ensuring data quality throughout your oracle environment_v3

Ensuring Data Quality Throughout Your Oracle EnvironmentStoring growing mounds of data isn’t enough these days—you have to make sure it’s relevant, too. That’s where data quality comes in. In this e-book, learn what options are out there for im-proving data quality in your Oracle databases and applications. Find out about the differences in content and the complexity of customer data quality compared with product data quality. And finally, get some tips on managing your Oracle data environment, including a handful of best practices. By Larry P. EngLish

Data Quality: Why It Matters to Your Business

Improving Your Company’s Data Quality: A Few First Steps

Mission Possible: Outlining Best Practices for Data Quality Projects

Weaving a Sustainable Data Quality Culture Begins With People, Process

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Data Quality: Why It Matters to Your Business

s businesses create more and more electronic data, IT shops are bur-dened with the

responsibility of keeping track of it all. But simply holding a lot of data isn’t enough—you have to make sure it’s relevant, timely, adds value and meets expectations from IT and business users.

That’s where data quality comes in. According to research group For-rester Inc. in Cambridge, Mass., the data quality market is nearly $1 billion and growing. And those running Ora-cle databases and applications must, like other shops, ensure data quality in their IT environments.

History of Data Quality anD Quality ManageMentThe idea of data quality and quality management has often been attributed

to W. Edwards Deming, a 20th-century American statistician, professor and author. Deming is often credited with the quote, “In God we trust; all others must bring data.” Deming led the qual-ity revolution in the 1970s and 1980s in the United States and Japan. While there are other quality giants, Deming captured the market and created his 14 points of quality management. Let’s examine a handful of these points and look at how they affect data quality to-day.

The first point is to “create con-stancy of purpose toward improve-ment of product and service, with the aim to become competitive and to stay in business and to provide jobs.” As with many IT trends today, data quality projects must lead back to their impor-tance within the context of business goals and, as Deming put it, help com-panies “stay in business.” Management has two sets of problems, those of to-day and those of tomorrow. It is easy to

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stay bound up in the tangled knots of today’s problems, but companies must look toward future problems if they ex-pect to stay afloat.

Deming’s second point is to “adopt the new philosophy. We are in a new economic age. We must awaken to the challenge, learn their responsibili-ties and take on leadership for change.” Point two really means a transforma-tion of management. We cannot com-pete in the age of information without quality information, and data quality projects can help achieve that.

The third point is to “cease depen-dence on mass inspection to achieve quality. Eliminate the need for inspec-tion on a mass basis by building quality into the product in the first place.” This can be summed up in the shorter, more common phrase, “garbage in, garbage out.” Data quality must be a priority from the get-go, so that the right kind of information is being sent to a data quality system.

The fourth point is to “end the prac-tice of awarding business on the basis of a price tag. Instead, minimize total cost. Move toward a single supplier for any one item on a long-term rela-tionship of loyalty and trust.” Reliable quality and shared information reduces costs and increases value.

Skipping ahead a bit, the last point is “put everybody in the company to work to accomplish the transforma-tion.” It calls for the executive leader-ship team to take action to accomplish

the data quality transformation. Execu-tives must create a culture of continu-ous information process improvement.

Deming applied the 14 points as absolutes; the aim: to transform the quality of products and processes and prevent recurrence of failures caused by defective processes. In the new in-

formation age, companies that don’t adopt information quality manage-ment will flounder or fail. Note that all data-quality-related software is not necessarily useful or valuable. Many software tools create more problems and are more expensive. Don’t waste time in data correction activities. In-stead, send the defective data back to the originating information producers to be corrected and updated. n

larry P. englisH is the president of Information Impact International, which consults on informa-tion and data quality. English developed the Total Information Quality Management methodology, applying Kaizen quality principles to information quality management. He chairs information quality conferences around the world and co-founded the International Association for Information and Data Quality.

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in the new informationage, companies that don’t adopt information quality managementwill flounder or fail.


Improving Your Company’s Data Quality: A Few First Steps

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ata Quality must meet the needs of all customers and knowledge workers so they can perform

their work effectively. Data quality characteristics must be defined for shared use to support other end users and knowledge workers who depend on the information.

Data values must have the following characteristics:

ppThey must be clearly defined with values representing real-world ob-jects or events, ensuring a common world-view of real-world objects or events, along with people, products, finances and other priority entities.

ppThey must be captured at a point in time that enables information pro-ducers and knowledge workers to perform their work effectively and efficiently.

ppThe content must have specific val-ues with specific meaning that will serve knowledge workers’ require-ments.

ppThe information must be formatted and presented in a way that is clear to all.

ppThe information must be verified as accurate through a comparison of the data representing a real-world object or an event being analyzed—for example, the accurate spelling of a given name.

finDing tHe rigHt Data Quality VenDorData quality is a major concern for IT departments and business users. About 41% of organizations believe that data quality is one of the biggest data management challenges, accord-ing to SearchDataManagement.com’s

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“2011 Reader Challenges and Priori-ties Survey.” Data quality has become a bigger issue as companies have come to realize that data quality issues are business issues.

The same survey found that 77% have started or are planning to start a data governance program. That’s a 9% jump over the previous year. Clearly, organizations are looking to get a hold of their data and control it properly.

There are dozens of data quality vendors out there. Connecticut-based Gartner’s most recent Magic Quadrant report on data quality listed details on 15 of them, from the major players down to the niche vendors. The report also listed scores of others as com-panies that weren’t classified for one reason or another but that could be considered by companies looking for a data quality system.

Oracle is fairly new to the data qual-ity market as a vendor, but its acquisi-tion of Datanomic in 2011 put it on the map as a major player. This technol-ogy is now under the umbrella called Oracle Enterprise Data Quality. The software provides features such as data profiling, data standardization and handling challenges regarding cus-tomer and product data quality. Oracle also recently acquired another data quality vendor, Silver Creek Systems, which focuses largely on product data quality.

Because these acquisitions are fairly new, don’t expect deep integrations with Oracle databases and applica-

tions quite yet. But Oracle will build in those integrations over time; it wants its customers to go with a completely “red stack”—that is, all Oracle prod-ucts all the time.

There are plenty of third-party data quality vendors out there as well. Some of them have been around for a while and have large, stable customer bases. Others are smaller and may only ca-ter to a particular facet of data qual-ity, such as address cleaning. But they may be able to provide more personal support.

As with many IT decisions, it of-ten comes down to preference. If a company likes the idea of having “one throat to choke,” it may already have mostly Oracle databases and applica-tions and want to go with data qual-ity software from the same vendor for better integration. Others might not want to put all their eggs in one bas-ket. They may have a widely hetero-geneous IT environment that includes Oracle and non-Oracle software. Going with a third-party data quality vendor

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oracle is fairly new tothe data quality marketas a vendor, but its acqui-sition of Datanomic in 2011 put it on themap as a major player.


chaPtEr 2: imProving your comPany’s Data QuaLity: a FEW First stEPs

that can work with all the various data-base and application software may be the right way to go.

According to Gartner, organizations must keep a few things in mind when choosing a data quality vendor. The first is to consider the product’s fea-tures—data profiling, parsing, stan-dardization, matching, monitoring and enrichment, among others. But it is equally important to take into account the business users’ perspective. All the features in the world are useless if us-ers don’t know how they function.

Other factors to consider:

ppHow well the new software can plug into existing business intelligence

and master data management soft-ware and projects;

ppWho will be using the tool; if users are somewhat nontechnical, the or-ganization must consider ease of use a priority;

ppThe software delivery mode: Will you be starting this project on-premises in your own data center, as Software as a Service, in the cloud or a hybrid?;

ppFinally, according to Gartner, buyers must account for the total cost foot-print as well as the size and viability of the vendor. n

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eVeloPing best prac-tices is one way to make sure a data quality project suc-ceeds. There are

many steps along the way, but the first is to establish a “data quality organi-zation” that will develop and lead the data quality unit. This should be led by the chief data quality officer. Informa-tion about ensuring data quality in Or-acle Database is crucial, and so is data quality in Oracle application environ-ments such as E-Business Suite, Peo-pleSoft and JD Edwards.

ConneCt Data Quality to tHe businessImproving data quality without con-sidering its business implications is a blind venture that will most likely hurt chances for success. Data quality er-rors are related to how they may affect the business; therefore, anything mea-

suring the success of the data quality project must take the impact on busi-ness into effect. For example, simply looking at data quality issues might lead an IT organization to study what data is missing or corrupted or what records are duplicated. But business goals and effects may be quite differ-ent—for example, data errors might have any number of effects on pur-chasing, sales or staffing for a particu-lar fiscal quarter.

It is crucial to have IT and the busi-ness work together, not against each other, in a data quality project. Too often, business users claim that IT is solely responsible for cleaning data be-cause of the technology needed to do so. On the flip side, IT often throws out the phrase “garbage in, garbage out,” telling the business side that it must be more careful entering data and improv-ing data quality.

The truth is that both sides are usu-ally responsible for poor data qual-

Mission Possible: Outlining Best Practices for Data Quality Projects

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ity, and both are responsible for stellar data quality. The business side should ensure there are good rules and proce-dures, while IT should be responsible for the technology that supports and enforces them.

Data quality errors can hurt the busi-ness, but different errors will affect it in different ways. It seems obvious, but when combined with the advice to connect IT to the business, it’s ex-tremely valuable. Data quality projects often strive to achieve so-called per-fect data, but it’s more important to first fix data flaws that will have a sig-nificant impact on the business and then move on to errors that won’t af-fect the business as much.

Don’t Worry about loyaltyA recent Gartner survey found that data quality vendors have not secured the loyalty of most customers. Why is that? Well, according to the survey, vendor satisfaction simply isn’t high enough to cultivate loyalty.

So if a company is beginning a new data quality project, it’s wise to look at several data quality vendors, not just the big players or the one currently used.

The survey also found that different vendors excel in different aspects of

data quality, aspects that include prod-uct features and pricing, among oth-ers. Overall, survey respondents were least impressed by vendor pricing and licensing. An organization may be able

to use this to its advantage when go-ing back and forth with several vendors over price discounts and such.

Master data management (MDM) is here to stay. Improving data quality can provide value on its own, but it’s more powerful when combined with a plan for MDM.

Some tools have data quality fea-tures to them, but buyer beware. Sometimes they lock you in or make integration difficult. Oftentimes it is better to keep data quality and MDM separate but able to easily communi-cate with each other. n

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chaPtEr 4

t is iMPortant to develop a data quality culture. This includes creating information policies, procedures and standards, and incorporating the proven prin-

ciples, processes and techniques for conducting continuous information process improvements.

Training is critical. The organization should provide data quality training for all IT and business users and incorpo-rate data quality principles, processes and techniques. Training should also be provided for all information produc-ers, knowledge workers and manag-ers, including the executive leadership team.

Self-reliance is also important. Al-low information producers and knowl-edge workers to improve their own processes, and provide a healthy infor-mation process improvement culture. Any software should align with an or-ganization’s data quality management principles. These principles should be

incorporated into executive leadership team meetings by the chief data qual-ity officer.

soliDify Data Quality ProCessesImplement regular assessments for the most problematic information pro-cesses to identify broken processes and defective data. Then address broken processes using the plan-do-check-act cycle, common in manage-ment for process improvement. This identifies root causes to establish the right process improvement to error-proof the original broken process. The plan-do-check-act cycle enables you to identify the quality of the information you are assessing. It identifies how to eliminate bias that can skew data qual-ity assessments.

Develop relationships among differ-ent business areas to understand in-formation, consumer and knowledge worker information requirements. This

Weaving a Sustainable Data Quality Culture Begins With People, Process

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goes back to Chapter 3, in which I dis-cussed the importance of connecting IT and the business.

Use a Pareto chart to identify un-acceptable variations in data quality. Then conduct a plan-do-check-act in an effort to implement error-proofing. The chart can identify and show the degree of variation in a business’ data quality processes.

Use poka-yoke error-proofing tech-niques, such as multiple verifications, so that duplicate data values match expected data values. Also use tech-niques that connect to authorita-tive proven points of data value for confirmation.

You can also take advantage of SI-POC (Supplier, Input, Process, Output, Consumer), a Six Sigma management tool, to identify information consum-ers’ requirements and the need for in-formation process improvement.

Identifying all information product specification quality characteristics is important. Here are some steps toward doing that:

ppSingle out the information group for quality assessment. This in-cludes entity types, attribute values, business rules and descriptions of customers and customer relation-ships.

pp Identify information workers and groups. Determine the real informa-tion requirements of information producers and knowledge workers.

ppStandardize information product specs. Information processes will fail without clear definitions and at-tributions.

ppAssess information architecture quality.

ppEvaluate the information quality as-sessment. The quality assessment identifies the core entity and attri-bute types that will be measured.

pp Identify the information value circle, including what process steps are in-cluded.

ppAnalyze SIPOC results to identify which processes are most impor-tant.

pp Identify accuracy verification sources. These are sources that can confirm the accuracy of data values and business rule specifications.

ppMeasure the costs and risks of poor quality information.

ppMeasure the direct costs of fail-ure due to poor data or information quality.

In Chapter 1 we learned about the history of information and data quality and its godfather, W. Edwards Deming. So it’s fitting that we would come back to him. Deming’s “System Profound Knowledge” can go a long way toward

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helping a business develop a healthy data and information quality culture. The system has four pillars:

pp Introduction to a system: A sys-tem must have an aim. Without it, there is no system. Management’s job is to direct all efforts toward the aim. The first step is clarification: All people involved must understand how to direct their efforts toward the aim. Above all, everyone must understand this: A team that seeks to become a selfish, independent profit center is a danger to the whole organization.

ppKnowledge about variation: Life is all about variation; thus, there will always be variation in a system or process.

ppTheory of knowledge: The theory of knowledge helps us understand that management in any form is pre-diction. A simple plan: “What route should I take to get home?”

ppPsychology: Psychology helps us to understand people, interactions be-tween people and circumstances, interactions between customer and supplier. Every person is different, and each requires understanding of the differences found in groups or organizations from around the world or in their own communities, busi-ness or otherwise. n

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Ensuring Data Quality Throughout Your Oracle Environment is a

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