News & Perspectives

CCWIS Data Management

16. August 2018 Shell Culp Human Services

The final rule for the Comprehensive Child Welfare Information Systems (CCWIS) is more focused on the data rather than the system itself.  This is an important change from the previous Statewide Automated Child Welfare Information System (SACWIS) focus, and one that will have significant impact to states and tribes.  While there are still elements of the rule that affect systems design, the new focus of the CCWIS is bi-directional data exchanges with courts, education, and Medicaid claims processing systems. 

Exchanging data with external partners isn’t really new – what’s new about it is the “bi-directional” nature of the exchange.  Agencies aren’t just receiving data from a source or sending data to another entity.  The “bi-directional” nature of the exchange means that agencies are both sending and receiving data from at least three other external partners. To make this work, agencies should expect to have “negotiations” regarding what data each entity needs to provide better outcomes for kids in care. 

The nature of the dialog between agencies was covered in another article <link>.  The topic on the table now is how to manage the data within and between agencies.  There is a time-sensitive opportunity for Child Welfare agencies to establish Data Management infrastructure with their CCWIS projects.

Data is an asset for the organization.

Data is widely viewed as one of the most important assets organizations possess.  What value might you ascribe to your data?

  • Does it save or improve a life?
  • Does it help to understand clients?
  • Can it be used to make more effective decisions?

As an asset, we would want to manage data – just as we manage buildings or vehicles or people.  Data management is the program that helps us manage our data assets.

What is data management?

Data exchanges are enigmatic – they seem simple, but there is lot going on in the background. At some point data exchange partners must agree on how data is defined, labeled, and passed from one partner to another.  Does it get changed along the way? Who gets to change it?  When does it get to change? Who is in charge of data integrity? How is integrity ensured?  These are the kinds of the questions that Data Management answers.

It is important to note that no organization, whether in the public sector or private industry, has perfect data.  Because of this the Administration for Children and Families (ACF) recognizes that “Intentional and rigorous data management practices are essential for data gathered … as well as for external data accessed by (others)…” in human services programs. As we begin to use and share more data from multiple agencies and organizations, data management and data governance must consider issues that multiple data sharing partners may bring.

Data management is ideally a part of the business program and includes components of strategy, quality, governance, and operations that are shared with the information technology group. The components listed here are high-level functions that include interrelated processes and practices, but the categorical level is sufficient for this discussion. Data management is a program that helps your organization manage one of its most valuable assets – its data.

How do organizations allow poor quality data? 

Certainly, poor quality data is not a goal of any organization, but not understanding the effects of poor quality data on the organization and mission success is a big contributor to poor quality data.  Bad planning, or no planning at all, is another hurdle – if you don’t know what you need, you won’t know if you have it. And “siloed” systems play a big role in data quality for an enterprise – it is not uncommon for each system to have its own data definitions that don’t match anything else, making it difficult to establish the “source of truth.”  Organizations often permit incomplete documentation, and lack of standards and/or governance will also lead to incomplete and/or inaccurate data (or worse – no data).  But perhaps most importantly, the failure to see data as an asset – lack of organizational commitment to data quality – is the biggest contributor to poor data quality.

The CCWIS rule requires “intentional and rigorous” data management practices involve establishing plans, policies, and practices that help manage data quality from the point of input to the point of archive. Some of these may be in place already, but it is likely for most states and tribes that data management practices need to be addressed. 

It is no secret that for many agencies, the current quality of child welfare data is a challenge.  A data quality plan (DQ Plan) will provide a good roadmap for your agency to focus on maintaining high quality data.  Your plans should include a discussion of how your agency will be intentional and rigorous in ensuring the quality of the data.  Specifically, you should include a description of how you’ll ensure timeliness of the data, how you’ll achieve completeness and accuracy, how you’ll measure and monitor these dimensions, and what steps your agency will take to encourage compliance by Child Welfare Contributing Agencies with data quality processes and goals.

Data governance with external data exchange partners.

The bi-directional data exchange requirements suggest that there will be cross-sector challenges for agencies to address with their external data exchange partners.  A network of stakeholder agencies will be helpful in starting and sustaining the dialog around data and its governance vis-à-vis CCWIS operations.  You should expect that without a standard for exchange, you won’t be speaking the same data language. It would be a mistake to just match up the technical experts from your agency with similar staff from the other agencies. Why? Because you’ll miss opportunities to develop relationships, both vertical and horizontal, that will help sustain the effort over time as staff turnover occurs. You’ll also miss opportunities to consolidate efforts and data for improved data quality and outcomes for the clients. 

Start by defining stakeholders that need to be part of the discussion. Coordinate the larger goals and establish common priorities and objectives across sectors before you get to discussions about data exchange standards. This will help you develop a charter that will guide the effort. With these basic steps, the team(s) can sail through the other tasks associated with bi-directional data exchanges.

Who should be involved with data management?

A common misconception is that data management belongs in the Information Technology group within an organization.  While this is where data management activities often get started, everyone should be concerned with data quality and how it is managed:

  • Data creation and usage are the most critical points in the data lifecycle. Front line workers are key to data quality – make sure they understand and are actively encouraged to enter data that reflects your data quality goals.
  • It takes planning to manage data – so managers and executives are key to quality as well. You’ll want to ensure that they understand the importance of the data quality goals to effectively manage the data assets. 
  • Managing the rules of how data is acquired, used, and stored is important, especially between data exchange partners, and contributes to data quality. These are key points to cover with your steering committee discussions.
  • Managing the securing of data is important to comply with data sharing agreements, rules, and regulations.

The good news is that there are familiar tools to help manage data quality.  Start with your people, and make sure they have a good understanding of how data is created, and how quality data is defined. Co-create that understanding and add some goals and principles to help embed these notions in a culture of data quality.  Clearly defined roles and responsibilities will help ensure processes for quality assurance are developed and followed.  Make sure you take advantage of this opportunity to do more than just “check the boxes.” Take decisive action toward improving outcomes through better quality data.


About the Author

Shell Culp is a Senior Advisor for Public Consulting Group and former Agency Information Officer for California Health and Human Services Agency.  Shell is also a Senior Fellow at the Center for Digital Government.