The Comprehensive Child Welfare Information System (CCWIS) Final Rule represents the evolution of the earlier Statewide Automated Child Welfare Information System (SACWIS). It also represents a bit of a sea change. Moving the focus from monolithic automated information systems to shared data enables myriad ways for Title IV-E agencies to meet their automation needs, and at the same time, provide important data to the Child Welfare community.  Focusing on data helps us focus on evidence-based practices that provide better results for kids – something we are all concerned with. Healthy and happy children have the best chance of growing into healthy, happy, and well-functioning adults. Our well-being as a society depends on this progression.

Sharing data works best when that data is timely, complete and accurate.  Data quality is at the heart of evidence-based decision making, which means it is also key to promoting better outcomes for kids.  In this article, we dive a bit deeper into what data quality is, how to improve your existing data quality, how to fund your efforts in this regard, and how you maintain high quality data. We’ll focus on the CCWIS Final Rule requirements, but you’ll likely see where these activities can benefit your entire enterprise.

An Organizational Investment

Any discussion of data quality is worth a short discussion on organizational culture.  We’ve all heard the old adage, “Garbage in, garbage out,” and that phrase actually references the idea that poor quality input will lead to poor quality output.  Making sure you have a culture of quality inputs – complete, timely and accurate data – will help ensure you have quality outputs.  This means that everyone who generates data must understand and commit to the importance of complete, timely, and accurate data (more on this later).

High quality data is a deliberate and significant undertaking for any organization.  Roles and responsibilities throughout the lifecycle of the data need to be clear and enforced – all the way up and down the chain. And data quality must be continuously monitored to ensure its integrity.  All of this may seem like a daunting challenge (it is), but there are good tools available to help get your data quality where you want it to be.

Data Quality Plans

The CCWIS Final Rule requires CCWIS States and Tribes to provide a Data Quality Plan (DQ Plan), and that is a logical place to start.  A plan for data quality sets expectations for everyone who will generate, collect, maintain, and use child welfare data.  The DQ Plan is also a useful teaching tool to help everyone understand the importance of accurate data for decision making and to establish a shared sense of value for high quality data. The community of data generators and users must understand that missing data points or late inputs or incorrect values will lead to incorrect decisions that can have life and death consequences.

Let’s take a closer look at the DQ Plan components:

Timeliness – A backlog of data that has not made its way into the system helps no one.  New, modular technology enables mobile solutions for data entry to help get data into the system faster and easier – the result is not just quicker data entry, but more time to spend with clients.

Completeness – Often, data fields are left empty for myriad reasons.  New technology tools make completion of data records easier for the case worker with tags, flags, and reminders on the simple end of the scale to draw attention to incomplete data.  On the more sophisticated side, augmented/assisted/artificial intelligence tools can supply values for some of the data elements, freeing a case worker’s time for the more “unstructured” data that describe complex relationships and complete the record.

Accuracy – Accurate data is pretty obviously correct data. To be correct, a stored data value must be the right value, must be consistent, and unambiguous.  But accurate data is more than data values that are correct – accurate data creates opportunities for service that would otherwise not be seen.  Accurate data helps paint an accurate picture of a child’s situation.

Monitoring – A DQ Plan must include an approach for monitoring data quality.  And you already know why: “what gets measured (monitored), gets managed.”  Data quality reporting is an opportunity for improvement.

Incentives/Enforcement – This will be especially important with data partners outside the agency. The good news is that strengthening knowledge about the benefits of data quality helps strengthen the relationships that feed data into the system.  Distributing the work of compliance is a key tool in this regard.

Your DQ plan is a part of your CCWIS investment, and can be paid for by federal matching funds.  Establishing your data management infrastructure should be a part of that too.  Establishing and maintaining data quality is not a “one and done” effort – you must ensure data quality on a continuous basis.  To do so, you’ll need policies and procedures and infrastructure – be sure to make these key elements part of your CCWIS project investment.

Culture of Data Quality

In some cases, you may have to overcome a culture of not reporting data because the quality is poor. It is not uncommon to find examples of agencies ignoring data quality until a report is requested. Often, this is due to a general failure to understand the importance of data quality by the end users and generators. Agencies that experience this kind of data dysphoria must understand that they need a culture of better data to drive better outcomes.

Building a culture of data quality cannot be done in a vacuum, and the data quality culture should ideally echo throughout the community of data generators and data users.  A DQ Plan must be supported by strategies that will make the plan successful throughout the community. The strategies should be earnest and thorough – including processes and resources needed for the community to provide quality data. Commitment from all parts of the community to work toward data quality will help reinforce the importance of quality data to the community.

The distribution of roles and responsibilities for data quality span the entire community, and require ownership by all. End users of a new system that collects and reports data are the ones who interact with clients, and are critical for collecting accurate information.  But they are not alone in the data quality puzzle - the roles of the providers in ensuring quality data must be defined and monitored as well.

In spite of the community-wide nature of data quality, a single DQ Plan is not necessarily the answer.  The organizations within the community can (and should) have their own DQ Plans that can reflect different levels of quality focus, but must adhere to a baseline minimum standard for quality.  The strategies for various agencies and organizations work together to strengthen the larger community focus on creating and maintain quality data.