Data is of paramount importance as migration to SAP S/4HANA looms large in the world of SAP. However, a new report by Syniti paints a vivid picture of the struggle that most SAP professionals have with data management. The study shows that while 70% of organizations now have a data strategy, 86% fear that data management issues will stall their AI adoption.
With SAP S/4HANA migrations amplifying these challenges, Kevin Campbell, CEO of Syniti, (Part of Capgemini), discusses the report’s findings with ERP Today. He explores why this gap between strategy and execution exists and gives his expert advice on overcoming the data quality and skills hurdles that are holding businesses back.
The report reveals that 79% of organizations see data management as a challenge for their SAP S/4HANA move, and confidence is low. Given that the biggest roadblock isn’t technology but people, specifically, the availability of business resources for data cleansing, what does a true data-first culture look like in practice?
A true Data First culture means that data isn’t just an IT issue; it’s a business priority. That starts with executive commitment and cascades down through every level of the organization. You need to embed data ownership into daily operations, not just project plans if you want transformation success.
Change management is also critical. You must engage business users early, make data quality part of their KPIs, and give them the tools and support to succeed. In this area, as in so many others, AI has the chance to leverage and reduce the workload on business users, which is promising but success still requires humans in the loop.
Overall, if you want your SAP S/4HANA migration to succeed, you must expect more from your data right from the start.
While AI is a huge business driver, 86% of leaders admit that poor data management will slow down AI adoption. How can organizations flip the script and start using AI not just as an end-goal, but as a tool to actively improve the data quality that’s holding them back?
I’m glad you mentioned that. Many companies don’t realize the relationship goes both ways; you need high-quality data to fuel AI, but you can also use AI to help keep that data quality high. AI is only as good as the data it’s fed. So, if your enterprise is dealing with inconsistent and scattered data, no amount of AI wizardry will save you.
Instead, business leaders should use AI to automate cleansing, flag duplicates, enforce governance at scale, and detect anomalies in real time. With the right approach, AI becomes a force multiplier for governance and quality and will help your organization unlock both trusted data and faster adoption.
The risk comes when data isn’t owned by the business. It can lead to data that technically loads and looks fine on the surface, but it causes huge downstream problems.
Your survey pinpoints exactly where SAP S/4HANA projects get derailed. Based on your experience, what is the single most common, yet avoidable, trap that migration teams fall into?
The number one mistake I see is companies not realizing that every migration is a data quality project. Too many organizations dive headfirst into design and planning, assuming they can clean up the data later. The result? Delays, inaccurate analytics, and high costs. That’s because data is not a side task, it’s the foundation.
The result is technically migrated data that still creates costly downstream issues. Every migration must be approached as a data quality project first, not just an extract, transform, load (ETL) exercise, if you want first-time-right success.
Another trap in migrations is treating ETL as the whole project. It is just 10–15% of the effort, but when under-managed, you end up with technically migrated data that disrupts business operations. This creates data quality, validation, and scalability issues. To avoid these mistakes, load your data early and often. The truth is, beginning data cleansing six to eight months before design is not only possible, it’s essential. I’ve been in this industry for over 30 years, and I’ve never heard someone say, “We started cleaning our data too early.” So, put your Data First, secure leadership buy-in, and make high-quality data your primary goal. It will be the difference between a transformation’s success or failure.
The report’s commentary argues that data strategy must ultimately be owned by the business. Why is this distinction so critical, and what are the risks when the people who feel the business’s pain aren’t the ones driving this strategy?
When the business owns the data strategy, it ensures that outcomes or results are tied to the data and is aligned with real operational needs, decision-making, and outcomes. Business teams understand the context, processes, and goals that data supports, so they’re best positioned to define what good data looks like and how it should be used.
The risk comes when data isn’t owned by the business. It can lead to data that technically loads and looks fine on the surface, but it causes huge downstream problems. Inventory records show products are available, but none of them can be shipped. Vendor files pass validation but are missing key banking details. Customer data looks clean but leads to billing mistakes and compliance issues. Without true business ownership, these gaps go unnoticed until they become costly setbacks.
Your data is your company’s most valuable asset, so transformation success requires the business at the table from day one, taking responsibility and treating data as a strategic asset.
How does this reliance on disparate, single-purpose tools contribute to the silos they’re trying to break, and what tangible business value does a unified data platform deliver in a complex SAP environment?
Relying on a patchwork of single-purpose tools only reinforces the silos that organizations are trying to eliminate. Each tool serves a narrow function, each operating in isolation. This creates more work, inconsistent standards, and limited visibility across the enterprise. Instead of enabling collaboration, teams end up working from different versions of the truth. This slows decision-making and puts the success of the whole transformation at risk.
A unified platform changes the game by supporting the full data lifecycle from migration, pipelines, and quality, to governance, and analytics in one place. It makes sure your data is accurate and aligned with real business objectives, and just as important, it’s built for collaboration, enabling IT and business to work side by side with the same tools and methodologies. In a complex SAP environment, that means data is optimized, validated, and made fit for purpose. The business gets actionable insights, IT delivers trusted systems, and together they unlock real transformation value.
Looking ahead, the report paints a stark picture of a persistent skills gap. As data complexity grows and projects like SAP S/4HANA and AI become non-negotiable, how should leaders rethink their approach to talent?
Business leaders need to realize good enough isn’t good enough when it comes to your data or your team. Too many companies think buying the latest tech will deliver transformation, but without skilled, business-savvy teams, even the best systems fall flat.
The answer is building a workforce fluent in both business and data. Organizations need skilled data specialists who understand data in context, not just generalists or developers. That means investing in training, certifications, and continuous learning so your people can keep pace with complexity. Embedding data experts in cross-functional teams keeps business goals and data initiatives aligned, putting your enterprise in the best position for SAP S/4HANA migration success.
