Data and its impact on our daily lives increases exponentially every year — from how we consume news and information, to the way we engage with each other via social media, to how we transact for goods and services online. From sensor readings, machine learning, and the prevalence of AI-powered natural language processing and image generation, the daily volume and variety of data being created and parsed is extraordinary. This upsurge has led to a global recognition of the value of data science skills across all industries.

A recent report by ExcelinEd and the Burning Glass Institute underscores the increasing demand for data science skills in the U.S. job market. According to the report, nearly one in four job postings in 2023 required at least one data science skill, with some states seeing even higher percentages.

Across industries and countries, organizations are realizing the potential of data-driven insights to drive innovation, improve efficiency, and gain a competitive edge. “Historically, employers seeking workers with data science skills were largely operating in a small collection of industries and sectors mostly focused on science, technology, engineering, and math (STEM). Over the past decade, however, that has changed. Today, to varying degrees, data science touches every American industry. Between a quarter and a third of job postings seek workers with different data science skills – sectors far outside the scope of tech.” (Data Science is For Everyone, Burning Glass Institute, February, 2024)

The global demand for data science skills is reflected in the growing number of job openings for data scientists and related roles. According to a report by the World Economic Forum, data and AI-related jobs are among the fastest-growing occupations, with an expected 11.5 million new jobs being created by 2026. This growth is driven not only by the increasing volume and complexity of data but also by the recognition that data science skills are essential for driving innovation and staying competitive in the digital age. From healthcare and finance to manufacturing and retail, the ability to collect, analyze, and interpret data is becoming a critical skill for success in the modern economy.

The demand for data scientists is particularly acute in industries undergoing rapid digital transformation. For example, in the healthcare sector, data science is used to develop personalized treatment plans, predict disease outbreaks, and improve patient outcomes. In the financial industry, data scientists are leveraging machine learning algorithms to detect fraud, assess credit risk, and optimize investment strategies. Even industries once considered far removed from data science, such as agriculture, are using it to optimize crop yields, reduce waste, and improve supply chain efficiency. In construction, data scientists leverage sensors and machine learning to monitor safety conditions, predict maintenance needs, and improve project management.

Specific to data science applications within law enforcement, Benchmark Analytics has been at the forefront — with first-of-its-kind technology that has transformed how agencies manage their personnel and mitigate risk exposure. First Sign® — our data-driven, research-based early intervention system (EIS)—is designed to help agencies use the power of data to identify officers who may be at risk of engaging in misconduct or experiencing mental health issues. By analyzing a wide range of data points, including use-of-force incidents, citizen complaints, and performance evaluations, First Sign can alert supervisors to potential problems early on, allowing for proactive intervention and support.

The benefits of data science in law enforcement extend beyond operational efficiency and risk reduction. By embracing data-driven approaches, agencies can also enhance transparency and accountability, building trust with the communities they serve.

However, integrating data science into law enforcement personnel management requires guardrails for implementational success. Ensuring the privacy and security of sensitive data is of the utmost importance, requiring robust data governance policies and secure infrastructure. Additionally, thorough system training ensures that supervisors have the necessary data literacy skills to effectively leverage these tools and interpret the insights they provide.

As the demand for data science skills continues to grow worldwide, law enforcement agencies that embrace data-driven approaches are better positioned to meet the challenges of the 21st century. When agencies partner with Benchmark, they unlock the full potential of their data to create an accountability and officer support system that boosts morale and increases retention . . . reduces internal agency resource needs . . . and ultimately improves community relations.

The future of law enforcement is data-driven. At Benchmark, we’re committed to helping agencies leverage data science for elevating their overall agency performance. Contact us today to learn more about how our team of data scientists and data-centric solutions is helping agencies make more well-informed decisions about how they manage, support, and elevate their workforce.


EIS PolicyLaw enforcement personnel have a unique position in our society. They are responsible for the safety and security of all those who reside in, work in, and visit their jurisdictions. As such, they have great responsibility to carry out their duties and exercise their authority within the bounds of established policies and procedures, which are an essential component of any law enforcement agency. Policies address pertinent matters, such as what entails acceptable behavior by employees. Procedures within a policy define a sequence of steps to be followed in a consistent manner — for example, the actions that need to be taken for an out-of-policy event.

As a general roadmap, policies ensure:

  • Strategic Alignment: Policies safeguard that law enforcement agencies’ actions are aligned with their mission, goals, and objectives.
  • Integrity: Clearly written policies help law enforcement personnel understand agency values and be more responsible for their actions.
  • Fairness and Consistency: Policies ensure all law enforcement personnel are treated impartially and in a reasonable manner.
  • Efficiency: Setting expectations and rules ahead of time saves time and any costs associated with inefficiencies.
  • Safety and Risk Management: Policies can prevent some problems from occurring or getting worse.

Policies and Procedures are Key to your EIS Success

Early Intervention Systems (EIS) are designed to identify officers who may be at risk of incidents, enabling targeted education and support to prevent potential issues. These systems are integral to modern policing strategies, focusing on officer wellness and performance improvement.

Having well-defined policies and procedures around an agency’s Early Intervention System has many important benefits, including:

  • Facilitating the prompt review of work-related occurrences involving their personnel that is non-disciplinary in nature.
  • Helping supervisory personnel make informed, fair, reasonable, and consistent decisions regarding the behavior and/or performance of their personnel.
  • Assisting agencies in exercising their responsibility to identify and support their personnel whose actions or inactions indicate possible stressors and/or need for intervention.

For agencies looking to enlist an Early Intervention System, establishing and maintaining specific EIS policies will improve the likelihood of getting off-track officers back on track; aid supervisors in their planning and support of officers; and build trust that the EIS at its core is a support tool. In the end, it will impact the overall integrity and success of your EIS.

Exploring EIS Policy Best Practices

In Benchmark’s recent installment of our Data Dialogue webinar series, Chief Partnerships Officer Chris Casula led a best-practice discussion on EIS polices. Titled “Early Intervention Systems: Exploring Best Practices for Establishing Your Agency’s Policies”, the webinar included an esteemed panel of agency leaders who have implemented – or currently are implementing – and EIS for their agencies, including Major Mike Harris and Captain Stephen Flatt at Charlotte-Mecklenburg PD; Sergeant Darwin Naval from San Francisco PD; and Senior Corporal Jared Nielsen with Dallas PD.

Each panelist highlighted their department’s unique approach to early intervention, discussing key issues surrounding policy, including:

  • Establishing an overall process for prompt review of flagged officers
  • Determining key stakeholders
  • Categorizing levels of at-risk behaviors
  • Developing non-punitive interventions
  • And developing training and mentoring resources

Policy in Action — Responding to Alerts

The discussion commenced with a summary of the four prevalent policy and procedural strategies agencies employ when responding to EIS alerts — with panelists weighing in on which one their agency uses and why. The four approaches – which were discussed in-depth in a previous session – include:

  1. Centralized: A chosen group of department leaders addresses each alert. Although efficient, this method can seem detached in larger agencies.
  2. Centralized Review: Here, a dedicated risk management team supervises all alerts. While this approach leverages specialized knowledge, it can inadvertently create a divide between the unit and the broader group of officers, possibly engendering an “us vs. them” attitude.
  3. Decentralized: Here, front-line supervisors are responsible for responding to their officers’ alerts. While fostering close bonds, this might result in inconsistencies due to diverse supervisory styles.
  4. Capacity Building: This is a combined effort where the risk management team collaborates with supervisors to provide training and expertise in deciphering alerts. It promises consistency and local responsibility, connecting various stakeholders and fostering trust.

Charlotte Mecklenburg PD’s decentralized model empowers the immediate chain of command to address alerts, believing supervisors are best positioned to understand and support their officers. According to Major Mike Harris, Charlotte Mecklenburg PD: “Ours is decentralized, pushing everything to the chain of command because with 1800 officers a centralized EIS couldn’t get to the granular issues like direct supervisors can. It’s meant as an intervention and employee wellness program.”

In contrast, San Francisco PD adopts a hybrid model, combining centralized review with decentralized action, depending on the alert’s severity. Senior Corporal Jared Nielsen, Dallas PD: “The officer’s chain of command reviews the alert and meets with them to create an action plan if needed. The goal is helping officers perform better.” Dallas PD is overhauling its program to transition from a threshold-based model, focusing on supporting officers through various non-punitive measures.

Across the board, panelists emphasized the importance of differentiating EIS from disciplinary measures. The systems are designed to support officers through voluntary training or incremental guidance and assistance, depending on the situation’s specifics. The goal is to help officers perform better and achieve more favorable career outcomes.

Building Trust through Policy Communication

A recurring theme was the need to build trust and ensure communication about the intent of an EIS — before, during and after the implementation process. Educating officers about the non-punitive nature of these systems is crucial. Many officers are unaware of the EIS in their departments, so informing them about its supportive purpose is key. This approach involves offering various options, like training or counseling, to assist officers effectively.

Sergeant Darwin Naval, San Francisco PD: “The biggest obstacle is communicating to officers that the system is non-punitive and supportive. Many don’t know we have an EIS. Educating all levels of the department has been crucial.”

According to Major Harris, “I think the most important thing I’ve learned in developing this program with Benchmark and First Sign is first clearly defining your ultimate goal for your EIS. For us, although the public likes terms like ‘early intervention,’ the backdrop is officer wellness.”

The panelists agreed it’s important that agencies clearly communicate an EIS as a supportive employee wellness initiative rather than a punitive tool. Conveying positive intent behind intervention policies and procedures proves vital for trust, participation, and outcomes. A cohesive internal team for continuity and momentum is recommended for achieving an agency’s communication goals.

EIS Customization and Flexibility is Key

The panelists agreed on the importance of customization and flexibility with an EIS. The systems should be tailored to individual departments and officers’ needs. Rigidity in such programs can be counterproductive. Continuous collaboration between law enforcement agencies and their EIS partner is vital to ensure the systems meet specific requirements and effectively support officers. According to Captain Flatt, “We held classes on the policy and system for all supervisors, stressing its supportive, non-punitive purpose as an early warning tool to make officers better. The flexibility to customize officer action plans has brought surprisingly good ideas from sergeants and lieutenants.”

Customization to each department’s policies, data sources, and localized needs makes platforms markedly more effective than inflexible one-size-fits-all systems. Frequent in-person communication from leadership around the progress and impact of their EIS can also be invaluable.

Addressing Performance and Wellness with Confidence

This final webinar of the year underscored the importance of Early Intervention Systems as a personnel management tool — and how having thoughtful EIS policies and procedures in place will enhance and impact results for your agency. The insights shared by the panelists provide valuable guidance for agencies looking to implement a new EIS.

Enlisting an advanced, data-driven solution like First Sign Early Intervention is crucial for adopting a proactive approach to officer wellness and performance. It goes beyond standard systems by establishing benchmarks that more accurately identify levels of behavior in need of support.

EIS Blueprint for Success

An Early Intervention System (EIS) can be a crucial asset for law enforcement agencies interested in managing their risk, in part by identifying officers who need assistance or support. The right system should monitor officer behavior and performance data to identify potential issues early, enabling focused interventions to minimize misconduct. However, the successful adoption of an EIS involves nuanced considerations in change management, data utilization, stakeholder engagement, implementation, and outcome measurement. This blueprint outlines essential factors in each area and serves as a roadmap for those agencies considering an EIS for optimizing officer performance.

Managing Change with Data

Introducing an EIS to an agency constitutes a significant cultural and technological shift that requires meticulous planning. According to an IACP policy document published in May 2020, agencies should consider several essential factors before moving forward with an EIS, such as:

  • The time commitment to administer the program
  • Deciding which agency-specific data points are critical for tracking and identifying performance trends
  • Establishing how that data will be collected, tracked, and used
  • Establishing policy for mapping potential actionable next steps once that data is extrapolated
  • Having alignment on who will be managing the execution and oversight of those next steps

Change management within any organization is never a light undertaking; it requires a strong commitment to achieve the objective at hand. For law enforcement agencies adopting an EIS it can mean the difference between helping struggling officers get back on track to become more productive in a non-punitive way — versus missing the opportunity to give them the incremental attention they need.

Using Data Effectively

The effectiveness of an EIS hinges on the quality of its data. Best practices for data application are:

  • Indicator selection: Prioritize in-depth data points that correlate closely with risk, such as arrest history, use of force incidents, internal affairs complaints, and missed court appearances. As stated in PERF’s 2015 article, Managing the Risks of Officer Misconduct and Failure through Early Intervention Systems: “Careful selection of data indicators based on those most predictive of risk is crucial for an EIS to flag situations accurately.”
  • Context analysis: Understanding the situations surrounding data points is critical for distinguishing meaningful trends — driven by complex, nuanced factors, such as adverse incidents, sequence of events, patterns of behavior and peer comparisons.
  • Ongoing indicator updates: Regular evaluations can guide adjustments for iterative learning, so that your EIS gets smarter and more efficient over time.
  • Data system integration: an EIS should be built on a modern suite of software with structured and accessible data — so that it’s easily integrated with incident data-capture systems, including computer-aided dispatch (CAD) systems and record management systems (RMS) — as well as any existing personnel management systems in place, for a holistic ‘data in’ view that connects disparate information.

Measuring Outcomes

Quantifiable metrics are vital for realizing the impact of an EIS. Best-practice performance indicators include:

  • A predictive model that identifies patterns of problematic behavior and patterns of exceptional conduct
  • Understanding context of activity to distinguish between Quality and Quantity of activity to eliminate excessive flags and investigations
  • Account for detailed officer activity relative to immediate peer groups to determine risk levels
  • Provide explainable, actionable alerts with non-punitive, non-disciplinary interventions
  • Transform risk management by significantly reducing exposure to rising liability costs

By consistently tracking such metrics, police departments can validate the advantages of an EIS for officers, departments as a whole, as well as the communities they serve.

Grounded in Research

It is critical that any data analysis is informed by research focused on utilizing performance data of officers so that the EIS can identify officers needing incremental support. First Sign® Early Intervention is the only EIS that uses national research combined with the patterns of data generated within an individual agency over several years to identify those law enforcement personnel with the greatest need for intervention.

Data scientists, who are experts in the field, developed First Sign based on a holistic view of available information that is indicative of risk. Drawing from multiple indicator categories, the First Sign system calculates overall activity and behavior, as well as trends compared to peer groups based on rank, nature of assignment, geography, and deployment time.

Because of this expertise, First Sign is a proven, predictive, and preventative system unlike any other to identify officers at risk for problematic behavior:

  • First Sign has seen an average model precision of 85%. For comparison, traditional early warning systems have a model precision of roughly 30%.
  • With a great degree of confidence, First Sign can identify an average of 5% of officers at risk within an agency.
  • Additionally, that 5% is responsible for 66% of injuries (both officer and citizens) and disproportionate use of force incidents.

Assessing Levels of Risk and Courses of Action

The effectiveness of any EIS largely depends on a department’s ability to manage a systematic set of actions to assist officers displaying at-risk behaviors. Upon identifying such behavior, it is advisable for agencies to have a process for assessing the officer’s level of risk. Subsequently, a specific, monitored plan that is non-punitive and non-disciplinary should be developed and implemented to provide the officer with the necessary support.

To facilitate this crucial phase, Benchmark offers a platform known as C.A.R.E. (Case Action Response Engine®). This course-of-action platform aids law enforcement agencies in managing officers identified as at-risk with First Sign, by featuring research-based case management modules. These modules are tailored for officer-specific interventions and include benchmarks for best practices at various levels of intervention. The goal of C.A.R.E. is to assist departments in ensuring that no officers displaying at-risk behavior go unattended.

A Skilled Implementation Team is Key

Getting to go-live in order to harness the full power of an EIS requires a seasoned implementation team — preferably one comprised of people who have either served in government roles or have substantial work experience serving complex municipal and government customers specifically. Certainly, all team members should have deep experience deploying configurable off-the-shelf software to customers.

Ideally, you should anticipate ongoing investment and research that constantly increases functionality, provides guidance on best practices, and allows access to research on personnel development.

Finally, the team should consist of a strategic mix of implementers, data scientists, and engineers to ensure an effective and efficient implementation.

The Path Forward: Navigating the Road to Early Intervention Success

Adopting an effective early intervention system requires a collective dedication to change, while the rewards to agencies can be substantial — from improved officer performance to enhanced community relations.

If your department is considering implementing an EIS — or you believe you can do better than your current system, contact Benchmark Analytics to speak with a solutions expert about First Sign® Early Intervention System. As the only data-driven, research-based EIS available today, First Sign empowers law enforcement agencies to harness their data for exceptional personnel management.


In an ever-evolving society, the roles and expectations placed on law enforcement officers – including how they engage and interact with the communities they serve – are continually changing. The same is true for how they are managed and supported for optimal on-the-job performance. As part of that infrastructure, the right early intervention system can become an indispensable tool for agency leaders aiming to discern and act on any potentially problematic patterns in officer behavior.

Such a system would be designed to identify and address these patterns before they develop into major incidents, ensuring the public’s safety and the officer’s well-being. Yet, the thoughtful implementation of a successful EIS requires careful consideration, adept change management, and a comprehensive understanding of an agency’s culture.

This summer, Benchmark Analytics presented the second installation of their Data Dialogue webinar series, led by CEO Ron Huberman, titled “Navigating EIS Alerts: Mastering the Right Approach for Your Agency.” Among the panel of participants were Benchmark’s Chief Research Officer, Nick Montgomery, Vice President of Data Science, Dr. Ugochi Jones, and Director of Data and Enterprise Analytics, Riley Maloney.

Four Approaches to EIS Alerts

Riley Maloney kicked off the discussion by outlining the four prevalent strategies that agencies employ in response to EIS alerts, signaling that an officer might need intervention. These strategies included:

  1. Centralized: A chosen group of department leaders addresses each alert. Although efficient, this method can seem detached in larger agencies.
  2. Centralized Review: Here, a dedicated risk management team supervises all alerts. While this approach leverages specialized knowledge, it can inadvertently create a divide between the unit and the broader group of officers, possibly engendering an “us vs. them” attitude.
  3. Decentralized: Here, front-line supervisors are responsible for responding to their officers’ alerts. While fostering close bonds, this might result in inconsistencies due to diverse supervisory styles.
  4. Capacity Building: This is a combined effort where the risk management team collaborates with supervisors to provide training and expertise in deciphering alerts. It promises consistency and local responsibility, connecting various stakeholders and fostering trust.

Riley observed that the efficacy of each model is contingent on the agency’s internal intervention mechanisms. More and more agencies are turning to Benchmark – with its First Sign® Early Intervention and C.A.R.E. platforms – and as a result, these agencies can discern the most fitting approach for their organizational design.

“In each of these systems, it’s vital to remember that an early intervention system’s strength lies in the interventions allocated in response to an alert. If an alert arises but is not acted upon, it’s futile. Benchmark dedicates significant time collaborating with agencies during the rollout phase to identify which of the four methods, or perhaps a new one, is most suited for their specific context. The aim is to determine how an agency can respond most effectively to an EIS alert.”

The Importance of Documentation

Often, in decentralized models, sergeants – due to their close ties with officers – are the first responders to EIS alerts. Yet, some supervisors might hesitate in documenting interventions, choosing instead to address matters informally. Despite good intentions, more documentation is needed to ascertain the efficacy of these interventions.

The panel acknowledged the importance of maintaining productive relationships with officers. However, they also emphasized that thorough documentation is indispensable for gauging success.

Challenges and Solutions in EIS Implementation

The discussion evolved toward potential obstacles in implementing an early intervention system. For instance, in larger agencies, supervising officers may become disconnected from frontline officers, complicating meaningful interventions. Additionally, some supervisors might need more training to formally document interventions due to existing cultural norms within the agency or lack of training.

Benchmark’s Chief Research Officer Nick Montgomery championed the capacity-building model, underscoring its balance between immediate alert responses and aiding supervisors in interpreting data and devising meaningful interventions.

“In any department, promotions are inevitable. Officers ascend to the rank of sergeants, and sergeants get promoted to lieutenants, among other shifts. There will be departures and new inductions, signifying change. This capacity-building method isn’t just about managing this flux. It’s centered on empowering individuals with the requisite skills to flourish in this dynamic setting. This isn’t confined to logistical details but extends to enhancing communication with officers, interpreting data accurately, and formulating robust strategies. Ultimately, it prepares the department for sustained improvement.”

Dr. Ugochi Jones delved into the shortcomings of casual interventions and emphasized the need for careful documentation. “In my discussions with supervisors, many who aren’t deeply engaged with the system (First Sign) still favor informal intervention. While they value effective communication when addressing potential issues among officers, they feel documentation makes it overly formal. We must consider this sentiment in our approach.”

Dr. Jones stressed the importance of constructive, data-driven exchanges with officers and the imperative to shift the perception and reality of interventions as a punitive measure to a supportive tool for officers. Meanwhile, Riley Maloney advocated for the inclusion of diverse stakeholders when shaping EIS policies, positing that this broad-based approach bolsters system trust.

The Path Forward

Effective communication and supervisory advancements are crucial. Benchmark’s First Sign is the only peer-to-peer, research-backed early intervention system — and has the potential to become a force multiplier for positive organizational transformation, with its implementation varying from agency to agency. The consensus among the panel was clear: for agencies with over a hundred officers, the capacity-building approach appears to be the most fitting. Meanwhile, smaller agencies benefit from a centralized or centralized review approach. However, the panel emphasized that agencies should not choose an approach based solely on size.

No single method can achieve widespread organizational change. Successful implementation requires a comprehensive strategy, strong stakeholder engagement, and the careful integration of technology.


predictive early intervention systems

An early intervention system (EIS) can be an instrumental tool for law enforcement agencies looking to track and address problematic patterns in officer behavior. The ideal EIS should be proactive in nature by enabling leadership to identify off-track behavior before it becomes a real problem — and should work seamlessly within the agency’s unique organizational needs and culture. Conversely, an EIS should also be able to identify positive patterns of behavior in officers exhibiting exemplary performance.

This spring, Benchmark Analytics conducted a webinar delving deep into this subject as part of our ongoing Data Dialogue series. Panelists included Ron Huberman, CEO of Benchmark Analytics and Nick Montgomery, Chief Research Officer at Benchmark. The dialogue centered around four principal areas concerning EIS: its evolution, data significance, its departmental adaptation, and perceptions towards its daily use by agencies and officers.

Evolution of Early Intervention Systems

Huberman, who rose through the ranks of the Chicago Police Department to serve as Assistant Deputy Superintendent, shed light on the progression of EIS within police departments — tracing its origins from the 1970s up to the recent advancements of today. Initially, departments utilized rudimentary “trigger-based” systems. However, these systems often produced “false positives” and “false negatives.”

As Huberman explained, “The University of Chicago published a lot of compelling research that showed trigger-based systems typically had a 70% false positive problem, which means 70% of the time it was flagging officers that were doing their job as they should. Furthermore, they had about a 40% false negative problem, meaning they were actually missing officers who were really struggling out there.”

While policing continued to evolve around more data-centric solutions, early intervention systems failed to keep up — until the introduction of First Sign® by Benchmark. First Sign offers a research-driven early intervention system that utilizes various data sources, including arrest records and use-of-force reports, with machine learning to identify predictive patterns. The digital transformation of departmental records and advanced algorithms provide a level of accuracy that trigger-based systems lacked at the time and still do to this day.

The Power of Integrated Datasets

Benchmark’s Chief Research Officer Nick Montgomery emphasized the power of converging various departmental datasets. An amalgamation of data, including over twenty event markers, can lead to over ninety predictive variables for each officer. This holistic approach greatly enhances the understanding of officer behavior compared to analyzing singular incidents. As he so powerfully stated, “…a research-based early warning system takes all of the data inside a police department looking back over five, ten, fifteen years and uses those various patterns of behavior to create a system that is far more predictive and can accurately identify officers likely to have an adverse event based on past events.”

Proactive Prevention of Harm

Early intervention systems can offer valuable insights by identifying officers exhibiting at-risk behavior, allowing for prompt intervention through training or counseling before problems escalate. Research conducted with the University of Chicago demonstrates that traditional EIS platforms using threshold-based triggers have a 70% fallacy rate for flagging at-risk officers. In contrast, First Sign boasts an 85% efficacy rate. Furthermore, our findings suggest that no more than 5% of officers contribute to over 60% of excessive force incidents.

Addressing the challenges with this specific group can significantly enhance community trust. Constructive, non-punitive supervision remains crucial for the efficacy of early intervention systems. In Huberman’s words, a modern research-based system like First Sign enables supervisors to say, “‘Hey, Officer Smith – I know you’re a good guy – and a good officer – but you were flagged in the system, so it’s important I intervene. Let’s talk about next steps to correct your at-risk behavior and how you’re engaging the public.'”

Signs of Impact

Preliminary data suggests that the implementation of an EIS can yield tangible results. Huberman shared data indicating a 50% reduction in the severity of force used by flagged officers post-supervisory intervention. Complaint rates against these officers also saw a significant decrease. Importantly, essential enforcement activities remain largely unaffected. And how do we know this? Benchmark has the world’s largest database on officer performance — and we’ve validated all predictive analytics through a standardized national model developed in partnership with the University of Chicago.

Embracing Modernization: The Future of Policing

In today’s rapidly evolving world, modernization touches every corner of our lives, from how we communicate to how we conduct business. Similarly, the realm of law enforcement isn’t exempt from this wave of change. The adoption of research-based methodologies combined with technological innovation reflects the evolution of policing in line with modern trends.

Historically, police reform efforts have been broad and overarching, often applying wide-reaching solutions like general de-escalation training for entire forces. But as we’ve seen, these blanket approaches might not always address the nuances and individual needs of officers. In many cases, the emphasis is shifting to more tailored tactics and tools, most likely powered by data science and analytics.

By harnessing the capabilities of modern data science, First Sign Early Intervention allows for a more focused course of action, directly targeting officers who genuinely need support. As a result, law enforcement stands at a pivotal moment where trust can be fostered within the communities they serve, internal personnel practices improved, and genuine change affected.

As Ron Huberman eloquently summarized, “There’s a tremendous opportunity before all of us in this profession – who view it as a noble calling and care deeply about policing – to say, ‘Let’s make a difference. Let’s turn this corner now because we have the tools to do so.’ I truly believe this is the moment we’re at.”

Benchmark Analytics was invited to participate in a roundtable discussion on March 15, 2023, hosted by the White House and the Office of Science and Technology Policy. The topic of conversation was President Biden’s Executive Order from May 2022 on Advancing Effective, Accountable Policing and Criminal Justice Practices to Enhance Public Trust and Public Safety.  Specifically, the gathered team was seeking to assess the current state of data collection, use, and transparency practices in law enforcement agencies across the country.

Denice Ross, the U.S. Chief Data Scientist, opened the session by laying out the key principles of accountable and equitable data, including:

  • Ability to Disaggregate
    Basically, how can you slice and dice data? Can you metaphorically pick up the information, reposition it and look at it from another angle? All while balancing data privacy and transparency, of course.
  • Maximum Utilization
    There is a tremendous amount of underutilized data that exists in the world today. How can we leverage information already being captured to gain new insights?
  • Analytic Capacity
    Is there the ability to dig into the data? That includes data quality, systems/tools for analysis, and the human capital/skill to understand the data and make it actionable.
  • Open and Shared
    Successful data initiatives should be available through diverse partnerships for various perspectives on content, analyses, and outcomes.
  • Accountable and Transparent
    Last, and far from least, the data has to be accountable and transparent. Full stop, period.

From there, the discussion transitioned to law enforcement data transparency and collection. It was a vibrant group dialogue echoing a collective and clear passion for service. The conversation took us through several interesting challenges around data collection. As one example, an agency might have multiple different sets of reporting standards for the exact same data points. Stated differently, use-of-force data reported to the FBI is, in many cases, dramatically different than use-of-force data required by state legislation — which can also be dramatically different from data required by settlement agreements (consent decrees) or civilian oversight. Today, the federal government collects the following data:

    • Incidents (arrests)
    • Hate/bias crimes
  • Use of force
  • Law enforcement demographics
  • Law enforcement/workforce characteristics
  • Police-Public Contact Survey
  • National Crime Victimization Survey

This is a good start – but this list lacks so many key elements. Only capturing whether a force incident occurred (or did not occur) misses valuable scenario context:

  • What was the force encountered?
  • Were there any attempts at de-escalation?
  • How did proportionality of force play into the situation?
  • What is the officer’s peer group and the expected force utilization patterns for that peer group?
  • Moreover, how do OTHER parts of that officer’s behavior pattern impact the force utilization and proportionality on any given day?

These are the meaty questions that need to be answered to build truly equitable data and enhance public trust.

Not surprisingly, the federal government is looking to augment current data collection with the following:

  • De-escalation incidents
  • Patrol locations
  • Complaints
  • Officer training
  • Community engagement
  • Vehicle pursuit
  • Other

These data points help tell the full story of an incident and an entire pattern of behavior. Still, software platforms cannot be unduly burdensome. Law enforcement officers are amazing civil servants tasked with protecting and guarding communities, not with building complex data schemas and reports. As a partner and software provider, we are constantly asking ourselves questions – how can we improve design and deployment of data collection tools to make life easier for the officer on the street while still maximizing the value of the resulting data? How can we provide law enforcement agencies with collaboration and education on best practices to simplify data collection and prioritize accountability and transparency?

Ultimately, everyone agreed that data transparency is the key to trust. With the power of analytic platforms, law enforcement agencies can harness the power of data to help tell a full and accurate story of officer behavior.

This session was part of a series of roundtables; OSTP also met with community members, academia, law enforcement agencies, and various other key stakeholders. Ultimately, all their learnings will feed a final report on Equitable Data due to President Biden on May 25. If that date is not immediately familiar to you – it’s the third anniversary of George Floyd’s death in Minneapolis. That significance isn’t lost on the Working Group; they understand the importance of this work and the unique role data plays in identifying, predicting, and ultimately preventing negative incidents.

Only through equitable and actionable data will we begin to bridge the divide and enhance public trust in public safety.

As political leadership shifts with each election cycle, law enforcement agencies are often on the front lines of implementing the policing policy priorities of new administrations and congressional leadership. Among these policy changes, Department of Justice (DOJ) consent decrees are generally agreed to have the most far-reaching impacts on an agency’s day-to-day operations, compelling the cooperation of departmental leaders to deploy often drastic changes to operational policies and those dealing with data collection and reporting. That need to overhaul data collection and reporting can be the source of the kinds of implementation and paperwork nightmares that keep chiefs up at night.

The success of a consent decree, in large part, hinges on how policy changes are implemented, documented, and reported. While they have little input on the consent decree terms, chiefs and departmental leaders are in the driver’s seat when it comes to fulfilling the terms of the decree and those of the monitor.

Over the next several articles, we’ll be exploring the lifecycle of a consent decree by incorporating conversations with prominent chiefs and law enforcement thought leaders who spoke at Benchmark’s Digital Science Leadership Series held at IACP 2022 in Dallas. Their real-world experience and thoughts on consent decrees form the bedrock of this series.

Consent Decree Beginnings

While the nuts and bolts of DOJ consent decrees vary from agency to agency, they all share a similar origin story. Initially, there is an incident or pattern of incidents that draws scrutiny from the media and the wider public. In the past, this could take months to develop into an issue of national attention, but in the age of 24-hour news and social media, a particularly striking example of alleged misconduct can be reported around the world before the day is over. Without a doubt, the perception of policing as a local issue is changing due to technological advances.

Once an agency is on the radar of the DOJ, federal officials investigate to determine if there is a pattern of practice involving misconduct or the deprivation of constitutional rights. In political eras where the DOJ is issuing consent decrees, they tend to move relatively quickly, relative to the level of bureaucracy involved. Using the contemporary example (late 2022) of the Louisville Metro Police Department, the trigger incident occurred less than two years prior to the expected announcement of a DOJ consent decree. Once a department is under a consent decree, daily operations shift and management duties change drastically.

“I don’t wish a consent decree on any police chief or on any police department. It is an enormous undertaking unlike anything I have ever experienced in my career,” said Chief Jason Armstrong – currently chief of Apex (NC) Police Department and whose prior experience includes managing the Ferguson (MO) Police Department’s consent decree. “I had real-life problems in the community that needed our focus, but so much of my time and energy had to be pulled away to work on the consent decree,” he shared during Benchmark’s Leadership Series panel discussion at IACP 2022.

Policing Versus Paperwork

A common observation of consent decrees, especially those that fail to reach a timely conclusion such as at Oakland, California is that the requirements imposed by the DOJ aren’t aligned closely enough with the broader goals of reducing crime, improving public safety, and contributing to positive community relations. This can create tension within a department and can be a source of stress for chiefs and other departmental leaders attempting to balance their day-to-day duties and larger mission with achieving the performance criteria associated with the consent decree.

Speaking at Benchmark’s IACP2022 Digital Science Leadership Series, Virginia Gleason, former deputy director of the Oakland Police Department and 2022 Harvard Advanced Leadership Initiative Fellow, noted the potential for disconnect between a department’s operations and the stipulations of a consent decree.

“There needs to be meaningful interaction between the monitoring team, the court, and the department about what is feasible, what is affordable, and what is practical for that jurisdiction to achieve the principles of [the consent decree] instead of chasing numbers.”

Chiefs are under extraordinary pressure to ‘hit the numbers’ of a consent decree – in many cases, their job depends on it. Collecting, collating, and reporting data types that haven’t been dealt with before significantly strains an unprepared department. Without carefully considering data analysis needs, this can disrupt the balance between administrative tasks and actual, patrol-oriented police work.

Rethinking Data

Data reporting, especially when there can be a perception of moving the goalposts, is more than just an annoyance in a department – it can take a severe toll on morale and retention, contributing to the staffing crisis experienced by agencies around the country. To help mitigate the effects of enhanced data collection and analysis, chiefs and agency leaders must be intentional in their methodology and policies.

Breaking down complex and multifaceted compliance tasks into smaller, more manageable pieces produced results for Ben Horwitz, Co-Founder of AH Datalytics and speaker at Benchmark’s 2022 Digital Science Leadership Series.

“You have to start by measuring something with body worn camera compliance” Horwitz said, speaking of his time managing the New Orleans Police Department’s consent decree. Step one was ‘do we turn it on when we’re supposed to?’. Within three months of command staff seeing the performance [and acting on that data], we went from 70-80% compliance to 95% and above”.

The path towards compliance in this instance involved not just breaking down reporting into more manageable steps but reporting that data up the chain of command to enable more impactful decision-making. Because that data was intentionally managed, it became more valuable as it was used for both compliance and leadership’s decision-making within the department.

Next Steps

Consent decrees are an arduous process that taxes an agency’s capacities and resources in many ways. They are expensive, time-consuming, and, without clear goals and effective management, a drain on a department’s morale that can have measurable impacts on its ability to improve public safety. While some of the processes are outside of the departmental leadership’s hands, they are anything but powerless.

Understanding the intersection of leadership and data analytics is a powerful tool when navigating the ins and outs of a consent decree. Benchmark Analytics is committed to both contributing to the conversation surrounding these core elements and building the best data analytics tools – tools that help law enforcement leaders navigate the demands of data-driven policing and personnel management.

Advancements like drones, virtual reality training, and other hardware-based technologies are said to be “revolutionizing” policing, giving law enforcement leaders a vast array of tools to confront rising crime rates and public demands for action. Useful as these tools are, they cannot inform policy decisions or point to improvements in personnel management strategies –measures that show the greatest potential in positively impacting public trust and the effectiveness of police operations as a whole. The emerging field of data science, however, has the capability to move the needle on some of these “big picture” problems.

Without intentional and research-based data collection and analysis practices, policing leaders are effectively flying blind when shaping policies and allocating resources within their agencies. Just as troubling, they may miss out on substantial cost-savings that often come with an investment in their data science capabilities. This article looks at the basics of data science in law enforcement and its potential to guide meaningful and positive changes in law enforcement.

What is Data Science?

Data science is a relatively new technical field, with the term only coming into common usage in the last 25 years. Though those in the field still debate some of the finer points of what encompasses data science, Amazon Web Services (AWS), a global leader in cloud computing, defines it in the following way.

“Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.”

Many in the field differentiate data science from statistics because it focuses on questions and problems unique to the digital age. Essential to this definition is the notion that data science is not a singular pursuit but, instead, a set of intersecting skill sets and techniques used to study a problem. Crucially, data scientists in fields like law enforcement and public policy use these insights to craft evidence-based solutions to these problems.

Crime Side Advances

Tracking largely with the personal computer revolution of the 1980s – which brought computing and data processing out of university labs and into homes and smaller offices – data science found an immediate use-case in policing.

COMPSTAT is one of the earliest and most recognizable examples of the use of data science methodology in law enforcement. Pioneered in the NYPD in the early 1990s, COMPSTAT incorporated crime mapping and trend analysis and was thought to have significantly impacted crime rates in cities that have adopted the practice.

Building on the fundamentals of COMPSTAT, a new generation of predictive analytics shows impressive potential as a crime-fighting tool – with substantial cost-savings as well. According to the National Institute of Justice (NIJ), predictive analytics build on established policing strategies while leveraging growing data sources to inform newer, proactive tactics. It also offers substantial cost-savings, enabling policing leaders to deploy their resources more efficiently and, ultimately, effectively.

Informing Policy and Personnel Management

In addition to its demonstrated success in field operations, data science also has a vital role on law enforcement’s administrative side. The 2015 final report from The President’s Task Force on 21st Century Policing emphasized the importance of data collection and analysis, stating, “(A) lack of relevant data impacts the ability of communities and law enforcement agencies to make informed policy and practice adjustments based on good information.” The report called for enhanced data collection efforts as a means to increase transparency and accountability – ultimately in service of improving public trust in policing.

Accurate and reliable data is crucial to modern law enforcement personnel management strategies. For instance, early intervention systems rely on this data for predictive analysis, and the stakes couldn’t be higher. False negatives derived from faulty analysis are potentially costly, contributing to an agency’s exposure to the risk of a lawsuit or civil rights claim.

In building First Sign®, Benchmark’s data scientists and engineers leverage the power of the world’s largest multi-jurisdictional officer performance database while incorporating iterative learning that uses cumulative analytics to get “smarter” and more efficient over time. This technology gives supervisors a more holistic picture of an officer’s performance, especially relative to others, and enables them to engage in more targeted and meaningful interventions.

Finally, the use of data science in personnel management has the potential for substantial cost savings. According to a recent paper published by the Ash Center for Democratic Governance and Innovation at Harvard, every one dollar of the cost associated with data analytics can return up to nine dollars in value to agencies. At a time of economic uncertainty when municipal budgets are strained, the cost-savings inherent in a data-informed personnel management strategy cannot be ignored.

Looking Ahead

At Benchmark Analytics, our purpose is guided by data science and evidence-based analysis. We specialize in public safety personnel management – it is our area of unique expertise. When we gather and analyze data sets, we use the product of that work for personnel management, professional standards, and early intervention. Taking this a step further, we work in partnership with our academic research consortium and use this data to contribute to a broader understanding of policing for the public good.

Incorporating data science more thoroughly into law enforcement operations allows law enforcement leaders to make smart and more cost-effective decisions, from personnel decisions to deploying resources in the field. Benchmark Analytics is proud to be on the leading edge of using data science to produce better policing outcomes while improving community relations.