Struggling With Slow Data Product Delivery? Here’s Where to Automate
In today’s data-driven economy, the speed at which you deliver data products directly affects your organization’s ability to make timely decisions.
Yet many teams still face bottlenecks across the data pipeline, from ingestion to serving.
If your data delivery is sluggish, automation is not just an option; it’s a necessity.
But not all parts of the data lifecycle are equally ripe for automation. Let’s break it down.
Prime automation points in your data product pipeline.
Opportunities from Bronze to Gold
The modern data stack often follows a layered approach — Bronze, Silver, and Gold, each representing a maturity stage in the data processing pipeline.
Ingest
Complexity: Medium
This is where raw data enters your systems. You must try to automate the things that can be easily automated, think of:
which tables and columns to load
basic things specific to a specific source
…
Risk: I’ve seen organizations try to reinvent the wheel here. Use common sense.
This is typically a set and forget task where the cost of automation is often higher than the profit.
In the end, every source has it’s own “speciallekes”
Define structure
Complexity: Medium
This is where you define the structure of you parquets, CSV’s, JSON’s, Images, … Here is option to automate per filetype. However keep in mind, this is often more parametrization than automation. The good thing of parametrization is that it forces you to use a pre-defined pattern which ensures consistency in developments.
Historize
Complexity: Easy
Every input is structured and it is clear that this needs to be historized. Easy peasy. create the code to easily based upon the input apply the required steps to historize the data in the target table, typically delta parquet in this stage. Note: Make sure you support schema drift.
Business Logic
Complexity: Hard
Hard. Did I mention real hard? This is the crux. This is where the data of your source systems is translated towards a data model that is fit for purpose. This can be the first step towards facts and dimensions or just a big table as a feature store for machine learning.
Serve
Complexity: Easy
This is where you already have the transformations and structure for your tables in place that meets your fit for purpose. You still need you to load these to facts and dimensions, tables for your feature store(s) or other data products. This loading mechanism can be heavily automated and parametrized. Doing this speeds up data product delivery and consistency in the architecture.
Did I not mention cleaning? You are right. Cleaning can be done in multiple stages of the medallion architecture. Preferably as close to the source as possible. It is important to have a cleansing approach in place. However the rules itself are not easy to automate.
Where to automate to increase data product delivery?
Based on complexity and return on investment, here's where automation gives the biggest profit:
Automate ingestion where the cost of automation is clearly lower than the profit
Automate the historization pattern for sure!
Automate serving using predefined patterns. Think about: dimensions, facts, feature stores, …
⚠️ Be cautious on one of these promises:
Please pick us, we have a framework that automates 60, 70 or more % of the work. NO! In time spend this is not feasible.
Please pick us, we automate bronze & silver. This is 2 out of 3 layers, which automates 66% of the work. NO again.
Have realistic expectations. 30% can be automated. Which already provides hughe gains. It’s even not only about time spend, also about having a clean & consistent implementation patterns that matter for supporting the solution.
Conclusion
Delivering data products quickly and reliably is essential—but bottlenecks across ingestion, historization, and serving are slowing teams down.
While only ~30% of the data lifecycle is truly ripe for automation, smart, targeted automation delivers outsized returns in speed, consistency, and maintainability.
The biggest wins? Automating historization, serving logic, and ingestion patterns. All areas where Plainsight can immediately drive impact.
Plainsight brings realistic, ROI-driven automation, not inflated claims of "70% automation"—we focus where it counts: accelerating delivery without sacrificing quality or flexibility.
