Data platforms with security and governance built in

Data platforms and analytics is the work of collecting, storing, and presenting operational data on a cloud platform so analysts and engineers can answer questions from a maintained dataset instead of a one-off export. SEYA Group designs and builds that platform for the sources you name, and sets up the reporting that sits on top of it.

Who this is for

CIOs and CTOs whose leadership team gets three different numbers for the same metric depending on who ran the query. IT managers who own a warehouse that cannot take a new source without a long project. Data and analytics leads whose analysts spend their week stitching exports together. Solution architects who have been asked to put a data platform on the cloud and need a design that the current team can run.

If your models already exist and the problem is getting them trained and released repeatably, that is MLOps. This page is about the platform underneath.

Problems we solve

• Data held in separate systems, with no agreed place where it comes together.
• A warehouse or set of application databases that cannot answer the questions the business has now.
• Decisions made from spreadsheets that were exported last quarter.
• Reports that nobody trusts because one person reloads them by hand.
• Access that is either wide open or so locked down that analysts work around it.

What you get

• Data strategy. A recommendation of which platform services and tools fit the sources in scope, which questions the platform is meant to answer, and in what order the sources should come on. Your team keeps the choice.
• Platform build. Ingestion pipelines, storage, and an access model for those sources, built as code on the cloud platform you select, with a streaming path where a named source and consumer need one.
• Data model and quality checks. Agreed definitions for the measures that matter, and checks in the pipeline that catch broken loads before they reach a report.
• Reporting and visualisation. Dashboards or reports on the datasets the platform holds, aimed at the decisions you name.
• Analysis and models. Analysis of those datasets and, when the brief asks for it, a first machine-learning model, with the handover to a repeatable release path scoped under MLOps.

How an engagement runs

• Scoping call. You describe the sources, the questions nobody can answer today, and who will run the platform.
• Discovery. We review the source systems, the current reporting, and the access rules with the owners of each, and agree the first set of sources and measures.
• Design and first build. The platform design is reviewed with your engineers and security owners, then the first sources and reports are built end to end.
• Extend and hand over. Further sources follow the same pattern, and your team takes over the pipelines, with runbooks for the loads and the access model.

Technical scope

Platforms are built on AWS, Microsoft Azure, or Google Cloud, using that platform's managed storage, warehouse, and pipeline services where they fit and open-source tools where they fit better. Infrastructure is defined as code, in Terraform where your team uses it, so environments can be rebuilt. Reporting uses the visualisation tool your organisation already licenses where possible, so analysts do not have to learn a new one.

What you need in place

Owners for each source system who can grant access and explain the data, a decision on which cloud the platform lives in (or an appetite to make that decision in discovery), and engineers or analysts who will take over the platform. Where no cloud foundation exists yet, the landing-zone work under cloud platform and migration comes first.

Out of scope here: sources beyond the agreed list, which are a further brief; training and releasing models on a repeating pipeline, which is MLOps; and pipeline security scanning, which is security and DevSecOps. When the work is building the data product, our sister company Mars Innovation Technology takes that build. SEYA's scope on this page is access, quality, and the security of the platform.

Related work

• Browse SEYA case studies across cloud foundations, migration, and delivery pipelines

Questions buyers ask

Do we need a data lake, a warehouse, or both?

It depends on the sources and the questions. Discovery recommends a storage shape for the first set of sources and explains what would change the recommendation as more come on.

Who owns the platform and the data?

You do. The platform runs in your cloud account, the pipeline code lives in your repositories, and the data never passes through SEYA systems.

How do you work with our analysts and engineers?

They are part of the build. Analysts define the measures and test the reports; engineers pair on the pipelines so they can extend them after handover.

How long until we see the first report?

The first build is scoped to a small set of sources so a working report appears early and the pattern can be checked. How early depends on how clean and accessible those sources are, which discovery tells you.

Can you work with the data we have on premises?

Yes. Sources can stay where they are and feed the platform over secured connections, or move as part of a wider migration. The strategy says which.

If your organisation needs one trusted place to ask questions of its data, book a data platform scoping call and bring the question nobody can currently answer.