Data & Enablement
The fundamental rule of data processing states: incorrect inputs lead to incorrect results. Incomplete or erroneous master data, missing KPI definitions, and unclear responsibilities inevitably lead to unreliable reports. Data management is therefore not a technical problem but rather the necessary prerequisite for reliable and credible analytics.
Key areas
Many organizations lack precise knowledge of data quality in their systems. Only through systematic data profiling do problems become visible: duplicate customer records, missing required fields, and inconsistent data formats. Data cleansing is necessary but insufficient without subsequent continuous monitoring. Without such control mechanisms, data chaos recurs regularly.
When master data is replicated across multiple systems, multiple different data versions result. SAP, Salesforce, data lakes, and decentralized Excel files each contain different customer lists and material numbers. This creates inconsistencies and wrong decisions. A unified data foundation (Single Source of Truth) is therefore not a visionary goal but an operational necessity for reliable analysis.
The question of responsibility is central to functioning data management structures. Without clearly defined ownership, an incoherent system emerges where various people independently take on data tasks with no one holding overall responsibility. Established roles like data stewards, transparent responsibility matrices, and documented standards create necessary structure without generating unnecessary bureaucracy.
Illustration: Without master data management, SAP, Salesforce, data lake and Excel each keep their own version of the master data. With MDM, they are consolidated into one golden record as the single source of truth.
Data quality problems emerge in various forms: duplicate customer records, materials with multiple different SKU numbers, or suppliers that are deleted but still appear in active transaction records. These problems cause actual business losses through incorrect decisions. They are often discovered only when their effects manifest in erroneous reports. By then, significant damage has already occurred.
Extensive, multi-hundred-page governance policies are often ineffective and not read or followed by staff. What works instead: designate clear owners for each data area, articulate rules concisely, and establish simple, understandable processes. This creates leaner, less bureaucratic structures. Critically, these rules must be actually implemented and lived in practice, not just documented.
A chaotic S/4HANA go-live often indicates data cleansing occurred too late in the implementation. Post-cutover cleansing activities are reactive and costly. A smarter approach involves identifying data quality issues before go-live and remedying them in the legacy system during the parallel-run phase. This prevents bad data from migrating to the new system.
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