Key Performance Indicators and Key Risk Indicators

This section presents the analytics process as a way to develop the measurement system in the organization, which is the same as seeing the analytics process as the nervous system that activates the organization’s intelligence in order to achieve its goals. The concept of connecting key performance indicators (KPIs) and key risk indicators (KRIs) is based on the theory and frameworks related to enterprise risk management and strategic risk (Figure 3.3).

Foundations of ERM

Figure 3.3 Foundations of ERM.

A business measurement system requires three components: goals, for example, related to sustainability, performance, and the components of a balanced scorecard—benefits, customers, processes, and employees. Next, metrics are the explicit identification of the methods of calculation of the factors to control and adequate values definition. There are metrics of several areas and types, such as quick ratio, and dimensions of the metrics, such as time, region, etc. A method to develop metrics is based on the following steps: connecting the desires of the organization and the structure of the management control system components

Connection of what the organization wants to do with a management control system

Figure 3.4 Connection of what the organization wants to do with a management control system.

(see Figure 3.4), embedding the concepts of efficiency and effectiveness in the definition of the metrics and the context of the strategy, and directing the general concepts of the organizations as a whole to the specific areas of the organization, manufacturing, marketing, and so on.

Once the areas are defined in terms of the measurement systems, at least two big tasks have to be performed:

a. Identification of costs associated with the measurement system

b. Identification of potential relationships of cause and effect of the results based on trees

The combination of KPI and KRI is based on the analysis of the possible events that can create variation of the KPIs. For example, the growth of market share can be part of the KPIs of the organization. The target is defined by a number, and the organization is prepared to achieve that target. In several cases, the targets are not exactly the final numbers from the results evaluation. The results of the market share are not exactly what the target was or better than the expected market share. In some periods, the target can be above or below the real value, creating variation through time that represents a measure of risk related to that KPI. The question for developing management control systems is how to improve the knowledge to reduce the uncertainty related to the market share. The actions that organizations take have risk associated with them, and the effects can be part of the causes for changes in the goal achievement. Table 3.1 shows the review of strategy goals; KPIs; expected results; circumstances or risks that can affect the achievement of the target, type of data, and possible steps in the analytics process that can be used.

The analytics process is related to the way to reduce risks related to that KPI and, at the same time, to the analytics knowledge creation to understand how the market share can be affected by market factors and competitive conditions of the organization.

With this section, the first part of the book has introduced the main concepts of the analytics process, and the second part will review the application of the analytics process to different sectors and several problems in various countries, using a variety of data and tools and following the steps to convert data into actions in organizations.

Chapter 4 introduces the value of the analytics process in the way that many other management tools are connected and required to keep alignment for better understanding of adding value in the organization. This presentation of the association of management concepts and the analytics process opens the description in the subsequent chapters about the way that analytics can be involved in several economic sectors.

Table 3.1 Finding the Relationship among Performance, Risk, and the Analytics Process

Review of the control and


The strategic goal is to improve the market share. The reason is it is assumed that better market share can bring benefits in profitability (this is not always the case).

As KPIs, it is possible to develop a metric that is just the ratio of value of revenues of the company to the total revenues of the sector. However, many other components can be required, for example, product definition, time, marginal changes, etc.

The targets or expected results will be achieved through the strategies. The analytics process can input into the target definition process the value of the metrics that can be expected.

Some conditions of the company, market, and business environment can affect the achievement of the goal, for example, cost increment, reduction of distribution channels, production capacity, mistakes, customer satisfaction, etc.

Data that can be required

Market indicators, company production capacity, economy expectations, etc.

Values of historical experience, values of other metrics, values of related financial indicators, values of related customer behavior, and engagement with the organization.

It can depend on the variables that are included in the metric development. It could be only numerator and denominator in the case of ratios, but these numerators and denominators can be deterministic or stochastic. They can be the results of additional model outcomes.

Each potential source of modification of expected results requires data. For example, changes in distribution channels can be related to costs, locations, etc. How can each event affect the achievement of the goal?




Descriptive visualization, exploratory data analysis, quantitative and qualitative analysis of the internal and external conditions of the organizations.

Review of the processes and data that each step uses and provides. There is a need for involving many variables and developing tables and statistical metrics to review relationships.

Performance models in the case that variables are used in combination. Identify if the probability distributions are possible.

Review the probability distributions of variables used, and identify approaches to describe variability.


Anthony, R.N., and Govindarajan, V., 2007, Management Control Systems, McGraw-Hill/ Irwin, Boston.

Dickinson, G., 2001, Enterprise Risk Management: Its Origins and Conceptual Foundation, The Geneva Papers on RCk andInsurance, 26(3^ pp. 36°366.

Hamel, G., 2011, The Big Idea: First, Let’s Fire All the Managers, Harvard Business Review, 89(12), pp. 4-13.

Mintzberg, H., Lampel, J., and Ahlstrand, B., 1998, Strategy Safari: A Guided Tour through the Wilds of Strategic Management, Free Press, New York.

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