End-to-end data partner for manufacturers
Datalloy connects plant systems, engineers the pipelines, and builds the warehouse and Power BI layer on top. Production, quality and finance stop assembling group figures by hand.
Azure · Databricks · SQL · Power BI. You talk to the people who build it.
We work from Bengaluru with manufacturers who already have ERP — SAP, Dynamics, or more than one plant system — and a Power BI file nobody will sign.
Data Architecture
Flowing
Source Systems
Data Integration
Connect and ingest
Data Engineering
Clean and transform
Data Warehouse
Store and historise
Data Modeling
Facts and dimensions
Power BI
Report and explore
Business Insights
Decide and act
What we do
Plants already have systems. What they lack is one modelled definition of product, plant, time and cost. Until that exists, every report is an argument.
ERP, MES, quality, WMS and the file sources still sitting in email.
Ingestion with incremental loads, retries and a schedule someone actually watches.
Clean and standardise part codes, plants and cost centres once, not in every workbook.
Facts and dimensions at a grain ops can use — production, inventory, quality, cost.
One definition of yield, scrap, OEE, on-time and margin.
Power BI for the decision, not a chart type. Lineage when someone asks where the number came from.
The difference
We don't just build dashboards.
We build the data foundation behind them.
A report is only as good as the pipeline, the model and the definitions underneath it. Datalloy takes responsibility for the whole chain — which is why the number on the dashboard holds up when someone asks where it came from.
Data Engineering
Pipelines that move and shape the data
Data Warehousing
Structure and history the business can rely on
Data Modeling
Facts, dimensions and governed measures
Business Intelligence
Reporting built around real questions
Business Decisions
The reason the platform exists
Services
Most work starts with one subject area — inventory, production or P&L — then grows. Not a twelve-month enterprise platform before the first trusted number.
Pipelines that run without supervision
Ingestion, transformation and orchestration built as engineered software — version controlled, parameterised, monitored and repeatable.
One definition of the business
Dimensional warehouses and data marts that hold history, resolve conflicting definitions and stay fast as volumes grow.
Reporting built around decisions
Power BI semantic models, governed measures and dashboards designed around the questions the business actually asks.
Numbers people stop arguing about
Validation, reconciliation and governance built into the pipeline, so quality is measured continuously instead of discovered in a meeting.
Azure data platforms, built properly
Lake, warehouse and orchestration layers designed on Azure with environment separation, cost control and deployment automation.
Move the data, keep the meaning
Legacy, ERP and database migrations executed with mapping, reconciliation and parallel-run validation before anything is switched off.
Architecture
Each layer has one job. Nothing quietly does two things at once.
Source Systems
Data Ingestion
Azure Data Factory
Data Lake
Raw and curated zones
Data Engineering
Databricks transformation
Data Warehouse
Azure Synapse
Data Modeling
Facts and dimensions
Semantic Model
Governed measures and hierarchies
Power BI
Dashboards and reporting
Business Insights
Decisions people can act on
Ingest
Source connectivity, scheduling and incremental loads into the lake.
Engineer
Cleansing, standardisation and business rules applied once, centrally.
Serve
Modelled data, governed measures and reporting people actually use.
Counts describe the reference architecture shown here.
Technology
Tooling follows the plants, not a slide deck. We typically build on Azure, Data Factory or Fabric, Databricks where volumes need it, SQL or Synapse for the modelled layer, and Power BI for the pack.
Microsoft Azure
Cloud platform
Azure Data Factory
Ingestion & orchestration
Azure Databricks
Data engineering & processing
Azure Data Lake
Lake storage
Azure Synapse Analytics
Analytical warehouse
SQL Server
Relational database
Azure SQL
Managed relational database
Power BI
Semantic layer & reporting
Talend
Data integration
Python
Engineering & automation
REST APIs
Source integration
Technologies Datalloy works with. No vendor partnership, certification or endorsement is implied.
Why Datalloy
An alloy is stronger than the metals it is made from. A data platform is the same — value is in how well the parts are joined.
One team accountable from ingestion to the number on the pack.
Source control, environments and monitoring as standard.
Every report answers a question someone actually asked.
You speak to the people who will do the work. Enquiries: contact@datalloy.com.
Industries
Multi-plant production, quality and supply data sitting in separate systems, with different part and plant codes. That is the work we take first.
Other sectors we will take when the pattern is the same (masters, month-end, ERP cutover): distribution, finance ops, logistics. We do not lead with healthcare.
Process
Seven stages, in order. Decisions before code.
Understand business requirements and data sources.
Who needs which answer, how often, and which systems hold the data behind it.
Analyze existing systems, data quality and architecture.
Profile the data, find the gaps and identify what is safe to build on.
Design the target data architecture.
Layers, storage, modelling approach and orchestration, agreed before build starts.
Build ingestion and transformation pipelines.
Parameterised, source-controlled pipelines with logging and alerting from day one.
Create the data warehouse and semantic model.
Conformed dimensions, facts at a defined grain and governed measure definitions.
Build Power BI dashboards and reports.
Reporting designed per audience, tested against the model and documented.
Monitor, improve and scale the solution.
Tune load windows and query performance, and extend the platform as needs change.
Examples
Representative engagements — problem, what was built, what changed. These are illustrative, not named clients, and they contain no invented figures. Real, approved stories will replace them.
Multiple ERP systems across plants produced inconsistent reporting. The same product and cost centre existed under different codes in each system, so group-level figures had to be assembled manually and rarely reconciled.
A move to a new ERP platform put historical reporting at risk. Legacy data structures were not compatible with the new system, and the business needed to compare performance across the cut-over point.
Month-end reporting was assembled from exports and spreadsheets. Figures could not be traced back to source, and every restatement required the previous version to be rebuilt by hand.
Fixed-scope first week
A fixed-scope first week. We map sources, grain and the three reports that matter. You get a one-page source-to-dashboard sketch and a fixed-price offer for slice one. No obligation after that.
Typical first slice: inventory, production or P&L. Weeks, not a year-long programme.
Tell us the ERPs, the plants, and which pack currently breaks. If we are not the right fit, we will say so.
