Your maturity results

Reading your results…

How to read the score

The same tiers and recommendations the assessment scores against, in full.

The five maturity tiers

Ad Hoc 0–20 / 100

Your data and analytics capability is still ad hoc. Information is scattered, work is manual, and most decisions run on instinct rather than trusted numbers — which means talented people spend their energy wrestling data instead of using it. This isn't a failing so much as a starting line, and the upside is large: a short push on inventory, one central home for the data that matters, and one trusted dashboard will change how the organization feels almost immediately. Start narrow, prove value fast, and build from there.

Emerging 21–40 / 100

You're emerging. The instincts and some of the building blocks are here — a few reliable reports, informal owners, pockets of good data — but they aren't yet connected into anything dependable, and too much still rests on specific people and manual effort. The priority now is to consolidate: centralize your critical data, formalize who owns it, and turn your best reports into governed, self-refreshing assets. You're closer than it feels; the gains come from making what already works repeatable.

Structured 41–60 / 100

You've reached a structured level of maturity. Core platforms, dashboards, and definitions exist and are used, and data is a regular input to how you run the business — a real accomplishment. The work now is consistency and depth: close the gaps where spreadsheets and bespoke integrations still fill in, make governance and quality systematic rather than reactive, and tie analytics work to measurable outcomes. This is the stage where deliberate investment compounds fastest.

Scaled 61–80 / 100

Your capability is scaled. You operate a modeled platform, trusted BI, working governance, and a culture that expects decisions to be grounded in data — most organizations never get here. Your focus shifts from building to leverage: reusable data models, reliable integration by default, self-service on certified data, and measured ROI steering the roadmap. You're also well-positioned to move into advanced and predictive analytics on a foundation that can actually support it.

Optimized 81–100 / 100

You're operating at an optimized level — data and analytics are a genuine strategic asset, not a support function. Your platform, governance, integration, and decision culture reinforce each other, and advanced analytics sits on a foundation that can carry it. The risks at this altitude are complacency and sprawl, so the work is disciplined evolution: keep governance proportionate, prune what doesn't earn its keep, and adopt new capabilities deliberately. The opportunity is to turn this maturity into durable competitive advantage.

What we would recommend, by dimension and tier

Six dimensions, five tiers each. Your report above quotes the one line that matches your score; the rest are here for context on what the next tier up asks for.

Data Foundations & Architecture

Ad Hoc: Start by making the invisible visible: inventory every source that feeds a decision — ERP, finance system, CRM, HRIS, spreadsheets, SaaS exports — and name an owner for each. You don't need a platform yet; you need one documented list and a single, backed-up home for the data that matters most. This is the cheapest, highest-leverage week you'll spend all year.
Emerging: You have informal knowledge — now codify it. Publish a living data catalog and consolidate critical data into one analytics-oriented store rather than the operational systems it comes from. Prioritize the three or four sources that drive the most decisions and get them landing in one place on a predictable schedule.
Structured: Your central store exists but grew ad hoc; invest in structure before it calcifies. Define clear raw, curated, and serving layers, standardize naming across domains, and document your models so a new analyst can orient in a day. This is the right moment to evaluate a modern lakehouse such as Microsoft Fabric to fold in the pockets still living in local files.
Scaled: With a modeled warehouse or lakehouse in place, focus on reusability and resilience. Build shared, certified data models that multiple teams reuse instead of rebuilding, and harden the platform with monitoring and capacity headroom. Treat your curated layer as a product with a roadmap, not a project that is 'done'.
Optimized: Your foundation is a genuine asset — protect and extend it. Keep the catalog trusted and current, retire redundant pipelines, and steer platform investment toward emerging needs such as real-time data, advanced analytics, and cost optimization. Your edge now is disciplined evolution: adopt new capabilities deliberately rather than chasing every platform trend.

Integration & Data Sources

Ad Hoc: Manual extracts and PDFs won't scale, and they quietly drain your team. Pick your single most important source — usually the ERP or finance system — and establish one repeatable, scheduled ingestion you don't have to babysit. Getting one clean feed working end to end, landing in Fabric or Azure, teaches you the pattern for all the others.
Emerging: You're stitching CSV and Excel exports by hand; the goal now is repeatability. Standardize how you pull from your core ERP, finance, and HRIS systems, document the mappings, and stop reinventing each extract. Lean on native connectors and Data Factory pipelines so the next integration starts from a template, not a blank page.
Structured: You connect your primary systems, but each new SaaS or operational tool is bespoke. Build a small, reusable ingestion pattern so onboarding a new source becomes estimable rather than heroic. This is the point to standardize on API-based connectors and parameterized pipelines — a reusable pattern pays off every time another tool or entity needs to be brought in.
Scaled: With repeatable integrations in place, make connectors and pipelines your default. Productize your ingestion patterns so a new SaaS tool or acquired business is a configuration rather than a project, and add freshness monitoring so consumers can trust timeliness. You're close to integration being a competitive advantage rather than a cost.
Optimized: Integration is a strength — now leverage it. Give internal teams well-documented, reliable access to unified data from every core system, and push toward near-real-time where the operational use case justifies it. Guard against sprawl by folding one-off connectors back into your standard framework as they appear.

Reporting & BI

Ad Hoc: Spreadsheet chaos is costing you both accuracy and hours. Choose the five metrics leadership looks at most and build one governed dashboard for them in Power BI or your preferred tool, sourced from a single dataset. The point isn't more reports — it's one version of those numbers that everyone can trust.
Emerging: Hand-built Excel reports are eating your team's capacity. Move your recurring reports onto a BI platform backed by a shared dataset so they refresh themselves, and standardize the definition behind each metric as you go. Retire the manual version once the dashboard earns trust — running both defeats the purpose.
Structured: You have dashboards, but coverage is patchy and spreadsheets fill the gaps. Map where people still export to Excel and close those gaps with governed content, and define your KPIs so a metric means the same thing everywhere. Aim for the point where the routine question is answered without anyone building a new spreadsheet.
Scaled: Your BI layer covers the majors — now deepen adoption and self-service. Publish curated, certified datasets and enable business teams to explore them safely, so analysts move from report-building to higher-value analysis. Add drill-down and clear KPI definitions so trust in the numbers keeps compounding.
Optimized: Your BI is trusted and widely used; keep it sharp. Prune stale content, watch adoption and performance as living metrics, and make sure every certified dataset stays governed as the business changes. Your focus shifts from building dashboards to ensuring the right decisions get made from them.

Governance, Quality & Security

Ad Hoc: Ungoverned data and unclear access are real risk, not just untidiness. Start with two moves: name accountable owners for your most important domains, and lock down who can see sensitive data with role-based access and basic logging. Being able to answer 'who can see this, and is it right?' is table stakes.
Emerging: Stewardship is informal and quality is caught by accident — formalize the essentials. Stand up a lightweight data dictionary for core metrics, make stewardship part of a few people's actual roles, and add basic automated checks for completeness and validity on critical datasets. Tighten access reviews so least-privilege is real, not assumed.
Structured: You have partial ownership and manual checks; make governance systematic where it counts. Extend accountable stewardship and a maintained glossary across your critical domains, and replace after-the-fact quality checks with automated rules that alert on issues. Put sensitive-data access on a regular review cycle with logging you'd be comfortable showing an auditor.
Scaled: Governance is working — mature it into a trusted operating rhythm. Trend data quality as a metric with owners and thresholds, keep the business glossary living and used, and test your security controls (access, encryption, audit) rather than assuming they hold. Governance should enable speed and trust, not add red tape.
Optimized: Your governance is a strength; keep it proportionate and proactive. Treat data quality as a product metric, run periodic security testing across the data lifecycle, and ensure governance stays light enough that people use it willingly. The goal is trust by design, sustained as the organization and its data grow.

Analytics-Driven Operations

Ad Hoc: Decisions running on instinct cap how fast you can improve. Pick one recurring operational decision and commit to bringing data to it every time — a weekly metric review is enough to start. Small, visible wins here are what build the appetite for more.
Emerging: Data informs the occasional big call but not daily operations. Choose a few key operational KPIs, put them in front of the people who own those outcomes, and make reviewing them a standing habit. As you go, note the results — you're starting to build the case that analytics work pays off.
Structured: Data is a regular input, but ROI and delivery are inconsistent. Define expected outcomes up front for your next initiatives so you can measure what they returned, and adopt a prioritized backlog with regular delivery cycles instead of reacting to whoever shouts loudest. Tie the work to revenue, cost, or quality so its value is legible.
Scaled: Most significant decisions use data and delivery is iterative — sharpen the value loop. Measure ROI on major initiatives and let those results steer your roadmap, and push metrics closer to frontline workflows so they drive action, not just review meetings. Keep delivery agile and outcome-focused without piling on process.
Optimized: Data is woven into how you operate — protect that culture and its ROI discipline. Keep pruning analytics work that doesn't earn its keep, invest where measured return is highest, and sustain fast, lightweight delivery as you scale. Your advantage is an organization that acts on trusted numbers by default.

AI & Advanced Analytics Readiness

Ad Hoc: You're descriptive today, and that's fine — resist the AI hype until the fundamentals are ready. The highest-value move is improving the data quality and structure that any future AI depends on, not launching a model. Pick one domain and get its data clean, complete, and well-documented; that groundwork is the real prerequisite.
Emerging: Interest is real, but the data isn't ready yet. Rather than chase a flashy pilot, identify one genuine problem where prediction would change a decision, and assess honestly whether the underlying data has the quality, history, and labeling to support it. Fix those gaps first — a disciplined 'not yet' now saves an expensive failure later.
Structured: You've piloted advanced analytics — the challenge is operationalizing, not experimenting more. Take one promising use case and put it into a real workflow with owners, monitoring, and a clear success metric. In parallel, invest in the data readiness — volume, quality, labeling — of the domains where advanced work will matter most.
Scaled: Several advanced use cases are live — make the capability repeatable and governed. Standardize how you identify, build, and deploy predictive and AI use cases, and put lightweight governance around them: ownership, monitoring, and bias and risk review. Curate the priority domains toward being feature-ready so the next use case starts faster.
Optimized: Advanced analytics is a genuine capability — now govern it deliberately as you scale. Keep experimentation disciplined with clear success criteria, maintain oversight for bias, drift, and risk, and be as willing to stop what isn't working as to scale what is. Your edge is a responsible, repeatable engine for turning data into advantage.

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