DATA & ANALYTICS
Your first analytics project on AWS starts with a business question
Choose one decision, agree the metrics, inspect the data, and validate a focused analytics pilot before expanding it.
1. Start with a decision someone needs to make
The first analytics project for a growing Thai business should answer a question that changes an action. “We need a dashboard” describes a deliverable. “Which product groups need replenishment before the next weekly order?” describes a decision, an audience, and a deadline. AWS guidance similarly puts business goals ahead of technology selection.
Choose a question that matters to one business owner and can be tested with accessible data. Write down the current process, the delay or uncertainty it creates, and what the owner will do differently with the answer. Use these answers to judge whether the project deserves expansion.
- Name the person who makes the decision and how often they make it.
- Identify the action the analysis should support and the consequence of a wrong answer.
- Record a baseline, such as report preparation time or the number of unresolved stock questions.
2. Agree what the numbers mean
Teams can share a chart while disagreeing about its meaning. Before building, define the metric, its calculation, its level of detail, and its exclusions. For sales analysis, distinguish orders placed, invoices issued, cash received, cancellations, and returns. Select the definition appropriate to the decision.
ASEAN expansion introduces details that can distort comparisons: currencies, reporting cutoffs, local time zones, and different product codes. Agree how these are handled and show the reporting basis in the output. Keep original values where needed to explain a transformed result.
- Create a small glossary with an owner for each business definition.
- For currency conversion, record the rate source, rate date, and chosen reporting currency.
- Choose whether the comparison uses order date, shipment date, or another agreed event.
3. Check the data before promising the answer
Inspect a representative sample from the systems that actually hold the records. Include normal transactions and exceptions, such as duplicate orders, missing product codes, partial shipments, and late returns. Data availability means more than obtaining a file: the information must arrive in time and be understandable enough to support the decision.
Make a source register that records ownership, access, refresh timing, known quality issues, and fields required for the pilot. Limit personal information to what the question needs. Confirm permitted users, storage locations, and retention requirements with the responsible business stakeholders before copying data.
- Check whether records can be joined using stable identifiers across systems.
- Define what happens to incomplete records: reject, flag, or include with a visible limitation.
- Assign a person to resolve source-data issues rather than silently fixing every problem downstream.
4. Choose an AWS data flow that fits the question
Sketch a short path from source data to a usable answer: ingest, store, transform, query, and present. AWS offers service categories for these stages, including object storage, data integration, analytical querying, data warehousing, and business intelligence. Select components against the actual volume, freshness, access, and operating needs.
A weekly purchasing decision may work with scheduled batch updates. A use case requiring immediate action needs a different evaluation. Start with the smallest complete flow that meets the agreed need, then document how it will be operated. Include logging, failed-load handling, permissions, and expected usage in the design.
- Check that the required AWS capabilities are available in the intended Region.
- Estimate costs using expected data volume, processing frequency, queries, storage, and users.
- Name the owner for refreshing the data and investigating failed or delayed updates.
5. Validate the answer and the working process
Give users a pilot with a limited set of agreed metrics. Reconcile its results against known source records and explain discrepancies before widening access. Test whether the intended audience can interpret the output and decide what to do next without the project team standing beside them.
Define acceptance criteria before the demonstration. They should cover data accuracy, freshness, access boundaries, and the usefulness of the decision. Measure the same metrics again after real use and compare them with the baseline. A dashboard being viewed is useful feedback, but it does not establish that the business outcome has improved.
- Show when data was last refreshed and flag missing or incomplete periods.
- Test access using the actual user roles planned for the pilot.
- Record user decisions, remaining limitations, and who supports the report after handover.
6. Illustrative scenario: replenishment across two markets
Imagine a fictional Thai consumer-goods distributor selling in Thailand and Malaysia. Its first question is which product groups need review before the weekly purchasing meeting. The pilot combines order lines, stock snapshots, and purchasing lead times for a limited product range. This example illustrates a method; it is not a customer result.
The team agrees how to treat cancelled orders and reserved stock, then checks a sample with purchasing staff. The output highlights groups for review and explains its inputs; buyers retain the purchasing decision. Expansion to more products or markets depends on validated data and demonstrated usefulness, with operating costs reviewed alongside benefits.
- Write a one-page brief covering the question, owner, sources, definitions, and acceptance criteria.
- List unanswered data questions and resolve those that could change the decision.
- Choose a review date to continue, revise, or stop the pilot based on evidence.