The Importance of Marrying Qualitative and Quantitative Analytics in Public Safety

The Importance of Marrying Qualitative and Quantitative Analytics in Public Safety

Qualitative data adds much needed context to qualitative stats.

When most organizations think about analytics, they are envisioning quantitative data: measurable numbers and specific statistics.

In fact, many organizations using analytics today remain fixated only on the hard data rather than gathering and evaluating other useful details that can’t be expressed by numbers alone.

Often overlooked are qualitative analytics: intangibles such as subjective opinions, attitudes, and other attributes that aren’t expressed numerically.

When semi-trailers on the interstates display the question, “How’s my driving?” with a phone number, the trucking firm are looking to gather qualitative data.  

When companies and organizations apply analysis to qualitative data, they can reveal answers where mere quantitative numbers cannot, such as:

  • Why customers initially chose to buy or contract with a company
  • How individuals rate their experience with an organization
  • What people value most about particular programs or services
  • Whether they would recommend a company to their friends and family
  • How an organization can improve its services

Qualitative analytics can generate data that helps shape policy and planning. If done correctly and using careful guidelines, qualitative analytics produces small datasets that can help inform and shape future planning and strategy.

And perhaps even more importantly, qualitative analysis can explain the “why” behind quantitative data.  

Rather than making assumptions about why statistics are playing out in a certain way, organizations can use qualitative research help to validate or dismiss certain assumptions before spending resources to address said statistics.  

Tips for gathering quantitative data

When collecting and applying qualitative analytics, it’s important to use proper research methodology because these metrics deal with subjective data rather than objective figures. If not handled correctly, bias can inadvertently influence the responses and return only self-serving results.

Also, the mined qualitative data should be mission specific. Without a strong goal guiding data collection, it is easy for surveys and the like to return unfocused and unrelated (and therefore worthless) comments.

How public safety groups use analytics

Public safety groups already often use quantitative analytics as a metric to assess a wide range of performance and service results, such as the average response time to a 911 call. And law enforcement has been using analytics for decades to help predict probable criminal activity.

To fully maximize the power of analytics, though, it’s important to go beyond the numbers. A comprehensive and equitable picture can only be brought into reasonable focus when quantitative metrics are carefully integrated with qualitative analytics.  

Advocates wanting to use quantitative analytics alone to predict behavior or activities are working with an incomplete view of reality.  

Employing quantitative analytics as a predictive tool without including the qualitative factor distorts the meaning of the data by omitting the human factor.

Using mission-driven quantitative analysis to increase effectiveness

If a police department, for example, is aware through quantitative data that there’s been a string of school and vehicle burglaries within eight zones of a city, watch commanders may make officers aware at the beginning of shifts so extra vigilance can be focused on those areas.

With many police departments, the analysis stops there—but those departments are forfeiting the complete picture available to them.

In reviewing the department’s assembled qualitative data on its officers’ roster, a supervisor notes that four of the department’s officers reside near the victimized schools and two have children attending those schools.

That means four of those officers are personally familiar with the neighborhood and are more likely to know people who would be willing to talk to them when canvassing those areas after an incident.

Thus, the department may see better results by assigning the identified officers to those areas, rather than simply asking everyone to be more vigilant.

Using qualitative analytics for internal monitoring

In addition to outward-facing, prevention purposes, there have been efforts—especially in law enforcement—to use quantitative analytics to identify personnel who may be underperforming or prone to inappropriate behavior.  

However, these efforts haven’t been nearly as successful as those predicting criminal activity.

Reasons for that include:  

  • The data collected on individuals frequently only tells part of their story and focuses almost exclusively on the numbers (quantitative) rather than the intangibles (qualitative).
  • Subjective information is frequently housed and maintained in disparate management systems that don’t communicate or work with one another.
  • Personnel analytics often ignore key factors for poor performance such as inadequate training or poor oversight.

Incorrect or incomplete data may cause supervisors to (at best) miss clues of possible misconduct, or (at worst) draw wrongful inferences.

If predictive data analytics is to be used as an early warning system for errant behavior, those analytics should be integrated into an internal affairs case management system that also offers hiring and training components to make the data more actionable and suited for preparing personnel.

Such a system can help organizations meet evolving statutory requirements, maintain legally defensible records, improve internal communications by standardizing the internal affairs case management process, and help supervisors better oversee internal cases involving separation and disciplinary action.

Data management software customized for today’s public safety needs can help improve an organization’s effectiveness and efficiency by adding the human quotient to every analytics assessment.

Ultimately, qualitative data combined with a robust data management system establishes a comprehensive approach that is more effective than merely using quantitative metrics to try to predict how individuals might perform in specific situations.

Qualitative data adds much needed context to qualitative stats.

When most organizations think about analytics, they are envisioning quantitative data: measurable numbers and specific statistics.

In fact, many organizations using analytics today remain fixated only on the hard data rather than gathering and evaluating other useful details that can’t be expressed by numbers alone.

Often overlooked are qualitative analytics: intangibles such as subjective opinions, attitudes, and other attributes that aren’t expressed numerically.

When semi-trailers on the interstates display the question, “How’s my driving?” with a phone number, the trucking firm are looking to gather qualitative data.  

When companies and organizations apply analysis to qualitative data, they can reveal answers where mere quantitative numbers cannot, such as:

  • Why customers initially chose to buy or contract with a company
  • How individuals rate their experience with an organization
  • What people value most about particular programs or services
  • Whether they would recommend a company to their friends and family
  • How an organization can improve its services

Qualitative analytics can generate data that helps shape policy and planning. If done correctly and using careful guidelines, qualitative analytics produces small datasets that can help inform and shape future planning and strategy.

And perhaps even more importantly, qualitative analysis can explain the “why” behind quantitative data.  

Rather than making assumptions about why statistics are playing out in a certain way, organizations can use qualitative research help to validate or dismiss certain assumptions before spending resources to address said statistics.  

Tips for gathering quantitative data

When collecting and applying qualitative analytics, it’s important to use proper research methodology because these metrics deal with subjective data rather than objective figures. If not handled correctly, bias can inadvertently influence the responses and return only self-serving results.

Also, the mined qualitative data should be mission specific. Without a strong goal guiding data collection, it is easy for surveys and the like to return unfocused and unrelated (and therefore worthless) comments.

How public safety groups use analytics

Public safety groups already often use quantitative analytics as a metric to assess a wide range of performance and service results, such as the average response time to a 911 call. And law enforcement has been using analytics for decades to help predict probable criminal activity.

To fully maximize the power of analytics, though, it’s important to go beyond the numbers. A comprehensive and equitable picture can only be brought into reasonable focus when quantitative metrics are carefully integrated with qualitative analytics.  

Advocates wanting to use quantitative analytics alone to predict behavior or activities are working with an incomplete view of reality.  

Employing quantitative analytics as a predictive tool without including the qualitative factor distorts the meaning of the data by omitting the human factor.

Using mission-driven quantitative analysis to increase effectiveness

If a police department, for example, is aware through quantitative data that there’s been a string of school and vehicle burglaries within eight zones of a city, watch commanders may make officers aware at the beginning of shifts so extra vigilance can be focused on those areas.

With many police departments, the analysis stops there—but those departments are forfeiting the complete picture available to them.

In reviewing the department’s assembled qualitative data on its officers’ roster, a supervisor notes that four of the department’s officers reside near the victimized schools and two have children attending those schools.

That means four of those officers are personally familiar with the neighborhood and are more likely to know people who would be willing to talk to them when canvassing those areas after an incident.

Thus, the department may see better results by assigning the identified officers to those areas, rather than simply asking everyone to be more vigilant.

Using qualitative analytics for internal monitoring

In addition to outward-facing, prevention purposes, there have been efforts—especially in law enforcement—to use quantitative analytics to identify personnel who may be underperforming or prone to inappropriate behavior.  

However, these efforts haven’t been nearly as successful as those predicting criminal activity.

Reasons for that include:  

  • The data collected on individuals frequently only tells part of their story and focuses almost exclusively on the numbers (quantitative) rather than the intangibles (qualitative).
  • Subjective information is frequently housed and maintained in disparate management systems that don’t communicate or work with one another.
  • Personnel analytics often ignore key factors for poor performance such as inadequate training or poor oversight.

Incorrect or incomplete data may cause supervisors to (at best) miss clues of possible misconduct, or (at worst) draw wrongful inferences.

If predictive data analytics is to be used as an early warning system for errant behavior, those analytics should be integrated into an internal affairs case management system that also offers hiring and training components to make the data more actionable and suited for preparing personnel.

Such a system can help organizations meet evolving statutory requirements, maintain legally defensible records, improve internal communications by standardizing the internal affairs case management process, and help supervisors better oversee internal cases involving separation and disciplinary action.

Data management software customized for today’s public safety needs can help improve an organization’s effectiveness and efficiency by adding the human quotient to every analytics assessment.

Ultimately, qualitative data combined with a robust data management system establishes a comprehensive approach that is more effective than merely using quantitative metrics to try to predict how individuals might perform in specific situations.

The National Decertification Index (NDI) is a national registry of police officers whose law enforcement credentials have been revoked due to misconduct.

For more than 10 years, the NDI has provided police departments, state agencies, and other organizations with decertification data about potential hires.