DQ-Tip: “An information centric organization...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“An information centric organization is an organization driven from high-quality, complete, and timely information that is relevant to its goals.”

This DQ-Tip is from the new book Patterns of Information Management by Mandy Chessell and Harald Smith.

“An organization exists for a purpose,” Chessell and Smith explained.  “It has targets to achieve and long-term aspirations.  An organization needs to make good use of its information to achieve its goals.”  In order to do this, they recommend that you define an information strategy that lays out why, what, and how your organization will manage its information:

  • Why — The business imperatives that drive the need to be information centric, which helps focus information management efforts on the activities that deliver value to the organization.
  • What — The type of information that you must manage to deliver on those business imperatives, which includes the subject areas to cover, which attributes within each subject area that need to be managed, the valid values for those attributes, and the information management policies (such as retention and protection) that the organization wants to implement.
  • How — The information management principles that provide the general rules for how information is to be managed by the information systems and the people using them along with how information flows between them.

Developing an information strategy, according to Chessell and Smith, “creates a set of objectives for the organization, which guides the investment in information management technology and related solutions that support the business.  Starting with the business imperatives ensures the information management strategy is aligned with the needs of the organization, making it easier to demonstrate its relevance and value.”

Chessell and Smith also noted that “technology alone is not sufficient to ensure the quality, consistency, and flexibility of an organization’s information.  Classify the people connected to the organization according to their information needs and skills, provide common channels of communication and knowledge sharing about information, and user interfaces and reports through which they can access the information as appropriate.”

Chessell and Smith explained that the attitudes and skills of the organization’s people will be what enables the right behaviors in everyday operations, which is a major determination of the success of an information management program.

 

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DQ-Tip: “The quality of information is directly related to...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“The quality of information is directly related to the value it produces in its application.”

This DQ-Tip is from the excellent book Entity Resolution and Information Quality by John Talburt.

The relationship between data and information, and by extension data quality and information quality, is acknowledged and explored in the book’s second chapter, which includes a brief history of information theory, as well as the origins of many of the phrases frequently used throughout the data/information quality industry, e.g., fitness for use and information product.

Talburt explains that the problem with the fitness-for-use definition for the quality of an information product (IP) is that it “assumes that the expectations of an IP user and the value produced by the IP in its application are both well understood.”

Different users often have different applications for data and information, requiring possibly different versions of the IP, each with a different relative value to the user.  This is why Talburt believes that the quality of information is best defined, not as fitness for use, but instead as the degree to which the information creates value for a user in a particular application.  This allows us to measure the business-driven value of information quality with technology-enabled metrics, which are truly relevant to users.

Talburt believes that casting information quality in terms of business value is essential to gaining management’s endorsement of information quality practices within an organizaiton, and Talburt recommends three keys to success with information quality:

  1. Always relate information quality to business value
  2. Give stakeholders a way to talk about information quality—the vocabulary and concepts
  3. Show them a way to get started on improving information quality—and a vision for sustaining it

 

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DQ-Tip: “Undisputable fact about the value and use of data…”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Undisputable fact about the value and use of data—any business process that is based on the assumption of having access to trustworthy, accurate, and timely data will produce invalid, unexpected, and meaningless results if this assumption is false.”

This DQ-Tip is from the excellent book Master Data Management and Data Governance by Alex Berson and Larry Dubov.

As data quality professionals, our strategy for quantifying and qualifying the business value of data is an essential tenet of how we make the pitch to get executive management to invest in enterprise data quality improvement initiatives.

However, all too often, the problem when we talk about data with executive management is exactly that—we talk about data.

Let’s instead follow the sage advice of Berson and Dubov.  Before discussing data quality, let’s research the data quality assumptions underlying core business processes.  This due diligence will allow us to frame data quality discussions within a business context by focusing on how the organization is using its data to support its business processes, which will allow us to qualify and quantify the business value of having high quality data as a strategic corporate asset.

 

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DQ-Tip: “Data quality tools do not solve data quality problems...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality tools do not solve data quality problems—People solve data quality problems.”

This DQ-Tip came from the DataFlux IDEAS 2010 Assessing Data Quality Maturity workshop conducted by David Loshin, whose new book The Practitioner's Guide to Data Quality Improvement will be released next month.

Just like all technology, data quality tools are enablers.  Data quality tools provide people with the capability for solving data quality problems, for which there are no fast and easy solutions.  Although incredible advancements in technology continue, there are no Magic Beans for data quality.

And there never will be.

An organization’s data quality initiative can only be successful when people take on the challenge united by collaboration, guided by an effective methodology, and of course, enabled by powerful technology.

By far the most important variable in implementing successful and sustainable data quality improvements is acknowledging David’s sage advice:  people—not tools—solve data quality problems.

 

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DQ-Tip: “There is no such thing as data accuracy...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“There is no such thing as data accuracy — There are only assertions of data accuracy.”

This DQ-Tip came from the Data Quality Pro webinar ISO 8000 Master Data Quality featuring Peter Benson of ECCMA.

You can download (.pdf file) quotes from this webinar by clicking on this link: Data Quality Pro Webinar Quotes - Peter Benson

ISO 8000 is the international standards for data quality.  You can get more information by clicking on this link: ISO 8000

 

Data Accuracy

Accuracy, which, thanks to substantial assistance from my readers, was defined in a previous post as both the correctness of a data value within a limited context such as verification by an authoritative reference (i.e., validity) combined with the correctness of a valid data value within an extensive context including other data as well as business processes (i.e., accuracy).

“The definition of data quality,” according to Peter and the ISO 8000 standards, “is the ability of the data to meet requirements.”

Although accuracy is only one of many dimensions of data quality, whenever we refer to data as accurate, we are referring to the ability of the data to meet specific requirements, and quite often it’s the ability to support making a critical business decision.

I agree with Peter and the ISO 8000 standards because we can’t simply take an accuracy metric on a data quality dashboard (or however else the assertion is presented to us) at face value without understanding how the metric is both defined and measured.

However, even when well defined and properly measured, data accuracy is still only an assertion.  Oftentimes, the only way to verify the assertion is by putting the data to its intended use.

If by using it you discover that the data is inaccurate, then by having established what the assertion of accuracy was based on, you have a head start on performing root cause analysis, enabling faster resolution of the issues—not only with the data, but also with the business and technical processes used to define and measure data accuracy.

 

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DQ-Tip: “There is no point in monitoring data quality…”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“There is no point in monitoring data quality if no one within the business feels responsible for it.” 

This DQ-Tip came from the Enterprise Data World 2010 conference presentation Monitor the Quality of your Master Data by Thomas Ravn, MDM Practice Director at Platon.

It reminds me of a similar quote from Thomas Redman: “It is a waste of effort to improve the quality of data no one ever uses.” 

A common mistake made by those advocating that data needs to be viewed as a corporate asset is discussing data independent of both its use and its business relevance.  Additionally, Marty Moseley recently blogged about how data quality metrics often do a poor job in relaying the business value of data quality, strategic or otherwise.

Data profiling can help you begin to understand your data characteristics and usage.  A full data quality assessment can help you create the metrics that establish an initial baseline measurement, as well as continue monitoring your data quality over time.

However, without data quality metrics that meaningfully represent tangible business relevance, you should neither expect anyone within your organization to feel responsible for data quality, nor expect anyone to view data as a corporate asset.

 

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Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Start where you are

Use what you have

Do what you can.”

This DQ-Tip is actually a wonderful quote from Arthur Ashe, which serves as the opening of the final chapter of the fantastic data quality book: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information by Danette McGilvray.

“I truly believe,” explains McGilvray, “that no matter where you are, there is something you can do to help your organization.  I also recognize the fact that true sustainability of any data quality effort requires management support.  But don't be discouraged if you don't have the ear of the CEO (of course that would be nice, but don't let it stop you if you don't).”

McGilvray then suggests the following excellent list of dos and don'ts:

  • You DON'T have to have the CEO's support to begin, but . . .
  • You DO have to have the appropriate level of management support to get started while continuing to obtain additional support from as high up the chain as possible.

     

  • You DON'T have to have all the answers, but . . .
  • You DO need to do your homework and be willing to ask questions.

     

  • You DON'T need to do everything all at once, but . . .
  • You DO need to have a plan of action and get started!

“So what are you waiting for?” asks McGilvray. 

“Get going: build on your experience, continue to learn, bring value to your organization, have fun, and enjoy the journey!”

 

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DQ-Tip: “Data quality is about more than just improving your data...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality is about more than just improving your data.

Ultimately, the goal is improving your organization.”

This DQ-Tip is from Tony Fisher's great book The Data Asset: How Smart Companies Govern Their Data for Business Success.

In the book, Fisher explains that one of the biggest mistakes organizations make is not viewing their data as a corporate asset.  This common misconception often prevents data quality from being rightfully viewed a critical priority. 

Data quality is misperceived to be an activity performed just for the sake of improving data.  When in fact, data quality is an activity performed for the sake of improving business processes.

“Better data leads to better decisions,” explains Fisher, “which ultimately leads to better business.  Therefore, the very success of your organization is highly dependent on the quality of your data.”

 

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DQ-Tip: “...Go talk with the people using the data”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“In order for your data quality initiative to be successful, you must:

Walk away from the computer and go talk with the people using the data.”

This DQ-Tip came from the TDWI World Conference Chicago 2009 presentation Modern Data Quality Techniques in Action by Gian Di Loreto from Loreto Services and Technologies.

As I blogged about in Data Gazers (borrowing that excellent phrase from Arkady Maydanchik), within cubicles randomly dispersed throughout the sprawling office space of companies large and small, there exist countless unsung heroes of data quality initiatives.  Although their job titles might be labeling them as a Business Analyst, Programmer Analyst, Account Specialist or Application Developer, their true vocation is a far more noble calling.  They are Data Gazers.

A most bizarre phenomenon (that I have witnessed too many times) is that as a data quality initiative “progresses” it tends to get further and further away from the people who use the data on a daily basis.

Please follow the excellent advice of Gian and Arkady — go talk with your users. 

Trust me — everyone on your data quality initiative will be very happy that you did.

 

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Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality is primarily about context not accuracy. 

Accuracy is part of the equation, but only a very small portion.”

This DQ-Tip is from Rick Sherman's recent blog post summarizing the TDWI Boston Chapter Meeting at MIT.

 

I define data using the Dragnet definition – it is “just the facts” collected as an abstract description of the real-world entities that the enterprise does business with (e.g. customers, vendors, suppliers).  A common definition for data quality is fitness for the purpose of use, the common challenge is that data has multiple uses – each with its own fitness requirements.  Viewing each intended use as the information that is derived from data, I define information as data in use or data in action.

Alternatively, information can be defined as data in context

Quality, as Sherman explains, “is in the eyes of the beholder, i.e. the business context.”

 

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“Don't pass bad data on to the next person.  And don't accept bad data from the previous person.”

This DQ-Tip is from Thomas Redman's excellent book Data Driven: Profiting from Your Most Important Business Asset.

In the book, Redman explains that this advice is a rewording of his favorite data quality policy of all time.

Assuming that it is someone else's responsibility is a fundamental root case for enterprise data quality problems.  One of the primary goals of a data quality initiative must be to define the roles and responsibilities for data ownership and data quality.

In sports, it is common for inspirational phrases to be posted above every locker room exit door.  Players acknowledge and internalize the inspirational phrase by reaching up and touching it as they head out onto the playing field.

Perhaps you should post this DQ-Tip above every break room exit door throughout your organization?

 

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Additional Resources

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DQ Problems? Start a Data Quality Recognition Program!

Starting Your Own Personal Data Quality Crusade