RWDG Webinar: Achieving Data Quality Through Data Governance

Achieving Data Quality Through Data GovernanceHarald Smith, Director, Product Management

TRILLIUM SOFTWARE, A Harte Hanks Company 2

Big Data Business Insights & Decisions

Big Data has changed our world. Social media, Internet of Things (IoT), Cloud sources, …

With it, it has brought new business insights with the promise of better business decisions

It’s also brought disruptive technologies and practices Streaming data, Self-service data preparation, Machine Learning, …

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Critical Business Decisions driven by TrustChallenging with in Volume, Variety, Velocity

YES?

Option 1

Option 2

Option 3

NO?

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What you don’t know can hurt you!Changes to data quality quickly undermine Trust

Missinginputs

Noise

Signalloss

Differingaggregations

Lack ofcontext

Incorrectdefaults

Unxpressedassumptions

YES????NO?

Cognitivebias

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Not only imperative to Understand your dataNecessary to actively Record, Monitor & Measure

YES!

NO!

ENUMERATEEstablish the criteria defining goals, relevance, and fitness for purpose

ACQUIRECapture the metadata for data

sources being considered and used

DISCOVERProfile the data sources which are required for the desired analysis

VALIDATEEvaluate the data sources for the identified and required qualities

DOCUMENTDocument and store the findings

about data sources and processes

CATALOGProvide and communicate findings about data sources and processes

for others to utilize

Option 1

Option 2

Option 3

TRILLIUM SOFTWARE, A Harte Hanks Company 6

Information Governance becomes central to supporting Business Insights & Decisions

These activities are all part of an active Information Governance program

Thanks!

For more information go to: trilliumsoftware.com

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Copyright © 2016 Robert S. Seiner – KIK Consulting & Educational Services / TDAN.com

Non-Invasive Data Governance™ is a trademark of Robert S. Seiner & KIK Consulting #RWDG @RSeiner

Real World Data GovernanceAchieving Data Quality Through Data Governance

Monthly Webinar Series Hosted by DataversityRobert S. Seiner – KIK Consulting / TDAN.com

October 20, 2016 – 2:00 p.m. EST

Robert S. SeinerKIK Consulting & Educational Services, LLC - KIKconsulting.com

The Data Administration Newsletter, LLC - TDAN.com

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• Real-World Data Governance – Monthly Webinar Series– November 17, 2016 – Corporate Data Governance – The CDO is the Data Governance

Chief – with Special Guest Anthony Algmin– December 15, 2016 – Using Agile to Justify Data Governance

– Monthly on the Third Thursday at 2 pm EST– Register On-Line at DATAVERSITY.net or TDAN.com or KIKConsulting.com

Achieving DQ Through DG2016 Upcoming Webinars

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• Non-Invasive Data Governance:The Path of Least Resistance and Greatest SuccessISBN 9781935504856 / Technics Publishing / Amazon.com

• KIKConsulting.comHome of Non-Invasive Data Governance™

• DATAVERSITY Events (with DEBTech International)

Data Governance Financial Services ConferenceNovember 14-18 – Delray Beach, FloridaTutorial – Three-Tiers of Building and Governing a Business GlossarySession – Data Governance in the Internet of Things Age

Real-World Data GovernanceIn the News

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• The Data Administration Newsletter (TDAN.com)– Bi-Monthly Publication– October 2016 Issue Available– New Content 1st & 3rd Wednesdays– Articles, Columns, Features, Blogs– Published by Bob Seiner– Produced by DATAVERSITY– Subscribe On-Line for Updates

• Just Announced– DATAVERSITY Training Center– On-Line Course Plan – 7 Courses– Non-Invasive Data Governance– Produced by DATAVERSITY

Real-World Data GovernanceIn the News

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• Data quality requires sustained discipline around the management of datadefinition and production.

• Data Governance is a large part of that discipline.

• The relationship between how well data is governed and the quality of thedata is obvious.

• You cannot have high quality data without active Data Governance.

Achieving DQ Through DGAbstract

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• In this webinar, Bob will discuss:– How Data Governance leads to data quality– Core principles of Data Governance and data quality success– Quality metrics based on governance practices– Relationship between quality and governance roles– Steps to achieve quality through governance

Achieving DQ Through DGAbstract

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• Data Governance is the execution and enforcement of authority …

• Data Stewardship is the formalization of accountability …

• Non-Invasive Data Governance™– The practice of:

• applying formal accountability & behavior • through a non-invasive framework of roles & responsibilities• to existing and / or new processes• to assure that the definition, production & usage of data• improves regulatory compliance, security, privacy, protection, quality.

– Non-Invasive describes how governance is applied to assurenon-threatening management of valuable data assets.

– The goal is to be transparent, supportive, collaborative.

Achieving DQ Through DGDefinitions

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• Data Quality does not only focus on data content quality.

• To achieve success with Data Quality requires …– Quality Data Definition– Quality Data Production– Quality Data Usage

Achieving DQ Through DGHow Data Governance Leads to Data Quality

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• NIDG is the practice of:– applying formal accountability & behavior – through a non-invasive framework of roles & responsibilities– to existing and / or new processes– to assure that the Definition, Production & Usage of data– improves regulatory compliance, security, privacy, protection, quality.


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• Quality Data Definition– Assures that someone is responsible for managing data definition– Assures a physical definition format that is recorded– Assures that the definition is accessible– Assures that the definition of data is consistent and approved


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• Quality Data Definition– Assures that the data naming contains context– Assures that you stay away from “cheeseburger” definitions– Often a quality definition is a result of a data modeling effort– Assures the involvement of the “right” people in the defining of data


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• How does the improvement of Data Definition lead to quality?– Hard to know what’s wrong if you don’t know what is right.– Improve consistent understanding and usage of data– Improve knowledge of the data that exists– Avoids duplication of data


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• Quality Data Production– Assures that people know the impact of the data they produce– Assures that people are given incentive to produce quality data– Assures that applications manage data production– Assures that data transfer checks and balances occur


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• Quality Data Production– Assures the trust (validation) of data sources– Assures that data validation is multi-faceted (data combinations)– Assures that data is made available in a timely manner– Assures that data is certified for analytical purposes (if important)


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• How does the improvement of Data Production lead to quality?– Improves confidence in data– Improves efficiency around data use– Improves effectiveness around data use– Improves formality around data production


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• Quality Data Usage– Assures that only the “right” people can see the data– Assures that people know and follow the data handling rules– Assures that data is shared appropriately– Assures that data reporting is compliant


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• Quality Data Usage– Assures that the “right” data is used for the “right” purposes– Assures that people know how the data can and cannot be used– Assures that people know the most appropriate place to get the data– Assures that people follow the rules associated with usage


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• How does the improvement of Data Usage lead to quality?– Improves data protection– Improves efficiency around data analytics and reporting– Improves effectiveness around data analytics and reporting– Improves formality around data reporting


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Achieving DQ Through DGCore Principles of Data Governance and Data Quality Success

• Core Principles of Non-Invasive Data Governance– Data Must Be Governed as a Valued & Strategic Enterprise Asset– Data Must Have Clearly Defined Accountability– Data Must Be Governed to Follow Internal & External Rules / Regulations– Data Quality Must Be Governed Consistently Across the Enterprise

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• Data Must Be Governed as a Valued & Strategic Enterprise Asset– Emphasis on data at the Senior Leadership Level– Data part of Strategic and Board-level Discussions– Formal Data or Information Governance Program in place– Senior Leadership Support, Sponsor and Understand Data Governance Activities– Requires attention of Chief Data Officer or similar position

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• Data Must Have Clearly Defined Accountability– People’s accountabilities based on their relationship to data– People know where they can go to get information pertaining to data quality– People understand the rules for handling data– Accountability is policy and built into people’s job description– Accountabilities should be communicated effectively

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• Data Must Be Governed to Follow Internal & External Rules / Regulations– Data Quality Rules and Standards– Business Rules– Classification Rules– Data Protection Rules (examples PII, PHI and don’t forget IP)– Compliance and Regulatory Rules

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• Data Quality Must Be Governed Consistently Across the Enterprise– Data definition, production and usage must all be managed– Cradle to grave management of data– Consistent applications of quality rules across the enterprise– Consistent classification and protection of data across the enterprise– Data Quality must be built into data processes

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• Dimensions of Data Quality– Accessibility– Accuracy– Completeness– Consistency– Relevance– Timeliness– Uniqueness

Achieving DQ Through DGQuality Metrics Based on Governance Practices

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• Accessibility– Measure how people have governed access to the data they need

• Accuracy– Measure how data compares to the standards

• Completeness– Measure comprehensiveness of data and available metadata

• Consistency– Measure the uniformity of quality metrics


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• Relevance– Measure data fit for purpose

• Timeliness– Measure data availability compared to business use

• Uniqueness– Measure how many versions of data exist and the system of record


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• Data Governance Roles– Executive / Board > Strategic > Tactical > Operational > Support

Achieving DQ Through DGRelationship Between Quality and Governance Roles

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• Executive / Board Level – Steering Committee– Senior-most level of the organization– Support, Sponsor and Understand relationship between DQ and DG– Hold someone accountable for DQ and DG results– Assure representation of the entire organization at the strategic level– Limited but informed involvement


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• Strategic Level – Data Governance Council– Decision makers and representatives from Business Areas– Attend regular Council meetings– Act virtually when necessary– Review metrics and adjust program leadership as necessary– Resolve data quality issues escalated to the Strategic Level


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• Tactical Level – Data Domain Stewards, Steward Coordinators– Responsibility (ownership) associated with a domain or a functional area– Work with Data Governance Team or Manager to facilitate quality improvement– Cornerstone Data Governance role when it comes to Data Quality– Subject Matter Experts and Enterprise Data Stewards– Hardest role to fill because it breaks down silos


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• Operational Level – Data Stewards– Stewards based on people’s relationship to data– Data Definers, Data Producers and Data Users– Accountable for improving quality at the operational level– Focus primarily on quality data within their Business Unit– Do not necessarily look at data across Business Units


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• Support Level – Data Governance Partners– IT, PMO, Compliance, Audit, Legal, Communications, HR …– Responsible for representing governance within each of their disciplines– Participate on Data Governance Team or work alongside DG Manager– Actively involved in defining governed processes related to their discipline– Actively involved in enforcing governed processes related to their discipline


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Achieving DQ Through DGSteps to Achieve Quality Through Governance

• Steps for Data Governance to Achieve Data Quality – Define Data Governance and Data Quality Best Practices– Assess Existing Practices Vs. Best Practices– Identify Strengths, Opportunities to Improve– Articulate Gaps & Risks Associated with Gaps– Define, Identify & Document Data Governance Roles

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• Steps for Data Governance to Achieve Data Quality – Select Project(s) for Data Governance to Focus on Data Quality

• Master Data Management• Data Warehouse• Building Out of Analytical Data Resources• Data Integration / ERP Implementation


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• Steps for Data Stewardship to Achieve Data Quality – Define Stewardship Roles and Responsibilities

• Tactical• Operational

– Recognize People into Roles / Engage People in Roles– Apply Stewards / Governance

• Data Definition Efforts• Data Production Efforts• Data Protection Efforts

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• In this webinar, Bob discussed:– How Data Governance leads to data quality– Core principles of Data Governance and data quality success– Quality metrics based on governance practices– Relationship between quality and governance roles– Steps to achieve quality through governance

Achieving DQ Through DGSummary

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• Real-World Data Governance – Monthly Webinar Series– November 17, 2016 – Corporate Data Governance – The CDO is the Data

Governance Chief – with Special Guest Anthony Algmin– December 15, 2016 – Using Agile to Justify Data Governance

• Questions and Answers

Real-World Data GovernanceConclusion

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• Robert S. SeinerKIK Consulting & Educational Services – KIKconsulting.comThe Data Administration Newsletter – TDAN.comPost Office Box 112571, Upper St. Clair, Pennsylvania 15241412.220.9643, 412.220.9644 (Fax)[email protected]@tdan.com@RSeiner#RWDG

Real-World Data GovernanceContact Information

RWDG Webinar: Achieving Data Quality Through Data Governance

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