Data and Analytics
Trusted, governed data connected to the decisions that matter
Most organisations do not have a data shortage. They have one number reported three different ways, a month-end that runs on spreadsheets, and no agreed owner for the definitions everyone argues about. We fix the foundation first, then the reporting layer, then the forecasting, so analytics becomes something the business acts on rather than re-checks.
Choose your platform
Business outcomes
Reporting your executives trust, produced once, governed properly and ready for the AI work that comes next.
What changes for the business, and how we agree to measure it before work starts.
- One agreed set of operational numbers
- Finance, operations and the executive team work from the same definitions of revenue, margin, utilisation and stock position instead of reconciling three versions in the meeting. The measure agreed up front is how many reported metrics have a single owner, a written definition and a traceable source.
- The reporting cycle stops eating the week
- The people who currently rebuild the same workbook every period get that time back for analysis and exception handling. We baseline the hours spent on manual preparation before we start, then measure the same hours once the pipeline is live.
- Numbers that arrive before the decision
- Operations and project managers see yesterday's production, dispatch or site position at the start of the day rather than waiting for a monthly pack. The agreed measure is data latency: how old a number is at the moment someone acts on it.
- Forecasts that can be argued with
- Demand, cash and resourcing forecasts come from a documented model with visible assumptions, so a planner can challenge one input instead of distrusting the whole output. Forecast accuracy is tracked against actuals over agreed periods and reported openly, including the periods it misses.
- A foundation an AI project can stand on
- When the business is ready for assistants, extraction or prediction, the underlying data is already modelled, permissioned and catalogued, which removes the most common reason pilots stall at the proof stage. Readiness is scored against a checklist agreed at assessment covering lineage, ownership, sensitivity classification and access control.
- Platform cost you can attribute
- Platform owners see capacity, storage and query cost broken down by workload and business unit, so an expensive report becomes a conversation rather than a surprise on the invoice. We agree a monthly cost view and the thresholds that trigger a review.
Common client problems
What we usually hear first.
These are the sentences that start most engagements, and what we do about each one.
Every department brings a different number to the same meeting and we spend the first twenty minutes arguing about whose is right
We trace each contested metric back to its source field, write down the definition the business will actually agree to, and implement it once in a shared semantic model. Reports then reference that model instead of each team's private calculation.
Our month-end runs on a spreadsheet that one person maintains, and she is going on long service leave
We work through the workbook with the person who owns it and rebuild its logic as documented, version-controlled transformations in the platform. The output stays familiar to the audience while the process stops depending on one laptop and one memory.
We bought a BI tool two years ago and people still export everything to Excel
That usually means the model does not answer the question people actually have, or they do not trust it yet. We sit with the heaviest exporters, work out what each export is really for, then rebuild the model and the measures around those decisions before anyone touches a visual.
We have a data lake and nobody can tell me what is in it or who is allowed to see it
We catalogue the estate, assign ownership and sensitivity classification, and archive or delete the datasets no one can justify keeping. Access is then granted through identity groups tied to roles, so who can read a table becomes a configuration item rather than a guess.
Our reports are always a month behind, so by the time we see a problem the money is already spent
We separate the small number of measures that genuinely need to be current from the many that are fine monthly. Streaming or incremental ingestion is applied only to that subset, which keeps the cost proportionate to the value of knowing sooner.
We want to do something with AI but we keep being told our data is not ready and nobody explains what that means
We turn readiness into a specific, testable list: which entities are modelled, where lineage breaks, which fields carry personal information and what access control exists today. You get a remediation plan with sequencing and effort attached, not a verdict.
Capabilities
What this domain covers.
- Data platform strategy and target-state architecture
- Data estate and AI-readiness assessment
- Lakehouse and warehouse design
- Pipeline engineering and orchestration
- Legacy database and warehouse migration
- Dimensional and semantic modelling
- Self-service business intelligence enablement
- Real-time and streaming analytics
- Data quality rules and reconciliation testing
- Master and reference data alignment
- Cataloguing, lineage and sensitivity classification
- Forecasting and predictive modelling
- Row-level and column-level access design
- Capacity, storage and query cost optimisation
How we deliver
From assessment through to the day we are still operating it.
- 01
Assessment and advisory
We start by establishing what your data estate actually is, what it costs to run and where the distrust in the numbers comes from. The output is a costed, sequenced plan rather than an architecture diagram.
- Inventory every source system, extract, database and reporting spreadsheet currently feeding a management report, including the ones maintained privately on desktops.
- Interview the people who prepare and consume the top reports, and record how long each pack takes to produce and where it is corrected by hand.
- Trace three to five contested metrics end to end, from source field to the figure on the slide, and document exactly where the versions diverge.
- Review current licensing, capacity and storage spend so the business case for change compares like with like.
- Score readiness across ownership, definitions, quality, lineage, sensitivity classification and access control, then rank each gap by what it blocks.
- Produce a sequenced roadmap with effort, dependencies and the success measures we will report against after go-live.
- 02
Architecture and implementation
We build in increments that each deliver a report someone uses, rather than a platform programme that shows nothing until year two. The first release is normally one business domain taken end to end.
- Design a layered architecture with raw landing, cleansed and conformed, and business-ready serving layers, using naming standards agreed before the first table is created.
- Build ingestion per source with incremental loading, watermarking and replay, so a failed run can be re-executed without duplicating rows.
- Implement transformation logic as version-controlled code with a deployment pipeline across development, test and production environments.
- Model the business layer dimensionally with documented measures and hierarchies, so new reports extend the model instead of recreating its logic.
- Add data quality tests at each boundary: schema checks, referential integrity, null and range thresholds, and reconciliation back to the source system total.
- Migrate legacy reports deliberately, retiring the ones nobody opens rather than rebuilding an entire back catalogue by default.
- 03
Security and governance
Analytics widens who can see what, so the controls have to arrive with the platform rather than after the first awkward discovery. We design access around roles the business already recognises.
- Classify datasets by sensitivity and record the owner, the retention position and the basis on which personal information is held.
- Implement role-based access through identity groups, with row-level and column-level restrictions wherever a report crosses business units.
- Register lineage and technical metadata in the catalogue so a reported figure can be traced to the system it originated in.
- Separate development, test and production environments, and mask or synthesise personal data in every non-production copy.
- Enable audit logging on access to sensitive datasets and route it into the monitoring the security team already reviews.
- Document the control set against the framework your organisation is aligning to, such as the Essential Eight or the Australian Privacy Principles, and record where gaps remain open.
- 04
Adoption and enablement
A dataset nobody opens is a cost. Adoption is planned as part of delivery, with named owners on the business side and training pitched at what people actually do each week.
- Agree named data owners and report owners before go-live, and record in writing what each one is accountable for.
- Run separate sessions for report consumers, self-service authors and the small group who will maintain shared measures.
- Publish a plain-language data dictionary listing each measure, its definition, its refresh schedule and who to contact when it looks wrong.
- Agree retirement dates for the spreadsheets and legacy reports being replaced, and confirm reconciliation before switching them off.
- Set up a request path for new measures and reports, with a short review step that decides whether a request extends the shared model or stays local.
- Track usage after launch and revisit any report with no activity after an agreed period.
- 05
Managed service continuation
Pipelines fail quietly, upstream systems change schema without telling anyone and cost drifts upward. We stay on afterwards to run the platform and keep improving it.
- Monitor pipeline and refresh outcomes, alerting on failures, late arrivals and row counts that fall outside expected ranges.
- Investigate and resolve load failures, including reruns and backfills, with the cause and the fix recorded each time.
- Review capacity, storage and query cost on a monthly cycle and recommend specific changes such as archiving, partitioning or resizing.
- Apply upstream schema changes in a controlled way, with the impact on downstream models and reports assessed before release.
- Report monthly on refresh reliability, data quality test results, usage and cost against the measures agreed at the start.
- Maintain a prioritised improvement backlog and work through it in agreed increments alongside business-as-usual support.
Related industries
Where this work has the most leverage.
Manufacturing and Distribution
Demand and inventory intelligence, production analytics and supplier automation built on data your planners already trust.
Logistics and Warehousing
Shipment and document automation, proof-of-delivery processing and exception dashboards that let a small team run a large network.
Construction and Property
Tender intelligence, addenda tracking and project reporting that keep estimators and contract administrators ahead of the documents instead of buried in them.
Professional Services
Governed enterprise search, document intelligence and secure copilots that respect matter confidentiality and conflict boundaries.
Related services
What usually comes with it.
Artificial Intelligence
AI that is chosen for a reason, costed before it is built and governed once it is live.
Cloud Modernisation
Ageing systems become a cloud platform your team can change safely, recover predictably and account for line by line.
Security and Governance
Close the ways in, know exactly who can do what, and answer an auditor or a client questionnaire from current evidence rather than memory.
Free discovery workshop
Start with a data and analytics discovery workshop.
Bring one challenge in this area. We will map the opportunity, the readiness gaps and a recommended next step.