Case Study: State of Arizona Implements Model for State-Wide Data Governance

By   /  November 29, 2018  /  No Comments

Data Governance The Arizona state government wants to improve outcomes for the people they serve. Part of their plan to improve those outcomes involves a robust state-wide Data Governance program to change the culture and elevate the state of Data Management for a hundred Arizona agencies.

Jeff Wolkove – State Data Management Architect for the Arizona Department of Administration, Arizona Strategic Enterprise Technology Office (ADOA-ASET) – and Melanie Mecca –  Director of Data Management Products and Services at Capability Maturity Model Institute (CMMI) – recently shared how Wolkove used CMMI’s Maturity Model to assess and implement Data Governance policies and create a program to train 20,000 Data Stewards throughout the state.

The Challenge

When Wolkove arrived six years ago, the state had no Data Governance policies in place. A desire to provide residents with easier access to needed services resulted in multiple departments and agencies being combined. For example, the Department of Economic Security is now a “one-stop shop” that combined nine different agencies for child support, elderly, and disabled services. These nine agencies had different missions, disparate funding sources with different federal and state funding requirements, and different cultures around data. As a result, the department was unable to provide accurate reporting on the number of clients served.

Key data is shared among agencies. For instance, a Department of Corrections recidivism initiative crosses multiple agencies: the Arizona Health Care Cost Containment System (AHCCCS) manages parolee health care and different areas of the Department of Economic Security provide job programs and other services to people released from prison, but none of these departments defines “person” using the same terminology.

Mecca noted, “Data sharing is a big, big issue in government.” Without a Memorandum of Understanding among agencies, Wolkove said, “It’s pretty much all over the place. Ask for data and you can get an answer in a day – or you can get an answer in 60 days – or never.”

Choosing a Solution

Implementation of any program or policy in Arizona government can be complicated. “Arizona is a very decentralized state. Each agency does their own thing when it comes to IT and management, and our role is to provide general leadership,” Wolkove explained, so he can’t force compliance. Departments are strongly encouraged to follow policies, “but if they have other business reasons not to or if it’s too burdensome, they don’t have to.” Thus, getting buy-in from other agencies is a key piece of the puzzle for implementing state-wide policies.

Wolkove addressed buy-in by encouraging participation in the process of any policy development. Providing iterative commenting periods and sharing revisions shows participants how their feedback was incorporated into the final policy, “So we don’t get much pushback.” Another way to establish credibility with state agencies is to base policies on a standardized framework that is widely accepted in the industry, he said. “And that’s why we chose to use the Data Management Maturity (DMM) model from CMMI “to assess current Data Governance maturity and develop an improvement plan for the state.

CMMI’s Data Maturity Model

CMMI works for the advancement of best practices in people, process, and technology. The Institute provides the tools and support for organizations to benchmark their capabilities and build maturity by comparing their operations to best practices and identifying performance gaps.

The Data Management Maturity model is a process improvement and capability maturity framework for the management of an organization’s data assets and corresponding activities. It contains best practices for establishing, building, sustaining, and optimizing effective data management across the data lifecycle, from creation through delivery, maintenance, and archiving.”

The DMM can be used in a wide variety of settings because it is technology-neutral and adaptable to any industry. While it defines the requirements for effective Data Management and provides resources for companies to build their own customized roadmap for improvement, it is not prescriptive. It focuses on ‘what’ is done rather than ‘how’ it is accomplished, allowing for many variations on how organizations reach capability maturity.  Even within the same industry, organizations proceed according to their specific business priorities, leading to differing emphasis on Data Management topics.

Implementation

In 2016, Wolkove started a conference series, and that year consultants from Data Blueprint took participants through the Data Management Body of Knowledge (DMBOK) over a two-day period. They also arranged for morning executive briefings and asked the Governor’s office to request that state directors and senior leadership attend. “That was the first time Data Management ever came on the radar for state leadership, and that was very positive,” he said. He split the team into workgroups that met separately to start addressing top level initiatives:

  • Policy Workgroup: Policy development and approval
  • HR Workgroup: Creating job descriptions for Data Management positions
  • RFP Development: To create state-wide standardized RFPs for Data Management consulting services
  • Vendor Selection: To select vendors for major Data Management tools

After the conference, those workgroups grew into a steering committee from 25 of the state’s top agencies. Policies developed by the workgroups were posted and approved by the CIO a few weeks before the 2017 conference, where they were introduced along with a one- to two-year implementation timeline, Wolkove explained. In addition, each agency was required to develop an internal data policy council.

In 2017, Mecca presented a three-day course entitled “Building Enterprise Data Management Capabilities” to 23 steering committee members representing eleven different agencies. The course helped participants understand:

  • What does it mean to an organization to do a great job in my particular area?
  • What obstacles do I face?
  • How can I assure implementation success?

Steering committee members were able to immediately begin implementation of new policies in their own agencies, and one committee member – Lisa Williams, Enterprise Data Management Leader of Water Resources – built a Data Management Strategy for her entire agency based on what she learned from Mecca’s course and presented a case study at the Data Governance and Information Quality Conference.

CMMI then conducted a series of assessments against the DMM with the Department of Corrections, Department of Water Resources, AHCCCS, and Department of Economic Security. Wolkove said that one of the challenges of offering a solution is that some agencies don’t understand that they have a problem, “so the fact that we’ve been able to get four data management maturity assessments done is a success by itself.”

Next Steps

Assessments showed that data sharing policies and procedures across state agencies should be a major focus, as well as the creation of a state-wide Executive Governance Council. In line with the philosophy that everyone who works with data is a Data Steward, Mecca developed a statewide Data Stewardship course.

Wolkove wanted the training to be short – two hours or less – with appropriate vocabulary for stewards from the business side but still useful content for IT staff and materials provided to help speed implementation of ideas from class. More than 20,000 employees will take the course, he said. “Any state employee who has a computer in front of them is going to have to take this training course to learn exactly how to handle the data that they use every day.”

The goals of the training include:

  1. An understanding of the policy defining stewardship and how it can be implemented.
  2. The use of the DMM to measure ongoing progress in Data Management across the state.
  3. Specific skills and techniques for defining data, developing quality rules, partnering with IT to specify data requirements, and Data Working Groups.
  4. Implementation of Robert Seiner’s Non-Invasive Data Governance

Seiner’s philosophy is that you don’t have to hire people to be Data Stewards:

“Because they already exist in the organization by virtue of the fact that they already handle data. You just have to teach them how to do that properly and how to be accountable for it,” Wolkove said.

Wolkove sees opportunities for other government agencies to use this training program, and he has had some interest. “I just spoke to the federal Government Accountability Office (GAO) a recently and they’re very excited about this. They want to know more about it,” he said. The training is suitable for state and federal governments, or for any large organization. “What we’re trying to do is get this on the radar and change culture, and this training program is going to be what changes culture.”

 

Image used under license from Shutterstock.com

About the author

Amber Lee Dennis is a freelance writer, web geek and proprietor of Chicken Little Ink, a company that helps teeny tiny companies make friends with their marketing. She has a BA in English, an MA in Arts Administration and has been getting geeky with computers in some capacity since 1985.

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