Data & Analytics
Every failed AI project hits the same root, data that is inconsistent, fragmented or inaccessible. Architecture, engineering and analytics tied to results turn data into a basis for decisions and fuel for AI in production.
Do you recognize this scenario?
Three typical situations in mid-market and enterprise organizations that do not yet operate this capability as a system.
Three areas, three versions of the same metric
Monthly revenue changes depending on who answers. Board decision stays at the weighted average of opinions. Data does not fulfill function.
Dashboard grows, decision does not change
Each area has its BI. Each management consults its own. Integrated decision stays manual and late. Platform cost grows without proportional return.
AI over bad data
AI initiatives stall because data is not ready. Team accepts the state and fixes on the fly. Model enters production on a foundation nobody validated.
Five maturity levels, at your own pace
Nothing here is set in stone. In data and analytics, the assessment places the company at one of these five levels and shows the gap to the next. The climb follows the appetite, urgency and value of each front, and the first result shows up within the first days.
Initial
Data exists in silos without owners and without common definitions. Every area calculates the same number differently, reports contradict each other and decisions revert to intuition because available data is not reliable enough to sustain an argument at the board.
Managed
Data architecture and quality foundations are established. Data contracts define who owns each domain, the medallion architecture ensures raw data never reaches consumers directly and quality controls block bad data before it contaminates decisions.
Defined
Data flows reliably between systems. Pipelines with observability and SLAs, ingestion of multiple sources with versioned transformation and lineage traceability that enables auditing every number back to its origin.
Quantified
Analytics connects data to business decisions with criteria. Executive dashboards with a single source of truth, cohort analysis on real behavior and the cost of each insight calculable because the platform has an SLA and analyst time has a baseline.
Optimized
Data operates as a product with an SLA, an owner and a continuous improvement cycle. Each data product has a defined user, a quality contract and versioning. Business teams consume data as a service and data-driven decision becomes routine, not exception.
The dimensions we assess
The Assessment evaluates seven data maturity dimensions. Each one has an objective criterion and a mapped financial impact before defining where to begin.
Data architecture and governance
Architecture model, domain ownership, catalog, lineage and federated governance maturity.
Pipeline engineering and reliability
Orchestration, observability, freshness, data contracts and operational pipeline reliability.
Data quality and master data
Six Data Quality Score dimensions per critical dataset, MDM, deduplication and survivorship process.
Analytics and decision intelligence
Semantic layer, governed self-service, BI adoption and percentage of decisions supported by verifiable data.
Data products and internal marketplace
Number of certified products in use, consumer NPS, publication lead time and contract coverage.
Data-driven culture and decision-making
Data literacy by function, analytics adoption in recurring decisions and data governance maturity by business area.
Data compliance and security
LGPD and privacy regulation, sensitivity classification, profile-based access control, processing traceability and auditability.
Use cases
Where this capability already delivers, from business teams to operations. This list is only a starting point, the cases are many.
Demand forecasting and inventory optimization
Forecasting models by product, channel and region combining internal history, seasonality and external signals. Purchases and replenishment at the right level, without stockouts or capital tied up in excess inventory.
Unified customer view across all channels
Customer golden record unifying CRM, ERP, e-commerce and service. Every area of the company works with the same record, eliminating the duplicate customer and inconsistencies in contact data and purchase history.
Reliable financial reporting without manual reconciliation
Revenue and cost pipelines with automatically validated quality, traceable lineage and a closing process without reconciliation spreadsheets. A monthly close that currently takes five days has data available on time with confidence.
Anomaly detection in critical data pipelines
Data observability monitoring distribution, volume and schema of data in real time. Anomaly detected before the analyst discovers the problem in the number. Alert goes to the engineer, not to the board.
Self-service analytics for business teams
BI platform with semantic layer and governed access that lets the marketing, sales or finance analyst answer questions without depending on the data team. A question that used to wait days in a queue now has an answer in hours.
Data foundation for AI models in production
Training data with validated quality, traceable lineage, mapped privacy compliance and formal ownership. AI projects that arrived at data and discovered inconsistencies now start from a certified foundation.
Data audit for regulatory compliance
End-to-end traceable lineage that answers any regulatory question about processed data: where it came from, who accessed it, for what purpose and under which legal basis. An audit that is currently an investigation becomes a catalog query.
Executive dashboard with data the board trusts
Revenue, margin and result indicators connected to pipelines with an active Data Quality Score and a reliability history. A board that verified numbers before presenting now uses data as the basis for decisions.
Internal data marketplace that eliminates rework between teams
Data product catalog with DATSIS certification that makes the company data portfolio discoverable by any team. A department that needed to request data from another department now finds it and accesses it autonomously.
Financial forecasting with certified-quality data
Revenue, cost and margin forecasting models fed by data with validated quality and traceable lineage. A forecast that is currently a manual spreadsheet exercise now has a statistical basis with a defensible confidence interval for the board.
Which business decision do you want to support with reliable data? Describe the case and we evaluate the impact before starting.
Talk about your caseThe 5 pillars of Data & Analytics
The fronts that make up the capability, from foundation to evolution. Each one matures in its own time, within the same system.
Data Architecture & Governance
Data architecture that scales with the business without becoming a centralized bottleneck. Data Mesh, Data Fabric and governance that ensures quality without creating approval committees for every data access.
Data Engineering & Pipelines
Reliable pipelines that feed real-time decisions without failing silently. Modern ETL/ELT, streaming and batch. Data infrastructure treated as a product with an owner and SLA.
Analytics & Business Intelligence
Analytics that answers business questions, not just stacks dashboards nobody uses. Self-service BI that democratizes access without losing governance. Metrics connected to financial result.
Data Quality & Master Data
Data quality as a permanent discipline, not a cleanup project that repeats every cycle. Master Data Management that eliminates inconsistencies across systems. Data observability that detects issues before they affect decisions.
Data Products
Data treated as a product with an owner, SLA and defined consumers. Every dataset has accountability. Monetization strategy that transforms data from storage cost into revenue source.
Frequently asked questions about Data & Analytics
What is the difference between Data & Analytics and Artificial Intelligence?
Data & Analytics is the foundation: architecture, pipelines, quality and data governance. AI is the application: models that consume this data to generate predictions and automation. Without mature Data & Analytics, AI never leaves PoC. They are complementary capabilities. One enables the other. Investing in AI before fixing the data foundation is building on sand.
What is Data Mesh and when does it make sense?
Data Mesh is a decentralized architecture where each business domain owns its data as a product. It makes sense when the organization has multiple domains with distinct needs and centralization has become a real bottleneck. It is not a universal solution. The Assessment evaluates whether it is the right model for your context.
How to justify data investment to the board?
57% of companies do not have AI-ready data (Gartner). Without a data foundation, every AI project starts from scratch. The real cost is every AI initiative failing due to lack of reliable data, not the data investment itself. The Assessment quantifies this gap in measurable financial impact.
Clients
Market leaders evolve their capabilities with us. Organizations that turned technology capability into defensible financial result.
What we wrote about Data & Analytics
Capability, governance and result. Concrete analysis to help technology and business leaders defend investment with thesis, not slides.
Technology transformation consulting only creates value when it leaves capability working
A technology transformation pays the consultant or pays the client, and the acceptance criterion decides which one before the first workshop. Charging for a documentary deliverable produces archivable slides. Charging for capability working, with an owner, an indicator and economic impact after the project, changes what the company receives.
Technology evolution roadmap that generates ROI
An evolution roadmap chains investment, capability and result into a cause-and-effect line the board can audit. Without that chain, the plan becomes a list of deliverables, and a deliverable without a number is activity, not return.
How to measure the ROI of technology modernization without narrative
Modernizing swaps platforms. Capturing value is a different discipline. The return shows up when the company fixes a baseline, separates direct return from enabling return and governs the capture before the first investment. Built after go-live, ROI becomes justification. The difference between the two paths is where accountability for the number lives.
Team Topologies in technology strategy
Applying Team Topologies for real means redesigning dependencies, governance and cognitive load alongside the structure. Swapping only squad names preserves the same boundary conflict and pays for the reorganization without capturing the return.
Technology immaturity charges every month, even without a budget line
Technology immaturity has no budget line, yet it charges in margin, deadlines and risk every month. While the bill has no name, it looks inevitable. When it gains a number, an owner and a cadence, it becomes a capital decision.
Ready to evolve Data & Analytics?
Start with a maturity diagnostic. In 47 days, you'll have clarity on where you are, where to go, and how long it will take.