The Chief Data Officer's Agenda: What a CDO Needs to Know about Data Quality

www.cdovision.com

Moderator: Tony ShawCEO, DATAVERSITY

Speaker: Danette McGilvrayPresident and PrincipalGranite Falls Consulting

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What a CDO Needs to Know about Data Quality

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CDO WebinarSeptember 4, 2014

2 pm Eastern/ 11 am Pacific

Danette McGilvray

Granite Falls Consulting, Inc.President and Principal

+1 [email protected]

www.gfalls.com

Your SpeakerPart of a Series

About the Moderator

© 2005-2014 Granite Falls Consulting, Inc. www.gfalls.com 2

Tony Shaw is the Founder and CEO of DATAVERSITY. He is responsible for the business and content strategy of the organization, which conducts educational conferences, webinars, and publishing activities focused on information and data management. Prior to founding DATAVERSITY, Tony started a dotcom in the identity management space (which went the way of most dotcoms), and was the president of Technology Transfer Institute (TTI). He still facilitates TTI’s strategic technology forum for CTOs, called TTI/Vanguard.

About the Speaker


Danette McGilvray is president and principal of Granite Falls Consulting, Inc., a firm that helps organizations increase their success by addressing the information quality and data governance aspects of their business efforts. From strategy to implementation with a focus on bottom-line results, Danette helps organizations enhance the value of their information assets by naturally incorporating information quality management into the business. She also emphasizes communication and the human aspect of information quality and governance.

Danette is the author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann, 2008). An internationally respected expert, her Ten Steps™ approach to information quality has been embraced as a proven method for both understanding and creating information and data quality in the enterprise. A Chinese-language edition is available and her book is used as a textbook in university graduate programs

Danette McGilvrayGranite Falls Consulting, Inc.

President and Principal+1 510-501-8234

[email protected]

About the Webinar


Data is the ultimate cross-enterprise asset. Data and information flow throughout an organization and require both business and IT expertise to manage them effectively. The same data is available for multiple users, unlike physical products that once sold to a buyer cannot be resold to the next customer who walks through the door or places an online order. These properties create unique challenges for the CDO chartered to oversee the data management organization. The purpose of any data management work is to ensure high quality, trusted data and information, yet data quality is a profession within itself. Join us to learn what a CDO needs to know about data quality: The relationship between data quality, governance, and other data

management functions Options for structuring within your organization The difference between data quality programs and projects What a CDO can do to help both data quality programs and projects

succeed

Copyright Information


COPYRIGHT INFORMATIONThese materials, and any part thereof, are protected under copyright law. The contents of this document may

not be reproduced or transmitted in any form, in whole or in part, or by any means, mechanical or electronic, for any other use, without the express written consent of Danette McGilvray.

Portions of this work are from the book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, by Danette McGilvray, published by Morgan Kaufmann Publishers, Copyright 2008 Elsevier Inc. All rights reserved.

Portions of this work are from Danette’s chapter on “Data Quality Programs and Projects” in the book Handbook of Data Quality: Research and Practice. Shazia Sadiq, editor (Springer, 2013).

Portions of this work are from “Data Quality and Governance in Projects: Knowledge in Action”, a Cutter Consortium Executive Report by Danette McGilvray and Masha Bykin. Data Insight & Social BI, Vol. 13, No. 5. Available at http://www.cutter.com/offers/dataqual.html. Feel free to forward this link to others.

TRADEMARK INFORMATIONAll uses of The Ten Steps™, and Ten Steps to Quality Data and Trusted Information™ throughout this

document are protected by trademark law, and those terms are owned by Danette McGilvray and licensed to Granite Falls Consulting, Inc.

Copyright © 2005-2014 Danette McGilvrayGranite Falls Consulting, Inc.

All Rights Reservedwww.gfalls.com

[email protected]

A Typical Meeting


I’m showing our biggest decrease in sales is in the Southern region. Historically that is a strong area for us. We should

focus there.

Wait! The South is holding its own. It is the Eastern region that is in trouble!

My figures show the North has

dropped the most!

No! The largest decrease in

sales is in the West!

No Useful Action

© 2005-2014 Granite Falls Consulting, Inc. See www.gfalls.com 7

Feeling the Effects of Poor Quality Information

Complaints Conflicts and mistrust Wasted time Poor or deferred decisions Lack of confidence Delayed action Unhappy customers Hindered ability to carry out goals Non-compliance with legal

requirements Security breaches Unhappy customers

Higher costs Decreased revenue Declining profits Lower employee morale


Costs of Poor Quality Data


The average organization surveyed by Gartner said it loses $8.2 million annually through poor data quality. Further, of the 140 companies surveyed, 22% estimated their annual losses resulting from bad data at $20 million. Four percent put that figure as high as an astounding $100 million.

-- Jeff Kelly, SearchDataMangement.com, 25 Aug 2009 http://searchdatamanagement.techtarget.com/news/1365965/Poor-data-quality-costing-

companies-millions-of-dollars-annually

More Costs of Poor Quality Data

Sample of research and reports by industry experts, including Gartner Group, PriceWaterhouseCoopers and The Data Warehousing Institute:

75 per cent of organisations have identified costs stemming from dirty data

33 per cent of organisations have delayed or cancelled new IT systems because of poor data

$611bn per year is lost in the US in poorly targeted mailings and staff overheads alone

According to Gartner, bad data is the number one cause of CRM system failure

Less than 50 per cent of companies claim to be very confident in the quality of their data

Business intelligence (BI) projects often fail due to dirty data, so it is imperative that BI-based business decisions are based on clean data

Only 15 per cent of companies are very confident in the quality of external data supplied to them

Customer data typically degenerates at 2 per cent per month or 25 per cent annually


Source: “Drowning in dirty data?” by Richard Marsh, Database Marketing & Customer Strategy Management, Vol. 12, 2, 105-112. http://www.palgrave-journals.com/dbm/journal/v12/n2/pdf/3240247a.pdf

The Bottom Line


We need high quality, trusted data and information so we can make informed decisions and take effective action!

If we have high quality information it means …

© Granite Falls Consulting, Inc. See www.gfalls.com 12

• We can find the data and information we need– We can get to it and access it

• It is available when we need it– It is timely and not late

• It includes everything we need– Nothing is missing

• It is secure – safe from unauthorized access and manipulation

• We understand it – We can interpret it

• It is correct– It is an accurate reflection of what is

happening or what did happen in the real world

Because of all these things …

• We trust it when we get it

• We can use it with confidence

Simply put, it is having the right information available at the right time and place for the right people to run your business.

Is Your Company Suffering from SLIBD Syndrome?


Raise Awareness of Data as an Asset and a Resource


Information Has Value and Needs to Be Deliberately Managed

HumanResources

FinancialResources

InformationResources

Comparing Resources


HumanResources


Comparing Resources


FinancialResources


Management System for Data Quality


Data/information quality management is an essential component of the broader management system called data management

Each Resource Requires an Appropriate Management System

HumanResources

FinancialResources

Data and InformationResources

Data Quality Should Be Goal of All Data Management


While Data Quality is one function in the DAMA Framework, all the functions exist for the purpose of trusted, reliable data and information.

DAMA-DMBOK2 Definitions of Data Management Knowledge Areas

Source: DAMA-DMBOK2 Framework, Sept 11, 2012.

IQCPSM Information Quality Certified Professional

© 2005-2014 Granite Falls Consulting, Inc. See www.gfalls.com 19Source: International Association for Information and Data Quality (IAIDQ). For more info see http://iaidq.org/iqcp/exam.shtml

Six major areas of knowledge (domains) considered essential to an information quality professional.

Add Data to the Conversation


People

ProcessTechnology

From this …

Process

Technology

People Data

To this …

Agree on a Definition


• Real - accurate reflection of real world• Recent - up-to-date information• Relevant – focus on information our customers and the

business needs and cares about

Data vs. Info

Definition: Information or Data QualityThe degree to which information and data can be a trusted source for any and/or all required uses

What is Your Definition of Data Quality?


Peace!

The Information Life Cycle


DisposeApplyMaintainStore & Share

ObtainPlan

The Information Life CycleThe processes required to manage the

information resource throughout its life. Also known as: • Information Chain• Information Value Chain• Data Life Cycle• Information Resource Life CycleIncludes lineage and provenance

Remember POSMAD!

Applying Life Cycle to Other Resources


Plan• Prepare for the

resource

Obtain• Acquire the

resource

Maintain• Ensure the

resource continues to work properly

Apply• Use the resource

to accomplish your goals

Dispose• Remove the

resource when it is no longer of use

Store & Share

• ID objectives, skills needed, recruiting strategy

• Recruit• Hire• Contract

• Compensate• Provide

benefits and training

• Assign position• Use skills

• Retire• Downsize• Terminate

• Manage human resources data/infoHuman Resources

Financial Resources

• Capital planning

• Forecasting• Budgeting

• Borrow loan• Sell stock

• Pay interest• Pay dividends

• Buy other resources

• Pay off loan• Buy back

stock

• Manage financial data/info

• Update data• Match and

merge• Cleanse and

transform• Augment,

enhance, enrich data

• Acquire data• Create

records• Load data• Capture data

• ID bus objectives

• Bus rules, info arch, standards

• Model and design

• Complete manual transactions

• Run reports• Make

decisions• Run automated

processes• Consume data

• Delete data• Archive

information• Purge• Retire data

• Store data electronic-ally or as hardcopy

• Make available through distribution methodInformation

Resources

• Hold info about re-source, make available for use

• Manage financial data/info

• Manage human resources data/info

• Hold info about re-source, make available for use

Use Life Cycle Thinking


Information is a Reusable Resource


Difference between information as a resource and other resources ; when information is used, it is not depleted. It can be used again and again.

DisposeApplyMaintainStore & Share

ObtainPlanInformation Life Cycle (POSMAD)

Important implications: Quality is critical! If the information is wrong,

the same incorrect information is used over and over - with negative results

If you can use the same information inadditional ways, you can increase thevalue you are getting from it – often withno or little incremental costs

Downstream systems

Understand the Need for Data Governance


Definition: Data Governance outlines and enforces• Rules of engagement • Decision rights• Accountabilities

by organizing and implementing (the right level of)• Policies and procedures• Structure• Roles and responsibilities

in order to effectively manage our information assets. Based on definition by John Ladley, Danette McGilvray, Anne Marie Smith, Gwen Thomas

Data Governance and Data Quality


Data quality projects and activities need what governance provides: Accountabilities, appropriate representation, decision rights

Who is accountable for the data throughout the information life cycle Appropriate representation from business and IT, with knowledge about

data, processes, people and organizations, and technology Who has the right to make decisions about the data

Venues for interaction and rules of engagement How the various people/organizations will interact A place to raise and resolve issues Escalation paths Ensure changes are implemented

Communication paths Who needs to know what, and when, and how

Make use of a formalized Data Governance program if it exists If not, determine the above as they relate to data quality work Use what you learn in your data quality projects to help develop data governance

Structuring Data Quality and Governance Programs


Data Quality

data governance

Data Governance

Data Quality

DataGovernance

Data Quality

Data Quality

Data Governance

To sustain data quality, an ongoing data quality program is needed.

DQ-Related Situations: Projects and Production Processes


Situation

CarAnalogy

Description

Examples

Incorporate DQ activities into other projects/methodologies

Build the car

Include data quality tasks in a standard SDLC – in-house or from a third party vendor (Software/solution/system development life cycle)

• Application development project• Building business intelligence/

data warehouse functionality• Data migration project using a

third-party vendor methodology

Operational data quality fix or incident

Fix a flat tire

Fix issues that arise in the course of day-to-day operations, responsibility for managing data quality; or the work impacts data quality

• A problem reported through the help desk shows a DQ component to a problem needing to be fixed

Data-quality focused “projects”

Diagnose sounds and replace transmission

Address Issues that require a focused effort - larger or more complex than operational support fix

• Develop a business case for DQ work

• Establish a DQ baseline

• Determine root causes

• Implement improvements

Data-qualitycontrols

Maintenance such as oil change and tune up

Implement controls to sustain and improve data quality

• Create on-going monitoring and metrics

How Data Quality is Put Into Practice


Projects ProductionProcesses

Program

Balance the Needs


ProjectsProductionProcesses

Programs

You Are a Pioneer


The Small World of Data Quality

© 2005-2014 Granite Falls Consulting, Inc. All rights reserved. www.gfalls.com 34

Commit to Increasing Skills


Provide funding and encourage your people to increase their skills and make use of resources such as: Books Conferences and Webinars – by companies such as Dataversity Other online resources – blogs, newsletters, social media, etc. Training, education, certification:

Training courses (public and in-house) – by companies andby consultants such as myself

IQCPSM – Information Quality Certified Professional from IAIDQ http://iaidq.org/iqcp/exam.shtml.

Graduate degree program in Information Quality at UALR www.ualr.edu/informationquality/

Professional Associations IAIDQ - the International Association for Information and Data Quality. www.iaidq.org/ DAMA – Data Management Association. www.dama.org

Give your own time Learn more yourself Contact your alma mater or other education institutions and offer to be a guest lecturer

Upward Spiral


Increase awareness and skills within your company and show positive results

Share with others both within and outside of the data quality industry

The more the world in general knows about the importance and impact of data quality, the easier it is to get support within your company

Continue to show positive results

Critical Success Factors


Critical success factors necessary to see (“C”) and lead your data quality efforts to success

your way to success!

Commitment Communication and Conversation Collaboration, Coordination, and Cooperation Change Courage

A New Atmosphere of Action


I’m showing our biggest decrease in sales is in the Southern region.

I agree that the Southern region is

showing the biggest decrease in sales. But the Eastern region is

close behind. We need to discuss

which one to help.

Historically the South is a strong area for us. We

should focus there.

Do we have to choose one or

the other? Let’s consider how to help them both.

Your Next Steps


Start where you areUse what you haveDo what you can

--Arthur Ashe

Additional Resources


Handbook of Data Quality (Springer, 2013) Edited by Shazia Sadiq with chapters by Danette McGilvray, Tom Redman, John Talburt, Elizabeth Pierce, C. Lwanga Yonke and others. www.springer.com

Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ by Danette McGilvray (Morgan Kaufmann, 2008). Kindle, Nook, and Chinese language versions also available. store.elsevier.com

Discount for webinar attendees: Visit store.elsevier.comto browse all of Morgan Kaufmann’s Data books! Enter PRT314 at checkout to receive up to 30% off * their data titles! Offer expires Dec. 31, 2014.* There may be differences in some regions and some titles have automatic discounts that will not be overwritten by this code.

“Data Quality and Governance in Projects: Knowledge in Action”, a Cutter Consortium Executive Report by Danette McGilvray and Masha Bykin available at http://www.cutter.com/offers/dataqual.html. Feel free to forward this link to others.


Feel free to contact me if you have comments or questions.

Danette McGilvrayPresident and PrincipalCell: +1 510-501-8234Office: +1 510-651-4400Find me on LinkedIn

[email protected]

San Francisco Bay AreaCalifornia USA

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The Chief Data Officer's Agenda: What a CDO Needs to Know about Data Quality