 Every AI model depends on the data it is built on. For enterprise AI projects, that data almost always includes data from SAP. And getting SAP data into a state that an AI workflow can use is rarely straightforward. This post covers the data preparation problem specifically: what it involves, why SAP makes it harder than most other data sources, and how DVW Analytics products address it as part of a broader AI architecture. The data preparation problem in enterprise AIBefore a machine learning model can be trained, the data it will be trained on needs to be found, extracted, joined, cleaned, standardised and transformed into the structure the model requires. This is data preparation, and in most enterprise AI projects it accounts for a significant proportion of total project effort. For data that lives in SAP, the data preparation challenge is compounded by the nature of SAP itself. SAP data is highly structured around business processes and objects, spread across multiple modules and often distributed across multiple SAP systems. Extracting it requires SAP-specific knowledge and SAP-specific connectivity. Joining data across SAP modules, or between SAP and other enterprise systems, requires understanding of the SAP data model. Preparing it for AI workflows requires all of this to happen reliably, repeatably and on a schedule. Organisations that approach this with manual processes (e.g. scheduled database queries, flat file exports, hand-maintained spreadsheets) find that the data preparation step becomes a bottleneck that limits how current their AI models can be and how frequently they can be updated. Where DVW Analytics fitsDVW Analytics products are integration software. They connect SAP to analytics, automation and data platforms including Alteryx, KNIME, Dataiku, Power BI, Databricks and Snowflake using direct, native connectivity that does not require middleware, staging databases or ABAP development. In an AI context, that connectivity is the starting point for data preparation. DVW handles the SAP extraction layer: pulling data from SAP Tables, SAP BW Queries, SAP HANA Views, SAP OData services, SAP T-Codes and more, and making it available inside the platform where data preparation and model development happens. The platforms DVW connects to (e.g. Alteryx, KNIME, Dataiku and the DVW Flow Tool) all provide native data preparation capabilities: cleansing, transformation, feature engineering, joining across data sources, handling of nulls and outliers and more. DVW supplies the SAP data. The platform does the preparation work. The result is a pipeline in which SAP data is extracted, prepared and delivered to the AI workflow as a scheduled, automated process rather than a manual one. A practical example: demand forecasting on SAP dataConsider a demand forecasting model that needs SAP sales history, inventory levels and open purchase orders. Without direct SAP connectivity, a data engineer manually exports data from SAP SD, MM and Purchasing on a weekly basis, processes it into a usable format and uploads it to the data science environment. The model runs on data that is already several days old by the time it is used. With DVW, an Alteryx, KNIME or Dataiku workflow connects directly to SAP, extracts current sales, inventory and purchasing data, joins it across modules, applies cleansing and transformation steps, and delivers a prepared dataset to the model. The workflow runs on a schedule. The model always works on current data. The manual step is removed. The same pattern applies to other AI use cases built on SAP data: predictive maintenance on SAP PM data, customer scoring on SAP CRM and SD data, financial anomaly detection on SAP FI data, workforce modelling on SAP SuccessFactors data. Data from other sources alongside SAPSAP rarely tells the whole story on its own. Demand forecasting models benefit from external market data and weather data alongside SAP sales history. Customer models benefit from web behaviour data alongside SAP transaction data. Maintenance models benefit from sensor and IoT data alongside SAP PM records. Platforms like Alteryx, KNIME, Dataiku and the DVW Flow Tool are designed for exactly this kind of multi-source data preparation. DVW supplies the SAP connectivity within those workflows. Other source connectors supply the rest. The preparation and integration of all sources happens in the same workflow, producing a single prepared dataset for the AI model. This is the architecture DVW operates within: not a standalone tool, but the SAP data layer inside a broader data preparation and AI workflow. Writing AI outputs back to SAPData preparation for AI is not always a one-way flow. Many AI use cases produce outputs that need to find their way back into SAP: forecast figures posted to SAP Integrated Business Planning, risk scores written to customer records in SAP CRM, quality flags updated in SAP Quality Management, anomaly alerts raised in SAP workflows. DVW products support write-back to SAP using BAPIs, IDocs, OData services and other standard SAP write mechanisms. This means the full cycle (i.e. extract, prepare, model, write back) can be handled within a single workflow, without additional integration work. Getting startedIf your organisation is working on AI or machine learning projects that involve SAP data, DVW products are available as a free 30-day trial with full functionality.
If you use Alteryx, trial the DVW Alteryx Connector for SAP. If you use KNIME, trial the DVW KNIME Connector for SAP. If you use Dataiku, trial the DVW Dataiku Connector for SAP. The DVW Flow Tool reads and prepares SAP data and delivers it to Databricks, Snowflake and other platforms, supporting data engineering workflows that feed downstream AI and analytics systems. Free Trial | Contact Us | DVW Alteryx Connector for SAP | DVW KNIME Connector for SAP | DVW Dataiku Connector for SAP | DVW Flow Tool Comments are closed.
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