So, What’s New?
Based on our definition of analytics, you may be thinking that many of the disciplines within analytics are not new. You are correct. Many of them have been around for years. But many things are new. We’ll cover these in more detail as we move through the book. The following list will give you a flavor for where we’re going.
First, there is a now a realization that all these disciplines are related and part of the larger theme of analytics. New solutions become possible as you combine different types of analytics to solve a problem.
Second, the proliferation of data has opened the possibility of asking many new questions and uncovering many new insights and trends. Analyzing and summarizing that same data, however, requires sophisticated tools and methodologies because of the data volume and complexity. At the same time, to take full advantage of the opportunity presented, organizations need to be able to deploy tools and methods more widely; in the past, these tools and methods were stuck in the corner of an organization, run by experts. The more people who can look at and analyze the data, the better your chances of finding interesting trends and running your business better. In addition, this proliferation of data has unleashed a new wave of creativity. Early on, website developers realized that they could combine two or more different services or products to create a new one. This was called a “mash-up.” More abundant data has made mash-ups accessible to more managers. They can pull data from many different sources to solve new problems or to solve an old problem in a new way. Creating mash-ups requires creativity.
Third, the proliferation of data has given people incentive to create new tools, revisit old tools that didn’t work with limited data, and apply old tools in new ways. For example, when you have access to the whole universe of data rather than just a sample of it, you can analyze it with algorithms that might not have worked well with the smaller sample set of data. (We’ll give some nice examples of this in Chapter 2, “What is Driving the Analytics Movement?”) Also, with large data sets, mathematicians are rediscovering old fields. For example, the field of topology has been around as a purely theoretical field for 250 years. Now topology is being used to help people visualize large data sets. Finally, new tools (or updated versions of them), like machine learning algorithms (which we will cover later), are moving out of research labs and into the hands of businesspeople.
Fourth, with a lot of business moving online combined with the fast feedback of social media, analytics makes running tests much easier. For example, a company can show different versions of its website to randomly selected visitors and easily test which version leads to the desired results—such as more sales, more signups, or longer time on the site. With more business being done online, analytics can help make and influence more and more business decisions.
Another way to look at what is new in the field of analytics is to consider how it can change what managers need to do. The former president of a successful online financial services firm summed it up nicely. He said that his job wasn’t to figure out what decisions to make. Instead, it was to figure out how the decisions should be made. Once he was confident in how decisions should be made, algorithms could be programmed to make those decisions. The algorithms ran the business. The algorithms determined what webpages to show each visitor, what services to offer the visitor, and what price to charge. Management simply needed to make sure the algorithms stayed up to date and used additional data as it became available.
Finally, and possibly most importantly, the attitude toward analytics is new: With the abundance of data available and the abundance of tools to use that data, managers realize that more and more decisions can be improved through the use of analytics. If they don’t take advantage of that, they risk falling behind.