Connected data sources
Map relevant databases, application exports and available APIs. Build agreed ingestion and transformation pipelines, including checks for failed loads and missing records.
Data platform & analytics on AWS
Bring your data together.
Make decisions with clarity.
When sales, stock and operational data sit in separate systems, even a simple business question can require hours of spreadsheet work. We help SMEs and mid-sized businesses in Thailand and ASEAN build an AWS data foundation around the decisions their teams need to make.
Discuss your data projectFrom source systems to useful answers.
Start with a focused business question, then build the data flow and reporting needed to answer it.
An AWS data foundation
Choose storage and query services suited to your data and reporting needs. This may include a data lake, warehouse or a simpler starting architecture.
Consistent, governed data
Agree business definitions, access rules and data owners. Add quality checks for issues such as duplicate records, inconsistent dates and incomplete product codes.
Dashboards people can use
Build focused views for agreed questions, such as branch sales and inventory exceptions. Document measures, refresh timing and how users can interpret or investigate the figures.
Build around a business question.
Define the decisions
Choose priority questions, report users and measures of success with your business and IT teams.
Assess the available data
Review sources, access, history, quality and refresh needs. Identify gaps before agreeing architecture and delivery scope.
Build and validate
Develop the agreed pipelines and reporting. Reconcile sample outputs with source records and review definitions with business owners.
Hand over and improve
Document operation, access and refresh schedules. Train the agreed users and plan subsequent improvements from their feedback.
Illustrative dashboard · Fictional data
Sales and stock, in one business view.
A fictional monthly snapshot showing the kinds of questions a dashboard could support: which branches need attention, and where should the team investigate stock?
Swipe or scroll to see the full table.
| Business unit | Monthly sales (THB) | Product SKUs to review |
|---|---|---|
| Branch A | 1,250,000 | 18 |
| Branch B | 860,000 | 9 |
| Branch C | 1,040,000 | 14 |
All names and figures are invented for demonstration; they are not customer data or promised results. In a real dashboard, the reporting period, sales definition and stock-review rules would be agreed with your team.
Questions before you start
Can we start with spreadsheets?
Yes. We can assess structured spreadsheet exports as initial sources, including their format and ownership. Automated connections can be considered when source systems and project scope support them.
Do we need a data lake and a warehouse?
Not necessarily. We choose an approach based on data types, volume, reporting questions and operating needs. A focused reporting requirement may suit a simpler architecture.
Will dashboards update in real time?
Refresh frequency is agreed around business needs, source-system capabilities and cost. Scheduled updates may be sufficient; real-time processing requires a specific design and scope.
How do you handle access and data quality?
We agree data owners, permitted users, quality checks and exception handling. Sensitive fields, storage locations and retention requirements are considered during design; business owners validate the definitions.
What determines project and running costs?
Source count, data preparation, integrations and dashboard complexity affect implementation. Storage, processing, query volume, refresh frequency and reporting licences contribute to ongoing costs.
Your first step
Scope your first useful data project.
Start with a discovery discussion around one reporting challenge. We identify source systems, intended users and data gaps, then propose a manageable first scope.
Discuss your data projectHelpful for our first conversation
- A business question your existing reports struggle to answer
- A list of source systems and representative, appropriately redacted reports
- Expected users, refresh needs and known data-quality issues