SAP Analytics Cloud Services
SAP Analytics Cloud brings business intelligence, enterprise planning and predictive analytics into one tool. That combination is genuinely powerful — and it is also why so many SAC deployments end up as a handful of unused dashboards.
CssInfotech implements SAC with the data model designed first: live connections where real-time matters, imported models where planning needs write-back, and stories built around the decisions people actually make.
What SAP Analytics Cloud Actually Does
SAC is three products sharing one interface. Knowing which one you are buying determines how you should set it up.
Business Intelligence
Dashboards and reports over SAP and non-SAP data.
- Optimised Story Experience for responsive, modern stories
- Charts, tables, geo maps and linked analysis
- Drill-down, filtering and cross-story navigation
- Scheduled publication and distribution
Enterprise Planning
Budgeting and forecasting with write-back, not just reporting.
- Planning models with versions, currencies and hierarchies
- Data actions, allocations and currency conversion
- Value driver trees for scenario modelling
- Planning workflow with input tasks and approvals
Predictive & Augmented
Statistical support for forecasting, without a data science team.
- Smart Predict for classification, regression and time series
- Just Ask natural language querying
- Smart Insights and automated contributing factor analysis
- Predictive forecasts written back into planning versions
Analytics Designer
When a story is not enough and you need an application.
- Analytic applications with scripted behaviour
- Custom widgets and dynamic, parameter-driven content
- Guided workflows and complex user interaction
- Embedding into Fiori launchpad and SAP Build Work Zone
Connectivity & Modelling
The decision that determines whether SAC performs.
- Live connections to S/4HANA, BW/4HANA, HANA and Datasphere
- Import connections where transformation or planning is needed
- Model design, hierarchies, measures and calculated dimensions
- SAP Datasphere as the governed semantic layer
Embedded & Digital Boardroom
Analytics where the decision happens, not in a separate portal.
- SAC embedded edition inside S/4HANA Fiori apps
- KPI tiles and analytical apps on live CDS views
- Digital Boardroom for executive review sessions
- Mobile access for approvals and monitoring
Live Connection or Import Model?
This is the first real design decision in any SAC project, and reversing it later means rebuilding your models and stories.
| Consideration | Live connection | Import (acquired) model |
|---|---|---|
| Where data stays | In the source system - nothing copied | Copied into SAC |
| Data freshness | Real time, always current | As of the last scheduled import |
| Planning / write-back | Not supported | Supported - required for planning |
| Data blending across sources | Limited | Flexible |
| Transformation options | Must happen in the source | Available inside SAC |
| Governance and security | Source system controls apply | Managed inside SAC |
| Best for | Operational reporting on S/4HANA or BW | Planning, forecasting, blended analysis |
Our SAP Analytics Cloud Services
We work from the decision backwards to the data, which is the opposite of how most dashboard projects run.
Analytics Strategy & Design
Start with the decisions, not the available data.
- Decision and KPI workshops with the people who act on the numbers
- Data source assessment and gap analysis
- Live versus import architecture recommendation with reasoning
- Governance: ownership, definitions and a single source of truth
- Licence and tenant sizing guidance
Implementation & Story Build
Stories people open twice, not once.
- Tenant setup, connections and security configuration
- Model design with hierarchies, measures and calculations
- Story design following clear visualisation principles
- Row-level security and authorisation alignment with source systems
- Mobile and responsive layout validation
Planning Implementation
Where SAC delivers the most value, and takes the most care.
- Planning model design with versions, time and currency handling
- Data actions, allocation logic and currency translation
- Input forms, validation rules and calendar-driven workflow
- Actuals integration from S/4HANA or BW
- Rolling forecast and driver-based planning setup
Support, Training & Adoption
Adoption is the whole point, and it does not happen by itself.
- Author training so your team builds its own stories
- Content review to retire duplicate and abandoned stories
- Performance tuning for slow stories and models
- Ongoing enhancement on a retained basis
- Usage monitoring to see what is genuinely being used
Our SAC Implementation Approach
We deliver a working, used story early rather than a complete data model nobody has seen.
- Decisions firstWe ask what decisions need support and what action follows from each number. A KPI nobody acts on does not belong on a dashboard.
- Data reality checkWe verify the data genuinely exists at the required granularity and quality. This is where optimistic analytics projects usually break.
- Architecture decisionLive or import, direct connection or via SAP Datasphere, decided explicitly and documented with the reasoning.
- Model & prototypeOne priority story built end to end within a few weeks, put in front of real users to validate the approach before scaling.
- Build out & secureRemaining models and stories delivered in iterations, with row-level security tested against real user roles.
- Enable & measureAuthor training, adoption tracking and a review cycle that retires unused content rather than letting it accumulate.
Why Analytics Projects Fail — and How We Avoid It
We have been called in to fix enough abandoned dashboard estates to know the pattern. It is almost always one of these.
- Built from available data, not real questions — we start with the decision
- No agreed definitions — two stories, two revenue numbers, zero trust
- Wrong connection type — import chosen where live was needed, or the reverse
- Security bolted on late — row-level security designed with the model, not after
- Too many stories — we retire duplicates rather than adding to the pile
- No named owner — every story gets a business owner accountable for it
- Performance ignored — a slow story is an unused story
- No author enablement — your team must be able to build without us
Connected Systems & Technologies
The sources we connect to SAC and the surrounding tooling we work with.
SAP data sources
- SAP S/4HANA
- SAP BW/4HANA
- SAP BW
- SAP HANA Cloud
- SAP Datasphere
- SAP SuccessFactors
- SAP Ariba
- SAP Concur
- Universal Journal (ACDOCA)
Non-SAP sources
- Microsoft SQL Server
- Oracle
- PostgreSQL
- Snowflake
- Google BigQuery
- Azure Synapse
- Excel & CSV
- OData services
- Salesforce
SAC capabilities
- Optimised Story Experience
- Analytics Designer
- Smart Predict
- Just Ask
- Data Actions
- Value Driver Trees
- Digital Boardroom
- SAC embedded edition
- Multi Actions
- Calendar & workflow
Industries We Serve
Analytics and planning requirements differ sharply by sector. These are the ones we know.
Why Choose CssInfotech for SAP Analytics Cloud
Anyone can build a chart. The value is in building the right chart on a model that holds up and that people trust.
We start with decisions
Our first workshop is about what you need to decide and what action follows. Dashboards built from whatever data was handy are the reason analytics tools get abandoned.
Architecture before aesthetics
Live versus import, direct versus Datasphere — we get these right first. They are expensive to reverse once stories are built on top.
Planning experience, not just BI
SAC planning is a genuinely different discipline: data actions, versions, currency and workflow. Plenty of partners only do the BI half.
Security designed in
Row-level security and role alignment with source systems are designed with the model. Retrofitting them into a live tenant is painful and error-prone.
We retire content
Part of our engagement is deleting duplicate and unused stories. A trusted small set beats a large unreliable one, every time.
Enablement is the goal
We train your authors so you are not dependent on us for every new chart. Long-term dependency is not a service model we are interested in.
SAP Analytics Cloud FAQs
Do we still need SAP BW if we have SAP Analytics Cloud?
They serve different purposes. SAC is the presentation, planning and predictive layer; BW or SAP Datasphere is a governed data warehouse with harmonised, historised data. If your reporting is operational and sits on S/4HANA CDS views, SAC live connections may be enough. If you need cross-source harmonisation, complex transformation and history, you still want a warehouse layer underneath.
What is the difference between SAC and embedded analytics in S/4HANA?
S/4HANA embedded analytics uses CDS views to deliver operational reporting inside Fiori apps, on live transactional data with no extraction. SAC adds cross-source reporting, planning with write-back, predictive capability and executive-level storytelling. Most customers use both, and SAC embedded edition deliberately blurs the line.
Can SAC replace Excel for budgeting?
For the process, yes — that is precisely what SAC planning is designed for, with versions, workflow, audit trail and a single source of truth instead of emailed spreadsheets. Realistically, Excel will remain part of the picture, which is why we set up the Excel add-in for people who genuinely need it rather than fighting it.
How long does an SAC implementation take?
A focused BI implementation on an existing clean data source can deliver useful stories in six to eight weeks. A planning implementation is bigger — typically three to six months — because planning models, data actions, currency handling and workflow all need careful design and business validation.
Will SAC work on our non-SAP data?
Yes. SAC connects to SQL Server, Oracle, Snowflake, BigQuery, flat files and generic OData sources among others. Blending SAP and non-SAP data usually works best through an import model or with SAP Datasphere as a harmonisation layer, since live connections are more restricted for cross-source blending.
Can you train our team to build their own stories?
Yes, and we actively encourage it. We deliver author training alongside implementation, covering model concepts, story design and the common pitfalls. The goal is that your team handles routine new content and comes to us for architecture, planning logic and the harder problems.
Related SAP Services
Analytics projects usually depend on these adjacent services.
Turn Your SAP Data Into Decisions People Trust
Tell us the decisions you need to support. We will recommend the right connection architecture, build one priority story to prove it, and train your team to take it from there.