SIDEBAR: Shape It Up Before You Ship It Out
As a vice president of a large college, George Kahkedjian, chief information officer Eastern Connecticut State University, has provided leadership for Institutional Research (Columbus State Community College, 1997-2002) and developed processes for collection, storage, maintenance and data retention strategies.
Kahkedjian says the best way to approach the data quality discussion is from the broader context of an information hierarchy where the bottom of the pyramid is the data that comes from multiple sources (medical, financial, education, personal and so on), the middle is information (so if the data is not correct or correctly related to each other, there is possibility of working with the wrong information) and the top is knowledge.
Most of the current discussions about information and knowledge rely on the data quality/integrity that supports these higher levels because data quality has context: in order for us to make the correct decisions, the data have to be correct, timely, available and maintained, otherwise it is not useful and impacts our information and knowledge.
If the data is not accurate, Kahkedjian says it creates major problems in information systems. How can the CIO ensure that the data is accurate?
First, input the right data into the right system
Example: if you put my wrong blood type, it is a problem.
Second, maintain the data by updating it at appropriate intervals
Example: if I develop an allergy condition and my medical records are not updated, it is a problem.
Third, remove unnecessary data from the system (retention)
Example: after one year of credit card transactions, perhaps the data should be removed.
Fourth, use the correct data for the correct task
Example: use my medical records for medical purposes, not employment.
Fifth, make the data available when appropriate on a timely basis
Example: there is an emergency, but the right individuals cannot access it.
SISDEBAR: The Right Stuff
by Mary BrandelData stewards work with the IT and business groups to improve data quality and standardization
Acustomer is a customer is a customer, right? Actually, it's not that simple. Just ask Emerson Process Management, an Emerson Electric unit in Austin that supplies process automation products. Four years ago, the company attempted to build a data warehouse to store customer information from over 85 countries. The effort failed in large part because the structure of the warehouse couldn't accommodate the many variations on customers' names.
For instance, different users in different parts of the world might identify Exxon as Exxon, Mobil, Esso or ExxonMobil, to name a few variations. The warehouse would see them as separate customers, and that would lead to inaccurate results when business users performed queries.
That's when the company hired Nancy Rybeck as data administrator. Rybeck is now leading a renewed data warehouse project that ensures not only the standardization of customer names but also the quality and accuracy of customer data, including postal addresses, shipping addresses and province codes.
To accomplish this, Emerson has done something unusual: It has started to build a department with six to 10 full-time "data stewards" dedicated to establishing and maintaining the quality of data entered into the operational systems that feed the data warehouse.
The practice of having formal data stewards is uncommon. Most companies recognize the importance of data quality, but many treat it as a "find-and-fix" effort, to be conducted at the end of a project by someone in IT. Others casually assign the job to the business users who deal with the data head-on. Still others may throw resources at improving data only when a major problem occurs.
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