The Healthcare Cloud Big Data Checklist

The Healthcare Cloud Big Data Checklist

The Healthcare Cloud Big Data Checklist
You’re ready to start capitalizing on your data to uncover new insights. And you’re sold on the cloud to make big data simpler to implement. But not all big data platforms—and not all clouds—are the same. If you’re going to fast-track your journey to data-driven healthcare innovation, be sure your solution checks all of the following boxes.

With advances in Hadoop and open-source big data tools, it’s relatively easy today to build something that can collect lots of data (or connect to something that does that in the cloud). It’s much harder to create an enterprise-ready platform. How does the system address all the steps after basic data collection—parsing, cleansing, extracting—so that your data is analytics-ready? How does it handle secure access and encryption? How will it tie into your identity framework? If a system hasn’t been built from the ground up with those complex considerations in mind, it shouldn’t go anywhere near your production environment.

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Most healthcare organizations don’t want or need to build up in-house expertise in Hadoop and big data system architecting—effectively, launching a new tech startup inside their organization. They just want to get more insight from their data.

Make sure your solution is an actual “solution” to that problem: That it does all the heavy lifting to provide a cohesive, analytics-ready platform, instead of just aggregating your data and leaving you to figure out the rest. That it’s built from the ground up for security, granular access control and comprehensive auditing. And that you have a single point of contact if you run into problems.

There are all sorts of options now for pre-integrated and cloud-based big data platforms. But very few were designed with the unique complexities of healthcare in mind.

Make sure your solution has data integration features to quickly ingest, and automatically catalog and index all of your data—structured and unstructured, simple and complex. It should provide data curation and transformation features to extract and enrich data from the full range of healthcare sources—text documents, images, EMR data, microbiome profiles, and more.  And it should make it easy to use third-party and custom analytics and data science tools to mine that data for insights.

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