In the rush to modernise enterprise systems, data has become both an asset and a critical foundation for success. As the 2027 SAP ECC deadline approaches, organisations are increasingly turning their attention towards ERP migration strategies. Yet beneath the discussions around implementation timelines and technical capabilities, a more fundamental issue is often overlooked: the quality and readiness of the data itself, despite its critical role.
It is common for systems to evolve over time with new technologies and upgrades, yet many of the same data problems persist. Inconsistent records, outdated suppliers, duplicated materials – all of which can carry over and lurk in systems if left unchecked. It’s the classic case of ‘rubbish in, rubbish out’. And for all the emphasis on digital innovation, many organisations still treat data quality as a back-office task rather than a strategic priority. It’s time that changed, as Charles Smith, Sales Executive, Absoft, discusses below...
Why Governance, not Guesswork, Determines Transformation Success
Legacy systems rarely arrive at their current state by design. Over time, patchwork processes, localised decisions and staff both coming and going can create fragmented data landscapes. One team might create a vendor record that already exists under a different name, for example, another might tweak a product description, breaking the consistency needed for meaningful reporting. This may seem minor at first glance, but it soon adds up over time, slowly but significantly, leaving businesses operating day to day with a confused mixture of systems and processes.
The result is a cluttered, unreliable data set that undermines both day-to-day operations and long-term planning. When businesses begin planning a system migration or ERP upgrade, the instinct is often to ‘clean as we go’. But without a structured governance framework in place, that approach is both risky and reactive, and may be too late.
Sometimes, poor data quality doesn’t just slow operations — it causes major disruption. A striking example of this played out during the COVID-19 pandemic, when Public Health England lost nearly 16,000 positive cases due to an outdated Excel format reaching its data limit. The incident delayed critical contact tracing efforts and made international headlines. Though few businesses face consequences quite so publicly, the implications of poor data practices, especially during critical transitions or mergers, are just as serious.
Robust data governance doesn’t necessarily require complexity either. Clear responsibilities, controlled data creation processes, and regular audits can make a significant difference. What matters most is that governance is embedded into business-as-usual activity, and not left to IT alone, and definitely not only addressed only when it becomes a problem.
Leadership Needs to Set the Tone
Transforming how organisations view data requires more than updated spreadsheets or new workflows though. It’s a cultural shift, and like any cultural change, it starts at the top. Leaders who understand the commercial value of clean, consistent data are far more likely to allocate time, budget, and focus to address it properly.
Another one of the challenges within this is that data issues are often invisible until something goes wrong. An invoice fails to process. A supply chain report produces odd results. A forecasting model gives inconsistent outputs. In each case, the root cause may trace back to the same thing: poor data quality, and a lack of processes or business attention/focus to prevent it. Senior decision-makers must champion a ‘data-first’ mindset, ensuring that remediation and readiness are built into transformation plans from the outset. Waiting until the go-live phase to address data quality is like renovating a house and ignoring the foundations – problems may not surface immediately, but they inevitably will, and when they do, they can be incredibly costly.
This is particularly important in long-established businesses, where historical data may date back decades. Often, the people who originally entered it are long gone, and the rationale behind certain structures or naming conventions has been lost to time. Leadership teams need to acknowledge this complexity, and ensure there’s sufficient resource and expertise dedicated to working through it before migration, not after.
Technology Can Help — But it Can’t Replace Ownership
The rise of artificial intelligence (AI) and machine learning (ML) in the data space has brought new optimism to this debate. Today’s tools can analyse vast data sets, detect anomalies, and even suggest corrections at pace. It’s a powerful addition to the toolkit, especially in environments with thousands or millions of data points to manage, allowing businesses to spot deep-rooted inconsistencies and identify problem areas that require attention before a migration begins. More advanced solutions now offer automated profiling, trend analysis, and cleansing recommendations that are tailored specifically for ERP environments, enabling teams to work more efficiency and with greater visibility.
However, while these technologies are powerful, they are not a silver bullet. AI might highlight that two vendor records are similar, for example, but it takes human insight and nuance to know whether they should be merged, archived, or kept separate. Automation can accelerate the mechanics of data cleansing tasks, but it cannot determine strategic value or interpret the business context that is stuck in the brains of experienced staff or consultants. Technology can guide the process, but it is human expertise that ensures decisions are accurate and relevant, and importantly, aligned with business goals.
The best outcomes come when technology is paired with governance and business input. Clean data requires ownership, not just from IT teams, but from the business functions that create and use it every day. When those teams are engaged, and when technology is used to support rather than supersede decision-making, organisations stand a far better chance of building a sustainable data strategy.
Too often, data clean-up is seen as a one-off effort, but in reality, it’s an ongoing discipline. Even after a successful migration, if processes aren’t tightened and habits don’t change, the same issues will simply re-emerge like a game of whack-a-mole, where the same problems keep popping back up in different guises.
Looking Ahead: Small Steps, Lasting Impact
There’s no denying that clean data lacks the glamour of a slick user interface or a breakthrough automation tool. It perhaps rarely earns airtime in the boardroom either. But its absence is felt in every inaccurate report, every duplicated entry, and every slow decision. As businesses prepare for the next wave of digital transformation, clean data deserves a place much closer to the top of the agenda.
For those looking ahead to 2027 and beyond, now is the time to get your house in order. Not through grand, sweeping changes, but through pragmatic, focused effort. Identify what’s truly needed and retire what no longer adds value. And perhaps most importantly, put processes in place that stop the rot from returning.
Transformation doesn’t start with systems. It starts with trust, and that trust is built on data you can rely on. Those who recognise this early will not only smooth their migrations but will create a stronger foundation for whatever comes next.
Photo by Markus Winkler on Unsplash
