I have worked at the forefront of data engineering in the Healthcare world where the struggles with data quality and completeness can be the difference maker in effective patient care and organizations hitting or missing their revenue goals.
My experience truly opened my eyes to the importance of having a functional data engineering team.
Led by the Chief Data Officer and kept functional by a group of data product managers, data analysts, data scientists, and data stewards, the success of the organization is truly data-driven only if the data engineering team can build an effective data model.
Simplifying the complicated process of building a product strategy can be achieved by acquiring supporting data and continuously validating the flexible product strategy.
What is Data Modeling?
Data Modeling is organizing and structuring data to support business processes and executive decision-making for effective returns.
It might sound like an oversimplification but then it is good to be reminded that for any organization to be successful the decisions need to be based on underlying data and believe it or not the data needs to be complete and accurate.
Data Modeling can be done in a handful of ways and I won’t go into details of them but share a resource that will be helpful -
Conceptual
High-level view of data structures and relationships
Logical
Detailed view of entities, attributes, and relationships
Physical
A specific implementation of the logical model in a database
In Healthcare, the data model is set up within the context of a large data warehouse. The specifics of the facts like information regarding a patient visit need to be stored with metadata and the dimensions that describe the details of the patient such as name, age, and insurance information that is stored in the physical data model.
Being data-driven to implement a product strategy means having a detailed understanding of the conceptual and logical layer of the data model and also being able to effectively navigate through the physical layer (DBMS) to back up the product decisions with actionable data.
Key Elements in Driving Product Strategy with Data
Product Strategy should provide a clear vision, goal, and strategy. The statement is true regarding the data model of an organization as well.
For an effective Product strategy, there needs to be a clear data strategy. A few steps that are common in both cases -
- Understand the business needs.
- Understand the user’s needs.
- Conceptualize and Normalize the vision with data.
- Validate and Refine.
For example, in Healthcare, product decisions are driven by a collective factor of the ecosystem, interoperability, regulatory & compliance needs, scalability, and users.
When the product decisions are based on so many crucial factors, the organization must have a robust data collection process followed by data preparation and monitoring.
A successful product is backed by a winning strategy which is again supported by a continuous flow of accurate and consistent data.
Continuous Insight for Product Success
Imagine walking into a product refinement meeting armed with data regarding predicted user impact based on user segmentation, pricing strategy based on forecasting models, and prioritized feature list as per impacted user. It would be such a pleasant change, wouldn’t it?
The confidence in the feature to be successful that the product team is building comes from the power of data.
The luxury of having KPIs and OKRs backed by data with actionable insights is a consequence of an effective data pipeline constantly updated with the latest data.
For example, in Healthcare the deviation from norm can be a matter of life & death. An efficient data engineering team will make sure the measurable outcomes are always up-to-date with real-world timelines.
Key Takeaways & Familiar Tools
The success of a product is dependent on having deep insight into -
- Customers
- Market
- Competitors
The deep insight comes from high-quality data. To maintain the quality of the data, the entire process needs to be consistent and automated.
To keep building on the deep insight, the organization needs resources and the right tooling to help their cause.
A few of the tools I have worked on in the past provide a helpful hand and may want to cover more in-depth in later articles.
Data Modeling Tools like the below help in making the process more visual -
- Lucid Chart
- PowerDesigner
- MySQL Workbench
While, Data Integration Tools like the below help in the process of data extraction, cleanup, and creation of data pipeline -
- Informatica
- Pentaho
- MS SQL Server Integration Services
- Apache Kafka
The topic of Data Engineering is vast and in my experience when coupled with the Healthcare industry it can get quite complicated and daunting for someone trying to get into the industry and looking from the outside in.
I have worked in the healthcare industry with Data Engineering teams as a Healthcare Consultant and as an ETL developer.
I hope to share detailed insights with a deep dive into one of the tools mentioned above.