1. Connectivity Layer
The approach for connectivity should be planned in conjunction with your desired business use cases. This will dictate which sources are needed, which data elements should be pulled, and how performant your extraction method should be.
2. Data Lake
Once extracted, data must be loaded to a central repository. A data lake may be efficient to load and store data, but it’s raw in nature, and must be further processed for meaningful analytics.
3. Analytics and Enrichment
Data harmonization and enrichment is crucial to making sense of these data. This includes quality measure calculation, risk suspecting, cost & utilization analytics, and predictive analytics for patient identification. Furthermore, how well each of these activities are performed will influence your return on investment.
4. Data Warehouse
Consolidating all these data into a harmonized, enriched, data warehouse requires up front effort, but setting this up properly will allow your analyst teams to be significantly more capable and efficient for years to come.
5. Business Intelligence, Reporting and Application Integration
Your business intelligence and reporting needs should be planned out before you build your data pipeline. Ideally, you’ve built a data platform that makes these tasks easy and efficient from a technical standpoint. Furthermore, with the right vendor, you should also expect an expansive set of analytics content that you can deploy out of the box, that your teams can use as a starting point.