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Late Binding Analytics Platform Explained a Little More Simplistically

The term Late Binding dates back to the 1960s and refers to a computer programming mechanism in which the method of programming does not require the compiler to reference the libraries that contain the object at the time of compilation. Late binding proves to solve the problem of version conflicts and modification accidents. The process of mapping the data in the enterprise data warehouse (EDW). Mapping a data is just one of the processes required as data must undergo massive transformations. The Late binding technique is used to map the data into the enterprise data warehouse from source systems to standardized and central vocabularies and business rules and systems.

Having a Late Binding Analytics Platform is important and preferred because it avoids the consequences of linking data with volatile business rules or vocabularies too early. By waiting to bind data until it’s time to solve an actual clinical or business problem, analysts don’t have to make lasting decisions about a data model up front when they can’t see what’s coming down the road in two, three, or five years. It also makes it possible to quickly adapt to new questions and use cases and have the data they need to perform timely, relevant advanced analytics. This is done so the data can be brought together for analysis. A lot of large healthcare organizations have hundreds of analytics vendors supplying data. All that data has to be brought into the enterprise data warehouse (EDW)  in order to ensure the possibility of having a reliable reporting and analysis.

Knowing when and how tightly to bind data to rules and vocabularies is critical to the ability and success or failure of a data warehouse. In healthcare, the risks of binding data too tightly to rules or vocabularies are particularly high because of the volatility of change in the industry. Business rules and vocabulary standards in healthcare are among the most complex in any industry, and they undergo almost constant change. Late binding, methods, variables, and properties are detected and checked only at the time it is run. This implies that the data analyst or the person imputing the data does not necessarily have to know what kind of data or its properties is being entered.

Data storage is an important topic to discuss extensively especially in an industry like healthcare because it plays different roles in the healthcare industry most of which are foundational. Disk data storage has become outdated as there are many forms of remote data storage techniques like cloud computing. With the increasing popularity of big data, cloud storage has revolutionized the way data is being handled and stored. Also, the need for a centralized data access has led more and more institutions in the industry lean towards utilizing cloud data storage services for patient records. It also helps hospitals and health clinics to reduce costs and increase access. There also other benefits beyond reduced costs and centralized access.

The volume of healthcare data has increased exponentially over the years and is projected to continue to skyrocket. Most of this data is unstructured and so the storage method chosen plays a significant role in structuring the data. Hence the whole late-binding vs early-binding debate. But that aside, the traditional disk storage method is no longer viable even for smaller health organizations.

Before choosing a storage system or deciding between late-binding and early-binding techniques, health organizations need to consider the organizational needs for the storage, choose a data storage plane that will help the organization remain agile and form a data health storage plan.