About
Engineering Better Data.
Datalloy is a data engineering and business intelligence company focused on helping organizations turn fragmented data into a reliable foundation for decision-making.
We work across the whole path — connecting source systems, engineering the pipelines, modelling the warehouse and building the reporting on top. One team, accountable for the number at the end.
- Raw Data
- Reliable Data
- Structured Data
- Trusted Information
- Business Intelligence
- Better Decisions
The name
Data + Alloy
An alloy combines different materials to create something stronger and more useful than any of them alone.
That is the job. Datalloy brings different data sources, technologies and systems together into one reliable platform — and the result is worth more than the parts were separately.
It is also why we do not stop at the dashboard. The strength of a data platform comes from how well the layers are joined.
Positioning
What we take responsibility for
Datalloy is not a dashboard shop. The hierarchy below is the order the work happens in, and we own all of it.
- 01
Data Engineering
Pipelines that move and shape the data
- 02
Data Warehousing
Structure and history the business can rely on
- 03
Data Modeling
Facts, dimensions and governed measures
- 04
Business Intelligence
Reporting built around real questions
- 05
Business Decisions
The reason the platform exists
How we work
What clients get from us
Six things that shape every engagement, whether it is one pipeline or a full platform.
End to end
One team accountable from ingestion to the number on the pack.
Engineering first
Source control, environments and monitoring as standard.
BI around decisions
Every report answers a question someone actually asked.
Based in Bengaluru
You speak to the people who will do the work. Enquiries: contact@datalloy.com.
Process
How delivery runs
Seven stages, in order. Decisions before code.
- 01
Discover
Understand business requirements and data sources.
Who needs which answer, how often, and which systems hold the data behind it.
- 02
Assess
Analyze existing systems, data quality and architecture.
Profile the data, find the gaps and identify what is safe to build on.
- 03
Design
Design the target data architecture.
Layers, storage, modelling approach and orchestration, agreed before build starts.
- 04
Engineer
Build ingestion and transformation pipelines.
Parameterised, source-controlled pipelines with logging and alerting from day one.
- 05
Model
Create the data warehouse and semantic model.
Conformed dimensions, facts at a defined grain and governed measure definitions.
- 06
Visualize
Build Power BI dashboards and reports.
Reporting designed per audience, tested against the model and documented.
- 07
Optimize
Monitor, improve and scale the solution.
Tune load windows and query performance, and extend the platform as needs change.
Want to talk it through?
A short conversation about your sources and your reporting is usually enough to see the shape of the problem.