Real-World Data Governance: Setting Appropriate Business 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

Twitter About This Webinar at #RWDG

Robert S. Seiner KIK Consulting & Educational Services – KIKconsulting.com

The Data Administration Newsletter – TDAN.com

Real-World Data Governance Setting Appropriate Business Expectations

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

June 21, 2012 – 2:00 p.m. EST

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• Real-World Data Governance – Monthly Webinar Series

– July 19, 2012 – Meta-Data & Data Governance – 2pm EST

– August 16, 2012 – Master Data Management & Data Governance – 2pm EST

– September 20, 2012 – Business Glossaries & Data Governance – 2pm EST

– Register On-Line at Dataversity.Net

Setting Appropriate Business Expectations Upcoming Webinars

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• Organizations define Data Governance program success in different ways.

• For one organization success will be determined by the number of data quality issues resolved or the improvements in quality of data.

• For another, success will be measured in the ability of the organization to comply with rules and regulations, whether those rules are defined internally or externally.

• For even another, success will be determined by the ability to provide analytics that drive business decisions, or by the ability to manage data development and integration projects smoothly.

• This list can go on and on.

Setting Appropriate Business Expectations Abstract

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• Before an organization can claim Data Governance success, it is necessary for the Data Governance Team (those people responsible for the program) to level set with the business participants and sponsors and define effective expectations.

• These expectations should be expanded beyond traditional business value to include what “governing data” means to the organization and the effort that will be required to achieve that business value. Setting and communicating business expectations, and delivering on those expectations, will provide an avenue to deliver success in governing data.

• Join Bob Seiner and DATAVERSITY for the sixth in a series of Data Governance webinars titled “Real World Data Governance” where Bob will discuss how to level-set business expectations in the real-world with the approach and capabilities of your Data Governance program.

Setting Appropriate Business Expectations Abstract

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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]

Setting Appropriate Business Expectations Considerations for Setting Expectations

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• Articulate Expectations in Terms of:

– Existing Levels of Data Governance That Can Be Directly Applied

– Staying Non-Invasive in Your Approach to Governing Data

– The Technical Complexities of Governing Data

– The Process Complexities of Governing Data

– The Ownership Complexities of Governing Data


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• Top Avoidable Mistakes (in no specific order) :

– Lack of Executive & Strategic Level Understanding & Support

– Lack of Program Accountability & Responsibility

– Lack of Definition of Best Practices as a Target Behavior

– Lack of Critical Analysis of Present Environment

– Inability to Select & Follow Suitable Approach

– Lack of Planning and Ability to Support Meaningful Initiatives

– Framework of Roles & Responsibilities

– Making Data Governance & Data Quality Optional

– Lack of Focus on Communications

– Improper Expectations of the Data Quality & Data Governance Solution

– Failure to Stay Iterative and Incremental in the Approach


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• In What Areas Should We Set Expectations:

– Data Governance Basics

• Definition of Data

• Production of Data

• Usage of Data

– Risk Management

• Compliance & Regulatory Control

• Data Classification

• Security

– Disciplines

• Issue Resolution

• Quality Control & Improvement

• Proactive & Reactive Governing of Data


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• In What Areas Should We Set Expectations:

– Roles & Responsibilities

• Executive & Strategic

• Tactical

• Support (DGT & IT)

– Communications Planning

• Audience

• Messages

• Delivery Tools

– Governance

• Process / Technology Governance

• Stewardship / Ownership

• Execution & Enforcement

• Monitoring & Reporting


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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?



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• Concepts of Non-Invasive Data Governance™ – Overview

– Data Governance Programs are often perceived as being “invasive” and “over-and-above” the existing work culture of an organization.

– Follow the necessary steps to eliminate that perception.

– Take a “practical” and “pragmatic” approach to implementing Data Governance and Data Stewardship that follows several key concepts.

– Use the key concepts as a starting point and basis for the “selling” and “understanding” of your Data Governance and Data Stewardship programs.


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• Concepts of Non-Invasive Data Governance™

– Do Not give employees new job titles and recognize that the majority of their present work will not change.

– Data Stewards are Not hired.

Data Stewards Are identified & engaged according to their present responsibilities.

– A Data Steward is Not a position.

Becoming a Data Steward Is an accountability for the management of a subset of enterprise data.

– The Data Stewardship process will Not create new tasks or time commitments but instead will ensure that the appropriate Data Stewards are identified, engaged in each data related task.


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• Concepts of Non-Invasive Data Governance™

– Data Stewards Will Not be given additional work over and above their present responsibilities.

– Present responsibilities related to data management Will be formalized & recorded to ensure consistent and complete involvement of the appropriate data stewards.

– The time commitment of each Data Steward will depend on the number of projects and data issues which impact their area of expertise.

– Data Stewards will be provided the knowledge, tools, forums and processes to become more effective and more efficient data managers.


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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.

Setting Appropriate Business Expectations Messages for Management

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

1. What data / information do you use most often to achieve your objectives?

2. What is the primary source / system used by your area to obtain the data / information? Who do go to get information about this source / system / data / information?

3. What processes consume the data / information?

4. Do these processes have data / information challenges? If so, what are they?

5. What is the product or customer impact? How has this been tracked or measured?

Setting Appropriate Business Expectations Where Should We Expect to See Benefit?

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

6. Is there an associated risk – brand, compliance, legal, etc?

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

8. What other impacts should be noted?

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

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


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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 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: – 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 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

– “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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• Webinar Summary

– Define Setting Appropriate Business Expectations

– Considerations for Setting Expectations

– Messages for Management

– Expectations of Value Add

• Questions & Answers

Real-World Data Governance Wrap Up

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• Real-World Data Governance – Monthly Webinar Series

– July 19, 2012 – Meta-Data & Data Governance – 2pm EST

– August 16, 2012 – Master Data Management & Data Governance – 2pm EST

– September 20, 2012 – Business Glossaries & Data Governance – 2pm EST

– Register On-Line at Dataversity.Net

Setting Appropriate Business Expectations Coming 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]

Real-World Data Governance Contact Information

Real-World Data Governance: Setting Appropriate Business Expectations

Technology

metadata data governance

quality of data

noninvasive data governance

data governance success

control data

behavior of data

affairs of data

data development