Predictive policing is a technologically-driven law enforcement strategy that relies on mathematical analytics and other predictive models to anticipate potential criminal activity. The objective is to enable law enforcement to proactively address crime before it actually occurs.

• Comprehensive Forecasting: By utilizing data analytics, predictive policing software can project where and when a potential crime may occur or even predict potential offenders. It's a proactive approach crafted to pre-empt crime rather than react post-event.
• Data-intensive: Predictive policing relies heavily on voluminous datasets. This includes information like historical crime data, demographic trends, social media posts, and localized data. Despite the broader implications in terms of privacy and security, data-driven policing can contribute positively to effective law enforcement.
• Real-time assessment: Predictive policing software provides real-time crime predictions which can be integrated into the daily routines of law enforcement.
• Vendor Support: Vendors provide specialized training along with regular updates, maintenance, and support to law enforcement agencies implementing the software.
• Cost-Effective: Predictive policing methods are generally more cost-effective than traditional ones due to their ability to cut down on labor-intensive tasks, streamline operations, and improve crime-fighting efficiency.

Predictive policing sees extensive use across various law enforcement agencies due to its potential in enhancing efficiency and efficacy in crime prevention.

Effective implementation of predictive policing requires an in-depth understanding of agency needs, meticulous selection of suitable software, rigorous cost-benefit analysis, and careful evaluation of vendors. Finally, training and adaption are critical for successful integration of predictive policing software into the law enforcement work frame. Monitoring of the software deployment should be thorough and regular to ensure efficiency and effectiveness in achieving the desired outcomes.

Several inherent benefits make predictive policing an attractive investment, including:

• Cost-Effectiveness: Diversified costs over multiple agencies make predictive policing a more affordable option. Further, it saves on operational costs by improving patrols' efficiency and resolving cases quicker.
• Improved Efficiency: As the software analyzes and predicts crime patterns, it optimizes law enforcement schedules, leading to better allocation of resources and improved efficiency.
• Reliability: As more data is fed into the program, it increases the system’s functionality and stability. This, in turn, increases the reliability of the predictions over time.
• Constant Support and Updates: Vendors provide regular maintenance, security updates, and customer support, ensuring the software is up-to-date and secure.
• Integration Compatible: Most predictive policing software can integrate with existing law enforcement technologies, ensuring smooth transition and operability.
• Immediate Access to Advanced features: Law enforcement officers have access to advanced predictive features, thus equipping them to anticipate and respond promptly to potential crime incidents.

Despite its numerous advantages, some limitations prevail:

• Limited Customization: deriving insights from generic data limits this. Hence, it may not cater to specific unique processes or requirements in certain jurisdictions.
• Vendor Dependence: Dependency on vendors for updates and improvements can dodge issues, particularly if the software isn't maintained properly or if the vendor discontinues support.
• Integration Challenges: Despite the compatibility with pre-existing systems, the process and cost of integrating this technology with current platforms or older systems can present obstacles.
• Potential for Bias: If not appropriately governed, predictive interventions can embed or amplify existing biased police practices. Training, ethical standards, and robust oversight must accompany the deployment of predictive policing tools to mitigate these risks.
• Risk of Obsolescence: Rapid technological advancements can render these software obsolete quicker than anticipated, necessitating frequent updates or replacements.