Office of Data Quality Visualization

Analytics is the practice of using data to drive optimized systemic strategy and performance. In an effort to become an insight-driven organization and to align resources with the increasing demand for data analytics, the Office of Data Quality and Visualization (DQV) was formalized in August 2017. The establishment of this office corresponds with the need for data as a key component in quality monitoring and decision-making throughout the agency.


The mission of DQV is to advance the use of quality data through inspiration, collaboration, and empowerment. The DQV provides assistance to subject matter experts (SMEs), enabling programs to accurately and simply communicate their data.

Who We Are

  • Jodi Kuhn, Director
  • Ariel Unser, Data Reporting Specialist
  • Cari Hennessy, Statistical Methodologist
  • Zac Knitter, Data Analyst
  • Toyin Ola, Data Analytics Specialist

For general inquiries, please e-mail the team directly:

What We Do

The DQV team works with program area specialists within DBHDS on a variety of projects. Examples of the types of services offered include:

  • Analytic Consultation
  • Data Visualizations
  • Data Restructuring
  • Data Reconciliation
  • Methodological Development
  • Statistics
  • Data Cleaning Procedures
  • Survey Development and Analysis
  • Ad-hoc Report Requests
  • Process Mapping for Data Flow

Quality Improvement & Monitoring

The DQV assists programs to develop questions and further plan their analyses. This process has been facilitated by the OneSource Data Warehouse providing unprecedented ease of access to a wide breadth of information about providers and individuals receiving services.

While providing technical assistance to the programs is the majority of the DQV’s responsibilities, there are several data quality procedures inherent in how the DQV functions. These procedures are conducted in an effort to continuously monitor, measure, and improve data quality. In an effort to exercise the versatility of the process and establish models for ongoing quality monitoring for program areas, the DQV regularly applies a process established by Avedis Donabedian to the development of their quality monitoring efforts. General steps in quality monitoring and improvement:

  1. Determining what to monitor
  2. Determining priorities in monitoring
  3. Selecting an assessment approach
  4. Formulating criteria and standards
  5. Obtaining the necessary information
  6. Choosing when and how to monitor
  7. Constructing a monitoring system
  8. Bringing about behavior change