Data & analytics,
built to move decisions.
Modern data platforms, governed semantic layers, self-serve BI, embedded analytics and decision science, engineered as a product, not delivered as a stack of dashboards.
A complete analytics practice, engineered end to end.
From platform and modelling through BI, embedded analytics, decision science and governance, our practice covers the full delivery surface of a modern analytics estate.
Analytics strategy & operating model
Data and analytics strategy anchored to the decisions the business actually makes, target operating model, capability mapping, roadmap and business case, without the deck-ware.
Modern data platforms
Lakehouse and warehouse-native architectures on Snowflake, Databricks, BigQuery and Redshift, designed for scale, cost and openness, not vendor lock-in.
Analytics engineering
Warehouse-native modelling with dbt, versioned, tested and documented, so the numbers on a dashboard match the numbers on an invoice, and stay matched over time.
Semantic & metrics layer
One definition of revenue, active user and margin, expressed as code, tested in CI, and served consistently to BI, spreadsheets, embedded apps and AI. No more competing dashboards.
Self-serve BI & visualisation
Governed self-serve on Looker, Tableau, Power BI and Superset, with modelled data, documented metrics and an actual enablement plan, so answers stop bottlenecking on the data team.
Embedded & customer-facing analytics
Analytics shipped inside your product, dashboards, benchmarks and insights for your customers, engineered against multi-tenant, latency and cost envelopes appropriate to a paid feature.
Product & behavioural analytics
Event models, funnel and cohort analysis, experimentation and attribution, implemented on Amplitude, Mixpanel, Segment or open equivalents, instrumented to answer product questions, not vanity ones.
Decision science & advanced analytics
Forecasting, propensity, uplift, pricing, churn and marketing-mix modelling, engineered against the decisions they inform, so a number leads to an action rather than a slide.
Real-time & streaming analytics
Streaming pipelines on Kafka, Flink and warehouse-native streaming, powering operational dashboards, fraud, ops and personalisation with the freshness the use case actually needs.
Data quality & observability
Contracts, tests, SLAs, freshness and lineage, implemented as first-class platform capability, so data incidents are detected before the CFO sees them, not after.
Governance, privacy & AI-readiness
Cataloguing, access, masking, lineage and consent, aligned to GDPR, HIPAA, DORA and sector regimes, and structured so the same governed data is ready for BI, embedded analytics and GenAI.
Reverse ETL & data activation
Governed data pushed back into the systems that act on it, CRM, ad platforms, ops tooling, via reverse-ETL, so insight leaves the warehouse and lands where the work happens.
Move a decision first. Then move the platform.
A delivery model that ships useful analytics against a real decision within weeks, while the modelling, governance and platform work run in parallel underneath.
Frame
Working sessions to identify the decisions we're trying to move, audit the current data and analytics estate, and agree the measurable outcomes. The output is a phased plan with a first slice already scoped.
Model
Domain-driven data modelling in the warehouse, a shared metrics layer, and a documented set of KPIs the business actually agrees on. This is where most analytics programmes are quietly won or lost.
Deliver
Iterative delivery of the analytics surface, pipelines, models, BI, embedded views, decision science, integrated with your platforms and versioned in code, with tests and CI on the data itself.
Operate
Data quality, cost, freshness and adoption monitored as ongoing engineering disciplines. Enablement is designed in, so the team using the platform tomorrow is not the team that built it.
A modern analytics toolchain.
Warehouse, modelling and BI selection is driven by your existing estate, team capability and long-term economics, not by category analyst preference.
Snowflake
Databricks
BigQuery
Redshift
PostgresDdbt
AirflowDDagsterFFivetranAAirbyte
KafkaFFlinkSSparkLLookerTTableauPBPower BISSupersetMMetabaseCCubeAAmplitudeSSegmentHHightouchMCMonte CarloGEGreat Expectations
Python
Pandas
ElasticsearchAWS
AzureGCPProgrammes delivered on this practice.
What prospective clients ask us first.
If your question isn't addressed here, please reach us at hello@tekansa.com.
01Lakehouse, warehouse or both, how do you approach the platform decision?
It's a working decision, not a preference. For most organisations a warehouse-first architecture on Snowflake, BigQuery or Redshift is the fastest path to trusted analytics, with a lakehouse layer on Databricks or open table formats added where ML, unstructured data or open-format economics genuinely earn it. We size the platform against your data volumes, query patterns, cost envelope, existing skills and long-term openness, and we design so components can be swapped without rewriting the world.
02Why do you insist on a semantic / metrics layer?
Because in most organisations 'revenue', 'active user' and 'margin' quietly mean different things in different dashboards, and a lot of executive time is spent reconciling numbers rather than acting on them. A metrics layer expresses those definitions once, in code, with tests and version control, and serves the same numbers into BI, spreadsheets, embedded views and AI assistants. It is the highest-leverage engineering investment we see in most analytics programmes.
03How do you handle data quality, reliability and trust?
As first-class engineering, not as a quarterly clean-up. We implement data contracts between producers and consumers, tests on models in dbt, freshness and volume SLAs, lineage that traces a number back to the source system, and observability that pages the right team before the CFO opens a dashboard. Incidents are triaged the same way software incidents are, with owners, runbooks and post-mortems, so trust in the data compounds rather than erodes.
04Where does AI fit into your analytics practice?
In two places. First, as a way of using the data, forecasting, propensity, recommendation, anomaly detection and generative interfaces on top of governed data. Second, as a way of interacting with analytics, natural-language querying, semantic-layer-aware assistants and embedded copilots. In both cases the value is bounded by the governed data model beneath, which is why we invest so heavily in the semantic layer, quality and governance before layering AI on top.
05How do you approach data governance, privacy and compliance?
Cataloguing, access control, masking, lineage and consent are designed into the platform from day one, not retrofitted before an audit. We align to GDPR, HIPAA, DORA and sector-specific regimes where they apply, and structure the estate so the same governed data can safely serve BI, embedded analytics and AI use cases. Evidence trails, model cards, data contracts, lineage records, are engineered artefacts that ship with the platform.
06How quickly can we get value from an engagement?
First useful analytics typically ship within the first 6 to 10 weeks, a scoped slice against a real decision, on a real platform, with a real metrics layer beneath it. The larger platform work runs in parallel. We deliberately design engagements so a stakeholder sees a moved decision before the platform work is finished, rather than after, that's usually what earns the mandate for the rest of the programme.
Let's discuss your data & analytics programme.
Share a brief overview of the decisions you're trying to move and we'll arrange an introductory conversation with the relevant practice leadership. Every enquiry receives a direct response.