Pillar 08 · Data Lake Optimisation
A data lake is a centralised store of all your business data — structured, semi-structured, raw — in one place, queryable by anyone or anything (including AI). We design, build, and optimise data lakes so your business runs on real data, not stale Excel exports.
Data Lake · Explained
Right now your data lives in 8 systems. Your CRM has customers. Your accounting has invoices. Your ad platforms have campaigns. Your support tool has tickets. Your warehouse has stock. None of them talk to each other in a way that lets you ask a real business question.
A data lake is a single store where all of that lands, gets normalised, and becomes queryable. You can ask: "Which marketing channels drive customers who have the highest lifetime value AND lowest support load?" — and get an answer in seconds, not days.
Why "lake" not "warehouse"? A lake holds everything — structured tables, semi-structured JSON, raw files — without forcing it into rigid schemas upfront. You decide later what shape it needs to be in.
Why does it matter for AI? AI is only as good as the data it can read. A data lake gives every AI agent and every dashboard one consistent, fresh, complete view of your business. Without it, you're training agents on slices and getting fragmented answers.
What We Build
Architect from scratch. Pick the stack (Snowflake, BigQuery, Databricks, or self-hosted). Build ingestion pipelines from every system you run. Land in normalised, AI-ready schema.
You already have one but queries are slow, schemas are messy, freshness is poor. We audit, fix, document, and tune.
Structure your data so AI agents can query it efficiently. Vector embeddings where they earn it. Metadata layers. Semantic naming. Stop AI hallucinating because the schema is opaque.
Once the lake exists, every dashboard and every AI agent reads from one place. We build the BI layer (Looker, Metabase, custom) on top.
Stack Options
Snowflake, Google BigQuery, Databricks, Microsoft Fabric, AWS S3 + Athena, ClickHouse, or self-hosted on Postgres. Ingestion via Fivetran, Airbyte, Hightouch, or custom Python. Transform with dbt, SQL, or Python. dbt for transformation. Lookups via vector DB (pgvector, Pinecone, Weaviate) where AI agents need semantic search.
Data Lake FAQ
Do I really need a data lake?
+If you're under 50 employees with simple data needs — probably not. A few SQL views on top of your operational databases can be enough. If you're 50+ employees, running 6+ systems, and AI agents need to read across them — almost certainly yes.
What does it cost to run?
+Cloud platform costs scale with usage. We help you pick the right stack and cost curve for your stage — self-hosted vs. managed, regional vs. global, hot vs. cold storage. The audit gives you the numbers up front.
Where does the data live? Compliance?
+Your choice. AU-region cloud (Sydney) or New Zealand-hosted available. Full DPAs. We never use your data to train models. PII handling follows New Zealand Privacy Act + AU Privacy Act by default.
Free Data Audit
Show us your stack. We'll tell you whether a data lake fits, what it would cost, and what it would unlock.