The 2018 Data Governance Winter Conference, hosted by DebTech International and DATAVERSITY, took place from December 3 to December 7, 2018 in sunny Delray Beach, Florida. The conference offered tutorials, panels, presentations, and a vendor showcase.
In addition, it offered various networking opportunities with fellow attendees and data professionals. Although I am a seasoned data professional, the DGIQ conference enriched my knowledge of data governance and my appreciation of the many different challenges associated with making an Enterprise data governance program successful.
The conference was held in a beautiful setting— the Delray Beach Marriott hotel, right on the Atlantic Ocean. The weather was bright, warm and sunny every day. Coming from Long Island, it was great to get away from the cold weather.
This conference addressed all levels of experience, from the novices through seasoned veterans, while dealing with the day to day problems of an experienced organization. Even if you have attended this conference in the past, I would recommend attending it in the future. It is a good way to learn about new solutions and emerging technologies in the data arena. This is the best place to learn about all things data governance and data quality.
The question that is asked most often is, “How can you justify the cost of coming to a week-long conference?” If your organization has started to establish Data Governance, the best way to be prepared to optimize everything is by sending 1-2 key stakeholders (IT and Business) to this conference. The attendees cover every industry and are in various stages of development (Initial, Halfway there and optimization). The subject matter and learning experience mirrors every stage of the Maturity model (1,2,3,4,5). You can also network with others like yourself or get some private time with some of the experts who will be there. The conference was the largest winter conference with attendance of 350 professionals.
What I learned while I was attending the conference was amazing. I have attended this conference over the years and what I noticed was that the number of attendees have increased and they also seem to be much younger. That tells me that the conference isn’t just attracting veteran data professionals, but also younger people who want to learn what data governance really is all about. I particularly liked the way the program was balanced with Tutorials, Keynote speakers, and Panels. The reader can maximize the contents of the conference, I have summarized some of the highlights from the session I attended.
The first day December 3rd started with the Pre-conference Tutorials. There was a number of choices to make regarding which ones to attend. I chose to initially participate in AM2, Kelle O’Neal (First San Francisco Partners) “Data Governance isn’t about Data or Governance” – It’s about People. The Tutorial started with a Data Governance Timeline showing how data governance has changed from a Regulatory Framework to a strategy which is focused on People and Organizational Change Management. The purpose of the tutorial was to demonstrate that Governance was really about organizational culture, how people act and how we can get them to feel comfortable in doing things differently for the better good of the organization and sharing of data. Here are some of the major points covered in the Tutorial:
- Data Governance is about Change, Behavior and Culture (Roles, Process and Policies).
- What are the Obstacles to Change?
- How do we Facilitate Change?
- There is a need for a Change management framework
- Organizational Change Management (OCM)
- How do we Plan and Implement OCM
- How to measure success?
The tutorial began with a historical overview of Data Governance using a timeline. Initially it started due to regulatory and compliance and then handled challenges like Business semantic support, Migration to the Cloud, Data Lakes, Data Wrangling Self-service analytics, and finally Data Privacy. Each one of these made it much harder for people to adapt. Ultimately the major message conveyed was about Organization Change Management (OCM). Kelle reviewed that people can be against change. She also mentioned why we need a policy, process, and roadmap to introduce OCM into the organization. The primary message was Data Governance was about organizational change and how we can facilitate this to have a successful program by making stakeholders aware of why we need this change. In conclusion it was important to understand stakeholders, how to conduct an assessment of their level of support, and how they were part of the key factors of success for the program.
In the afternoon, I attended Tutorial PM3, which was Robert S. Seiner’s “Building and Using Data Governance Maturity Models.” A Data Maturity Model measures an organization’s maturity related to data management components. The purpose of the tutorial was to identify to the audience what a data maturity model was, show some examples, and then demonstrate how components could be used across the board in facilitating a data governance program. Here are the main components discussed during the presentation:
- Value of available industry maturity models – CMMI and DMM are the two major Maturity modeling frameworks available. Time was spent review of the history of maturity models their similarities and differences. Robert suggested to use the best of breed to compliment your data governance program.
- Components of a useful model – Here are some components of a useful Model: Roles (Identify authority), Processes, Communication (communicate change), Metrics (Measure Change), and Tools (How to leverage tools to facilitate success).
- Defining levels required – A maturity model consists of a series of levels. Each level depicts how well the organization in question performs in relationship to the component in question. Here are the five levels covered: 01 (Initial), 02 (Defined), 03 (Managed), 04 (Quantitatively Managed), 05 (Optimized).
- Artifacts that improve maturity – The following artifacts improve maturity: Formalize roles, Formalize Process, Formalize Communication, Formalize Metrics, and Formalize Tools.
- Requirements for advancing levels – Robert reviewed some of the requirements that need to be tracked to advance to a higher level of maturity. This was to periodically monitor and analyze the artifacts that improve maturity and enhance if necessary.
- Steps to prevent Maturity Demise – Robert offered that there were certain steps that could be taken to safeguard an organizations maturity level as well as providing for future advancement.
In conclusion, the tutorial was a fine introduction to maturity models and how the components of a model can be leveraged to create sustainable data governance programs.
Tuesday morning started early with the Data Governance Professionals Organization (DGPO’s) Annual Winter Meeting. Anne Buff, VP Communications, introduced us to the organization, and the framework. Initially the goals of the organization were as described:
- Delegation and Enforcement of Accountability
- Forster Collaboration
- Protect, enable and support our stakeholders
- Allow Flexibility and extensibility
Immediately after that, the framework was discussed consisting of the following:
- Fundamentals – Terminology, Scope/Boundaries, Priorities and definition of success
- Organization – Roles/Responsibilities, Stakeholders, Reporting Structures, Documenting Accountability, Work within culture
- Process– Policies, Process, Stewards enforcement, clearly identify accountability, Document ease for maintenance requirements
- Metrics– Set targets, be specific, do you measure Value?
- Communicate- Bi-Directionally, Multiple channels, provide training, tell stories that matter
- Stewardship – Owner, champion, Accountability, nobody is indispensable
In conclusion the overview was received with much energy and understanding.
The conference officially opened with a number of keynote sessions. The following is a summary of the sessions I personally attended:
The first keynote session in the morning was led by Kelle O’Neal (First San Francisco Partners), who presented “The Future of Data Governance is Now.” The purpose of the session was to provide the attendee insights for the approaches, concerns, and trends that will drive information management strategies in the years ahead including why cloud solutions are truly the new normal; the growing importance of data privacy and ethics; the criticality of metadata and its governance; and how governance’s overall scope must be broader than “Just Data” and be scalable for operationalization.
Kelle started her presentation by describing who data governance has evolved over the last ten years. The chronology started with Regulatory and Compliance business drivers causing organization to realize the need for data governance. Then along came Big Data and Data Lakes, the Cloud and new challenges with machine learning and GDPR (Privacy). With the growth of data making demands on organization along came challenges. Here are some examples of some of these challenges:
- Data Understanding
- Data Privacy
- Data Governance Expansion
- Data Governance in the Cloud
Data Understanding – with the growth of data things like Source (Trust), Data Lineage, Content, and Context (Frequency, Derivation, Business Rules), Cost of understanding (Support and Trust). Stakeholders and data professionals were demanding to better understand all of these things to measure cost and impacts to existing architecture and application portfolios. To support this the following would be needed:
- Create a business-driven metadata strategy
- Define enough data lineage to have an impact
- Make information about data widely available
- Reward people for contributing to information knowledge management
Data Privacy & Ethics – the growth of data will introduce concerns related to privacy and social ethics. These concerns were from Regulatory, Dynamic Public Sentiment. To support this the following is recommended:
- Promote a culture of data privacy
- Leverage metadata to address privacy and ethics
- Create a data ethics framework
- Ensure adaption through organization change
- Be prepared to adapt as privacy and ethics standards evolve
Expansion of Data Governance – expansion of data governance due to data growth. To support this, the following challenges are recommended:
- Organization demand
- Purpose and value
- Its not just about data
Data Governance in the Cloud – expansion of data in the cloud will require that data governance also reside in the cloud. The following are recommended:
- Data in the cloud
- Data governance in the cloud
The afternoon keynote was Data Governance Readiness – The Route to Success presented by Danny Sandwell, Product Marketing Director, erwin, Inc. The session started by explaining about the Industry Assessment that was conducted by erwin. The assessment identified why organizations established their data governance programs (Business drivers). We reviewed our industry research, the State of Data Governance 2018, and the 5 Pillars of DG Readiness gleaned from this report, help attendees evaluate their organization’s readiness for Data Governance based on the success factors identified below. The log-term success of any data governance program is dependent on a number of factors, including but not limited to:
Executive Support– Sponsors are important— without executive sponsorship your initiative will have problems obtaining funding, resources, support, and alignment for implementation. If sponsors are passive or not engaged, figure out resources way to increase their participation.
Organizational Support– Data Governance must be integrated into the company’s current culture. Scope and success factor must be defined to establish parameters and metrics for evaluation.
Data Governance Resources– Data Governance requires continued funding which should come from the enterprise not a particular project. It should be ab active participant in all data governance activities. Understanding the relationship between data governance and data management is very important.
Enterprise Data Management Methodology– Data Governance is the foundational component of enterprise data management (IT, data architecture, data quality and metadata) and critical to its success.
Delivery Capability– Data quality requirements should be based on the enterprise’s business goals. Enterprise data modeling and data analysis are enabling force in the performance of data management and data governance success. Having the capability to manage all data governance operations and coordinate data stewardship activities can have a positive effect by giving governance administrative support for program management.
The next session was Successful Reference Data Governance and Management Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners The purpose of this presentation was to explain what Reference data is, why it’s important and what is needed to establish a program that works. This presentation covered the following components regarding reference data:
- What reference data is, how it differs from other classes of data in its governance and management needs
- The structures needed for successful reference data governance management
- How the semantic needs of reference data can be addressed
- How reference data is produced and distributed
Malcolm mentioned that Reference data – often simply known as codes, lookups, or domains – is an area of enterprise data management that is becoming increasingly important. However, many enterprises have difficulty formulating governance programs and management practices for reference data. One of the reasons for this is because usually at the enterprise level, there can be different versions of a type of reference data. An example of this might be Country codes. There can be many different sources for country codes. Finance might use ISO Country code tables. However, Product management might have another source specifically in the EMEA (European Union) Region which might be different.
The first thing that must be accomplished is to conduct a survey of what types are used and what their sources might be. The next step might be to pick the best to be used at the enterprise level (Master). Now here is the challenge. On Day One you cannot simply turn on the master version you want to be governed and expect everyone to use it. Other organizations might use different versions. You must conduct a mapping (Crosswalk) of the Master version to all of the other versions used across the enterprise. The main reason for this is so that whenever the master version (Governed) changes and is updated, you need to notify all of the data stewards who maintain the other versions, so the mappings are kept current. Overtime this process will go away and you can use the Master version across the enterprise. Most data governance organizations have tools to facilitate this process. Some examples are Collibra, Erwin, Informatica, and many others.
On the following day, the morning started with an unusual session – a meditation / data governance hybrid hosted by Len Silverston, “Zen with Len – What does Zen Have to Do with Data Governance? The Answer is ‘Everything’. Find out why”. It was billed as an enlightening, wonderful session to start your day in a relaxed and receptive state of mind! Len led us through several meditation exercises, while relating them to data governance. A simple example is ‘stop’. Meditation gives you a chance to stop, empty your mind, and relax. In data governance, a good conflict management tool is also to stop and think of what it is you are trying to do evaluation and if necessary decide if it makes sense to move forward with it. Namely, before reacting to a conflict, take time out to think things through. This simple technique can reduce / help you avoid conflicts and escalations, for a more harmonious and successful data governance program! We attendees did indeed start the following session in a relaxed, receptive state of mind. As such, I’ve accepted Len’s challenge to find just one minute every day for simple meditation, and I am continuing to enjoy my moments of Zen!
Immediately after attending Len’s morning Zen session I attended, Implementing a data quality framework to accelerate measuring of data quality by Mark Blanchette (Seacoast Bank). The purpose of this session was to demonstrate that metrics were an important part of any data quality program. During the Bank’s 90+ year’s history, the organization did not have a formal data governance program. With numerous bank acquisitions over the last several years the time was right for Seacoast Bank to gain the benefits that Data Governance and Data Quality would bring to the organization.
With an emphasis on speed to delivery and to implement a scalable and comprehensive solution, the bank developed a Data Quality Framework that laid the foundation for measuring and monitoring data quality. The framework consisted of the following:
- Data Quality Dimensions used to measure (Accuracy, Completeness, Conformity, Consistency, Uniqueness)
- Data Quality Lifecycle (Definition, Assessment, Remediation and metrics monitoring)
- Data Quality Process to support the Lifecycle
- Data Quality Metrics used to monitory program
With the framework in hand, the team rolled out a series of deliverables that increased not only the visibility of data quality issues but helped to ensure a high-level quality of data for use in the banks very successful data analytics program. Along the way, the bank leveraged its innovative use of data virtualization to help logically centralize sources of data.
Afternoon Panel “Lessons from the Trenches – Takeaways from Successful Practitioners”, featuring moderator Len Silverston (Universal Data Models), Yolanda Eyre (CAS), Jim Johnson (Scripps Health), Janice McKaughan (Focus on the Family) and Michael Randall (Nationwide). In this keynote panel discussion session, successful data governance practitioners from diverse industries discussed the challenges and obstacles they overcame during their execution strategies. Here are some of topics that were discussed by the panel:
- Identifying where and how to start your data governance program- most organizations leverage a major event such as a regulatory or audit report to show the need. Also to make sure you have the budget, leverage a major data initiative such as CRM, Product management system to piggy back your program. Most cases this might be a tactical approach bottom up to minimize the financial impact. Later on a parallel strategic effort could be started to leverage what was learned at the tactical level.
- Right sizing your data governance efforts-Don’t overreach. Pick solutions such as the “Low hanging fruit” to add value and communicate success.
- Demonstrating the value of your data governance program and developing the right metrics– most members agreed that you could not overreach to demonstrate examples of the value of data governance. You need to leverage metrics that show value to your leadership. For data governance some examples might be the number of issues that were discussed by data council members and how they were resolved.
- Innovative ideas for communicating and getting the word out– a communication plan is important for determining the way you would communicate with each stakeholder type. For data stewards, it might be through emails and for leadership status reports. It is important to figure out the communication media most used by that stakeholder.
- How to engage more participation from other departments or lines of business– some examples used were publishing a monthly newsletter as well as a monthly “Lunch and Learn” program to be used as a training program and let the rest of the business and IT organization to stay current with the data governance program.
I believe this session was one of the most informative and had the most value to me because participants could clearly understand a particular issue and learn how the expert practitioners resolved the issues.
Sponsors and Vendors
This year the conference not only had a record number of attendees, but also a number of great sponsors. This was reflected in the exhibit hall, which was filled with a broad variety of vendors covering both tools and services.
There was plenty of time to talk with the vendors on Tuesday (first while enjoying ice cream, and later drinks and appetizers) and again on Wednesday (while enjoying dessert). The vendors were generous with their gifts, including several drawings. The conference schedule allotted several half-hour time slots specifically for vendor presentations. These half-hour slots were very helpful to those who were reluctant to talk with vendors directly (i.e., shy), and presented another helpful means to learn more about various vendors and their offerings. There was a list of sponsors in attendance, which included a brief summary which was available on the conference website, if you didn’t get a chance to talk to all of them during the conference.
The strength of this conference is the fact that it offers a various of formats to communicate. There are a wide variety of short presentations as well as the deeper dives offered by the tutorials and seminars. Coupled with the vendor show and the networking, it’s been a valuable learning experience. It is the complete package and I’m very glad I attended the conference. I have another reason for attending. I am the President of the New York City chapter of DAMA and I have reached out to potential vendors and speakers who might be interested in participating in our annual program. It was well worth the time and effort attending the conference.