BeyeNETWORK - The Global Vision for BI and Beyond

A Systems View of Business Analytics, Part 1

Introduction to Systems Thinking

Published: August 5, 2008

In this first part of a three-part series, Dave Wells explains how systems thinking provides the cause-and-effect connection that is key to real analytic value derived through insight, understanding, reasoning, forecasting, innovation and learning.

Many of today's business intelligence programs focus intensely on analytics. The business wants scorecards, dashboards and analytic applications, and the technology to deliver them is mature. Still we struggle to deliver high-impact analytics that are purposeful, insightful and actionable. The key to high-impact analytics is a strong connection with cause and effect – the essence of understanding why and deciding what next. Systems thinking offers the cause-and-effect connection. It holds the key to real analytic value that is derived through insight, understanding, reasoning, forecasting, innovation and learning.

Systems Theory

So let’s look beyond analytics and think about systems. I don’t mean computer systems here, although computer systems are one type to which systems theory can be applied. But it applies just as readily to human, organizational and business systems.

Fundamental truths for all systems, regardless of their type, include the following assertions:

• A system is a collection of interacting parts.
  
  
• Behavior of any part is influenced by interaction with other parts.
  
  
• A system boundary defines the set of parts that comprise a system.
  
  
• A system may interact with things outside of its boundary.
  
  
• External interaction is less influential on system behavior than internal interaction.
  
  
• Behavior is understood by examining the entire system, not individual parts.

Systems Thinking – Applied Systems Theory

Systems thinking applies systems theory to create desired outcomes or change. It offers a unique approach to problem solving that views problems as part of an overall system. Traditional problem-solving approaches tend to focus on one or a few parts of a system, believing that changes to those parts offer a solution. The systems-thinking approach focuses less on the parts and more on interactions and influences among them as the core elements of solving problems.

Understanding of systems is achieved through identification, modeling and analysis of relationships and interactions among the parts of a system – a distinctly different and more in-depth analysis than is possible with structural models of a system. Systems modeling is performed by representing the parts of a system and the interactions among those parts.

The most basic concept of systems theory is that a system is a collection of interacting things. I use the word “thing” to avoid the context-based connotations that might occur with terms such as entity, object or component.

Things in a system are of many types. They may include (but are not limited to) entities that are familiar to data modelers, objects that are familiar to object-oriented systems analysts and components as they are understood by software developers. Things in a business system encompass artifacts such as resources, capacities, limits, gaps, goals, desires, actions, results, plans, processes, rules, standards and much more.

Influence is a behavioral characteristic of interaction. Interaction between two things in a system is directional – one thing has influence on another thing. System behavior is important to understand why things happen in a system and to predict what may happen in the future. Analysis of influences is the key to understanding system behavior.

Systems Thinking Models – Causal Loop Diagramming

Visually representing system behavior is widely practiced in systems thinking with a causal loop diagram (CLD). Causal loop diagramming is a form of cause-and-effect modeling. The diagrams represent systems and their behaviors as a collection of nodes and links. Nodes represent the things in a system and links illustrate interactions and influences.

Influences are of two types – same direction and opposite direction. A same-direction influence means that the values of two things move in the same direction when change occurs: When employee morale increases, employee productivity goes up. An opposite direction influence means that the values move in opposite directions: When employee stress increases, employee productivity decreases. Figure 1 illustrates how these two examples are modeled. Note that a plus (+) indicates same direction and a minus (-) is used for opposite direction.

The diagramming technique is called causal loop diagramming because real understanding comes from understanding the system as a whole. Cause-and-effect is typically not linear. It is circular with a sequence of influences producing a feedback loop. Loops are closed structures that represent a sequence of system interactions without a beginning or an end. A loop may contain any number of interactions greater than one. Feedback is a characteristic of loops in systems.

Feedback is a process by which the results of an activity or action are returned to the actor in a way that influences the behavior of that actor. Positive feedback occurs when the cumulative effect of all interactions in the loop is one of growth, amplification or acceleration. Positive feedback loops are often called reinforcing loops. Negative feedback occurs when the cumulative effect of all of the interactions is stabilization or equilibrium. Negative feedback loops are also known as balancing loops or goal-seeking loops.

Figure 2 illustrates both kinds of feedback loops. Note that the kind of feedback loop – positive or negative – is indicated using a polarity symbol at the center of the loop. Polarity describes the positive or negative feedback property of a loop. Determining loop polarity is relatively easy. Simply count the number of subtractive interactions in the loop. An odd number indicates negative polarity, and an even number indicates positive polarity.

Individual feedback loops are a step toward understanding cause and effect, but they only scratch the surface. It is often the interactions among loops that provide real insight into system behaviors by breaking down stovepipe views of the parts of a system. Figure 3 illustrates this principle with only one minor change to the diagrams shown in Figure 2. The new model shows a connection between the two feedback loops. Finding these kinds of connections is the first step to developing a holistic view of a system.

In reality, a system consists of many loops and many interactions among those loops. It is that total system view that helps to achieve depth of understanding and real insight into the behaviors of complex systems. The intersection nodes – those that participate in two or more loops – are the core of system complexity, and they provide the greatest opportunity to discover side effects, hidden influences and unintended consequences.

Determining the boundaries of a system model can be challenging. Every system is a part of some larger system. Therefore, it is possible to continue modeling infinitely. Stop modeling when you have acquired the knowledge and information that satisfies the purpose of the model. Stopping too quickly, however, brings the risk that you’ll overlook side effects and unintended consequences. Figure 4 illustrates the nature of this challenge.

Systems Thinking and Business Analytics

This article provides only a brief introduction to systems thinking, a subject that is deep, complex and very much related to business analytics. Only by understanding system dynamics can we provide the most meaningful measures and deliver analytics that are purposeful, insightful and actionable. Sometimes that means measuring things, but more often it means measuring interactions and influences.

The discipline of systems thinking includes several archetypes – generic models that represent recurrent patterns in systems. The names of the archetypes are fascinating in themselves: accidental adversaries, fixes that fail, drifting goals, tragedy of the commons, etc. But even more interesting is the clear and certain relationship that exists between these archetypes and the patterns seen in time-series analysis.

The systems thinking approach also includes other modeling techniques. Causal loop diagrams illustrate influences. Another technique called stock-and-flow provides the means to quantify influences. Quantification enables simulation, and simulation is at the heart of “what if” analysis and predictive analytics.

I will expand on these topics as parts 2 and 3 of this series of articles to describe a systems view of business analytics.

If you found this article helpful and would like to receive the latest insights each week from the experts featured on the BeyeNETWORK, please subscribe to the BeyeNETWORK Newsletter.