Taking a Data First approach is critical for your S/4HANA migration

For many CIOs, the S/4HANA migration that was once in the distant future is now looming. And with 2027 and 2030 deadlines approaching, it’s time to move from high-level planning into concrete decision-making. But before you do, have you considered where the real risks lie? And how you could avoid some of the costly mistakes that plague major digital transformation projects? This is the moment to expect more from your data, ensuring it’s not just migrated, but governed and business-ready.

For most organisations, the biggest barrier to S/4HANA success isn’t the software itself. It’s the data. It’s the way data is governed, the way it’s managed, and quality of data that determines whether the project runs to schedule and keeps to budget. All too often, this pulls teams away from the critical work of migration and into aligning data to support new processes. And that can dictate whether a go-live runs smoothly or falls flat.

But while the technology roadmap may be clear, confidence in data readiness is far less certain. According to this year’s UKISUG survey of SAP user organisations in the UK and Ireland, 76% of respondents believe data management presents a significant challenge to their S/4HANA migration.

That’s an enormous percentage. So why aren’t enterprises leading with a Data First approach? Instead of prioritising data from the beginning and carrying that discipline throughout the life of the project, enterprises often push it to the last minute – or simply lift‑and‑shift and hope for the best.

Why data, not technology, causes migrations to stall

We need to stop thinking of the move to S/4HANA as a technology transformation. It’s a business transformation - enabled by data.

Here’s why. Fragmented, poor-quality and ungoverned data happens. Over time, just about every organisation accumulates duplicated records, inconsistent definitions and siloed systems, with unclear ownership.

Even when those organisations are not attempting digital transformation, poor quality data can negatively impact business outcomes. But throw a major transformation into the mix and these issues become more visible, more complex and more expensive to resolve.

The result can be stalled programmes, disrupted migrations and increased compliance risks. And when it comes to moving to S/4HANA - it can become impossible to realise the benefits set out in the original business case. Real-time analytics and process optimisation capabilities simply won’t deliver on their promise if they’re battling with out-of-date, inaccurate or siloed data. And AI capabilities, a key reason for moving to S/4HANA, are unlikely to succeed if the underlying data isn’t business-ready.

This is the moment that organisations should expect more from their data. Not just that it moves cleanly from one system to another, but that it can be trusted, understood and actively used to drive better decisions. An S/4HANA migration should raise the bar for data, not carry old problems into a new platform.

Taking a Data First approach can make the difference. By focusing on data from the very start of the programme - rather than treating it as a mop-up task in the final stages - CIOs can identify issues early, avoid surprises and ultimately improve the whole organisation’s outcomes.

Even for programmes already underway, it’s not too late to refocus on data. This doesn’t need to slow progress, but can instead unlock faster, more confident outcomes.

Missed opportunities: tools and automation

Despite the scale and complexity of S/4HANA migrations, when it comes to data migration and validation many organisations still rely on outdated approaches like Extract, Transform and Load (ETL) tools, Microsoft Excel or Microsoft Access. These tools are not designed for governed SAP migrations, and as a result validation often happens late, business users lack visibility and auditability is weak. Errors aren’t discovered until go-live, manual work increases and data owners are forced into last-minute fixes that can derail a transformation.

This doesn’t have to be a manual process. Data management platforms with automation, rule-based validation and AI-driven capabilities can identify data issues earlier at greater scale and improve accuracy. Freeing up business experts to focus on strategic decisions rather than spreadsheet-driven cleansing exercises. 

And these specialist tools also support repeatable, governed processes - helping organisations move away from one-off fixes that are not guaranteed to work long-term and can’t be replicated.

Don’t leave data to the last minute

One of the most common failure patterns in SAP migration programmes is addressing data quality too late in the programme. This cannot be bolted on late in the programme. At that point, pressure mounts on data owners, manual validation increases and timelines are put at risk - or the challenge is deferred and only addressed once it causes major operational issues

Instead, data quality must be embedded as a foundational capability that supports both the migration itself and the organisation’s long-term data strategy.

Introducing governance at the start of a project and taking a Data First approach allows organisations to phase work and align stakeholders early rather than scrambling to retrofit controls under deadline pressure.

What does strong data governance look like?

Effective data is data that is business-ready. Data governance is not about rules for their own sake. At its best, it is closely linked to business goals for better decision-making, operational efficiency and compliance.

In fact, when it’s aligned to organisational priorities, governance becomes an enabler rather than an obstacle. In my experience, the strongest governance tends to include the following:

• Clear ownership and accountability: for the best results, data needs named owners who are responsible for quality, definitions and use during the migration and beyond go-live.
• Standardisation and consistency: agreed definitions, validation rules and data standards reduce ambiguity and duplication, particularly across complex organisational structures.
• Scalability and adaptability: governance frameworks must be flexible enough to evolve as the business changes, rather than locking organisations into rigid models.
• Embedded processes: data quality controls and governance workflows should be integrated into day-to-day operations, so that improvements last beyond the migration project.

Governance as a strategic enabler

Your S/4HANA migrations can be transformational - an opportunity to reset how data is managed, governed and used across the organisation. Treating data governance and data quality as a strategic capability, rather than a compliance afterthought, is the difference between a smooth migration and one that stalls. And can impact your business way beyond go-live.

CIOs who put Data First thinking at the centre of their S/4HANA plans will not only reduce risk, but also be far better positioned to deliver lasting value. And by expecting more from your data, you can unlock its full potential, driving insights, efficiency and lasting impact across the business.