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Taking data culture to the next level to serve better

Embedding data culture is the first step to taking your data capabilities to the next level so that agencies serve and help better.

In brief

  • Developing a data culture in human services agencies is the foundation for advancing data and analytics.
  • Using meaningful data and analysis can improve client outcomes.
  • Institute high standards of security and confidentiality and guiding principles to govern how this data can be used ethically. 

Human service agencies collect and maintain vast quantities of data on their clients and the services they provide. There are millions, often billions, of data points within a single state, county or city agency. Yet, when asked, few are satisfied with the value derived in improving client outcomes. Every leader articulates a desire to use data to make informed decisions, and every agency has more reports and dashboards than they will ever need or use, so what is missing? In the past, leaders often had to wade and sift through thick reports consisting of data elements told in every depiction possible. Eventually they could determine if their organization was doing things right or on time, but it was difficult to know if they were truly helping families. 

Create an internal data culture

While the desire is to be data focused and outcomes-driven, the true success to this mantra is to establish a foundational enterprise culture that understands data constructs, frameworks and the organization, utility, and interpretation of good data.

Today, a general assumption is that pursuing more advanced methods, such as artificial intelligence (AI), machine learning and predictive analytics will solve data needs. Intuition says the more advanced the analysis, the more rewards. While this can be beneficial under certain conditions, building a mature data infrastructure and data team to master the fundamentals of data delivery through a purposeful approach substantively enhances operations for human service agencies leading to improved client outcomes.

Building a data culture should focus on building business processes which require the consumption and interpretation of data to inform the optimization of service delivery. It does not initially require AI or advanced analytics and is often successful with a focused portfolio with simple but meaningful metrics that inform the business. Building a focused and specialized team for delivering data and analytics, investing in infrastructure, and establishing processes and support systems that yield high-quality services is tactical and is something from which to build.

Grow a rich data field for outcomes-based decision-making

How does a human services agency develop a practical framework that leads to a data behavioral approach and sound tactical advice?

  • Attract and retain data-oriented talent, starting with leadership who understands the value of data and the culture required for it to live and thrive in the organization.
  • Cultivate a data culture across the agency that requires the incorporation of data into critical business decisions; use evidence to support decisions.
  • Establish an enterprise framework which creates user understanding and interprets data to move agencies to outcome focused reporting — transactional reporting is required; outcome reporting is needed.
  • Develop and implement an end-to-end data and analytics operating model and make it known to the agency.
  • Prioritize data quality through robust data management.
  • Implement data governance to focus on key outcomes and initiatives.
  • Invest in infrastructure that matters.

The use of data in human services is critical to continuous improvement efforts. It enables better decision-making, improves the quality of our work, and optimizes resource allocation. Using the right data, organizations can evaluate performance, identify key areas of concern, implement necessary changes, and track the outcomes of those changes. For example, human service agencies can use data to understand the demographics and needs of populations served more effectively thereby improving program and service delivery. However, using data effectively is much easier said than done. 

When we use data in continous improvement, we can divide data into three groups: operational data, outcomes data and compliance data:

  • Operational data measures activities like how many tasks were completed, how many benefits were distributed and how timely the benefits were administered. 
  • Compliance data verifies adherence to relevant rules, regulations, standards and laws that govern agencies such as HIPPA compliance and quality standards.
  • Outcomes data tracks the impact of our activities such as user or client satisfaction, improved health outcomes or food stability, or reduction in poverty. 

Each measure requires different perspectives and source data; however, outcomes data is the key yet hardest to track. Outcomes measurement requires long-term commitment, continual analysis and a deep understanding of how to use data sources.

Gain external support for next level impact

Once an enterprise data culture and infrastructure are developed, the time comes to really put the power of data to use. By nature, government and social services tend to be reactionary entities that don’t come into play in an individual’s life until that person is in crisis and needs help. When individuals and families come to social services for help, the effective use of data can be a crucial tool for delivering services tailored to their needs. But what if we could draw on data to not only improve the experience of human services intervention but also use predictive modeling and data analytics to identify these needs before they even need services?

We know data can make intervention more effective and by harnessing the power of data, agencies can interact with and empower vulnerable populations in groundbreaking, impactful ways. The next level of data use in social services would involve putting a far wider variety of public information to work. It will enable better informed decisions and predictive work of great benefit to families and their communities. There are hurdles: we need to address legitimate concerns about how governments collect and use an individual’s information and operate under the strict legal framework governing use of that data. But these are opportunities, not barriers. There is great potential to use data to make decisions that address immediate needs and to craft impactful, longer-term intervention strategies on the individual and family level.

Today, agencies share some aggregate data, but what if we could do it more? Think of all the information collected at the beginning of school, such as poverty level and free and reduced lunch. What if we could drill that down to the community level and make decisions that are not just based on schools but neighborhoods within school districts? While that kind of aggregation doesn’t violate the law, agencies nevertheless approach it apprehensively and hesitate to put it into action. Some states are implementing this data sharing, but we can go further.

The data agencies need for this transformative change exists, but the risk-averse culture of government agencies prevents that data from being used in the most effective way possible. If we could pool data from disparate sources and analyze that data holistically, we could provide predictive support. When we discover — through, for example, court records, arrest records and other public information — that an individual family is at or on the verge of a crisis point, we can deploy earlier intervention strategies. Court records show the family is about to be evicted. School records indicate the child’s attendance rate has dropped. With that information in hand, caseworkers know they need to act quickly rather than wait for their monthly check-in and can approach that family with solutions for immediate problems and long-term sustainability strategies.

In 2019, research by Ernst & Young LLP and Imperial College London’s Institute of Global Health Innovation explored the use of data and analytics by health and human services providers. A data-driven UK homelessness prevention program illustrates how predictive modeling can protect vulnerable populations by recognizing warning signs of trouble and providing support before the problem becomes insurmountable. In one example, case workers saw a woman facing eviction was eligible for rental assistance and reached out to her through a text alert in time to help her keep her home.

Despite the potential for data to help government agencies make better informed decisions and mobilize resources to keep families from facing catastrophic losses, the public generally feels uneasy about government collection and use of private individuals’ information. And, of course, legal barriers prevent some data sharing, including strict government regulations concerning the use of children’s school information. While government agencies need to operate within the guidelines of the law, they often go beyond the law, approaching legal data sharing with unnecessary caution. 

Tension exists between the need for robust data use and government workers’ approach to data. The strict regulatory oversight of government agencies creates hesitancy about sharing data, even when it is legal and beneficial to do so. The first step in resolving that tension is to reframe data sharing as an opportunity, both for government services and for the people they serve. Many states have demonstrated that we can create unique data-sharing agreements that allow us to combine publicly available data with some private information. A public information campaign explaining how governments use citizens’ data, the benefits of using that data and the respect given to the right to privacy could help soothe concerns among the public.


The end goal is to serve a family better and, through doing so, to build stronger communities. By helping a family in crisis, we also help the family’s community become stronger. Data culture, governance and modeling has the potential to be a powerful tool that can help agencies not only serve better but help better. Through foundational data tenets, and robust data and analysis tools, we can unlock more seamless delivery of services, serve families better and help build stronger communities.

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