Salt Lake City (PRWEB) January 22, 2014 -- PublicEngines, a provider of cloud-based solutions that facilitate crime analysis, supply actionable intelligence and increase community engagement for law enforcement, schools, and governments today announced the availability of a new product feature and service to help reduce data and reporting errors that plague law enforcement. Recent audits by PublicEngines show that up to 25% of a typical agency’s Records Management System (RMS) or Computer Aided Dispatch (CAD) data is incorrect. Generally these errors are due to mis-labeled or mis-mapped crime – something that occurs at the point of data entry.
Launched this week as a new feature in PublicEngines’ CommandCentral Analytics™ and CommandCentral Predictive™ products, the Data Quality and Validation (DQV) tool is essential for crime analysis because it helps reduce common errors of mis-mapped and mis-labeled crimes - which often result in poor analysis and inaccurate reporting of crimes. Additionally, PublicEngines is launching a data quality and validation service for those agencies that need assistance in cleaning up their data errors.
According to a recent survey by the International Association of Chiefs of Police, 94% of agencies agree they are being asked to take on greater responsibility with fewer resources. That has spurred an increased reliance on data to make smarter decisions. However, without the necessary tools to clean up data issues, much of that analysis will be based on erroneous data.
The DQV Tool is built into PublicEngines’ CommandCentral products - the industry’s leading cloud-based crime analytics software - to provide the ability to evaluate, clean up, and validate data errors, providing any agency clean data that results in improved analysis, reporting, and decision-making capabilities. The new DQV tool is available free as part of the CommandCentral suite.
"Accurate, timely and easily accessible data is fast becoming the life-blood of effective law enforcement," said William Kilmer, CEO of PublicEngines. "The DQV tool will help correct data errors and improve agency effectiveness. We see this as a key element to our offering and one that is unique to the CommandCentral product suite.”
“In particular, this is essential to CommandCentral Predictive, our predictive crime analytics platform,” said Kilmer. ”At the heart of solid predictions is accurate data. We are the only vendor in the market to offer built-in clean up tools, facilitating accurate crime prediction.”
In addition to the DQV tool, PublicEngines is also offering a service as part of its PublicEngines Insight™ services that will provide data clean up and validation on behalf of the agency. “When we spoke to agencies about DQV, they were enthusiastic about it but they acknowledge that they just didn’t have the man power to perform their own data clean up and asked us if we could provide it as a service,” said Kilmer. “The data quality service is available as a one-time or ongoing service for those agencies who want to practice good data hygiene but don’t have the personnel to do it.”
Because data quality is so important to proper analysis, PublicEngines is holding an online Webinar on the topic as part of its monthly Webinar series. The Webinar covering common data quality issues and how to resolve them, will be held on January 28th 2014. More information on the free Webinar series, entitled Best Practices for Better Crime Analysis, can be found at http://www..publicengines.com/2014CrimeAnalysisTraining.
About PublicEngines:
PublicEngines develops easy-to-use, cloud-based software that helps law enforcement, government, defense, and other organizations gather, analyze, and share intelligence to improve public safety. Used by more than 2,000 organizations worldwide, our market-leading solutions are accessible wherever you are, and are easy-to-deploy with minimal training and no additional IT burden or expense. For more information visit http://www.publicengines.com.
Public Relations, PublicEngines, http://www.publicengines.com, +1 8018282708, [email protected]
SOURCE PublicEngines
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