The Foundation Every
Decision Stands On

Each system names customers, parts and suppliers its own way, so joining data means endless manual matching and every report tells a slightly different story.

• Build a harmonised master for customers, parts and suppliers
• Map and de-duplicate identities across the source systems
• Keep the master governed as new data flows in

One consistent version of the core entities, so reports finally reconcile and joins stop being guesswork.

After a merger, incompatible data models make group-level reporting a manual reconciliation each period, and leadership can't see the combined business clearly.

• Reconcile the two data models onto common definitions
• Harmonise hierarchies, codes and units across both
• Stand up unified reporting on the combined base

The merged business reporting as one, without a manual reconciliation every close.

There's no trusted, clean layer, so each analyst re-does the same cleaning, differently, and the numbers never quite agree.

• Build a governed, harmonised layer analysts share
• Encode cleaning and business rules once, centrally
• Retire the private spreadsheets that fork the truth

A shared clean layer everyone builds on, ending the re-cleaning tax and the disagreement it caused.

Moving to a new ERP or platform means migrating years of data no one fully understands, and a bad migration corrupts the exact records the business runs on.

• Profile and map source data before anything moves
• Cleanse, transform and reconcile against source counts
• Validate the target against the source before cutover

A migration that lands complete and reconciled, with the risk of silent loss designed out.

Legacy systems limp on purely because their history is locked inside, and every one kept alive costs licence, support and risk.

• Extract and preserve the history worth keeping
• Archive the rest in an accessible, compliant store
• Clear the path to decommission the old system

Legacy systems retired with their data safe and reachable, cutting the cost of keeping them alive.

The cutover is the scary moment: pull it too early and the business breaks, too late and the project drags and overruns.

• Migrate in staged, reversible phases with checks at each
• Run source and target in parallel to prove parity
• Cut over only once reconciliation passes

A migration that goes live on evidence of parity, not on a held breath.

When data quality is unmanaged, every team keeps a private version 'they can trust', which multiplies the versions and destroys the trust further.

• Define ownership, definitions and quality rules for key data
• Measure and publish data quality against those rules
• Certify the trusted sources and retire the shadow copies

Certified data teams actually trust, so decisions run on one source instead of many private ones.

When the regulator asks where a number came from, the answer takes weeks to assemble by hand, and gaps in lineage become findings.

• Capture data lineage from source to report
• Enforce access, retention and privacy controls
• Keep evidence audit-ready as a standing state

Compliance that's demonstrable on demand, not reconstructed under audit pressure.

Errors entered upstream flow through untouched and surface as failed orders, wrong reports and rework far from where they started.

• Profile data to find where quality actually breaks
• Add validation and cleansing at the point of entry
• Monitor quality continuously and alert on breaches

Errors caught at source, so downstream processes stop paying for upstream mistakes.

Without a shared KPI framework, each function optimises its own metric and the definitions conflict, so the business pulls in different directions.

• Define a KPI tree linking activity to strategic outcomes
• Agree single definitions and owners for each metric
• Cascade it so every level sees its line of sight

One aligned set of KPIs, so teams measure success the same way and toward the same goals.

Leadership drowns in reports but still can't answer the few questions that matter, because signal is buried under volume.

• Distil the vital few metrics leadership decides on
• Design one clear executive dashboard with drill-down
• Show trend, target and exception at a glance

A single executive view that answers the real questions, with detail one click away when needed.

Years of ad-hoc report requests leave a sprawling estate of overlapping, contradictory dashboards that cost money and erode trust.

• Audit the report estate for usage and overlap
• Consolidate onto a governed, consistent set
• Retire the redundant and the unused

A lean, trusted set of reports that agree with each other, at lower cost to run.

Business users can't answer their own questions, so the BI team is a bottleneck for even trivial asks and the backlog never clears.

• Build governed, well-modelled datasets users can explore
• Give them templates and training to answer common questions
• Keep governance so self-serve doesn't fork the truth

Users answering their own routine questions safely, with the BI team freed for the hard ones.

Ungoverned self-service let everyone build their own metrics, so now there are ten definitions of revenue and no one knows which is right.

• Publish certified datasets and metric definitions
• Separate a governed core from a sandbox for exploration
• Guide users to the trusted source by default

The freedom of self-service with the discipline of one agreed definition underneath it.

Expensive BI licences sit idle because users were never brought along, so they fall back to spreadsheets and the investment is wasted.

• Meet users at their real skill level with fit-for-purpose content
• Run enablement tied to their actual daily decisions
• Track adoption and close the gaps that block it

Analytics tools genuinely adopted, so the licences bought turn into decisions made.

Platform choices rest on polished vendor demos and analyst rankings, not on how the tool performs against the organisation's own data and needs.

• Turn real requirements into weighted evaluation criteria
• Run a structured PoC on representative data
• Score vendors on evidence, not on the pitch

A platform chosen on how it actually performs for you, not on how well it demos.

Proofs of concept drift without defined success criteria, so they end inconclusively and the decision falls back to opinion anyway.

• Define measurable success criteria before the PoC starts
• Scope it to the questions that actually decide the choice
• Judge the result against the criteria, not vibes

PoCs that give a clear, defensible verdict instead of an inconclusive shrug.

Requirements get discovered after purchase, when the shiny tool can't do the one thing the business most needed, and switching is expensive.

• Draw requirements from the people who'll use the tool
• Test the make-or-break needs explicitly in evaluation
• Surface gaps and workarounds before signing

Critical needs proven before purchase, taking the nasty surprise out of go-live.

Budgets are set on licence cost alone, ignoring infrastructure, integration, support and people, so the real bill lands as an unpleasant surprise.

• Model the full cost of ownership across the stack and its life
• Include infrastructure, integration, run and people cost
• Compare options on total cost, not sticker price

Decisions made on the true cost of ownership, with the hidden run-cost visible up front.

The monthly cloud bill keeps rising and no one can tie the increase to value, so finance can't tell efficient growth from waste.

• Break spend down by workload, team and use case
• Tie cost to the value each workload delivers
• Target the waste and right-size the rest

Cloud analytics spend understood and controlled, with cost mapped to the value behind it.

Analytics gets funded on faith and, later, no one can show the return, which makes the next round of investment a hard sell.

• Define the value the investment is meant to create
• Measure benefit against cost over time
• Report ROI in terms the business accepts

A quantified return that justifies the spend and earns the next round of investment.

The vision is bold but there's no roadmap, so everything is attempted at once, nothing finishes, and momentum drains away.

• Break the strategy into sequenced, deliverable phases
• Front-load quick wins that fund and prove the programme
• Map dependencies so the order actually works

A phased roadmap that delivers value early and builds toward the vision without stalling.

Competing demands with no objective ranking mean the loudest stakeholder wins, not the highest-value work, and the roadmap becomes politics.

• Score initiatives on value, effort and dependency
• Rank them into an objective, agreed backlog
• Make trade-offs explicit when priorities collide

A backlog ordered by value and feasibility, so effort goes to what matters, not who shouts.

A long build with no interim value loses sponsor confidence and funding before it ever lands the payoff it promised.

• Design an incremental delivery with value at each step
• Ship a working slice early and build on it
• Use each win to fund and de-risk the next

A programme that proves value continuously, keeping sponsors and funding on board to the finish.

Access grew organically and no one has pruned it, so sensitive data is far more exposed than anyone intends, a breach waiting to happen.

• Map who can access what against who should
• Enforce role-based, least-privilege access
• Monitor and review access as a standing control

Sensitive data locked to those who need it, shrinking the exposure surface.

Personal and sensitive data is scattered across systems with no map, which makes privacy compliance, and answering a subject request, nearly impossible.

• Discover and classify sensitive data across the estate
• Map how it flows and where it rests
• Apply controls, masking and retention to policy

A clear map and control of sensitive data, so privacy obligations can actually be met.

When security is an afterthought, the data platform ships with gaps that are expensive and disruptive to close later.

• Design encryption, access and audit into the architecture
• Segregate environments and secure the data in motion and at rest
• Bake in the controls auditors and regulators expect

A platform secure from the foundation, not patched after the fact.