In the 1990s Kaplan and Norton developed a new approach to strategic management based upon a multi-dimensional view of the organization. According to this approach, traditional financial measurements provide a "too little too late" snapshot of the organization, which is inadequate for companies in the new economy.



Today, efficient internal business processes, good customer relations, and long-term strategic investments in technologies and employees are what make companies successful.

The balanced scorecard proposes that companies be viewed and measured in each of four major dimensions1:

· The traditional financial dimension

· The customer dimension

· The business process dimension

· The learning and growth dimension

The organization's overall vision and strategy are particularized for the various organizational structures within each of these dimensions-e.g., accounting for the financial dimension, marketing and customer support for the customer dimension, order and warehouse management for business processes, and human resources and business development for the learning and growth dimension. Metrics for gauging performance against the specific strategic goals of the organization's structures are then devised.

Finally, data is collected and analyzed on an ongoing basis to evaluate performance against the goals and to provide decision makers with the information needed to identify problems and trends and to make adjustments while the data is hot. Quality information is the foundation of a balanced scorecard.

Getting Good Raw Data:

Producing quality information begins with getting good raw data. Above all, good data is data that is directly related to the larger informational needs as determined by the scorecard. That is, just because it is true and accurate does not mean it is good data. The data must be relevant to the strategies within the scorecard dimensions. Rarely is there a shortage of data. Often we are overwhelmed with data, much of it not relevant or helpful, and we are forced to do our own faulty filtering. As a result, insignificant or misleading pieces of data are emphasized, and poor decisions are made.

Good data must be accurate and fresh. Getting "clean" data is often the greatest impediment to effectively using the sophisticated business intelligence tools now available. It is estimated that over 25% of critical data used by major corporations is flawed due to human data-entry errors and a lack of corporate data standards2.

Companies are spending much more money and effort analyzing data than they are on ensuring its accuracy.

“Flawed data not only compromises customer service, marketing campaigns, and supply-chain forecasting, but skews the scorecard and misleads decision makers."

A number of data-cleansing software packages are available, but data quality degrades quickly over time. Obtaining clean data should be viewed as an aspect of ongoing business processes rather than as a one-time operation of IT organizations. Business units must take ownership of their data and treat it as a critical resource and product of its operations.

Transforming Data into Information:

Raw data is not information. Data becomes information when it is analyzed and transformed using the scorecard information goals as a guide. Data must be grouped and placed into context. It must be compared to other data and to older versions of the same data. How does the data relate to other aspects of the scorecard, and what trends are emerging overtime?

“Transforming data involves not only analyzing and distilling it into useful

information related to the organization's strategic goals, but requires that the information be presented in a form that is best for the audience."

Delivering the Data:

Information must be delivered to the right persons at the right time. This may seem obvious, but often it is the most difficult part of the information generation process, particularly for large organizations. For these organizations, delivery may mean more than simply making the information available. Such mass delivery can result in the information being overlooked. Targeted delivery to only those people who are responsible for acting on the information focuses the information process and highlights the importance of the information. If no one is directly responsible, why is the information being generated?

In addition, the persons receiving the information must understand how it relates to their objectives. Without direct relevance to their organization, work, and goals, the information is little more than interesting reading material.

Finally, the timing of delivery is a crucial consideration, which may determine the type of data collected. If an operations manager needs to predict sales quantities in the future so that materials can be ordered in advance, historical sales data may not be as relevant as proxy information that is highly correlated to sales. Timing may also affect data analysis when there are data dependencies.

What Information Quality Means for My Organization:

No matter what your organization does, it relies on information to make strategic and operational decisions, which ultimately determine its level of success. This information most likely includes financial data. It should also include a broader spectrum of information related to other dimensions of the organization. Whatever the breadth of the information, it must be directly related to strategic goals or it is a distraction.

The quality of the information must similarly be measured by how directly it relates to the strategic goals. Data which is not culled and cleaned as guided by the strategic goals will distract and mislead decision makers. And data analysis and delivery, which transform data into actionable information, can only be effectively performed when strategic goals are understood. The quality of the information generated by your organization is ultimately measured by its overall relation to the organization's goals.

1 www.balancedscorecard.org

2 "Getting Clean," CIO Insight, August, 2004.

About Ralph Dandrea:

Ralph Dandrea is the President of ITX Corp., and leads its Business Performance practice. He is experienced in business and information technology management and holds graduate degrees in business and law.