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Using Metrics to Measure Corporate Performance as Part of an Enterprise-Wide BI and DW Program

BI Solutions

My recent online column, "Making the Case for an Enterprise-Wide Business Intelligence (BI) and Data Warehouse (DW) Capability" (June 2006) highlighted the need for businesses to "get serious" about their approach to developing an enterprise BI and DW capability. When pursuing this capability it is important to adopt a holistic view, followed by disciplined investment and execution. To develop the future vision for this capability, you should consider seven interrelated areas:

1. Strategy

2. People
3. Process
4. Metrics
5. Applications
6. Data
7. Architecture

This column explores the key considerations of the "metrics" focus area. An enterprise BI and DW journey consists of many implementation projects over time - each project focused on the creation or enhancement of a BI application. These applications are the means to deliver metrics about the business to decision-makers. Before building these applications, you'll need to know what measurements you want to provide.

Enterprise Metrics

It is important to recognize that there are some metrics that should be in place to help measure the performance of the overall enterprise. To determine these metrics - the type that would show up on enterprise-level scorecards - taking a look at the corporate strategy is the ideal place to start. Many companies have an annual planning process where the strategy for the company and related objectives are defined.

As an example, there may be a corporate objective to "grow the XYZ product line" as part of the company's overall growth strategy. But in order to measure whether you are growing this product line - or are positioned well to grow this product line - many metrics may need to be put in place.

Some will be backward-looking, "rear view mirror" or "outcome" types of metrics. This will include measures for this product line such as:

  • Actual revenue generated,
  • Actual costs related to the production and distribution,
  • Actual number of customers buying this product,
  • Actual number of customer service calls, or
  • Actual number of units sold.

All of these can be indicators of how well this product line is doing.

Other metrics will be forward-looking, predictive in nature. This would include things like sales pipeline for the product and sales and expense forecasts. It would also include measures that are "drivers" of the outcome metrics, such as:

  • Number of employees hired or trained to manufacture, sell or service the product; and
  • Number of customers currently owning a related product.
All of these can be indicators of how this product line might do in the future.

As witnessed by the examples just mentioned, in order to get a complete picture, you'll need to have a "balance" of both outcome metrics and predictive metrics. The Balanced Scorecard methodology breaks these metric categories in to four distinct areas, which should also be balanced:

  • Learning and growth
  • Internal processes
  • Customer
  • Financial

It is also a good idea to have only a limited number of the most important metrics available for most users. If you have too many metrics, users may get lost in information overload, thereby eliminating some of the effectiveness of the BI application.

Operational Metrics

There are also metrics that are used to measure the performance of the operations of a particular function or process within the company. These are the types of metrics that would show up on operational dashboards. Some of these metrics would also appear on an enterprise scorecard, but the operational dashboard will have even more measurements - especially quite a bit more driver metrics.

Deciding What to Measure By

Business users think about their business dimensions hand in hand with their metrics. The dimensions are what they want to measure "by" - regions, product lines, time, customers, industry, etc. Getting your dimensions right is critical to the success of any BI solution.

Finding the Data

Ideally, the source data used to calculate all of your metrics or provide your dimensional information would come from information systems that already exist within your company. However, in many companies this is not the reality.

Especially when considering the forward-looking metrics, there are often gaps in the available data to support these. It is common to see companies implement new data collection capabilities in order to capture the data needed to calculate the unsupported metrics. This may take the form of simple Web forms to more sophisticated budgeting and forecasting systems that collect the forward-looking forecasts.

Additionally, the source systems often don't contain all the dimensions and hierarchies that are required for analysis. In many cases, different source systems actually have competing versions of the dimension values. Enterprise BI programs will have to determine an approach to managing and integrating their dimensional information to address these issues.

Ensuring Your Business Performance is Improving

Once you know what you want to measure, you'll need to think about how you are going to use the metrics to tell if your business is headed in a positive direction. Typical techniques used to do this are to:

  • Compare current metric values to the past in the form of trending reports;
  • Compare current metric values to forecasts or targets in the form of variance reports;
  • Compare metric values between dimensional members (such as comparing product XYZ performance to product ABC performance etc.);
  • Creating scores or quartiles to summarize many metrics into easy-to-understand categories; and
  • Evaluate the forward-looking metrics to see what they tell you about the future. 

Robert Farris, Hitachi Consulting Vice President and Business Intelligence Capability Practice Leader, has more than 19 years of information technology and consulting experience. He has served both in consulting organizations with Andersen Consulting, Navigator Systems and Hitachi Consulting, and in industry organizations with Bankers Trust and American Power Conversion. Farris specializes in developing the strategy for a BI Program, specifically defining and implementing the team organization, architectures, technologies, methodologies and internal processes. Farris is a graduate from Purdue University with a Bachelor of Science in Industrial Management with a minor in Computer Science. He may be contacted at rfarris@hitachiconsulting.com.

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