Optimizing the climb up the enterprise information systems maturity model
The enterprise information system maturity model presents a general context for understanding enterprise systems. The maturity model provides criteria to evaluate enterprise system needs and help determine next steps to increase decision efficiency (and the bottom line). With care, it’s possible to move successfully from Level 2 (a centralized data repository) to Level 3, in which standard metrics and actionable information are used to optimize resources. The diagram shows the maturity model levels.
Optimizing resources is a continual effort
Although Level 3 is not the highest step in the maturity model, it’s the primary goal for most organizations. When implemented successfully, it enables a business to more efficiently understand and use its resources. This is achieved by using objective information (centralized for access in the previous maturity level) to make business decisions.
Success at this level differs greatly from the lower maturity levels. It is less about the initial implementation of a standard measurement system and more about the continual cultivation of an optimization process over time. The technological challenges at this level are not the primary barrier to success. Adopting the Level 3 methodology of standardization and metrics often requires a major shift in corporate culture. To have continued success at this level, you need to focus on getting started, getting everyone on board, and keeping it going.
A corporate shift
The most difficult challenge a business faces when implementing a standardized analytic methodology is its own corporate culture. Not only is the Level 3 approach different from most organization’s operational strategy, it requires a change in culture to implement successfully. Many corporations work in silos and foster unhealthy competition between units within their organization. Standardized metrics expose the inner workings of a business. This visibility is not usually welcomed. Obviously, without the cooperation of the stakeholders, the optimization of resources at all levels is not possible.
The key to success at this level is, oddly, failure. How failure is viewed by an organization directly affects the impact of analytics. The first reaction to any objective measurement is usually the realization that the business is doing a poor job. It’s important that the corporate culture is ready to accept failure and view it as something positive.
The famous quote, "If you can’t measure it, you can’t manage it," comes to mind here. Failure is a positive-when you know about it. It’s what allows resources to be optimized.
A business rewards visibility and collaboration at this level. Breaking down internal competitive barriers is a must. Business units that share information, especially in regard to their own failures, is what helps others fail less and succeed more. Recognizing failure is the basis for continual improvement.
Finding the right metrics
What is a metric? By definition, it is "a standard of measurement." For business metrics, the important thing to realize is what is being measured. This is not always easy to identify. Once you begin creating metrics using the volumes of information you’ve collected, some metrics will make sense and be easily correlated to a business output (such as widgets made or dollars spent). Others will seem more intangible. Both types of metrics may be important because they tell you about different parts of your business. Some metrics may find failure where others do not. The important task is determining the impact of a metric. For example, if you can increase a metric by 10%, what do you get for it?
Initially, finding metrics can be a difficult task. The best place to start looking is inside your organization. Look at what is currently used to measure success. Review this at all levels of management and operations. Resist the urge to drive metrics solely from the top-down or bottom-up; useful metrics are used every day to make business decisions, but rarely are they formalized.
Level 3 maturity represents the formalization of these metrics. Evaluate metrics across business streams. Seemingly disparate business entities still share the same goals, and their metrics often reflect it.
Don’t be afraid to link your metrics to the most important resources of your business: people and money. When centralized metric analysis is introduced, companies resist linking metrics to their human resources and financial systems. Although you can still optimize resources without these connections, the true impact of a metric is not fully known without them.
The next step is to look outside your organization. If your industry has government regulation and standard metrics are required for reporting, these are obvious candidates. Although most mandated metrics are simplistic and provide little actionable information, government regulated metrics are publicly available and provide data to compare against your competitors.
Also look to other companies in your industry. Metrics are a growing necessity for all businesses and are key topics at industry-based conventions. Most large industries have an organizational body (usually made up of the key corporate entities) that helps to determine objective ways to measure success.
Next, broaden the search by looking outside your industry. There are many instances in which approaches from one industry have become key ideas in another. For example, lean manufacturing metrics have had a significant impact on software development methodologies.
Last, there are numerous software metrics packages that are constructed with industry standards and commonly known metrics. These are great places to find new metrics and insights.
Using and implementing metrics
After a metric system is introduced to an organization, there is an immediate attempt to find the one metric that encompasses everything and can be used to measure all performance. Resist this temptation. This metric does not exist. And, even worse, concentrating on this metric will hide failures. Using several metrics provides a better view from many individual aspects and allows you to better correlate the impact to each metric.
The second fallacy that businesses attempt to follow is that of normalization. This is a common error and can lead to the misuse of metrics. Normalization is the altering or shifting of data values in an attempt to enable comparison among different items.
For example, in the energy industry, dividing energy use by square feet is a common normalization technique. Businesses may use this metric to determine the energy efficiency of their building portfolio. However, normalization is extremely difficult and is almost impossible when you evaluate the aspects of business. For energy efficiency, this metric is deceiving. A warehouse that leaves its lights on all day (an inefficiency) will report better energy use per square foot than a highly efficient data center. Based on this metric, the inefficiency at the warehouse will go unnoticed.
A better approach is to use normalization to narrow and conform to a standard. Then use that standard as a way to compare performance for the same item (a building, in our example) over time. It is also a good way to set goals for performance (for example, a 10% reduction of energy use over 6 months).
Finally, when setting goals with metrics, it’s important to understand the impact of their results. A good metric provides actionable information. When using a metric, it’s good practice to determine what changes you’ll make as a result of the measurement before you enter the performance evaluation period. It helps to make a result actionable if the action has already been prescribed.
Choosing an analytics package
This advice follows the same guidance given for previous maturity levels: ensure the analytics software you select is open. Your centralized data repository from Level 2 already meets open standards—demand the same from your analytics software. Also, the analytics field is young. New algorithms and technologies are changing the landscape each year. Don’t be afraid to try new software or metric methodologies.
Level 3 enables you get the most from a centralized repository and optimize resources through objective methods. Most companies that reach this level thrive by acting directly on their metric results. For most, this is the stopping point in the maturity model. For companies that continue to integrate optimization throughout their business, the goal is Level 4 maturity. At the next level, decisions are automated to better focus a workforce. The next and last article in this series will discuss how organizations can reach full maturity.
– Corey Stefanczak is senior system architect, Leidos. Edited by Mark T. Hoske, content manager, CFE Media, Control Engineering, email@example.com.
- Maturity models help with enterprise system integration.
- Level 3 helps optimize resources through objective methods.
- Normalization can help narrow and conform to a standard.
Have you looked at maturity levels when integrating systems?
This article online contains links to each part of this full five-part series on meeting the challenges of enterprise information systems looking at the maturity model introduction, taking the first step, gaining a competitive advantage, optimizing resources, and a highly mature enterprise.
Part 1: Understand the maturity model to better manage, integrate plant floor, enterprise systems Control Engineering (CE), August issue, Inside Machines section, p. M1
Part 2: Migrating toward enterprise information systems from Applied Automation (supplement to CE and Plant Engineering), October issue, p. A13
Part 3: Gain a competitive advantage, meet the challenges of enterprise information systems CE Weekly News enewsletter, Nov. 25
Part 4: Optimizing the climb up the enterprise information systems maturity model CE November issue, Technology Update, p. 34 [This article]
Part 5: Highly mature enterprise: Meet the challenges of enterprise information systems CE December issue, Inside Machine section, p. IM4