RMS (records management system) and CAD (computer-aided dispatch) are ac-ronyms that every police officer has probably heard at least once in his or her career. Similarly, predictive policing, intelligence-led policing, and problem-oriented policing are concepts that law enforcement agencies around the world use on a daily basis to engage their citizens and combat crime. The acronyms and concepts may seem like standalone cogs in the policing machine at first glance, but they are forever linked by a myriad of data pieces that must be obtained, integrated, and acted upon. The problem lies in getting the pieces to interconnect with and communicate accurate information to other technology items.

Data, in and of themselves, are not bad. Police departments have always collected data in some fashion. Data collection started with pen and paper, evolved into communication over the telephone or radio, and finally moved into varying digital iterations. The ever-growing need for more and better shared data has consumed all departments, large and small. The drive to implement emerging technologies into police departments is in full force. Citizens want to be able to view crime statistics from their local departments’ webpages, submit a police report over the Internet, and download digital copies of their accident reports, all from the comfort of their living rooms. The age of carbon copy or handwritten police reports is a distant memory (or unheard of) for most law enforcement personnel.
The way that a law enforcement agency deals with data can either be a headache or serve as a force multiplier to increase efficiency and citizen interaction. For most police administrators, it is probably a little of both. The challenge is that data can become overwhelming—and can also be a little intimidating—but correct data are the most important foundational cornerstones that must be laid before any new technology can be properly utilized. A carefully considered data philosophy and proper planning can help an agency minimize data obstacles.