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Data Utilization Critical to Organizational Capacity

In the fields of juvenile justice, child welfare, and behavioral health, we talk all the time about data-driven decision making. Recent advances in technology are making it easier than ever before to have data at our fingertips, and algorithms are enabling us to make future predictions based on historical outcome data.

And yet, most practitioners would agree that few human service agencies effectively use data to inform their decision making. There are common challenges or barriers that prevent agencies from collecting data, analyzing it for consumption, and applying the findings to inform decision making and practice.

As I work with agencies across the country, I have witnessed organizations facing common challenges including lack of time, inability to prioritize, identifying data-proficient staff, and archaic data collection processes. It may be helpful to take a few minutes to look at some of those challenges.

man wearing gray polo shirt beside dry-erase board


Barriers to Data Utilization

Time is one of the most significant barriers to data collection, often due to competing interests. Increasing demands on agencies make it difficult for staff to keep up with day to day priorities, making data collection nearly impossible. The myth “Everything is important” can complicate how staff prioritize their activities, creating the need for greater understanding of how data collection fits into their work environment.

Lack of understanding around data collection priorities can result in scarce human resources being utilized to capture meaningless data. Demands for the collection of multiple types of data creates confusion about what type of data will provide the greatest benefit for the organization.

Finding (and keeping) staff with the skill to collect, organize and analyze data is becoming increasingly challenging. These staff have exceptional skills and abilities that uniquely qualify them for special projects that are vital to the organization’s success. But once discovered, these staff become prime candidates for burnout because managers frequently overtask them with too many projects.

Archaic data collection processes can cripple efforts to collect meaningful data. Data collection processes that require excessive manual entry can bog down staff. The use of multiple programs and spreadsheets requires double the effort to make the data meaningful to decision makers. The vast amount of data that agencies are required to collect warrants platforms and technology that make it impossible for staff to keep up.

Fortunately, even a small amount of resources directed at data collection and utilization can demonstrate significant returns in organizational capacity. There are several ways that organizations of any size can improve their data readiness competency:

Define priorities to ensure staff resources are directed more efficiently and effectively toward data collection and analysis. It is not enough for leaders to define priorities through strategic planning. They must ensure that staff have the resources that are necessary to operationalize them. Leaders create engagement with staff by explaining how data-driven decision making, and the accompanying efforts around that objective, fit into the larger mission of the organization.

Recruit (and retain) staff who have the skill set to make sense of what is essential for an agency to make data-driven decisions.

Utilize artificial intelligence (AI) to predict outcomes. AI offers reliable and accurate insights into current and future behavior, helping organizations to make critical clinical and business decisions accurately and effectively. FirstMatch™, for example, relies on artificial intelligence to assist decision makers in the program matching and selection process for youth in the juvenile justice, child welfare, and behavioral health systems. This structured decision making tool compares the predictive factors of the referral to the historical outcomes achieved by unique programs for clients that present with the same or similar predictive factors. A trained algorithm is utilized to determine which program has the highest likelihood of producing the desired outcomes from this specific youth.

Ultimately, organizations that want to stay ahead of the curve are prudent to collect meaningful data and acquire the platforms necessary to process and analyze their data in an effort to inform decision-making.

How can your organization improve the use of data to fulfill its mission and meet the needs of its stakeholders?


Shawn Peck