Data Reuse
What is Data Reusability in Healthcare?
By creating data that is easily accessible and reusable, we can unlock the full potential of data and use it to improve the lives of patients.
Examine overlooking backward compatibility in dataset modifications and how to streamline work, minimize challenges, and expedite insights delivery.
In the fast-changing world of data analytics, developers refine reports to optimize their effectiveness. However, a prevalent challenge emerges when managing fields and tables within datasets, introducing potential complications.
This blog examines a common data reuse pitfall - overlooking backward compatibility in dataset modifications and advocates for a strategic approach to streamline work, minimize challenges, and expedite insights delivery.
The Pitfall: Altering Fields and Tables
While incorporating new fields and tables into reports typically poses no issues, the real risks emerge when renaming or removing existing columns. The possibility of disrupting the visuals within a report emerges when specific fields and tables are absent. It is critical to provide developers with precise guidance on the matter.
For example, developers can employ versioning terminology like “v2” when creating non-backward compatible transformations, ensuring continuity within the same workspace. It also aids developers in understanding dependencies and potential impacts before making alterations.
The data lineage perspective becomes an invaluable tool when confronting defective reports. It aids developers in understanding dependencies and potential impacts before making alterations.
Adopting a Data Product Mindset
Developers should consider themselves "data product" developers rather than "report" developers. When creating reusable datasets, it is essential to consider how changes may impact other reports or visuals relying on that dataset. This mindset encourages a more careful, big-picture approach to change management.
Addressing backward compatibility pitfalls minimizes disruptions and delivers insights faster. As developers embrace their role as “data product” creators, they enable a more sustainable, streamlined approach to evolving datasets.
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