Real-World Data Governance: Data Governance Expectations

Real-World Data Governance
Data Governance Expectations
Getting Them to Tell You How Governance Will Add Value
Monthly Webinar Series Hosted by Dataversity
Robert S. Seiner – KIK Consulting / TDAN.com
December 19, 2013 – 2:00 p.m. EST

Robert S. Seiner
KIK Consulting & Educational Services – KIKconsulting.com
The Data Administration Newsletter – TDAN.com

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Non-Invasive Data Governance™ is a trademark of Robert S. Seiner & KIK Consulting
Copyright © 2012 Robert S. Seiner – KIK Consulting & Educational Services / TDAN.com
2013

Twitter About This Webinar at #RWDG
#RWDG @RSeiner

Upcoming Webinars
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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

2
2013

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

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This may not be the best strategy to take.

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

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2013

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

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

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Join Bob Seiner and DATAVERSITY for this informative Real-World Data
Governance webinar that will focus on getting THEM to tell US where data
governance will add value.

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

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2013

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Knowing “Them”
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Who is “Them”?
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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 …

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2013

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Definitions
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Setting Expectations Around Data Governance
With Data Governance in General:
– Many organizations view Data Governance as being over-and-above normal
work 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 Data
Governance best practices because of a common belief that Data Governance is
about command-and-control.
– While I firmly state that Data Governance is “the execution and enforcement of
authority over the management of data”, nowhere in that definition does it say
that Data Governance has to be invasive or threatening to the work, people and
culture of the organization.
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Definitions
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With Non-Invasive Data Governance™:
– Data steward responsibilities are identified and recognized, formalized and
engaged according to their existing responsibility rather than being assigned or
handed to people as more work.
– The governance of data is applied to existing policies, standard operating
procedures, practices, and methodologies … rather than being introduced or
emphasized as new processes or methods.
– The governance of data augments and supports all data integration, risk
management, business intelligence and master data management activities
rather than imposing inconsistent rigor to these initiatives.

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Definitions
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With Non-Invasive Data Governance™:
– Specific attention is paid to assuring senior management’s understanding of a
practical and non-threatening yet effective approach to governing data that will
be taken to mediate ownership and promote stewarding of data as a crossorganization asset, rather than the traditional method of “you will do this”.
– Best practices and key concepts of the non-threatening approach are
communicated effectively, compared to existing practices to identify and
leverage strengths and enable the ability to address opportunities to improve.

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Definitions
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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

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Data Stewardship
– Data Stewardship is the Formalization of Accountability
Over the Management of Data and Data-Related Resources.
Robert S. Seiner
Recent Client Definitions
Formalization of behavior around the definition, production and
usage of data to manage risk and improve quality and usability of
selected data.
Formalization and guidance for behavior over the definition,
production and use of information and information related assets.

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Definitions
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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 assure
non-threatening management of valuable data assets.

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

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Considerations for Setting Expectations
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[to] gov·ern [data]

v. gov·erned, gov·ern·ing, gov·erns
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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]

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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

13
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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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Messages for Management
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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, …

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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 operating
model of roles and responsibilities that best suits our organization.
– At some level, there will need to be somebody that has an enterprise view and
responsibility for data that cuts across the silos and manage data as a shared
resource.
– This will be our biggest challenge because it is not natural for us to manage data
as 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 what
we are presently doing.
– There are people in our organization in operational, tactical, strategic and
support roles around data.
– We need to know who they are and put formal structure around who is
responsible, accountable, consulted and informed about the business rules and
regulations 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 risks
associated with compliance, classification, security, business rules ... of our data.
– People of our organization potentially put us at risk every day when they are not
assured of knowing the rules associated with the handling of data.
– Our efforts to improve the quality of data must be coordinated and cooperative
across business units using the formal structure mentioned above.
– Quality assurance requires that operational and tactical staff have the ability to
record, track and resolve known data quality issues.
– Our organization can immediately improve how we communicate about data
through 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 time
we put into it.
– Certainly Data Governance will require that an individual(s) spend the time
defining and administering the Program, but there is large misconception that
Data Governance must be over-and-above the existing work efforts of the
organization.
– We should avoid calling things "Data Governance processes" because that gives
people the impression that formal behavior around data definition, production
and usage of data is the fault of data governance rather than the glue that
assures 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 threatening
to the people of our organization that will participate in the program.
– Data Governance will require that both the business and the technology areas of
the organization take formal and shared accountability for how data is governed.
– The participants in the Data Governance Program already have "day jobs" and
we 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 and
collaborative.
– These concepts lie at the heart of the implementation of the Non-Invasive Data
Governance™ 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 Governance
can 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, most
successful organizations (data governance-wise) implement their programs
incrementally – incrementally in 1) the scope of the data that is governed
(domain-wise and organizationally) and/or 2) the level of governance (formal
behavior) 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 purchase
software or hardware that will BE your Data Governance solution. And most
people will agree that simple tools can be developed (i.e., the KIK's Common
Data Matrix) internally to help organizations govern people's behaviors (see #8).
– Technology can assist in formalizing people’s behavior. Data only behaves the
way people behave. Therefore, technology may help you to govern the
behaviors 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 the
definition, production and usage of data. Formalizing people’s behavior, not
data. Data only behaves the way people behave. Therefore, technology may help
you 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 Data
Governance to be a process-driven discipline. That is partially true. Getting
people 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 that
approach. 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 a
Data 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. Some
organizations focus on a combination of the two or three, specific domains
within specific business units using specific applications. There is no single
correct way to evolve into your company. But I can almost assure you that if you
treat it as a revolution, there will be a revolt.

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Where Should We Expect to See Benefit?
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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?

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6. What data do you use most often to achieve your objectives and describe what it
takes to get and use that data?
7. What is the primary source used by your area to obtain the data? Who do go to
get 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 or
measured?

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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.) of
the 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 these
solutions – Scoped, Funded, Planned, In-Flight, etc?
15. What data / information is on your “wish list” that would better enable your
area to deliver on our Core Competencies?

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I cannot …
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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 …
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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:
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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

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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 for
other raw materials
Compare data across regions, plants, … for planning,
development and delivery purposes
Identify where to build new plants to address customer
needs
Clearly define global data owners or those people with
Global responsibilities
Provide solid cost comparisons across the Company, by
plant or by region
Define, measure and report On-Time Delivery (OTD)
Provide consistency in how to measure and what to
measure 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 that
will enable the company to:
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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

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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:
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Don’t let perfection get in the way of progress.
Point of diminishing returns – to determine what data projects
should be completed.
Concerned of resources – people and time
Stay away from “data perfection” – address specific
inconsistencies that provide most value
Secondary – focus on “political” value of consistency in
product, category, customer definition
There are pockets of experts but no formal information about
who decision makers are
Need to improve overall maturity level around data and
information
Record and communicate how progress is being made in data
maturity
No single person has the ability to define the standards.
Estimate the time requirements of people participating in
Governance processes
Finance’s inability to resolve issues centrally prolong issue
resolution process
Need manner to leverage customer experience – what do
”they” want
Recent problems with data availability and time necessary to
translate the data for analysis

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“Everything we do” around costing and benchmarks is
difficult and time consuming, Very painful.
Gain agreement on priorities and let specialists gather the
information 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 will
deliver consistency in a few years time due to product
turnover and the natural progressions.
Global has adopted the ISO standard but not the U.S. as
only 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 what
you want” approach to data.
Huge disconnect to the tune of more that $1 million
dollars spent to ineffective communications associated
with product changes.
Lack of understanding of costs leads to problems
w/product mgmt – introduction, delivery, retention

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Wrap Up
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Webinar Summary
– Define Data Governance Expectations
– Considerations for Setting Expectations

– Messages for Management
– Getting Them to Tell Us
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Questions & Answers

38
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Coming Up
•

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

39
2013

#RWDG @RSeiner

Contact Information
•

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)
rseiner@kikconsulting.com
rseiner@tdan.com

@RSeiner
#RWDG

40
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Real-World Data Governance: Data Governance Expectations

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