Data Warehousing Special Report: Data quality and the bottom line
During the past 50 years, the developed world has moved from an industrial
economy to an information economy. Companies now compete on their ability to
absorb and respond to information, not just manufacture and distribute products.
Intellectual capital and know-how are more important assets than physical infrastructure
If information is the currency of the new economy, then data is a critical
raw material needed for success. Just as a refinery takes crude oil and transforms
it into numerous petroleum products, companies use data to generate a multiplicity
of information assets. These assets form the basis of the strategic plans and
actions that determine a firm's success.
Consequently, poor quality data can have a negative impact on the health of
a company. If not identified and corrected early on, defective data can contaminate
all downstream systems and information assets.
The problem with data is that its quality quickly degenerates over time. Experts
say 2% of records in a customer file become obsolete in a month because customers
die, divorce, marry and move. In addition, data-entry errors, systems migrations
and changes to source systems, among other things, generate bucketloads of errors.
As well, as organizations fragment into different divisions and units, interpretations
of data elements mutate to meet local business needs. A data element that one
individual finds valuable may be nonsense to an individual in a different group.
TDWI estimates that poor quality customer
data costs U.S. businesses a staggering $611 billion a year in postage, printing
and staff overhead (TDWI estimates based on cost-savings cited by survey respondents
and others who have cleaned up name and address data, combined with Dun &
Bradstreet counts of U.S. businesses by number of employees.). Frighteningly,
the real cost of poor quality data is much higher. Organizations can frustrate
and alienate loyal customers by incorrectly addressing letters or failing to
recognize them when they call, or visit a store or Web site. Once a company
loses its loyal customers, it loses its base of sales and referrals, as well
as future revenue potential.
Given the business impact of poor quality data, it is bewildering to see the
casual way in which most companies manage this critical resource. Most companies
do not fund programs designed to build quality into their data in a proactive,
systematic and sustained manner. According to TDWI's Data Quality Survey, almost
half of all firms have no plan for managing data quality.
Part of the problem is that most organizations overestimate the quality of
their data and underestimate the impact errors and inconsistencies can have
on their bottom line. On one hand, almost half of the companies who responded
to our survey believe the quality of their data is "excellent" or
"good." Yet more than one-third of the respondent companies think
the quality of their data is "worse than the organization thinks."
Although some firms understand the importance of high-quality data, most are
oblivious to the true business impact of defective or substandard data. Thanks
to a raft of new information-intensive strategic business initiatives, executives
are beginning to wake up to the real cost of poor quality data. Many have bankrolled
high-profile IT projects in recent years -- data warehousing, CRM and e-business
projects -- that have failed or been delayed due to unanticipated data-quality
For example, in 1996, FleetBoston Financial Corp. (then Fleet Bank) in New
England undertook a much publicized $38 million CRM project to pull together
customer information from 66 source systems. Within three years, the project
was drastically downsized and the lead sponsors and technical staff were let
go. A major reason the project came unraveled was the team's failure to anticipate
how difficult and time consuming it would be to understand, reconcile and integrate
data from 66 different systems.
According to TDWI's Industry Study 2000 survey, the top two technical challenges
firms face when implementing CRM solutions are "managing data quality and
consistency" (46%) and "reconciling customer records" (40%).
Considering that 41% of CRM projects were "experiencing difficulties"
or "a potential flop," according to the same study, it is clear that
the impact of poor data quality in CRM is far reaching ("Harnessing Customer
Information for Strategic Advantage: Technical Challenges and Business Solutions."
Data warehousing, CRM and e-business projects often expose poor quality data
because they require companies to extract and integrate data from multiple operational
systems. Data that is sufficient to run payroll, shipping or accounts receivable
is often peppered with errors, missing values and integrity problems that do
not show up until someone tries to summarize or aggregate the data.
Also, since operating groups often use different rules to define and calculate
identical elements, reconciling data from diverse systems can be a huge, and
sometimes insurmountable, obstacle. Sometimes the direct intervention of the
CEO is the only way to resolve conflicting business practices, or political
and cultural differences.
Every firm, if it looks hard enough, can uncover a host of costs and missed
opportunities caused by inaccurate or incomplete data. Consider the following:
* A telecommunications firm lost $8 million a month because data-entry errors
incorrectly coded accounts, preventing bills from being sent out.
* An insurance company lost hundreds of thousands of dollars annually in mailing
costs due to duplicate customer records.
* An information services firm lost $500,000 annually and alienated customers
because it repeatedly recalled reports sent to subscribers due to inaccurate
* A large bank discovered that 62% of its home-equity loans were being calculated
incorrectly, with the principal getting larger each month.
* A health insurance company in the Midwest delayed a decision support system
for two years because the quality of its data was "suspect."
* A global chemical company discovered it was losing millions of dollars in
volume discounts in procuring supplies because it could not correctly identify
and reconcile suppliers on a global basis.
* A regional bank was unable to calculate customer and product profitability
due to missing and inaccurate cost data.
In addition, new industry and government regulations, such as the Health Insurance
Portability and Accountability Act (HIPAA) and Bank Secrecy Act, are upping
the ante. Organizations are now required to carefully manage customer data and
privacy or face penalties, unfavorable publicity and loss of credibility.
What can go wrong?
The sources of poor quality data are myriad. Leading the pack are data-entry
processes, which produce the most frequent data quality problems, and systems
Not surprisingly, survey respondents cite data-entry errors by employees as
the most common source of data defects. Examples of errors include misspellings,
transposition of numerals, incorrect or missing codes, data placed in the wrong
fields and unrecognizable names, nicknames, abbreviations or acronyms. These
types of errors are increasing as companies move their businesses to the Web
and allow customers and suppliers to enter data about themselves directly into
Lack of validation routines. Interestingly, many data-entry errors
can be prevented through the use of validation routines that check data as it
is entered into Web, client/server or terminal-host systems. Respondents to
the TDWI survey mentioned a "lack of adequate validation" as a source
of data defects, noting this grievance in the "Other" category.
Valid, but not correct. But even validation routines cannot catch
typos where the data represents a valid value. Although a person may mistype
a telephone number, the number recorded is still valid -- it is just not the
right one. The same holds true for social security numbers, vehicle identification
numbers, part numbers and last names. Database integrity rules can catch some
of these errors, but firms need to create complex business rules to catch the
Mismatched syntax, formats and structures. Data-entry errors
are compounded when organizations try to integrate data from multiple systems.
For example, corresponding fields in each system may use different syntax (first-middle-last
name vs. last-first-middle name), data formats (6 byte date field vs. 4 byte
date field), or code structures (male-female vs. m-f vs. 1-2). In these cases,
either a data cleansing or ETL tool needs to map these differences to a standard
format before serious data cleanup can begin.
Unexpected changes in source systems. Perhaps a more pernicious
problem is structural changes that occur in source systems. Sometimes these
changes are deliberate, such as when an administrator adds a new field or code
value and then neglects to notify the managers of connecting systems about the
changes. In other cases, front-line people reuse existing fields to capture
new types of information that were not anticipated by the application designers.
Spiderweb of interfaces. Because of the complexity of systems
architectures today, changes to source systems are easily and quickly replicated
to many other systems, both internal and external. Most systems are connected
through a spiderweb of interfaces to other systems. Updating these interfaces
is time-consuming and expensive, and many changes slip through the cracks and
"infect" other systems. Thus, changes in source systems can wreak
havoc on downstream systems if adequate change management processes are not
Lack of referential integrity checks. It is also true that target
systems do not adequately check the integrity of the data they load. For example,
data warehouse administrators often turn off referential integrity when loading
the data warehouse for performance reasons. If source administrators change
or update tables, this can create integrity problems that are not detected.
Poor system design. Source or target systems that are poorly
designed can create data errors. As companies rush to deploy new systems, developers
often skirt fundamental design and modeling principles, which leads to data
integrity problems down the road.
Data conversion errors. In the same vein, data migration or conversion
projects can generate defects, as well as ETL tools that pull data from one
system and load it into another. Although systems integrators may convert databases,
they often fail to migrate business processes that govern the use of data. In
addition, programmers may not take the time to understand source or target data
models, and may therefore write code that introduces errors. One change in a
data migration program or system interface can generate errors in tens of thousands
The fragmentation of definitions and rules. A much bigger problem
comes from the fragmentation of our organizations into a multitude of departments,
divisions and operating groups, each with its own business processes supported
by distinct data management systems. Slowly and inexorably, each group begins
to use slightly different definitions for common data entities -- such as "customer"
or "supplier" -- and apply different rules for calculating values,
such as "net sales" and "gross profits." Add mergers, acquisitions
and global expansion into countries with different languages and customs, and
you have a recipe for a data-quality nightmare.
The problems that occur in this scenario have less to do with accuracy, completeness,
validity or consistency, than with interpretation and protecting one's "turf."
That is, people or groups often have vested interests in preserving data in
a certain way even though it is inconsistent with the way the rest of the company
For example, many global companies squabble over a standard for currency conversions.
Each division in a different part of the world wants the best conversion rate
possible. And even when a standard is established, many groups will skirt the
spirit of the standard by converting their currencies at the most opportune
times, such as when a sale was posted vs. when the money was received. This
type of maneuvering wreaks havoc on a data warehouse that tries to accurately
measure values over time.
Slowly changing dimensions. Similarly, slowly changing dimensions
can result in data-quality issues depending on the expectations of the user
viewing the data. For example, an analyst at a chemical company wants to calculate
the total value of goods purchased from Dow Chemical for the past year. But
Dow recently merged with Union Carbide, which the chemical company also purchases
In this situation, the data warehousing manager needs to decide whether to
roll up purchases made to Dow and Union Carbide separately, combine the purchases
from both firms throughout the entire database, or combine them only after the
date the two companies merged. Whatever approach the manager takes, it will
work for some business analysts and alienate others.
In these cases, data quality is a subjective issue. Users' perception of data
quality is often colored by the range of available data resources they can access.
Where there is "competition" -- another data warehouse or data mart
that covers the same subject area -- knowledge workers tend to be pickier about
data quality, said Michael Masciandaro, director of decision support at Rohm
Delivering high-quality data
Given the ease with which data defects can creep into systems, especially data
warehouses, maintaining data quality at acceptable levels takes considerable
effort and coordination throughout an organization. "Data quality is not
a project, it's a lifestyle," said David Wells, enterprise systems manager
at the University of Washington and the developer of TDWI's full-day course
on data cleansing ("TDWI Data Cleansing: Delivering High Quality Warehouse
And progress is not always steady or easy. Improving data quality often involves
exposing shoddy processes, changing business practices, gaining support for
common data definitions and business rules, and delivering education and training.
In short, fixing data quality often touches a tender nerve on the underbelly
of an organization.
One top executive leading a data-quality initiative said, "Improving data
quality and consistency involves change, pain and compromise. The key is to
be persistent and get buy-in from the top. Tackle high ROI projects first, and
use them as leverage to bring along other groups that may be resistant to change."
The University of Washington's Wells emphasizes that managing data quality
is a never-ending process. Even if a company gets all the pieces in place to
handle today's data-quality problems, there will be new challenges tomorrow.
That is because business processes, customer expectations, source systems and
business rules all change continuously.
To ensure high-quality data, firms need to gain broad commitment to data-quality
management principles and develop processes and programs that reduce data defects
over time. To lay the foundation for high-quality data, firms need to adhere
to the methodology outlined below.
Step 1. Launch a data quality program. The first step to delivering
high-quality data is to get top managers to admit there is a problem and take
responsibility for it.
The best way to kickstart a data-quality initiative is to fold it into a corporate
data stewardship or data administration program. These programs are typically
chartered to establish and maintain consistent data definitions and business
rules so the firm can achieve a "single version of the truth" and
save time on developing new apps and looking for data.
Step 2. Develop a project plan. The next step is to develop a
data-quality project plan or series of plans. A project plan should define the
scope of activity, set goals, estimate ROI, perform a gap analysis, identify
actions, and measure and monitor success. To perform these tasks, the team will
need to dig into the data to assess its current state, define corrective actions
and establish metrics for monitoring conformance to goals.
Step 3. Build a data-quality team. Organizations must assign
or hire individuals to create the plan, perform initial assessment, scrub the
data and set up monitoring systems to maintain adequate levels of data quality.
Step 4 and Step 5. Review business processes and data architecture.
Once there is corporate backing for a data-quality plan, the stewardship committee
-- or a representative group of senior managers throughout the organization
-- needs to review the company's business processes for collecting, recording
and using data in the subject areas defined by the scope document. With help
from outside consultants, the team also needs to evaluate the underlying systems
architecture that supports the business practices and information flows.
Step 6. Assess data quality. After reviewing information processes
and architectures, an organization needs to undertake a thorough assessment
of data quality in key subject areas. The purpose of the assessment is to identify
common data defects; create metrics to detect defects as they enter the data
warehouse or other systems; and create rules or recommend actions for fixing
the data. This can be long, arduous and labor-intensive work, depending on the
scale and scope of the project, as well as the age and cleanliness of the source
Step 7. Clean the data. Once the audit is complete, the job of
cleaning the data begins. A fundamental principle of quality management is to
detect and fix defects as close as possible to the source to minimize costs.
Prevention is the least costly response to defects, followed by correction
and repair. Correction involves fixing defects in-house, while repair involves
fixing defects that affect customers directly. Examples of repair are direct
mail pieces that are delivered to a deceased spouse, or software bugs in a commercially
Step 8. Improve business practices. As mentioned earlier, preventing
data defects involves changing attitudes and optimizing business processes.
"A data quality problem is a symptom of the need for change in the current
process," said Brad Bergh, a veteran database designer with Double Star
Inc. Improving established processes often stokes political and cultural fires,
but the payoff for overcoming these challenges is great.
Having a corporate data stewardship program and an enterprise-wide commitment
to data quality is critical to making progress. Under the auspices of the CEO
and the direction of corporate data stewards, a company can begin to make fundamental
changes in the way it does business to improve data quality.
Step 9. Monitor data continuously. Organizations can quickly
lose the benefits of their data preparation efforts if they fail to monitor
data quality continuously. To do this, companies need to build a program that
audits data at regular intervals, or just before or after data is loaded into
another system such as a data warehouse. Companies then use the audit reports
to measure their progress in achieving data-quality goals and complying with
service-level agreements negotiated with business groups.
Service-level agreements should specify tolerances for critical data elements
and penalties for exceeding those tolerances.
The above techniques, although they are not easy to implement in all cases,
can help bring a company closer to achieving a strong foundation on which to
build an information-based business. The key is to recognize that managing data
quality is a perpetual endeavor. Companies must make a commitment to build data
quality into all information management processes if they are going to reap
the rewards of high-quality data -- and avoid the pitfalls caused by data defects.