Business Drivers for Advanced Analytics

The Table outlines four categories of common business problems that organizations contend with where they have an opportunity to leverage advanced analytics to create competitive advantage. Rather than only performing standard reporting on these areas, organizations can apply advanced analytical techniques to optimize processes and derive more value from these common tasks. The first three examples do not represent new problems. Organizations have been trying to reduce customer churn, increase sales, and cross-sell customers for many years. What is new is the opportunity to fuse advanced analytical techniques with Big Data to produce more impactful analyses for these traditional problems. The last example por- trays emerging regulatory requirements. Many compliance and regulatory laws have been in existence for decades, but additional requirements are added every year, which represent additional complexity and data requirements for organizations. Laws related to anti-money laundering (AML) and fraud prevention require advanced analytical techniques to comply with and manage properly.

BI Versus Data Science

The four business drivers shown in Table require a variety of analytical techniques to address them properly. Although much is written generally about analytics, it is important to distinguish between BI and Data Science. As shown in Figure, there are several ways to compare these groups of analytical techniques.

One way to evaluate the type of analysis being performed is to examine the time horizon and the kind of analytical approaches being used. BI tends to provide reports, dashboards, and queries on business questions for the current period or in the past. BI systems make it easy to answer questions related to quarter-to-date revenue, progress toward quarterly targets, and understand how much of a given product was sold in a prior quarter or year. These questions tend to be closed-ended and explain current or past behavior, typically by aggregating historical data and grouping it in some way. BI provides hindsight and some insight and generally answers questions related to “when” and “where” events occurred.

By comparison, Data Science tends to use disaggregated data in a more forward-looking, exploratory way, focusing on analyzing the present and enabling informed decisions about the future. Rather than aggregating historical data to look at how many of a given product sold in the previous quarter, a team may employ Data Science techniques such as time series analysis to forecast future product sales and revenue more accurately than extending a simple trend line.

In addition, Data Science tends to be more exploratory in nature and may use scenario optimization to deal with more open-ended questions.

This approach provides insight into current activity and foresight into future events, while generally focusing on questions related to “how” and “why” events occur.

Where BI problems tend to require highly structured data organized in rows and columns for accurate reporting, Data Science projects tend to use many types of data sources, including large or unconventional datasets. Depending on an organization’s goals, it may choose to embark on a BI project if it is doing reporting, creating dashboards, or performing simple visualizations, or it may choose Data Science projects if it needs to do a more sophisticated analysis with disaggregated or varied datasets.

There are several things to consider with Big Data Analytics projects to ensure the approach fits with the desired goals. Due to the characteristics of Big Data, these projects lend themselves to decision sup- port for high-value, strategic decision making with high processing complexity. The analytic techniques used in this context need to be iterative and flexible, due to the high volume of data and its complexity. Performing rapid and complex analysis requires high throughput network connections and a consideration for the acceptable amount of latency. For instance, developing a real-time product recommender for a website imposes greater system demands than developing a near-real-time recommender, which may still provide acceptable performance, have slightly greater latency, and may be cheaper to deploy. These considerations require a different approach to thinking about analytics challenges, which will be explored further in the next section.