Real-World Data Governance: Data Governance Expectations

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Copyright © 2012 Robert S. Seiner – KIK Consulting & Educational Services / TDAN.com

Non-Invasive Data Governance™ is a trademark of Robert S. Seiner & KIK Consulting Twitter About This Webinar at #RWDG

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

Robert S. SeinerKIK Consulting & Educational Services – KIKconsulting.com

The Data Administration Newsletter – TDAN.com

Real-World Data GovernanceData Governance Expectations

Getting Them to Tell You How Governance Will Add Value

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

December 19, 2013 – 2:00 p.m. EST




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

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• Real-World Data Governance – Monthly Webinar Series – 2pm EST

– January 16, 2014 – A Different Way of Defining Data Stewards & Stewardship

– February 20, 2014 – A Governance Framework for Success

– March 20, 2014 – Agile Data Governance: Truth Be Told*

– Register On-Line at DATAVERSITY.Net * with guest

Data Governance ExpectationsUpcoming Webinars





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• When starting a Data Governance program, significant time, effort andbandwidth is typically spent selling the concept of data governance andtelling people in your organization what data governance will do for them.

• This may not be the best strategy to take.

• We should focus on making Data Governance THEIR idea not ours.

Data Governance ExpectationsAbstract





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• Shouldn’t the strategy be that we get the business people from ourorganization to tell US why data governance is necessary and what datagovernance will do for them?

• If only we could get them to tell us these things? Maybe we can.

• Join Bob Seiner and DATAVERSITY for this informative Real-World DataGovernance webinar that will focus on getting THEM to tell US where datagovernance will add value.

• Seiner will review techniques for acquiring this information and will shareinformation of where this information will add specific value to your datagovernance program. Some of those places may surprise you.

Data Governance ExpectationsAbstract





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• Who is “Them”?

– Anybody that Defines, Produces and Uses Data

– Anybody Who Must Support Data Governance in Order for it to be Successful

– Anybody Who is Supported by or Supports These People ↑

– Anybody that Sees Room for Improvement Associated with Data

– Anybody Who is Feeling the Pain of Having Crummy Data

– Anybody …

– Anybody …

– Anybody …

Data Governance ExpectationsKnowing “Them”





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• Setting Expectations Around Data Governance

With Data Governance in General:

– Many organizations view Data Governance as being over-and-above normalwork efforts and threatening to the existing work culture of the organization.

We should tell them – It does not have to be that way.

– Many organizations have a difficult time getting people to adopt DataGovernance best practices because of a common belief that Data Governance isabout command-and-control.


– While I firmly state that Data Governance is “the execution and enforcement ofauthority over the management of data”, nowhere in that definition does it saythat Data Governance has to be invasive or threatening to the work, people andculture of the organization.


Data Governance ExpectationsDefinitions





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With Non-Invasive Data Governance™:

– Data steward responsibilities are identified and recognized, formalized andengaged according to their existing responsibility rather than being assigned orhanded to people as more work.

– The governance of data is applied to existing policies, standard operatingprocedures, practices, and methodologies … rather than being introduced oremphasized as new processes or methods.

– The governance of data augments and supports all data integration, riskmanagement, business intelligence and master data management activitiesrather than imposing inconsistent rigor to these initiatives.






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With Non-Invasive Data Governance™:

– Specific attention is paid to assuring senior management’s understanding of apractical and non-threatening yet effective approach to governing data that willbe taken to mediate ownership and promote stewarding of data as a cross-organization asset, rather than the traditional method of “you will do this”.

– Best practices and key concepts of the non-threatening approach arecommunicated effectively, compared to existing practices to identify andleverage strengths and enable the ability to address opportunities to improve.






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• Data Governance– Data Governance is the Execution and Enforcement of Authority

Over the Management of Data and Data-Related Resources.

Robert S. Seiner

• Data Stewardship

– Data Stewardship is the Formalization of AccountabilityOver the Management of Data and Data-Related Resources.

Robert S. Seiner

Recent Client Definitions

Formalization of behavior around the definition, production andusage of data to manage risk and improve quality and usability ofselected data.

Formalization and guidance for behavior over the definition,production and use of information and information related assets.






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• Non-Invasive Data Governance™

– The practice of:

• applying formal accountability & behavior

• through non-invasive roles & responsibilities

• to existing and / or new processes

• to assure that the definition, production & usage of data

• assures 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.






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• [to] gov·ern [data]

v. gov·erned, gov·ern·ing, gov·erns

– To make and administer the public policy and affairs [of data]

– To exercise sovereign authority [in data]

– To control the speed or magnitude [of data]

– To regulate [data]

– To control the actions or behavior [of data]

– To keep under control [data]; to restrain [data]

– To exercise a deciding or determining influence [on data]

– To exercise political authority [over data]

– To have or exercise a determining influence [over data]

FreeDictionary.com [Bob Seiner]

Data Governance ExpectationsConsiderations for Setting Expectations





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• Be Able to Articulate Expectations in Terms of:

– Benefits from Existing Types of Governance

– Formalizing Accountability – but in order to do that … we need provision

– Technical Complexities of Governing Data

– Process Complexities of Governing Data

– Ownership Complexities of Governing Data


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• Top Expectations to Set (no specific order) :

1.Need for Executive & Strategic Level Understanding & Support

2.Need for Program Accountability & Responsibility

3.Need for Definition of Best Practices as a Target Behavior & Critical Analysis

4.Need to Select & Follow Suitable Approach

5.Need to Plan and Be Able to Support Meaningful Initiatives

6.Need to Deploy a Well Thought-Out Operating Model of Roles & Responsibilities

7.Need to Make Data Governance & Data Quality Not Optional

8.Need to Focus on Communications and Awareness

9.Need to Set Proper Expectations of the Solution

10.Need to Stay Iterative and Incremental in the Approach






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• Top Expectations Around Becoming a Data Steward

1. A Data Steward can be absolutely anybody.

2. Being a Data Steward describes a relationship to data and is not a position.

3. A Data Steward is not hired to be a Data Steward.

4. A Data Steward does not need to have the title of “Data Steward”.

5. A Data Steward does not have to be told how to do their job.

6. Public or Industry Data Steward Certification is a load of bunk.

7. There is more than one Data Steward for each type of Data.

8. Data Steward training should be focused on “Formalizing Accountability”.






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• The Burning Questions:

– What / Where should we expect to see (as) benefits from Data Governance?

– What is the work effort that will be required to achieve the benefit?

– How will we measure the benefit of Data Governance?

– What should / shouldn’t we tell Management about Data Governance?

– How can we get them to tell us where Data Governance adds value?


So Tell Us …





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• Expectations Associated with Benefits

– Return on Investment

– Cost Savings / Avoidance

– Compliance and Regulatory Risk Management

– Improvements in Efficiency & Effectiveness

– Improvements in Data Quality (But does anybody care about this unless it

represents some level of the first two above?)

– Improvements in Decision Making Based on Access to Data

– Improvements in Decision Making Based on Higher Quality Data, …

Data Governance ExpectationsMessages for Management





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• Expectations Associated with Work Effort

– Non-Invasive Impact – Formalize Accountability

– Formalize what? Execution and Enforcement of Authority

• Data Management is Governance

• Information Security is Governance

• Regulatory & Compliance is Governance

• Project Management is Governance

• Following an SDLC is Governance

• Issue Resolution is Governance

• Business Process Reengineering is Governance

• Data Modeling Best Practice is Governance

• Data Quality Management is Governance

– Escalation and Resolution Path






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• Expectations Associated with Measuring the Value Add of Governance

– What are we going to measure?

• Issues, Time to Market, Compliance, Productivity, Revenue, Savings, …

– What are we going to measure it against?

– How are we going to record data to report the measures?

– How are we going to report the measures?

– How are the measures related to:

• Corporate Goals

• Corporate Vision

• Key Performance Indicators

• Performance Evaluations






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• What We Should Tell Management About Data Governance

– We ARE already governing data (but we are doing it informally).

– We CAN formalize how we govern data by putting structure around what we are presently doing.

– We CAN Improve.

– We DO NOT Have to Spend A Lot of Money.

– We NEED structure.






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• What We Should Not Tell Management About Data Governance

– Do NOT Sell Data Governance as being A HUGE CHALLENGE.

– Emphasize that Data Governance is NOT A TECHNICAL SOLUTION.

– Emphasize again that PEOPLE BEHAVIOR IS GOVERNED, Not Data.

– Emphasize that Data Governance is an EVOLUTION, NOT REVOLUTION.






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1. We ARE already governing data (but we are doing it informally).

– There are already people in the organization that have responsibility for data.

– We should inventory who does what with the data and provide an operatingmodel of roles and responsibilities that best suits our organization.

– At some level, there will need to be somebody that has an enterprise view andresponsibility for data that cuts across the silos and manage data as a sharedresource.

– This will be our biggest challenge because it is not natural for us to manage dataas a shared and enterprise resource.

– A challenge but a "do-able" challenge nonetheless.


Should Tell Management





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2. We CAN formalize how we govern data by putting structure around whatwe are presently doing.

– There are people in our organization in operational, tactical, strategic andsupport roles around data.

– We need to know who they are and put formal structure around who isresponsible, accountable, consulted and informed about the business rules andregulations associated with the data they define, produce and use.







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3. We CAN Improve.

– Our Data Governance efforts can help us to improve how we manage risksassociated with compliance, classification, security, business rules ... of our data.

– People of our organization potentially put us at risk every day when they are notassured of knowing the rules associated with the handling of data.

– Our efforts to improve the quality of data must be coordinated and cooperativeacross business units using the formal structure mentioned above.

– Quality assurance requires that operational and tactical staff have the ability torecord, track and resolve known data quality issues.

– Our organization can immediately improve how we communicate about datathrough recording and sharing information about who does what with data.







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4. We DO NOT Have to Spend A Lot of Money.

– Data Governance is not a costly endeavor.

– Depending on the approach we take, Data Governance should only cost the timewe put into it.

– Certainly Data Governance will require that an individual(s) spend the timedefining and administering the Program, but there is large misconception thatData Governance must be over-and-above the existing work efforts of theorganization.

– We should avoid calling things "Data Governance processes" because that givespeople the impression that formal behavior around data definition, productionand usage of data is the fault of data governance rather than the glue thatassures these behaviors are handled properly.







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5. We NEED structure.

– We should consider the Non-Invasive Data Governance™ Approach.

– We must follow a proven approach to Data Governance that is not threateningto the people of our organization that will participate in the program.

– Data Governance will require that both the business and the technology areas ofthe organization take formal and shared accountability for how data is governed.

– The participants in the Data Governance Program already have "day jobs" andwe must add value, not interfere, with what they do as their daily jobs.

– The goal of Non-Invasive Data Governance™ is to be transparent, supportive andcollaborative.

– These concepts lie at the heart of the implementation of the Non-Invasive DataGovernance™ approach.







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6. Do NOT Sell Data Governance as being A HUGE CHALLENGE.

– If your management already thinks that Data Governance will be a big challenge,try to calm their nerves by referring to the first five messages. Data Governancecan be implemented in a non-threatening, non-interfering, non-culture changing… dare I say – “Non-Invasive” way that will reduce the challenge significantly.

– Data Governance doesn’t have to be implemented all at once. In fact, mostsuccessful organizations (data governance-wise) implement their programsincrementally – incrementally in 1) the scope of the data that is governed(domain-wise and organizationally) and/or 2) the level of governance (formalbehavior) that is applied to that data. See #9 for further thoughts on this.


Should Not Tell Management





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7. Emphasize that Data Governance is NOT A TECHNICAL SOLUTION.

– There will likely be a technical component to your Data Governance program.But then again, there might not be. Most people agree that you cannot purchasesoftware or hardware that will BE your Data Governance solution. And mostpeople will agree that simple tools can be developed (i.e., the KIK's CommonData Matrix) internally to help organizations govern people's behaviors (see #8).

– Technology can assist in formalizing people’s behavior. Data only behaves theway people behave. Therefore, technology may help you to govern thebehaviors of people, but it won’t – by itself – govern the data.







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8. Emphasize again that PEOPLE BEHAVIOR IS GOVERNED, Not Data.

– Data Governance is typically about formalizing the behavior of people for thedefinition, production and usage of data. Formalizing people’s behavior, notdata. Data only behaves the way people behave. Therefore, technology may helpyou to govern the behaviors of people, but the data does what it’s told.

– Since it is people's behavior that is governed, many organizations consider DataGovernance to be a process-driven discipline. That is partially true. Gettingpeople to do the right thing at the right time is a big part of governance;however, organizations that "sell" data governance as all new "governance"processes struggle because of the inherent (viewed) invasiveness of thatapproach. Governance should formalize behavior around existing processes first,and only add to people's workloads as a last resort.







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9. Emphasize that Data Governance is an EVOLUTION, NOT REVOLUTION.

– As was stated earlier, Data Governance will not be completed all at one time.There are a few different ways that organizations transition themselves into aData Governance state.

– Some organizations focus early on specific domains or subject areas of data.Some organizations focus on specific business areas, divisions, units,applications, rather than implementing all across the organization at once. Someorganizations focus on a combination of the two or three, specific domainswithin specific business units using specific applications. There is no singlecorrect way to evolve into your company. But I can almost assure you that if youtreat it as a revolution, there will be a revolt.







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• Questions to Get the Business to Describe Where to Expect Value:

1. What can’t you do that you think you should be able to do with the data?

2. What would you want to do if you had the data to support it?

3. What data would you like to see to help you do your job better?

4. How could your daily routine be made better?

5. What do you spend time on where that time could be reduced?

Data Governance ExpectationsWhere Should We Expect to See Benefit?





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6. What data do you use most often to achieve your objectives and describe what ittakes to get and use that data?

7. What is the primary source used by your area to obtain the data? Who do go toget information about this source data?

8. What processes that you follow consume the data?

9. Do these processes have data challenges? If so, what are they?

10. What is the product or customer impact? How has this been tracked ormeasured?






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11. Is there risk associated with the data – brand, compliance, legal, etc. ?

12. What is the operational impact (e.g. money, FTEs, manual work-around, etc.) ofthe challenges? Have you tracked or quantified these impacts?

13. What other impacts should be noted?

14. Have you identified potential solutions? If so, what is the status of thesesolutions – Scoped, Funded, Planned, In-Flight, etc?

15. What data / information is on your “wish list” that would better enable yourarea to deliver on our Core Competencies?






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• I cannot …

– Track the productivity of the wells across regions …

– Determine which salespeople have the clients with the highest lifetime value …

– Identify at-risk students based on …

– Report which loans are being paid back by …

– Compare raw material costs across …

– Compare production costs across …

– Project successful campaigns versus …

– Cross sell to clients in other parts of the business …

– Report on the most profitable parts of our business …

– Answer my boss’ questions without a lot of wasted …

– Determine the best time of year to …

– … and on … and on … and on … just like the naughty list!






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• I wish I could …

– Just press a button and get the data I need to …

– Find the business definition of this particular piece of data I use to …

– Know when data I use is updated and any changes that may have taken place …

– Have access to the system of record rather than jumping through hoops to …

– Understand the impact of changing the value of this data …

– Know who I can and cannot share my results with …

– Get the right people to live a day in my shoes …

– Get rid of all of my spreadsheets …

– Know who else cares about the data I produce …






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• Inconsistencies in the definition, production and use of specific critical pieces of data across the company prevent the ability to:– Compare costs across regions

– Identify the best place to get alternate or substitute materials

– Provide sophisticated ad-hoc cross-company analysis

– Leverage capacity and material use

– Manage planning for supply chain and demand globally

– Reduce overall investment in capital improvements

– Target areas where cross-company comparisons will add the most value

– Formally decide the best approach to build consistency in that data

– Complete the process of building consistency in the data

– Compare raw material usage across regions, plants

– Fully plan for and leverage material use

– Identify where raw materials are used globally

– Identify which raw materials can be substituted forother raw materials

– Compare data across regions, plants, … for planning,development and delivery purposes

– Identify where to build new plants to address customerneeds

– Clearly define global data owners or those people withGlobal responsibilities

– Provide solid cost comparisons across the Company, byplant or by region

– Define, measure and report On-Time Delivery (OTD)

– Provide consistency in how to measure and what tomeasure in regards to production, shipping, requests

– Provide consistent analysis by regions, costs,allocations,






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• Data Governance provides a formal framework of roles and processes thatwill enable the company to:– Formally address accountability and ownership of that data

– Formally investigate and report the benefit and gain from consistency

in that data

– Formally investigate and report the cost associated with the benefit

and gain

– Formally identify the resources and time required to achieve the

– benefit and gain

– Complete the process of building consistency in the data

– Control and manage raw material substitution

– Identify and plan for where raw materials are used

– Compare material usage across plants

– Identify alternate and substitute raw materials

– Improve metrics around data – ability to count products, materials,

customers, …

– Analyze and measure time required to load data into system globally

– Provide faster innovation and redefinition in processes, products

– Formalize and implement data standards for the most critical data across BOMs, routings, costs

– Communicate efficiently and effectively regarding product changes

– Utilize cost and production data globally to Identify and retire products that are less profitable

– Prevent rework associated with product changes not formally communicated and rejected by customers

– Formalize responsibility for communications across the manufacturing business areas

– Standardize the approach to our product management by improving the understanding of cost






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• Key Concepts Shared:– Don’t let perfection get in the way of progress.

– Point of diminishing returns – to determine what data projectsshould be completed.

– Concerned of resources – people and time

– Stay away from “data perfection” – address specificinconsistencies that provide most value

– Secondary – focus on “political” value of consistency inproduct, category, customer definition

– There are pockets of experts but no formal information aboutwho decision makers are

– Need to improve overall maturity level around data andinformation

– Record and communicate how progress is being made in datamaturity

– No single person has the ability to define the standards.

– Estimate the time requirements of people participating inGovernance processes

– Finance’s inability to resolve issues centrally prolong issueresolution process

– Need manner to leverage customer experience – what do”they” want

– Recent problems with data availability and time necessary totranslate the data for analysis

– “Everything we do” around costing and benchmarks isdifficult and time consuming, Very painful.

– Gain agreement on priorities and let specialists gather theinformation for decision making

– Issues everywhere, in everything I do

– “So important to get this done. I am supportive.”

– “Urgent issue and you have my support”.

– Standardizing product data for all new products willdeliver consistency in a few years time due to productturnover and the natural progressions.

– Global has adopted the ISO standard but not the U.S. asonly holdout.

– Causes problems with all units of measure.

– Product changes not communicated causing rework.

– Not using SAP to its fullest value because of the “do whatyou want” approach to data.

– Huge disconnect to the tune of more that $1 milliondollars spent to ineffective communications associatedwith product changes.

– Lack of understanding of costs leads to problemsw/product mgmt – introduction, delivery, retention






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• Webinar Summary

– Define Data Governance Expectations

– Considerations for Setting Expectations

– Messages for Management

– Getting Them to Tell Us

• Questions & Answers

Real-World Data GovernanceWrap Up





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• Real-World Data Governance – Monthly Webinar Series – 2pm EST

– January 16, 2014 – A Different Way of Defining Data Stewards & Stewardship

– February 20, 2014 – A Governance Framework for Success

– March 20, 2014 – Agile Data Governance: Truth Be Told*

– Register On-Line at DATAVERSITY.Net * with guest

Data Governance ExpectationsComing Up





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• Robert S. Seiner

KIK Consulting & Educational Services – KIKconsulting.com

The Data Administration Newsletter – TDAN.com

Post Office Box 112571, Upper St. Clair, Pennsylvania 15241

412.220.9643, 412.220.9644 (Fax)

[email protected]

[email protected]

@RSeiner

#RWDG

Real-World Data GovernanceContact Information