Data Analytics Services

We usually start with a short assessment focused on two or three high-value decisions, such as revenue forecasting or margin reporting. We map the sources those decisions depend on, assess quality and agree on KPI definitions. That scope is small enough to deliver a first release within weeks, yet it establishes the data model, pipelines and governance patterns that later use cases reuse, so you avoid a long, abstract platform program before anyone sees value.

Not always. If your existing warehouse or lakehouse is reasonably healthy, we build on it. If it is slow, expensive or fragmented, we recommend a modernization path, often on Microsoft Fabric, Databricks, Snowflake or BigQuery, and phase it so business deliverables continue in parallel. Our recommendation is based on your volumes, skills, licensing and cloud strategy rather than on any preferred vendor, and we explain the trade-offs in writing.

The main cost drivers are the number and complexity of source systems, the state of data quality, the number of KPIs and reports in scope, security and compliance requirements, and whether you need ongoing support. Licensing and cloud consumption are separate and depend on your platform choice. After the initial assessment we provide a fixed-scope proposal or a dedicated-team estimate with a clear breakdown, so you can compare options before committing.

For a focused scope, users typically see a working first release, such as a set of certified KPIs and dashboards, a first planning module or a validated model, within one to three months, depending on data readiness. We work in short sprints with weekly written progress updates and sprint demos, so you see progress continuously rather than waiting for a single big launch at the end of the project.

We follow your security policies and apply least-privilege access, encryption in transit and at rest, and row-level security where needed. Where possible we work inside your cloud tenant and accounts, so data does not leave your environment. Access for our engineers is individually named, logged and removed when the engagement ends. We can also work with masked or synthetic data during development when production data is particularly sensitive.

Yes, and that is often the best model. We can deliver a defined project, provide a dedicated team that works alongside your analysts and engineers, or run the solution as a managed service. In every case we document architecture, data models and runbooks and run knowledge transfer sessions, so your team can own and extend the solution. Our Hanoi delivery hub provides working-hour overlap with clients in Asia-Pacific, Europe and North America.