Properties of Big Data for Healthcare

Healthcare organizations must consider the essential properties of the analytics for big data that when commencing significant projects for analytics. Such features include relevance, reliability, soundness, diversity, stability, speed, and capacity.

Relevance refers to whether data is pertinent to specific use cases. Data scientists often cite the fact that correlation does not equal causation. Comprehending which elements of data connect to measuring and predicting the desired outcomes is essential for producing dependable results. For this purpose, healthcare organizations must grasp what features they have, whether these elements are sufficiently robust for analysis, and whether results are truly informative or merely exciting diversions. Establishing the viability of specific metrics and features require trial and error. Many projects for predictive analytics for big data currently emphasize identifying innovative variables for detailing certain behaviors of patients and clinical outcomes. This emphasis continues to be prevalent as more data sets become available.

Reliability refers to whether data can be trusted. This dependability may be even more important than access in the context of patient care. The integrity of datasets is challenging to confirm. However, healthcare organizations cannot utilize insights that data analysts may have derived from data that is noisy, biased, and incomplete. Data scientists, in general, spend a majority of their time cleaning up data before they apply it. Healthcare organizations are continuously struggling to improve the quality and integrity of their data. For example, healthcare systems allow unstructured entries such as free text and scanned images. Governance of data and information is a vital strategy that healthcare organizations must follow to ensure that their data is readily available, standardized, complete, and clean.

For more information on the other properties, refer to the 14-page whitepaper “Handling the Complexities of Analytics for Big Data for Healthcare”.

Click here to read the whitepaper.

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