Analytics Strategy and Roadmap
Current-state assessment, reporting needs, KPI alignment, modelling priorities and phased analytics implementation planning.
Turn raw enterprise data into trusted metrics, models and analytics-ready foundations.
Logiciel helps enterprises design, build and operate analytics engineering systems that connect data platforms with business decision-making. From semantic layers and BI-ready data models to cloud data architecture, data pipelines, governance, observability and managed analytics operations, we help teams create reliable reporting foundations that product, finance, operations and leadership can trust.
Most enterprises do not struggle because they lack dashboards. They struggle because the data behind those dashboards is inconsistent, duplicated or poorly governed.
We build analytics engineering foundations that make enterprise data easier to model, govern and use.
A clear analytics engineering roadmap tied to business priorities.
Trusted data models for finance, operations, product, sales and customer teams.
Semantic layers that standardise metrics, dimensions and business definitions.
Cloud data architecture designed for scalable analytics and AI-ready workflows.
Data quality checks, lineage and observability built into analytics pipelines.
Governance, access controls and documentation for reliable reporting.
A practical analytics operating model your teams can maintain after launch.
We cover the full analytics engineering lifecycle. Data models, cloud architecture, governance and operations need to work together.
Current-state assessment, reporting needs, KPI alignment, modelling priorities and phased analytics implementation planning.
Trusted metric definitions, dimensions, entities, business logic and reusable semantic models across BI and analytics tools.
Data modelling, transformation workflows, curated datasets, testing and documentation for reliable business reporting.
Cloud architecture design for warehouses, lakehouses, data lakes, pipelines, storage, compute and analytics access layers.
Cloud platform architecture, cloud based architecture, cloud computing architecture and cloud native application architecture for modern data environments.
AWS data lake architecture, AWS security architecture and AWS cloud architect practices for scalable analytics foundations.
Ongoing monitoring, incident response, data quality reviews, cost control, performance tuning and continuous improvement.
Dedicated Analytics Engineering Squad
A standing team of analytics engineers, data engineers, cloud solutions architects and platform specialists embedded into your analytics roadmap.
Analytics Advisory and Staff Augmentation
Senior analytics engineers, data analytics engineers and cloud architecture consulting specialists who strengthen your internal data, product or platform teams.
Outcome-Based Analytics Engineering
Fixed-scope engagements with defined analytics outcomes, delivery milestones and success baselines agreed up front.
Detailed assessment of dashboards, datasets, metrics, data models, pipelines, BI tools, cloud architecture and reporting pain points.
Metric standardisation, KPI modelling, dimensional modelling, reusable business logic, data marts and trusted reporting foundations.
Analytics-ready transformations, curated datasets, testing workflows, documentation, lineage and environment promotion.
Cloud data architecture, cloud platform architecture, cloud architecture design and cloud based microservices for scalable analytics systems.
AWS cloud solution architect support for data lakes, warehouses, pipelines, access controls, security architecture and analytics workloads.
Validation checks, schema monitoring, freshness tracking, lineage mapping, access controls, auditability and data quality dashboards.
Ongoing pipeline monitoring, dashboard reliability support, metric reviews, cost reporting, performance tuning and continuous improvement.
Patterns from our data platform engineering teams that help enterprises improve metric trust, analytics speed and reporting reliability.
How we structure ownership, metric governance, semantic layer reviews, data quality checks and continuous improvement across analytics teams.
A practical approach to ranking analytics priorities by business impact, metric inconsistency, data quality risk, cloud architecture maturity and AI readiness.
1. Analytics Diagnostic and Baseline
We assess dashboards, reports, metrics, data models, pipelines, cloud platforms, governance controls and business priorities.
2. Metric and Data Flow Mapping
We map core KPIs, source systems, transformation logic, ownership, reporting dependencies and downstream analytics workflows.
3. Analytics Model and Platform Engineering
We build semantic layers, curated datasets, transformations, cloud data architecture, BI-ready models and secure access foundations.
4. Reliability, Governance and Observability
We harden analytics systems with testing, lineage, freshness checks, access controls, documentation, alerts and operational dashboards.
5. Analytics Operating Model
We hand over a repeatable analytics engineering practice, including ownership, KPIs, governance reviews, runbooks and improvement cadences.
Ready to turn Analytics Engineering Services into a trusted foundation for reporting, automation and AI? Partner with Logiciel to build semantic layers, modern cloud data architecture and analytics systems your teams can rely on.
Analytics Engineering Services include analytics strategy, semantic layer development, data modelling, transformation workflows, BI-ready datasets, cloud data architecture, governance, observability and managed analytics operations.
An analytics engineer turns raw data into trusted, reusable models for reporting and decision-making. They build semantic layers, define metrics, create transformation workflows, document logic and ensure business teams can trust analytics outputs.
A data engineer focuses on moving, storing and processing data. A data analytics engineer focuses on modelling that data into business-ready metrics, datasets and reporting layers that analysts and decision-makers can use.
Yes. We support cloud architecture design, cloud data architecture, cloud platform architecture, cloud computing architecture and cloud architecture consulting for scalable analytics, reporting and AI-ready data systems.
Yes. Logiciel supports AWS data lake architecture, AWS security architecture, AWS cloud solution architect guidance and cloud data platform engineering for analytics and AI workloads.
Most engagements produce a diagnostic, roadmap and initial analytics engineering foundation within 4-8 weeks, while larger analytics programs run across phased implementation waves over several months.
You retain ownership of all data models, semantic layers, transformations, pipelines, dashboards, governance assets, cloud architecture documentation, runbooks and implementation materials.
Yes. We run managed operations with monitoring, incident response, data quality reviews, metric governance, cost tracking, performance tuning and continuous improvement.