A data platform that makes every team's numbers agree — before the reporting starts
We consolidate the ERP, POS, Excel files, Google Sheets, and online systems you already run into one central data layer, agree a single definition for revenue, cost, and margin, then feed reports and dashboards that refresh themselves without anyone merging files.
- One set
- of metric definitions everyone quotes
- Many systems
- ERP, POS, Excel, Sheets, and online platforms
- Full history
- kept even when source systems retain little
- No disruption
- we read data without touching daily operations
The short answer
What is a data platform, and how is it different from a dashboard?
A data platform is the central layer that consolidates data from every system a business runs, normalizes it into one shape, holds one definition per metric, and keeps history. A dashboard is a screen that reads from that layer. Businesses that wire dashboards straight into several source systems end up with numbers that disagree — a problem no dashboard can fix.
Key facts
- A data platform consolidates multiple systems and holds one definition per metric
- Every dashboard and report reads the same layer, so the numbers always agree
- Queries hit the central layer, not the systems people use to work
- The central layer keeps history even when source systems retain little
Last reviewed:
What you get
What a data platform project covers
Not just charts — making the data trustworthy before anything is displayed.
Source connections
Pull from every system you run, via API, database, or export file — whatever it supports.
Data cleaning
Reconcile product codes and customer names that differ per system so they refer to the same thing.
Central warehouse
Stored in a structure designed for analysis, with the history your source systems discard.
Metric dictionary
Revenue, returns, cost, and margin are defined once and computed from there in every report.
Automated refresh
Schedules matched to how fresh each source can be, with alerts when a run fails.
Reports & dashboards
Connect the BI tool you already use, or build screens around the questions you need answered.
Access control
Define who sees what — a branch manager sees their branch, not the whole group.
Handover & docs
Delivered with data-model documentation, so your team or another developer can carry it on.
The layers
A data platform has four layers
Most businesses do not need all four at once — we start where it hurts most.
Ingestion
Pull data out of source systems on a schedule, with checks that nothing is missing.
Transform
Normalize raw data into one shape and reconcile codes that mean the same thing.
Semantic
Business metrics are defined once, so every report computes them identically.
BI
Dashboards, scheduled reports, and digests delivered to LINE or email.
Try it
How data from many systems comes together
Step through the path from source systems to the screen an owner opens.
Sales
฿82K
Profit
34%
Stock
5 ⚠
How we work
From data scattered across systems to a layer you can trust
We start from the business questions, not from picking a tool.
- 01
Assess what exists
Map where data lives, how far each system can export, and which numbers currently disagree.
- 02
Pick the first questions
Choose a few business questions that must be answered, and design the layer around them.
- 03
Build the pipelines
Connections and refresh schedules, with checks for completeness and duplicates.
- 04
Agree the definitions
Sit with each team until the disputed numbers have one definition — then write it down.
- 05
Connect and hand over
Wire the BI tool, build per-audience screens, and hand over with documentation.
Dashboards wired straight into every system
- Each team reports a different revenue figure
- Reports slow down because they query live systems
- History is unavailable — sources keep little of it
- Every source-system change breaks the whole report set
A designed central data layer
- One definition and one number everyone quotes
- Reports read the central layer, not working systems
- Real history to compare trends against
- A source change means fixing only the ingestion layer
Best for
More on data
More on data and BI
Pick by where your business is stuck right now.
Data Dashboard & Analytics service
The full data service, from connecting sources to deeper analysis.
A Power BI / Looker Studio alternative
Off-the-shelf BI vs a purpose-built dashboard, against your budget and team.
SME dashboard — the whole business
Sales, stock, profit, and team on one owner screen.
Turn Google Sheets into a dashboard
Build on the files your team already uses, without changing how they work.
What is a data warehouse?
The definition, and the signals that say a business needs one.
What is BI?
What business intelligence covers, and where an SME should start.
FAQ
Data platform & BI FAQ
Short answers to what owners ask before starting.
How is a data platform different from a data warehouse?
A data warehouse is the store of normalized data used for analysis. A data platform is the whole system around it — source connections, transformation, the warehouse, the metric definitions, and the presentation layer. Put simply, the warehouse is one part of a data platform, not a synonym for it.
Does a mid-sized business need a data platform?
Not always. If data comes from one or two systems and volumes are modest, pointing a reporting tool straight at the source is enough. The signals that it is time: teams reporting different numbers, reports slowing down enough to affect daily work, or needing history the source systems have already deleted.
Do we need Power BI or a specific tool?
The tool can be chosen later, and should be chosen around the team that will use it. What determines the quality of the result is the ingestion, transformation, and metric-definition work that comes before the display layer. We work both with the off-the-shelf tool you already have and with purpose-built screens when per-user licensing stops making sense.
How long does a project like this take?
It depends on the number of sources and how clean the existing data is, not on company size. Starting from two or three sources and a few business questions, a usable result typically lands within weeks. Multi-company groups with mismatched codes take longer, because the real work is reconciling the data.
How secure is the data?
Access is granted by role, data is encrypted in transit and at rest, and the accounts used to pull data are separate from normal user accounts. Personal data is handled on PDPA principles — retained only as far as the analysis requires, with anything that does not need to identify a person masked.
What do we need if we want to use AI on our data later?
Clean data with clear definitions — which is exactly what a data platform produces. Most failed AI projects do not fail at the model; they fail because the data fed in was inconsistent. Getting the data layer right first is therefore an investment that serves both today's reports and any AI work later.
Learn more
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Want every team's numbers to agree? Start with a free data assessment
Tell us which systems your data lives in and which figures your teams report differently. We'll tell you plainly how far that data connects and which layer to build first.
Chiang Mai team — we work with the real data of Thai businesses running several systems at once.