Enterprises in many diverse fields are trying to make sense of the possibilities afforded by big data analytics. Taking advantage of the insights buried in the wealth of data that is available promises innovative solutions to stubborn problems and new ways to do business. Healthcare organizations and the patients they serve can achieve immense benefits through efficient big data analytics. Their challenge is coping with the flow of data effectively.
Big data is distinguished from traditional data by the three Vs which represent the volume, velocity, and variety of formats in which information is gathered for analysis. The amount of data and the speed at which it is generated overwhelms the capabilities of established methods for processing and analysis. Data is collected from a variety of sources, introducing both structured and unstructured to the mix.
Important Characteristics of Big Data
All data should not be treated equally. Some data sets or streams may be more useful for specific purposes. The first step in the productive use of big data is identifying what subset of information to use from all available sources. This entails taking a close look at a number of characteristics regarding the origin of the data to be used for analytics. These characteristics include:
Relevance – The data used for analytics must be relevant to the goals of the analysis. One of the challenges being faced by healthcare organizations is identifying the proper metrics and variables that will result in informative analytics.
Reliability – Unreliable data leads to incorrect analytical results and potentially harmful patient diagnosis. The unstructured nature of the information gathered in the healthcare field demands methods to standardize and clean data before it can be analyzed.
Accuracy and Stability – Data is generated dynamically in the healthcare industry. Keeping up with the constant changes while maintaining the accuracy of the information impedes the ability to perform viable analytics.
Diversity – The different types of data sources an organization is expected to assimilate poses a particularly difficult challenge. Using multiple data sources can lead to more robust analytics, but must be balanced with the problem of processing information in different formats.
Capacity – Handling the tremendous amount of data demands that healthcare organizations develop methods to store and access the information efficiently. They need to use the data for analytics without impacting the ability for it to be used by clinicians and practitioners.
Managing Big Data
The data that an enterprise gathers to perform analytics on it must be managed throughout its lifecycle. There are many aspects of managing the data that are critical to determining its present and future value to an organization.
Ingesting and cleansing the data is a required prerequisite to using it for analytics. The healthcare industry as a whole has been challenged with transforming the patient notes and charts associated with patient care into electronic health records that can be stored in a database. Data needs to be cleaned to ensure it is consistent, correct, and accurate to be useful for analysis.
Storing and protecting the accumulated data resources is the next challenge that needs to be surmounted. Security is essential in the healthcare industry and is a top priority driven by stringent regulatory guidelines. Data needs to be protected while it is stored and during processing and transmission. Failure to adequately address these concerns puts sensitive personal data at risk.
Maintaining data stores requires techniques such as data governance to ensure the validity of information used in clinical studies. One of the challenges with healthcare information is that some of it changes rapidly and dynamically while other items are fairly static. Examples are patient vital signs and addresses. Keeping diverse datasets coordinated adds complexity to the task of maintaining an organization’s data resources.
Finally, the data needs to be accessible so it can be used for reporting and presenting the results of analytic endeavors. Data may need to be shared by competing organizations such as when a patient changes healthcare providers or moves to a new state. It’s critical that the data can be manipulated so it can be used in multiple ways.
For a more in-depth discussion of big data and the healthcare field, download this IDERA whitepaper that explores these ideas further.
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