Definition:
Raw data is data in its original, unprocessed form. It has not been aggregated, averaged, filtered, or transformed in any way.
Granular data refers to data at its most detailed level — the individual records, events, and attributes that sit beneath the summary numbers you see on a dashboard.
In practice, the two terms are often used interchangeably. Both refer to the foundational layer of data that makes deeper, more precise analysis possible.
What is Raw Data?
Raw data is collected directly from its source such as an SDK, an API, a data pipeline, or a device event and stored without modification. It is the data before any processing has taken place.
On its own, a single raw data record might not tell you much. But in aggregate and when you have the freedom to query, filter, join, and analyze it yourself raw data becomes one of the most powerful assets available to a mobile marketing or product team.
Most analytics platforms display data in an aggregated form by default. They take the raw records and surface totals, averages, and summaries that are useful for quick reads on performance. Raw data is what lives underneath those summaries, and it is what you need when the summary is not enough.
What is Granular Data?
Granular data is data captured at the most detailed level possible. Where aggregated data gives you a total or an average, granular data gives you the individual records that make up that total each with its own set of attributes, timestamps, and identifiers.
A useful way to think about the difference:
| Data Type | What You See |
| Aggregated data | Total gross revenue from purchases on a given day |
| Granular data | Each individual purchase: product ID, currency, gross revenue, net revenue, quantity, timestamp, advertising ID, OS version, and more |
The aggregated view is fast and readable.
The granular view is where you can investigate, segment, and build.
Granular Data vs Aggregated Data
Aggregated data works well for monitoring. It tells you what happened at a surface level and flags when something looks off. Most dashboards are built around it because it is easy to read and quick to act on.
Granular data is what you need when you want to understand why something happened, or when the answer you are looking for cannot be found in a summary.
Some things you can only do with granular data:
- Calculate custom metrics that your platform does not surface by default
- Investigate a specific user's behavior or event sequence
- Join datasets from two different sources. For example, matching IAP revenue data with install attribution data to understand which ad network drove a purchasing user
- Build custom reports and dashboards tailored to your specific business questions
- Perform cohort analysis at a level of detail that goes beyond what pre-built reports allow
If your analysis requires more than what a dashboard can show you, you need access to raw, granular data.
Why Raw Data Matters
Freedom to Ask Your Own Questions
Dashboards answer the questions their designers anticipated. Raw data lets you ask questions that no one thought to build a report for and get true answers.
Precision Over Approximation
Aggregated data involves trade-offs. Averages smooth over variation. Totals hide the distribution underneath. Granular data gives you the full picture without those compromises.
Custom Metric Calculation
If you need a metric that your analytics platform does not calculate natively, granular data is where you build it. You define the logic, apply it to the raw records, and get exactly the number you need.
Dataset Joining
Some of the most valuable analyses require connecting data from multiple sources. To join IAP revenue data with attribution data, for example, you need raw records from both that share a common identifier like a user ID or device ID. Aggregated data cannot be joined in a meaningful way.
Audit and Investigation
When a metric looks wrong or a campaign result seems off, granular data is where you go to find out what actually happened. It gives you the receipts.
Raw Data and DataVault
This is exactly why Tenjin built DataVault and Raw Data Exporter. One is a data warehousing service that gives you direct access to your raw event-level data. Raw Data Exporter does the same, but is accessible directly from the Tenjin dashboard.
Rather than being limited to what Tenjin's dashboard surfaces, DataVault lets you query your own data, build custom reports, join datasets across sources, and run the kind of granular analysis that standard reporting cannot support. Your data stays yours in a stored, accessible, and ready to work way and ready use for your team needs.
For teams with specific analytical needs, data scientists who want to work directly with raw records, or anyone who has outgrown what a pre-built dashboard can offer, DataVault removes the dependency on aggregated views and puts the full dataset within reach.
Granular Data Examples in Mobile Marketing
To make this concrete, here are a few scenarios where granular data is the only way to get the answer you need:
Revenue Investigation
You can see total daily revenue on your dashboard. But to find out exactly how much a specific user spent on a specific product on a specific dat including the currency, quantity, and net revenue after fees, you’ll need the raw purchase event record.
Attribution Analysis
To understand which ad network drove an install that later converted to a paying user, you need to join the raw attribution record with the raw purchase event using a shared user identifier. Neither record is useful without the other.
Retention Cohort Analysis
Building a custom retention model for a specific acquisition cohort requires event-level session data for every user in that cohort, not a pre-aggregated retention rate.
Fraud Investigation
Spotting unusual patterns in click or install data, like patterns that might indicate invalid traffic, requires looking at the individual records, not the totals. If you spot these, you can use another one of our tools, Site ID Optimization, in the dashboard to block them on the site ID level.