Specialty
SAP Analytics Cloud
Dashboards, planning, and forecasts on SAP and non-SAP data, with a clear path for BusinessObjects users.
Data and analytics
Data and analytics: strategy, engineering, and AI for confident decisions.
Data strategy and governance, platform engineering, analytics, and AI for SAP and non-SAP data, delivered on Google Cloud, AWS, or Microsoft Azure.
Data monitor · daily loadProduction pipelines and data quality
- Completeness98%
- Consistency94%
- Timeliness91%
- Uniqueness99%
- Sales · SAP S/4HANALoaded
- Customers · CRMRunning
- Sales targets · spreadsheetsCheck rules
- Demand forecast modelRetrained
certified datasets in the catalog42
- ERP data and other sources in one architecture
- SAP + non-SAP
- from data assessment to operations and ongoing evolution
- 4 stages
- partner tier with Google Cloud
- Premier
Overview
Data managed as an asset, not a by-product of your systems.
Data and analytics is the practice of turning records scattered across ERP, CRM, spreadsheets, and applications into reliable information for making decisions, reducing risk, and improving processes. It spans strategy, governance, engineering, visualization, and artificial intelligence.
Grupo Intelsis starts by assessing your data maturity and priorities, then designs and builds the platform, delivers analytics and AI models in short iterations, and stays on for operations with governance and continuous improvement. We work on-premises, in the cloud, or in hybrid environments, with SAP and non-SAP data.
What sets us apart is deep SAP expertise combined with cloud platforms such as Google Cloud, where we are a Premier Partner, and AWS. The metric that reaches the dashboard keeps the business meaning it had in the ERP, and you can count on dedicated data teams or a center of excellence.
Highlights
- Data strategy, governance, and quality
- Data engineering and cloud platforms
- Dashboards, reports, and analytical models
- Machine learning and AI applied to the business
- Dedicated data teams or a center of excellence
Why Grupo Intelsis
Business, SAP, and cloud on the same data team.
Data projects fail when technology, processes, and people move separately. That is why we address all three together.
Strategy tied to outcomes
Every initiative starts from a business decision and a measurable gain in efficiency, cost, or growth, not from a tool.
SAP context in every data point
We know the SAP processes and rules that give numbers their meaning, which prevents rework and disagreements between departments.
Partner of the leading providers
SAP Gold Partner, Google Cloud Premier Partner, and AWS Partner: we choose the platform based on your scenario, cost, and cloud strategy.
Cultural change, not just technology
We support data-driven decisions with clear roles, team enablement, and regular routines for reviewing metrics.
Focus areas
From strategy to operations, across the data value chain.
We work on targeted projects or across the entire chain, based on your company's maturity and priorities.
Data strategy
Maturity assessment, use cases prioritized by value, and a roadmap aligned with business goals and your current architecture.
Governance and quality
Policies, roles, catalog, lineage, and quality rules so everyone trusts the same definition of each metric.
Engineering and platforms
On-premises, cloud, or hybrid architectures for high volumes of structured and unstructured data, with monitored pipelines.
SAP and non-SAP integration
Extraction and modeling of SAP ECC, SAP S/4HANA, and SAP BW data alongside CRM, e-commerce, files, and APIs, preserving business semantics.
Analytics and visualization
Dashboards, reports, and analytical models for every area, using SAP Analytics Cloud, Looker, or the tool that best fits the scenario.
Machine learning and AI
Forecasting, classification, and anomaly detection models, plus generative AI on governed data, with platforms such as Vertex AI.
Data teams and CoE
Dedicated squads or a center of excellence (CoE) that runs, supports, and evolves the platform and data products with your team.
Operations and continuous evolution
Monitoring of loads, costs, and quality, incident handling, and new sources and use cases added in every cycle.
Reference architecture
Five layers between the source and the decision.
Each layer has a clear role, and governance runs through all of them.
Sources
SAP ERP, CRM, e-commerce, spreadsheets, files, and partner APIs, each with its own owner and update frequency.
Ingestion and integration
Batch or near-real-time loads with orchestration, monitoring, and failure handling.
Platform and modeling
Data warehouse or lakehouse with layers for raw, cleansed, and consumption-ready data.
Governance
Catalog, lineage, quality, security, and privacy applied across every layer.
Consumption and AI
Dashboards, planning, machine learning models, and applications that rely on trusted data.
Data assessment
What we assess before recommending any platform.
An objective picture of maturity guides priorities, investments, and the order of delivery.
Strategy and governance
- Critical decisions and metrics by area
- Data owners and defined roles
- A single definition for each metric
- Access, privacy, and data protection policies
- Quality measured and tracked
Platform and engineering
- Inventory of SAP and non-SAP sources
- Integrations, loads, and dependencies
- Latency required by each use case
- Cost, scalability, and security
- Documentation and lineage
Analytics and AI
- Reports in use and duplicates
- Business user autonomy
- Viable machine learning use cases
- Data readiness for generative AI
- Adoption and management routines
How we work
Four stages, from assessment to operations.
Each stage produces usable deliverables, and the next one builds on what was learned.
Assessment and priorities
It starts with a free 30-minute conversation, followed by a maturity assessment, prioritized use cases, and a data roadmap.
Architecture and platform
Joint architecture design and the build of pipelines, models, and governance layers on the chosen platform.
Analytics and AI in short iterations
Dashboards, analytical models, and machine learning solutions delivered in short iterations, with frequent validation by business teams.
Go-live, operations, and evolution
Supported go-live, monitoring, ongoing support, and new sources and use cases, with dedicated teams or a center of excellence.
Frequently asked questions
Questions about Data and analytics
Where should a data project start?
With the decisions that matter most. During the assessment, we identify critical metrics, sources, quality issues, and initiatives already underway, then prioritize a few high-value use cases. That way, the platform grows guided by results rather than by a list of tools.
Do you only work with SAP data?
No. We integrate data from SAP ECC, SAP S/4HANA, and SAP BW with CRM, e-commerce, spreadsheets, APIs, and other applications. Deep SAP knowledge is an advantage because it preserves the meaning of business data, but the architecture is designed for your company's entire ecosystem.
Which data platform is right for my company?
It depends on your sources, volumes, use cases, team skills, and cloud strategy. We evaluate options such as SAP Datasphere, Google BigQuery, and AWS and Microsoft Azure data services, weighing operating cost, security, and SAP integration before making a recommendation.
How is this service different from the SAP Analytics Cloud specialty?
SAP Analytics Cloud is SAP's solution for BI, planning, and predictive analytics. The data and analytics service covers the whole chain, from strategy and governance to engineering, platforms, and AI, with or without SAP. In many projects, the two complement each other.
Can you run our data platform?
Yes. Beyond projects, we provide operations and continuous evolution: monitoring of loads, quality, and costs, support for pipelines and dashboards, and dedicated data teams or a center of excellence working alongside your team.
Keep exploring
Solutions that connect.
Next step
Which decisions is your data not supporting yet?
Schedule a free 30-minute assessment and decide where to start turning data into decisions.