Data is the new oil”1 has become a common mantra among today’s Big Data proponents. However, Clive Humby, the founder of dunnhumby (the well-known retail analytics arm of Tesco Supermarkets in the UK), has also said that “[data] unrefined cannot really be used.” For this reason, many skeptics argue that Big Data is not new and is really just a fad. Before you are convinced of the importance or insignificance of Big Data, let’s try to use the “data as oil” analogy and show you the remarkable impact oil has had on every aspect of life across the world and (by inference) data.
If you look at a photo taken in New York in the late nineteenth century, you don’t see cars and buses. Instead, you see commuters on horseback or in horse-drawn carriages and omnibuses. In fact, New Yorkers living in a city with a million people in the late nineteenth century enjoyed their night life lighted not by using electric or gas lamps, but by burning whale oil. In fact, at its peak, more than 10 million gallons of whale oil per year2 (see Figure 1-1) were consumed across the United States. Because this usage was one of the main causes of the near-extinction of whales, Samuel M. Kier may be said to have saved the whales by successfully refining the crude oil into usable fuels in 1851. Of course, he also helped the emergence of entrepreneurs such as Rockefeller, who started out by selling kerosene as one of the first products of the oil-refining process.
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Figure 1-1 U.S. whale oil imports (1805–1905)
Over the next century, the impacts of oil literally reached the sky when U.S. astronauts landed on the moon in 1967. It is fair to say that oil transformed every facet of human culture and existence in the last 100 years. Just as you couldn’t ask someone in the early 1900s to predict how oil would transform the world in 100 years, it is just as difficult to imagine today what a transformational role data and analytics might have in the rest of the twenty-first century.
Data Big and Small Is Not New
Although gasoline was new, crude oil was not “new” in the 1900s. In fact, crude oil has been known since prehistoric times. For 7,000 years, humans have been trying to find uses for crude oil.3 In fact, one of its main uses in the United States in the nineteenth century was as folk medicine. Likewise, data (some may argue even Big [voluminous] Data) has been around for decades. What is new today is our ability to refine it to uncover novel insights and discover new applications.
In this book, I will show how business analytics (BA) is today’s way of refining raw data, big or small, into strategic business opportunities. In many schematics for Hadoop and its popular Big Data-related tools such as Hive and others, these Big Data management tools are labeled the “Big-Data Refinery.”4 More appropriately, the Hadoop Distributed File System (HDFS) is more like an oil storage and distribution system than the actual refinery. The actual refining process happens right after the data has been properly stored and then subjected to a refining process, which is defined in this book as the applied business analytics process (BAP).
Definition of Analytics
At this juncture, we should define what the terms “analytics” and “business analytics” mean as they are used in this book. The standard dictionary definition of analytics, which is “the method of logical analysis” with a first-known use in 1590, clearly needs updating. I have also seen that the understanding of analytics in some businesses have not progressed much further. During an interview in 2010 of senior business executives with Professor Tom Davenport in benchmarking analytics competencies of major companies, I was shocked to hear one SVP of analytics consulting asserting that “Everyone in my team is doing analytics as they all work with numbers.” Unfortunately, “working with numbers” does not make one an analytics practitioner. A horse carriage has four wheels just like a car, but it is clearly not a car. We did not find any evidence that any “analytics consultants” were doing any analytics as defined here.
In this book, I define “analytics” (or “business analytics”) this way:
- More than just numbers—Analytics is more than just working with numbers and data to find and report observed correlations and/or statistical distributions.
- Knowledge and results-centric—Business analytics is focused on the process of discovery of actionable knowledge and the creation of new business opportunities from such knowledge.
- Tools-agnostic—Analytics can use any computation or visualization tools from statistics, computer science, machine learning, and operational research to describe and recognize patterns, seek and validate causal relationships and trends, and predict and optimize outcomes.
To refine data into “business solutions” using the oil analogy, today’s analytics “Rockefellers” must be able to do the following:
- Understand the data—Know the properties and nature of crude oil.
- Understand the technology—Know the different ways of refining crude oil.
- Understand needs—Know the human needs that are currently either barely met or unmet by today’s technology.
- Be creative—Possess creativity in linking newfound properties of refined crude oil to meet known needs, and even create new markets out of new needs such as synthetics in materials, cosmetics, and pharmaceuticals which did not exist before.
Let’s take a look at some of these business opportunities for applying analytics.