If you’re leading a data transformation project or migrating to SAP S/4HANA, you’ll know the burden it places on data owners and business users. They are the ones responsible for validating data quality, cleansing and enriching records, and making sure everything lines up with business requirements before it’s signed off for the new system. While their expertise is essential, the amount of manual work involved in cleaning up data issues can quickly become overwhelming.
It doesn’t have to be this way. When it comes to managing data quality during a transformation, AI models more specifically, transformer models, such as pretrained large language models, can take a major part of the load.
By automating the way we prepare data for migration, from discovering data quality issues to suggesting ways to fix them, AI can significantly reduce the manual labour forced on data owners.
Why data owners shoulder the burden
Major transformation projects are stressful - no one enters into them lightly. But data owners do seem to get a raw deal. Why do so many of the checks and balances fall to them throughout the project?
These individuals are experts, they have deep insights into their specific business processes. And so they become critical to the process of identifying which fields are essential, what the data means contextually, and how best to maintain or improve its integrity. The business typically leans on this expertise for decisions that can shape the success of the transformation.
As the migration draws closer, they’re called on to deduplicate entries, fill gaps, standardise attributes, and create entirely new records. Historically, this task has been manual and it’s easy for errors to slip in, especially under tight project timelines. So pressure continues to build.
Before the system goes live, data owners are called on again. Their in-depth knowledge means they’re often best-placed to validate data before the final migration.
This laborious process often involves checking numerous data sources, cross-referencing legacy systems, and ensuring alignment with new data models, all of which place a heavy demand on theirtime.
Easing the load: AI’s role in smarter data management
Enter AI. With its ability to automate complex and time-consuming tasks, the technology promises to deliver a reduction in manual hours and improved data quality leading to enhanced confidence for faster insights and more effective decision-making.
Those benefits can be felt throughout the transformation. Let’s start with AI’s ability to assess data quality. Essential at the start of any project, this process can be automated with targeted rules and a broad set of data quality checks. Large data sets can be quickly analysed, anomolies identified and potential errors grouped, so that data owners have time to prioritise strategic decisions, rather than hunting for issues line by line.
AI can also assist with data cleansing: offering smart suggestions for corrections by comparing existing structured data with unstructured inputs or by referencing common dictionaries, and detecting duplicates or misclassifications, saving data owners from manually combing through large tables or spreadsheets.
Once the data has been analysed, AI tools are poised to improve its quality. From identifiying gaps to interpreting context, these solutions are able to enrich and unify existing data so that, when it comes to it, the data can be migrated more smoothly.
Ahead of that migration, AI shows its value once more, acting as a first-pass validator. This validation stage is vital - especially in large scale transformations like the move to SAP S/4HANA.
Checking data against business requirements, governance standards, and technical rules is traditionally one of the most time-consuming and resource-intensive responsibilities for data owners. AI fundamentally shifts this workload by narrowing the scope of human review to only the highest-risk or most ambiguous records.
And for traceability, every AI-assisted validation is logged and fully traceable. Data owners gain a transparent audit trail that supports compliance requirements while reducing manual documentation effort. This ensures that migration sign-off is not only quicker but also defensible in audits and governance reviews.
First steps: introducing AI to your digital transformation project
Before you get started, there are tried and tested steps that I’d recommend:
1. Early integration
Incorporate AI-based quality checks and rules at the outset of the project. Detecting issues reduces last-minute scrambling before go-live.
2. Define clear objectives
Align AI activities, like data cleansing and enrichment, with clearly defined business outcomes and measurable KPIs. This ensures the technology is delivering true value rather than just novelty.
3. Maintain the human touch
Generative AI should enhance, not replace, the expertise of data owners. A feedback loop, where data owners refine AI’s suggestions, continually improves the system’s accuracy and reliability.
4. Iterative feedback
Encourage iterative cycles where AI proposes changes or flags anomalies, and data owners confirm or correct these outputs. This approach consistently enhances the AI’s future recommendations.
5. Governance and compliance
AI should respect data privacy, internal governance policies, and industry regulations. Ensure that all AI-driven transformations are transparent and traceable, maintaining audit logs for compliance.
Pairing human expertise with AI for confident migration
Preparing data for a migration has always been a huge lift. Generative AI is changing that.
Of course, people are still the key. These tools work best when guided by the business knowledge only data owners have. AI is a partner. It speeds up checks and reduces mistakes. Freeing up time for the experts to take a more strategic approach to their migration, and making it easier to sign off migrations with confidence.
