Performance Management or Just Performance Monitoring?

Performance Management or Just Performance Monitoring?

By James

t seems to me that there are two different perspectives for the use of performance management software (dashboards and such). There are the folks who really think deeply about performance management (like my friend Gary Cokins who just passed 100 monthly columns on Information Management) and those who seem to only care about performance monitoring.

Performance monitoring is by far the easier path. To monitor performance, it is sufficient to simply identify the KPIs, metrics and measures that matter to your business and put them on a dashboard or report. Systems are instrumented, data is collected and some thought is given to how best to display the data to make it clear what the organization’s performance has been or currently is. Many such systems also allow users to define “good” or “acceptable” performance and then send alerts when something is out of bounds.


So far so good. But what happens when something is not good, or not good enough, or becomes the focus of an effort to improve results? At that point, it often becomes someone else’s problem to actually manage the organization’s performance. The user of the system identifies the people who have to change their behavior, tries to find out what they are doing now, and engages them in some effort to drive change in the organization. There is no way for someone looking at the dials and graphs in their performance management system to do anything about them; the user must leave his or her dashboard and go off and tell someone to change something.

Cokins has a richer view of enterprise performance management – one truly focused on performance management. He likes to really think through how someone managing the performance of their organization against their strategic objectives (typically displayed in the strategy map of a balanced scorecard) could take action and change things to improve performance. As he says: “[Performance Management links an] executive team’s strategy formulation to operations for strategy execution. It gives context to its purpose.”

It is this linking of strategy formulation to strategy execution that is critical and defines for me the difference between performance monitoring and performance management.

I would add, though, that for a modern organization of any size “strategy execution” increasingly means information systems. In today’s 24×7, high performance, real-time world, operations are increasingly automated. To align operations for strategy execution, performance management systems must be linked to the behavior of operational systems. In particular, they need to be linked to the way operational systems decide, and this is where decision management comes in.

To build this linkage, we apply a straightforward decision management approach.

We identify the decisions, especially the day-to-day operational decisions, that impact our performance measures. This means we know what decision-making we have to change if we are to have an impact on a measure or KPI.
We model these decisions so we understand how we need or want to make these decisions. We identify the information we need and the know-how we require, and then describe how we will make these decisions.
We automate these decisions, wholly or in part, using an appropriate mix of business rules, predictive analytics and optimization technologies – the technologies of decision management systems.
Adding decision management to a performance management system in this way ensures that the strategy devised to achieve the desired performance can be rapidly and accurately applied to information systems. By ensuring that the results of individual operational decisions can be measured and improved by the businesspeople who understand the objectives, decision management increases the agility of an organization and allows it to become adaptive to change. When analytics, especially predictive analytics, are embedded in these systems, it can also become a truly analytic organization.

Consider an example. My dashboard shows me that retention in a segment of my customer base is falling. From my dashboard I can drill down and find out which kinds of customers are quitting, which segment is being most affected – performance monitoring. If my decisions are manual, not modeled or linked to performance management, then I am done. All I can do is pick up the phone and yell at someone.

But if I am focused on performance management and have been identifying, modeling and automating my decisions, I have new options. I can see which decisions have an impact on customer retention and review them. I can compare the way I made decisions – the business rules and analytics I used – for that segment with other segments. (Perhaps I have some rules that don’t work well and get triggered for customers in that segment.) I can analyze the customers in the segment that I did retain and compare them to the ones I did not retain to see if I made different decisions. If I am making the same decisions, then I know I have to adjust my decision-making for this segment because it’s clearly not working well. Perhaps I need a new offer or more aggressive pricing for this segment. Because I am using business rules to underpin these decisions, they are easy to modify and change; the same business users who see the problem in the performance management system could manage the rules changes themselves.

The use of decision management to automate these key decisions acts as the glue between the visibility of enterprise performance management and day to day operational systems. Decision management is a key element in moving from performance monitoring to performance management.

As Cokins often states, “It is not only about monitoring the dials but rather moving them!”

James Taylor is the CEO of Decision Management Solutions and is the leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. Taylor is a faculty member of the International Institute for Analytics and is the author of “Decision Management Systems: A practical guide to using business rules and predictive analytics” (IBM Press, 2011). He previously wrote Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions (Prentice Hall) with Neil Raden, and has contributed chapters on Decision Management to multiple books. He is a frequent contributor to Information Management and writes a regular blog at JT on EDM. You can follow him at @jamet123

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