Data Analytics vs. Data Science: The SR&ED Divide

"We process millions of rows of data using advanced algorithms."

This is a common opening line in SR&ED claims from software companies. However, simply handling a lot of data does not automatically qualify as Scientific Research and Experimental Development. The CRA differentiates sharply between standard data analytics and true data science R&D.

Routine Data Analytics (Ineligible)

If your team is using commercially available tools (like Snowflake, standard SQL, Tableau, or out-of-the-box AWS services) to aggregate, clean, and visualize data to generate business insights, this is standard data analytics.

Even if the data pipelines are complex and the resulting dashboards are highly valuable to your business, the technological problem of "how do we organize this data?" has already been solved by the creators of those database engines. Connecting APIs and writing complex SQL queries is considered routine engineering.

Experimental Data Science (Eligible)

Data work becomes SR&ED when the volume, velocity, or variety of the data breaks the commercially available tools, forcing your team to invent new ways to process it.

Example 1: Novel Machine Learning Architectures

Using a standard random forest algorithm from the scikit-learn library to predict customer churn is not SR&ED. However, if that standard algorithm cannot handle the dimensionality of your specific dataset without catastrophic memory failures, and you must invent a custom, distributed tensor-processing architecture to train the model, that underlying engineering work may be eligible.

Example 2: Real-time Ingestion Bottlenecks

If a company is trying to ingest and analyze streaming IoT data with latency requirements under 5 milliseconds, standard message brokers (like Kafka) might fail under the specific network topology. The effort spent developing a custom, low-latency data ingestion protocol to solve this uncertainty could qualify.

The Key Differentiator: The Goal

Ask yourself: Was the goal to find a business insight (e.g., "What product drives the most retention?"), or was the goal to solve a technological limitation (e.g., "How do we compress this specific binary data structure by 80% so it can be transmitted over a weak cellular network?").

Only the latter qualifies for SR&ED. When writing your claim, ignore the business value of the data and focus entirely on the computational hurdles required to process it.