CRM, OLAP Cubes, and Business Intelligence

Customer Relationship Management, as a concept, brings together a number of various systems from functions across the business (sales, marketing, operations, external, etc) that allow the enterprise to create, maintain, and grow positive and productive relationships with customers. We might think of it as being the glue that brings front office and back office together and allows the business to de-silo what would otherwise be proprietary information across the organization.

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But what good are all these data points if they aren’t utilized effectively? It would be easy to fall victim to information overload if we tried to explore the data from a particular axis or angle. This is where classic data mining and online analytical processing (OLAP) come in. If we think of various systems of record as one-dimensional axes on a graph, bringing these together in a three-dimensional cube and taking a particular block within that cube to analyze would be much more efficient. Rather than starting with the data and searching for questions to answer that might involve those points (as is tempting to do at times), we are able to start with a specific business question and use OLAP to answer it.

For example, assume I am a cosmetics manufacturer and want to know how much of my product actually goes out the door to consumers after it is sold to a distributor. I want to use that information to adjust my marketing efforts and potentially re-evaluate my production line. I have the following data points available by way of my existing business intelligence environment:

  • Production line data
  • Inventory balances in my warehouse
  • Marketing campaign data
  • Sales data from my company to the distributor
  • Sales data from the distributor to the end consumer

Rather than starting from one or two of these data points and throwing things against the wall to see what might stick, I can use OLAP capabilities to find the different relationships between these points, eventually driving my answer. Understand here that answering the initial question is simply a matter of reading one data point (the last one in this case)—however, a strategic approach that addresses the customer relationship is the end goal.

One caveat here. OLAP may be considered a predecessor to currently-understood data mining, depending on which view of business intelligence you find appealing. Strictly speaking, traditional OLAP has been used for a number of years already for marketing, forecasting, and sales. Data mining capabilities at present far surpass what has been traditionally available in the OLAP sense.

Reference

Connolly, T. & Begg, C. (2015).  Database Systems: A Practical Approach to Design, Implementation, and Management (6th ed.). London, UK: Pearson.

From Decision Support to Business Intelligence

Decision support systems (DSS) predate business intelligence (BI) by several decades. Sprague and Carlson (1982) define a DSS as “class of information system that draws on transaction processing systems and interacts with the other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in organisations.” This definition is very nearly interchangeable with that of a business intelligence system. We can think of DSS as more of a framework and model more than an actual software package. These have often been aided by computer resources, such as databases and online processing (OLAP), but they may also be offline. Any DSS involves a data or knowledge base, the business rules, and the interface itself. DSS systems may be classified by one of the following drivers (Power, 2000):

  • Communication-Driven
  • Data-Driven
  • Document-Driven
  • Knowledge-Driven
  • Model-Driven

Business intelligence can be viewed as the successor of DSS or the parent of it. I prefer to see it as a hybrid. As methods of collecting, storing, viewing, and analyzing data became more advanced, DSS systems came to be a specific part in a larger BI framework. A DSS is always dependent on “access to accurate, well-structured, and organized data” (Felsberger, Oberegger, & Reiner, 2016, p. 3). The various functions of business intelligence that have grown in recent years all serve to support the data points going into the DSS.

In a manufacturing environment, a practical example might be the evaluation and assignment of work centers. The knowledge base may include data such as what must go in, what must be produced, what constraints are in place, et cetera. Production and diagnostic data from the different work centers would be integrated via the BI capabilities of the organization, as well as forecasted production and schedule data. Business rules such as employee labor hours and machine lifecycle may also be included. The DSS would use all these data points to drive outputs; in this case, the desired outputs and decisions include production labor and machine scheduling that are most efficient to the company.

References

Felsberger, A., Oberegger, B., & Reiner, G. (2016). A review of decision support systems for manufacturing systems.

Power, D. J. (2000). Web-based and model-driven decision support systems: concepts and issues. In proceedings of the Americas Conference on Information Systems, Long Beach, California.

Sprague, R. H., & Carlson, E. D. (1982). Building effective decision support systems. Prentice Hall Professional Technical Reference.

The Courage to Step Back

On February 26, 2008, Starbucks stores across the country closed for 3.5 hours for what CEO Howard Schultz characterized as “a reaffirmation of [their] coffee leadership.” An estimated $4-6 million in sales were lost, rival coffee stores offered promotions taking advantage of the competitor’s down time, and reactions were wildly mixed. This was an incredibly bold move in the midst of the Great Recession. Just when consumers needed signs of confidence from their trusted brands, a staple goes dark? After expanding at a breakneck speed, why was Starbucks stepping back?

I had very little insight into what went into this decision before reading Schultz’s book, Onward. In it, he explains how the drive to grow had overtaken the fundamentals of the company. In an environment of increasing demands at the front line, Starbucks had fallen into bad practices, even with the best of intentions. It became necessary to take a step back. It was time to refocus, retrain, and recommit to the Starbucks Experience. As Jon Picoult notes, Schultz did not view this as a cost—it was “a smart investment in the education of his employees.” Beyond that, it was a courageous move . . . one that ultimately worked out in the company’s favor.

In any business environment, the prospect of shutting down and stepping back from production to refocus on internal housekeeping seems contrary to conventional wisdom. It may be interpreted as a sign of weakness or lack of organization. But that same race for deliverables and production can introduce corner-cutting or ad-hoc fixes that are never meant to be sustainable. In Starbucks’ case, for example, baristas were pre-steaming milk for lattes and cappuccinos. This compromised the beverage.

Business intelligence efforts are particularly susceptible to the race for deliverables. Think of an analytics group bombarded with report requirements from different business units. Their revenue depends on the justification for these reports. In some cases, the source data may be given to them without sufficient explanation or rationale, and they are asked to make sense of it on the fly. Billable hours and the drive to “just get it done” takes precedence. A cycle of short-term fixes emerges and no clear ownership of the data is established.

This is a process driven by fear. Being a martyr to productivity is not only selfish, it is irresponsible. Starbucks recognized they were slinging an inferior product and chose to refocus. Howard Schultz had the courage to stand up and step back . . . and that’s just coffee. In the business domain, what critical data products get rushed out to production and are mediocre at best?

Someone must recognize that the vicious cycle is untenable. It might seem contrary to the pressures of billable hours and deliverables, but ultimately is a smart investment in the sustainability of processes. Taking the time up front to stop and get the house in order precludes the repeated short-term fixes that would inevitably snowball. It’s a courageous move amidst competing pressures.