Data quality is fundamental to SAP S/4HANA migration success
If your company is preparing to move to SAP S/4HANA, stop thinking of data as an IT task. The success of that migration depends almost entirely on data quality. Skip over that, and you’re setting the stage for costly delays, system failures, and poor decision-making across the business.
Most companies think they’ll deal with data “later.” That’s a bad plan. Treat every migration as a data project first. Start cleansing and validating data six to eight months before design even begins. Don’t push it to the back of your roadmap. The technical side of migration, what’s often called extract, transform, load (ETL), only accounts for about 10–15% of the work. The rest is all about quality: how data is structured, how accurate it is, and whether it actually serves the business.
When this goes wrong, you still get data loaded into the new system, but it doesn’t work properly. That means incorrect analytics, inventory issues, billing failures, all things that cost time and money. The solution is simple: make clean, consistent data your top priority from the start. That gives your transformation real momentum.
A disconnect exists between having a data strategy and executing it effectively
Almost three-quarters of companies (70%) say they have a strategy for data. The real problem is this: most of them never make that strategy work. The plans sit on documents, disconnected from the day-to-day business.
That’s because people still think data belongs to IT. It doesn’t. If you’re serious about transformation, your data needs to be owned by the business. Integrate it into how people work. Set clear data KPIs for business teams. Bring them into the process early. And make sure they have the tools and training to handle it.
A true data-first approach starts at the top. Leadership must treat data as a core value. That means embedding responsibility for data into all levels of the organization. Otherwise, you’ll end up with siloed ownership, inconsistent results, and stalled progress.
Execution is where most strategies fail. Fixing this requires alignment across technical and business teams, and a cultural shift in how you treat data within the organization. That’s not a small task, but it’s essential if you’re going to see real ROI from your technology investments.
Ineffective data management is a major barrier to AI adoption
AI doesn’t work without clean data. AI systems can’t make accurate predictions or decisions if the data feeding them is incorrect, inconsistent, or incomplete. And right now, 86% of business leaders know that their data issues are either slowing down AI projects or stopping them before they start.
This needs to be reframed. Most companies treat AI as the goal and think data quality is something they’ll deal with once AI is up and running. That’s the wrong sequence. The real opportunity is using AI not just as an output but as a tool to improve the quality of the data itself. AI can flag anomalies, detect duplicates, and enforce governance policies at a scale that manual processes simply won’t match.
AI value depends entirely on the environment it operates in. If your data is messy or spread across multiple systems, AI won’t solve that for you. You need clean input before you can get useful output, and then you need AI to help you keep that data quality high over time.
This is where leadership makes the difference. If you own the data and prioritize its management, AI becomes a force multiplier. If you don’t, it becomes shelfware. The decision here is whether your business is prepared to support it with the right data architecture and ownership.
Business ownership of data is essential for strategic success
When data belongs only to IT, no one in the business feels responsible for outcomes. That’s a major vulnerability. You can have technically clean data that passes validation and still cause financial loss, mistaken customer invoices, missing supplier records, or inaccurate inventory details that disrupt logistics.
Data should be owned and governed by the business teams who use it every day. They understand context, workflows, and what “correct” actually means. When they lead the data strategy, the results align with business goals. When they don’t, gaps go unnoticed until something breaks, and by then, the cost of recovery is high.
Shifting responsibility doesn’t mean business teams have to become technical experts. It means they have to take accountability for accuracy, consistency, and usefulness of their data. That’s why embedding data KPIs across departments is so critical. It turns data into something that drives performance, not just system functionality.
This change depends on executive action. Leaders need to be involved from the beginning, not just funding the data strategy, but owning it. If data is your company’s most valuable resource, then make sure your most senior people are responsible for making it valuable.
Unified data platforms outperform disparate tools in breaking down silos
If your organization is using a mix of disconnected data tools, you’re slowing yourself down. Every tool that solves a narrow problem in isolation increases effort, fragmentation, and inconsistency. Teams operate off different standards. Reporting doesn’t align. Critical decisions get delayed, or worse, made using bad data.
The issue isn’t the tools themselves; it’s the fact they don’t work together. When you operate in silos, you lose visibility and trust across the organization. Projects take longer, require more coordination, and carry greater risk of miscommunication.
What moves the needle is using a unified data platform that supports your entire data lifecycle, migration, cleansing, governance, analytics, all in one environment, with one source of truth. That creates consistency. It also simplifies collaboration between IT and business stakeholders. Everyone uses the same platform, sees the same data, and executes with the same standards.
In SAP environments, this clarity becomes even more critical. You can’t afford inconsistent data processes when core systems drive global operations. A unified platform brings control and speed. It connects strategic objectives with day-to-day action and unlocks the full value of the technology investments you’ve already made.
For executives, standardizing on a unified platform is about efficiency. It’s a step toward faster transformation, tighter governance, and more consistent business intelligence, all of which directly support top-line growth and bottom-line performance.
A skilled, business-fluent data workforce is critical for transformation
Investing in top-tier technology means nothing without the people who can put that technology to work. There’s still a gap between what new tools can do and the skills inside many companies to take full advantage of them. This isn’t a minor issue, it’s a growing strategic risk.
Many organizations assume they can solve this by pouring money into software or infrastructure. That won’t work. You need talent that understands data, and understands what that data means in the business context. Developers alone can’t do that. You need data specialists who get both the technical and operational side, people who can assess what “good” looks like, validate results, identify gaps, and adjust systems to match business goals.
That type of talent won’t show up by itself. Companies have to invest in it. That includes targeted hiring, but more importantly, upskilling existing teams through training, certification, and embedding data experts directly into cross-functional initiatives.
Business leaders should view this as a long-term asset. Build a workforce that doesn’t just operate your systems but understands what your data is telling you, across finance, supply chain, customer operations, and beyond. That’s how you get transformation efforts that actually deliver meaningful business outcomes.
Executives need to drive this shift at the organization level. Developing talent pipelines, aligning roles to strategic initiatives, and integrating data literacy into leadership development must become priority items on the board agenda. Transformation outcomes start with your people.
Key takeaways for leaders
- Prioritize data quality at the start: Treat SAP S/4HANA migration as a data quality project from day one. Early cleansing, ideally six to eight months before design, avoids costly rework and system-level disruptions later.
- Bridge the Strategy-Execution gap: Having a data strategy is not enough. Leaders must embed data accountability into day-to-day operations and align KPIs across departments to ensure execution sticks.
- Use AI to improve data: AI initiatives require clean, consistent data to succeed. Use AI tools to automate cleansing, detect anomalies, and enforce governance at scale to boost data integrity over time.
- Make the business own the data: Business teams, not IT, should lead data strategy to ensure alignment with real operational needs. Hold them accountable for defining, maintaining, and validating the data they rely on.
- Standardize on a unified platform: Eliminate fragmented point solutions that reinforce silos. A unified data platform enables consistency, faster decision-making, and tighter collaboration between business and IT.
- Build a skilled, Business-Savvy data workforce: Invest in data talent that understands both systems and operations. Upskilling teams in data literacy and embedding experts into cross-functional efforts is critical for transformation success.
