Anne Marie Smith is one of the world’s leading experts on implementing Data Governance and Data Quality initiatives.
In this interview she provides answers to a range of challenges that include:
- What to expect in the early stages of data governance?
- How to create a data governance framework?
- Which soft skills are critical for data governance?
- Why is a business glossary so important to data governance?
- What are the common business glossary issues people will face?
- How can smaller organizations implement data governance?
Dylan Jones : For a business leader sponsoring a new data governance initiative what should they expect to see being delivered in those early stages?
Anne Marie Smith : For each initiative this will be different, based on what the data governance program is designed to accomplish. Many data governance programs are launched as part of an enterprise information (or data) management initiative, so these DG programs may have several goals to accomplish.
Leaders should expect to be involved in the planning at the strategic levels for a data governance program, since data governance must be an enterprise effort if it is to be successful.
Delivery at the strategic level would include (but would not be limited to) a readiness assessment for launching data governance, perhaps as part of an enterprise data/information management assessment where the current state of the management of data and information as assets is examined objectively and recommendations for improvement are made.
From that recommendation, an approach to the development of a data governance program for that organization can be started.
That would include a vision and a mission for the enterprise data governance program created by business leaders and the data governance consultant/expert, the development of a framework for the organization’s approach to implementation, selection of the first critical subject areas for treatment (chosen from the assessment), and identification of the initial teams that will be trained for the data governance activities.
Finding the right industry standard training for these candidate staff is a major challenge since many organizations do not give sufficient attention to training these days. DAMA International is a great source of education for those who are interested in any area of data management, including data governance, and DAMA has special interest groups for business people so they will feel comfortable as part of the organization.
Dylan Jones : Creating the initial data governance framework is something a lot of organizations struggle with. Do you have any advice to offer for developing the right framework?
Anne Marie Smith : Choosing the right framework is a matter of great debate!
There are industry standard frameworks such as the DAMA-DMBOK enterprise data management framework, the data management maturity model (DM3) framework, MIKE (information management open source). There are vendor-specific frameworks from consulting companies such as EWSolutions (EIM Methodology), Informatica, IBM, Oracle, SAS, etc.
Each framework/methodology starts as a template that must be customized for the individual organization, and this customization requires a fair amount of skill from experienced people in data management and data governance to ensure that the right aspects are retained for the specific organization, with the organization’s targeted needs highlighted to the proper level.
Some organizations think that since there are many ways to do data governance that “any way is right” – and that is not true at all! There are many ways to err in developing a data governance program, so using one of the established frameworks will help an an organization avoid many of the common errors.
I would recommend starting with one of the frameworks using an expert to customize it for the organization, knowing that using one of the hardware/software vendors’ offerings may require that the organization use their products and services as part of the price for the use of the framework.
Dylan Jones : I meet a lot of data governance practitioners and many view data governance primarily as a cultural and behavioural change discipline. This will no doubt confuse many executive sponsors who may perceive it as data-centric.
What skills do you find yourself drawing on when carrying out data governance consulting engagements and why are they so important?
Anne Marie Smith : I agree that some of the main skills I use in my work involve interpersonal communication, change management, behavioral and organizational aspects, as well as the data management capabilities. I have had to learn how to be a facilitator and change management consultant in addition to being an enterprise data management and data governance expert, which has been an interesting journey.
Data governance is a discipline that requires change to behaviors concerning the management of data and metadata, changing established but not effective practices, policies, procedures. Data governance also requires that the practitioners have a deep background in many areas of enterprise data management (general data management/administration, metadata management, data modeling, master data management, data quality, etc.) So, it is a data-centric discipline, but not exclusively.
Since everyone resists change, even change that improves a situation, I have had to learn how to encourage clients to consider the adoption of new approaches to managing data as an asset, to consider thinking about managing data through enterprise-wide processes rather than by individual decisions, and to adopt consistent standards for defining and naming data elements to enable reuse.
All of these positive changes can assist any organization in their quest to reduce costs through reducing data redundancy, enabling faster and better decisions based on accurate data, improving the ability of the organization to understand the rules that it uses to manage its operations thereby providing more access to information across the organization. However, many people in every organization resist any change, so leading these people to adopting the changes that are needed can be a long and challenging effort. The organization has to be ready for data governance and these changes; I cannot force them to accept these changes, no matter how beneficial the changes may be.
The human factors of data governance are so important for any data governance professional to learn and master, since they contribute to the overall atmosphere of the program (first) and to the organization (eventually) if the program is to be successful.
Dylan Jones : The concept of a business glossary crops up regularly in data governance circles but I know this confuses many of our readers. Can you describe why a business glossary is important to data governance and how organizations can go about creating one?
Anne Marie Smith : Having and sustaining a business glossary is extremely important to every organization, regardless of its size, since it is the record of the organization’s terms and their definitions. In one place, anyone can locate a term and its accepted meaning.
Having this glossary, with all the terms defined and those definitions accepted throughout the organization means there will be an absence of miscommunication among the organization’s members. No one will be misunderstood, especially at crucial points. No one in the organization will wonder what John meant when he used the term “customer”. Did he really mean “client”? Is there a difference? If this organization uses both terms, each will have been defined clearly in the business glossary, and everyone will know that Company X is a “customer” according to the definition, while Organization Y is a “client” according to the definition.
Dylan Jones : Looking back at some of your most recent projects where business glossaries were implemented, what were some of the biggest problems you encountered and how were they overcome?
Anne Marie Smith : Some of the largest problems with implementing business glossaries include the devotion people have to certain terms and definitions, and having them accept the need to agree to use a common term with a standard definition.
For people who have worked at the organization for a long time, certain terms and their meanings may have special meaning or value, so changing to using a new term or adopting a new definition for that term may be a major shift. Showing them how much their communication will improve, and how easy data integration will become once they have adopted a comprehensive business glossary helps to reduce their resistance to starting and maintaining the effort.
Another challenge is the technical issue to implementing a business glossary. Collecting all the sources of terms and definitions, scheduling the meetings to develop the common terms/definitions, building the glossary and sustaining its enhancement, training people to refer to it and use the correct term/definition – all are important points that should not be overlooked when planning to develop a successful business glossary.
It is not a small or simple project, but it can be a very successful one. Securing the services of experienced business glossary (metadata management and data governance) people, along with choosing the right technologies are also important aspects of a successful business glossary initiative.
“Right technologies” are choices that depend on many factors, including the organization’s platform, architecture, size, glossary usage plans, etc…so I cannot answer the question “which one would you recommend?”
Dylan Jones : A lot of our members are looking to take their data quality success to a wider audience in the organization. Why is data governance so important in achieving this?
Anne Marie Smith : Many organizations embark on a data governance program due to poor data quality. In my humble opinion, it is impossible to correct any data quality issue without a solid data governance program. If an organization attempts to correct data quality issues without having instituted a data governance program first, the organization will be correcting the same data quality issues over and over again.
Data governance establishes the practices, policies, procedures and standards for managing data as an organizational asset. One major result of improved data governance is the improvement of the quality of the data and metadata. Every organization that wishes to improve the quality of its data must design, implement and sustain a formal and enterprise data governance program that meets industry standards and adheres to proven practices, and that is aligned with a data quality management program that is also designed according to industry standards and with proven practices.
Dylan Jones : Many of our members in mid-tier organizations feel that the concept of data governance with its enterprise focus and governance councils is simply out of reach given their restricted size and funding.
Do you have any examples of a data governance success story that wasn’t a large corporation?
Anne Marie Smith : Yes, absolutely.
Data governance works at organizations of every size, every industry (including profit, non-profit, governments, etc…) I have developed several data governance programs for small organizations (fewer than 50 people) with the same structures as the larger organizations (just fewer layers) and these programs have been just as effective as the larger organizations’ programs.
In some cases, they have been more effective since the decision makers were more accessible than in the larger organizations, and their familiarity with the data and processes and business rules was more current.
Smaller organizations need data governance for the same reasons that larger organizations do: to establish the practices, policies, procedures and standards that enable the organization to manage data as an organizational asset.
The size of the organization or its industry or its profit sector does not matter – every organization manages data and every organization should view data as a strategic, organizational asset.
Dylan Jones : Can you give one example of a mid-tier project that went well and the type of results they obtained from implementing data governance so that our members can relate to the business benefits?
Anne Marie Smith : Every organization can realize many benefits from implementing a data governance program. However, each organization will see different benefits based on the goals they established at the start of their assessment and strategy, and based on how fast they proceed. Here are a list of common benefits that seem to pertain to most programs, regardless of size of organization, regardless of industry:
Reduction in data redundancy, since entities and attributes have been defined for the organization and are used consistently by all those who need them (not created similar attributes or entities for each project, making redundant data)
Reduction in confusion due to data redundancy, leading to better understanding of the data an organization has for use at every level (operations, decision support, analytics, etc.) This allows for faster decision making based on facts and data that can be supported rather than “I think this is the right answer or this is the right direction to go”.
Improved results for the organization based on better decisions made with well-defined data, leading to better financial position for the organization
Better organization concerning the management of data across the organization through the development of a data governance organization, regardless of the size of the organization.
Improved processes for the management of data as an asset (data definitions, data quality management, data valuation, etc.) so the organization has a clear understanding of data’s value to the organization’s asset base
Improved approaches to managing the quality of data through the development of data quality practices as a permanent team, regardless of the organization’s size
These benefits have been seen in small organizations as well as large organizations. Some of my clients that have seen these benefits were companies of fewer than 50 people, a few as small as 20, in various industries.
Dylan Jones : It’s great to hear such positive experiences, particularly for the smaller organizations. Thank you for sharing your insights today Anne Marie.
Anne Marie Smith : My pleasure Dylan.
About Anne Marie Smith
Anne Marie Smith, Enterprise Data Governance and Information Management Professional
Anne Marie Smith, Ph.D. is Principal Consultant with over 20 years experience in enterprise information management. She is a certified data management professional (CDMP) and is a frequent speaker and author on data management topics.
Anne Marie has consulted in areas such as: enterprise information management assessment and program development, data governance, data warehousing, business requirements gathering and analysis, metadata management, information systems planning and EIM project management. She has taught numerous workshops and courses in her areas of expertise.
Anne Marie holds the degrees Bachelor of Arts and a Master’s of Business Administration in Management Information Systems from La Salle University; she earned a Ph.D. in MIS at Northcentral University. She serves as an affiliated faculty member of the Information Quality program at the University of Arkansas-Little Rock and lectures on enterprise information management and data governance with University College Cork through the Irish Management Institute.