CHAPTER 1
Big Data and Analytics

Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data.1 In relative terms, this means 90 ­percent of the data in the world has been created in the last two years. Gartner projects that by 2015, 85 percent of Fortune 500 organizations will be unable to exploit big data for competitive advantage and about 4.4 million jobs will be created around big data.2 Although these estimates should not be interpreted in an absolute sense, they are a strong indication of the ubiquity of big data and the strong need for analytical skills and resources because, as the data piles up, managing and analyzing these data resources in the most optimal way become critical success factors in creating competitive advantage and strategic ­leverage.

Figure 1.1 shows the results of a KDnuggets3 poll conducted during April 2013 about the largest data sets analyzed. The total number of respondents was 322 and the numbers per category are indicated between brackets. The median was estimated to be in the 40 to 50 gigabyte (GB) range, which was about double the median answer for a similar poll run in 2012 (20 to 40 GB). This clearly shows the quick increase in size of data that analysts are working on. A further regional breakdown of the poll showed that U.S. data miners lead other regions in big data, with about 28% of them working with terabyte (TB) size databases.

Figure 1.1 Results from a KDnuggets Poll about Largest Data Sets Analyzed

Source: www.kdnuggets.com/polls/2013/largest-dataset-analyzed-data-mined-2013.html.

A main obstacle to fully harnessing the power of big data using analytics is the lack of skilled resources and “data scientist” talent required to exploit big data. In another poll ran by KDnuggets in July 2013, a strong need emerged for analytics/big data/data mining/data science education.4 It is the purpose of this book to try and fill this gap by providing a concise and focused overview of analytics for the business practitioner.

EXAMPLE APPLICATIONS

Analytics is everywhere and strongly embedded into our daily lives. As I am writing this part, I was the subject of various analytical models today. When I checked my physical mailbox this morning, I found a catalogue sent to me most probably as a result of a response modeling analytical exercise that indicated that, given my characteristics and previous purchase behavior, I am likely to buy one or more products from it. Today, I was the subject of a behavioral scoring model of my financial institution. This is a model that will look at, among other things, my checking account balance from the past 12 months and my credit payments during that period, together with other kinds of information available to my bank, to predict whether I will default on my loan during the next year. My bank needs to know this for provisioning purposes. Also today, my telephone services provider analyzed my calling behavior and my account information to predict whether I will churn during the next three months. As I logged on to my Facebook page, the social ads appearing there were based on analyzing all information (posts, pictures, my friends and their behavior, etc.) available to Facebook. My Twitter posts will be analyzed (possibly in real time) by social media analytics to understand both the subject of my tweets and the sentiment of them. As I checked out in the supermarket, my loyalty card was scanned first, followed by all my purchases. This will be used by my supermarket to analyze my market basket, which will help it decide on product bundling, next best offer, improving shelf organization, and so forth. As I made the payment with my credit card, my credit card provider used a fraud detection model to see whether it was a legitimate transaction. When I receive my credit card statement later, it will be accompanied by various vouchers that are the result of an analytical customer segmentation exercise to better understand my expense behavior.

To summarize, the relevance, importance, and impact of analytics are now bigger than ever before and, given that more and more data are being collected and that there is strategic value in knowing what is hidden in data, analytics will continue to grow. Without claiming to be exhaustive, Table 1.1 presents some examples of how analytics is applied in various settings.

Table 1.1 Example Analytics Applications

It is the purpose of this book to discuss the underlying techniques and key challenges to work out the applications shown in Table 1.1 using analytics. Some of these applications will be discussed in further detail in Chapter 8.

BASIC NOMENCLATURE

In order to start doing analytics, some basic vocabulary needs to be defined. A first important concept here concerns the basic unit of analysis. Customers can be considered from various perspectives. Customer lifetime value (CLV) can be measured for either individual customers or at the household level. Another alternative is to look at account behavior. For example, consider a credit scoring exercise for which the aim is to predict whether the applicant will default on a particular mortgage loan account. The analysis can also be done at the transaction level. For example, in insurance fraud detection, one usually performs the analysis at insurance claim level. Also, in web analytics, the basic unit of analysis is usually a web visit or session.

It is also important to note that customers can play different roles. For example, parents can buy goods for their kids, such that there is a clear distinction between the payer and the end user. In a banking setting, a customer can be primary account owner, secondary account owner, main debtor of the credit, codebtor, guarantor, and so on. It is very important to clearly distinguish between those different roles when defining and/or aggregating data for the analytics exercise.

Finally, in case of predictive analytics, the target variable needs to be appropriately defined. For example, when is a customer considered to be a churner or not, a fraudster or not, a responder or not, or how should the CLV be appropriately defined?

ANALYTICS PROCESS MODEL

Figure 1.2 gives a high-level overview of the analytics process model.5 As a first step, a thorough definition of the business problem to be solved with analytics is needed. Next, all source data need to be identified that could be of potential interest. This is a very important step, as data is the key ingredient to any analytical exercise and the selection of data will have a deterministic impact on the analytical models that will be built in a subsequent step. All data will then be gathered in a staging area, which could be, for example, a data mart or data warehouse. Some basic exploratory analysis can be considered here using, for example, online analytical processing (OLAP) facilities for multidimensional data analysis (e.g., roll-up, drill down, slicing and dicing). This will be followed by a data cleaning step to get rid of all inconsistencies, such as missing values, outliers, and duplicate data. Additional transformations may also be considered, such as binning, alphanumeric to numeric coding, geographical aggregation, and so forth. In the analytics step, an analytical model will be estimated on the preprocessed and transformed data. Different types of analytics can be considered here (e.g., to do churn prediction, fraud detection, customer segmentation, market basket analysis). Finally, once the model has been built, it will be interpreted and evaluated by the business experts. Usually, many trivial patterns will be detected by the model. For example, in a market basket analysis setting, one may find that spaghetti and spaghetti sauce are often purchased together. These patterns are interesting because they provide some validation of the model. But of course, the key issue here is to find the unexpected yet interesting and actionable patterns (sometimes also referred to as knowledge diamonds) that can provide added value in the business setting. Once the analytical model has been appropriately validated and approved, it can be put into production as an analytics application (e.g., decision support system, scoring engine). It is important to consider here how to represent the model output in a user-friendly way, how to integrate it with other applications (e.g., campaign management tools, risk engines), and how to make sure the analytical model can be appropriately monitored and...