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Revenue Cycle Management (RCM) is an important aspect of any healthcare business. It involves processes that ensure that patients are afforded the right healthcare treatments and are billed accurately, while healthcare service providers are paid by patients and reimbursed accordingly by insurance companies.

US Healthcare today is largely dependent on technology-enhanced systems for administrative and clinically-based functions such as claims and care management, computerized physician order entry systems, radiology, pharmacy, and laboratory systems and self-service applications. Through electronic health records (EHR) and protected health information (PHI), patient data can be accessed rapidly and from multiple locations, making healthcare service better.

“The target is always data” is a phrase that should probably be one of today’s enterprise mantras. A year of high profile cyber attacks has shown that enterprise organizations, not consumers, are now prime targets for data breaches. The breaching of vital user and customer information from the likes of T-mobile, Experian, Anthem, Inc. and Ashley Madison was among those high-profile stories. The effects were devastating; from ruined lives to massive financial loses. Hackers of extramarital affair site Ashley Madison exposed information of its 37 million users.

In an increasingly digital world, companies are relying on their data like never before. This overall reliance on data will keep increasing, not only for global business but for consumer, industry and government processes alike. The need to efficiently manage the mass of data within enterprises and to make good data available and protected has underscored the vital importance of data processes.

Using graphs or charts to present data is easier than having decision-makers sweat over spreadsheets or reports. Data visualization or visual analytics is the presentation of data in a pictorial or graphical format. Data visualization allows decision makers to understand data analytics better, grasp and convey difficult concepts quickly or identify new patterns.

Data preparation is now a necessary and crucial part of making business decisions using enterprise data. It is also often the most tedious one. Ask any data scientist, data analyst and IT person and they will tell you that data preparation is time consuming, taking 50 to 80 percent of a data professional’s time. However cumbersome it is, data preparation is the process that guarantees good data is pumped into the analytics process.