Data Consultancy Audits Licences, Tools, and AI Readiness

EpicData, a Belgian data and analytics consultancy, positions itself as a technology-agnostic partner that guides organizations from data strategy through implementation and adoption, declining to push any single vendor’s stack. The firm’s own framing names Microsoft, Qlik, SAP, and Databricks as the platforms it knows in depth, and it structures client work as either embedded staffing or fixed-scope project delivery. For SAP customers, the relevant thread is a set of packaged assessments that examine licensing, tool fit, BI reliability, and AI readiness across whatever platforms a company already runs.

Licence Reviews and Tool Selection Without a Vendor Stake

The licence audit examines effective consumption, active user counts, and component usage against current and projected need, then uses that picture to argue for better contract terms. The mechanism matters more than the promise. Most licence overspend hides in the gap between entitlements purchased and capacity actually consumed, and a vendor-neutral reviewer can map that gap without a quota to protect. EpicData claims independence here explicitly, presenting the review as advice rather than a sales motion, and it names SAP among the license models it works across.

Tool selection follows a similar logic earlier in the cycle. The free ToolCheck is a two-hour workshop that matches a technology, such as Microsoft Fabric, Databricks, or Qlik Cloud, to a stated set of business requirements, and it identifies quick-win use cases that can show value fast. For a landscape where SAP is the system of record, the practical question is usually which analytics layer sits on top of SAP data, and a scoped session aimed at that decision costs a team little to test. The firm treats the ToolCheck as an entry point that often leads into deeper strategy work it calls Vision Workshops.

BI Reliability and the Data Foundation AI Depends On

The Power BI audit, a fixed price engagement, traces a reporting environment from data sources through to visualizations to locate where refreshes slow, where calculations drift, and where access controls fail. EpicData describes a common failure pattern in which dashboards look finished while users quietly retreat to Excel because they cannot trust the numbers. The deliverable is an audit report with a prioritized action list and a roadmap covering governance and future evolution. Governance and row-level security carry weight for SAP shops, where finance and operational data pulled into Power BI still has to honor the segregation of duties enforced upstream in the ERP.

AI readiness extends the same emphasis on foundations. The free AI Kickstart Session assesses where an organization stands on data quality, availability, and usability before any model is built, and it produces a report with quick wins and a tailored roadmap. The session is aimed at CIOs, CFOs, CDOs, and data leaders who need either a boardroom business case or a fast start on a first use case. The reasoning holds for SAP estates, where clean, governed master data determines whether a model trained on ERP records produces reliable output or expensive noise.

What This Means for SAPinsiders

• Vendor-neutral license reviews can surface SAP overspend. An independent audit of consumption and active users gives SAP buyers an evidence base for renewal talks that a vendor’s own account team has no incentive to assemble. Teams renegotiating contracts should weigh whether a neutral third party strengthens their position.
• BI governance must extend SAP controls, not bypass them. When ERP data flows into Power BI, access rules and calculation logic can diverge from the source system. A reliability audit that checks security and refresh integrity protects the segregation of duties SAP teams already enforce upstream.
• AI value depends on ERP data quality first. Models trained on SAP records fail when master data is inconsistent or ungoverned. A readiness assessment that grades data before model work helps leaders decide where to invest, and it reframes AI budgets as data-foundation budgets.