Healthcare Intelligence Network

Recent trends and emerging technologies are converging and a truly real-time enterprise will soon be an achievable possibility. As we move beyond traditional batch data to include streaming data, healthcare systems are seeing an unlimited and unyielding flow of data. This constant flow gives enterprises the ability to act on the information as it originates. Additionally, the right infrastructure allows the information to be routed to a data lake where enterprises can employ modern business intelligence solutions to derive actionable insights.

Of course, not every organization will need to be able to utilize truly real-time data, all organizations need to consider how they can best manage the increasing flow of data. Following are five steps to consider as you develop your enterprise information management (EIM) strategy:

1. Define/identify business objectives – Is real-time data needed?: While the use cases are innumerable, real-time applications of data by their nature require a much higher level of network resources than data that is sent every hour or every day, as batch processes often are. Consider this: do you need data immediately or is once per hour sufficient? Organizations must first consider how frequently information is needed and then set the strategy.

2. Find your edge and manage devices: Advancements in integration, messaging software, and Internet of Things (IoT) are building a new edge of the network. Mobile devices in the modern context can be virtually anywhere. To have success organizations need a data and device strategy to ensure that they can “read” the data they need, when they need it. Asset management strategies are also necessary for these devices to ensure that the information on them is controlled, secured, and properly maintained. An increasingly common example of device management at the edge is in healthcare, where tablets and mobile phones are increasingly used at the point of care.

3. Let Data Streams Flow into Lakes: As organizations gather and use different kinds of often completely unrelated data forms, it makes a lot of sense to create a data lake. Whether this is required goes back to the context of use and the business objective, but in all cases, it is crucial to develop a strategy to consolidate, store, protect and back up the data.

4. How Do Users Consume the Data?: Information for the sake of information can be distracting. Real-time data is no exception. Again, it is critical that its use be considered in the development of strategy. Let’s use monitoring again as an example. Do users need to know what is happening all of the time, or just when something is wrong, or some other key milestone? If they only need to know at certain points (problem detected, report generated, etc.), what is the best way to relay that information—an alert, a color-coded dashboard? The possibilities are limitless but should reflect a keen understanding of how the information will be used when needed most.

5. Build in Analytics to Mine That Gold: Information is dynamic and so are the use cases that motivate different users to seek and apply it. For many, the information they gain is descriptive, for some it’s diagnostic in nature, and for others it’s predictive. An example can be found in the predictive analytics used to proactively identify equipment failure and to guide the resulting maintenance and repairs. For others it is prescriptive and informs what is happening currently to help define what should be happening. Regardless, the enormous range of use demands that organizations seeking to benefit from real-time data first establish the infrastructure necessary to run analytics in a way that pulls out actionable, relevant information.

A move to real-time enterprise will require changes to virtually every part of an organization. It will take a great deal of time, attention and hard work; however, the benefits will be significant. The five steps discussed here can help healthcare organizations find and stay on the right path to becoming a real-time enterprise.

About the Author:

Jennifer Schwartz is an accomplished professional with special expertise in enterprise information programs, consulting, strategic planning, and mobile solution architecture. She has 18 years of experience with improving operational efficiency, reducing costs, and formulating strategic plans for her clients. As the Enterprise Information Management solution lead for CTG, Ms. Schwartz focuses on business process management and automation, providing best practice guidance, and executing special projects that help transform data into action. Jennifer works across industries, advising clients on the execution of projects to realize efficiencies.