SCIENTE

Singapore analytics spend splits across BI platforms, data-engineering consultancies, and managed analytics services — and the costliest mistakes happen on data quality, governance, and lock-in long before a dashboard ships. Use this checklist to separate decision-grade vendors from those selling pretty visuals.

How to evaluate a data & analytics provider in Singapore

• Confirm where data engineering ends and dashboards begin: a serious partner spends most of the project on ingestion, cleaning, modelling, and governance, so be wary of anyone leading with visual mockups in week one.
• Pin down PDPA obligations before any data moves — agree who is data intermediary vs controller, where personal data is stored and processed, and how the PDPC breach-notification and retention duties are met across the pipeline.
• Demand a governed semantic layer (Power BI semantic models, LookML, ThoughtSpot Worksheets, or dbt) so 'revenue' and 'customer' mean one thing across every dashboard — not raw tables each team redefines.
• Model a 3-year TCO across the relevant cost shape (per-user, per-capacity, or per-query) and check PSG / EDG eligibility on the IMDA Tech Depot list, since the same tool through the wrong reseller may not be grant-eligible.
• Write data export and lock-in terms into the contract up front: full export of raw data, models, and metric definitions in open formats on exit, with no proprietary holdback.
• Require a written skills-transfer plan — documentation, paired delivery, and a handover phase — so your own team can sustain and extend the analytics rather than depend on the vendor forever.

Verify for SCIENTE

• Confirm key details directly with the vendor — this listing isn't vendor-managed yet.
• Ask for two recent Singapore client references you can speak with.
• Ask for a written scope of services before comparing quotes.
• Request evidence of relevant certifications and their current validity.

Questions to ask

• What share of this project is data engineering versus visualisation, and what is your plan for data quality and the governed semantic layer?
• Under PDPA, are you acting as our data intermediary, and where exactly will our personal and business data be stored and processed?
• On exit, what can we export — raw data, models, and metric definitions — and in what formats, with no proprietary lock-in?
• Can you share two Singapore clients of similar size and data maturity that I can speak to about decision-grade outcomes, not just dashboards delivered?

Prepared under the TechDirectory editorial byline with review credit June C; this label is not proof of identity, credentials, or a completed human review. This directory-authored checklist is not attributed to SCIENTE; editorial independence has not been independently audited. Editorial standards