A Late Binding Analytics Platform is a platform made available to healthcare organizations to help them in their data binding endeavors. Some organizations prefer to use Late-Binding techniques while others prefer to use Early-Binding techniques. Whichever the choice, both techniques have both advantages and disadvantages. We will discuss these below but first, let’s explain what each one means.
What is Late-binding
Late binding also known as dynamic binding can be defined as a computer programming mechanism in which the method being called upon an object or the function being called upon is looked up by name at runtime. To understand late-binding, think of when you take photos on your phone, you can choose to save them by name, location on in photos right away as you take them or you can choose to do that later or have your phone software do it for you later. In this case, the former is early binding while the later is known as late-binding. 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 the healthcare industry, 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 inputting the data does not necessarily have to know what kind of data or its properties is being entered. This way, the data can be stored and bound at a later time with the right names, codes and stored in folders for easy access. This also means more work later as you have to go through all the imputed data all over again and some find this time-consuming. In Late binding functions, methods, variables, and properties are detected and checked only at the run-time. It implies that the compiler does not know what kind of object or actual type of an object or which methods or properties an object contains until runtime. The biggest advantages of Late binding is that the Objects of this type can hold references to any object, but lack many of the advantages of early-bound objects
The Wisdom of Late-Binding
Health Catalyst’s Late-Binding architecture 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
- Quickly adapt to new questions and use cases
- Have the data they need to perform timely, relevant advanced analytics
- Minimize remodeling data in the data warehouse until the analytic use case requires it. Leverage the natural data models of the source systems by reflecting much of the same data modeling in the data warehouse.
- Delay binding to rules and vocabulary as long as possible until a clear use case requires it.
- Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to lock down for consistent analytics.
- Late binding in the visualization layer is appropriate for what-if scenario analysis.
- Retain a record of the changes to vocabulary and rule bindings in the data models of the data warehouse. This will provide a self-contained configuration control history that can be invaluable for conducting retrospective analysis that feeds forecasting and predictive analytics.
What is Early Binding
Early binding is the process where traditional data warehouses try to model the perfect database from the outset, determining in advance every possible business rule and vocabulary set that will be needed. This early binding practice is a time-consuming, expensive undertaking. In healthcare, business rules and vocabularies are dynamic and improve rapidly – and so do they use the cases that data linked across different source systems can serve. Mappings must be redone again and again as data models shift. Early binding architectures – like those espoused by Bill Inmon, Ralph Kimball, and others – force early data bindings into proprietary enterprise data models. Time has proven early-binding architectures to be inflexible, one-size-fits-all solutions, enforcing a compromised, least-common-denominator warehouse. When Early Binding is performed, an object is assigned to a variable declared to be of a specific object type. Early binding objects are basically strong type objects or static type objects. While Early Binding, methods, functions and properties which are detected and checked during compile time and perform other optimizations before an application executes. The biggest advantage of using early binding is for performance and ease of development. Put simply, early Binding means that the target method is found at compile time while in Late Binding the target method is looked up at runtime. Most script languages use late binding, and compiled languages use early binding