German software giant SAP’s S/4HANA data migration and management is a huge challenge. Organisations in the midst of transitioning to the platform lack comprehensive data strategies, find responsibilities split between departments, lack skills, can’t access data and report slow progress to use of artificial intelligence (AI) because of problems with data management.
Those are the key findings from a survey carried out among 52 senior decision-makers at SAP user organisations in the UK and Ireland about the challenges of moving to S/4HANA for data management consultants Syniti.
S/4HANA is the SAP’s latest generation of its enterprise resource planning software, which runs across cloud and on-premise datacentres. S/4’s innovation was to run the applications on SAP’s own HANA in-memory database, leaving behind its previous reliance on Oracle databases.
But S/4HANA dates from 2015, and migration to it by customers has been a long and drawn-out process, with data management and migration issues a big obstacle to smooth transitions.
Nearly four-fifths (77%) reported data management presented a challenge when moving to S/4HANA from SAP ECC 6.0. Only 7% found it not challenging.
The Syniti survey found that only for a minority (12%) did respondents’ organisational data strategy cover the whole organisation. For almost one-third (31%) it covers most of the organisation, and for 21% part of the organisation. Nearly one-quarter (23%) are still at the planning stage of their data strategy.
Chris Gorton, EMEA managing director and senior vice-president for Syniti, said customers often lacked a comprehensive data strategy that planned forward to desired outcomes in terms of the data. “Customers aren’t in control of their own destiny when it comes to data,” he said. “They have habits and attitudes to data that just don’t fit with the market today. Many still have the same approach they had 20 years ago, relying on untrained people, using Excel to build and migrate datasets, etc.”
What approach do customers need to take? “Data first,” said Gorton. “That means starting 12 to 18 months ahead and having the end goal in mind in terms of what you want to get from your data. You want to be able to transfer, enrich and validate your data with knowledge of what you want from its end state as you design the project.”
Responsibility for the organisation’s data strategy was found to be split between IT (62%), data analytics (23%), the CEO and chief financial officer (3%), while 5% reported they have a dedicated transformation team.
A staggering 70% of those questioned said they did not have the right business or data skills to make effective use of all their data. Just under one-quarter (23%) said they did.
When asked, a large majority were either not very confident (34%) or somewhat confident (54%) about the quality and accessibility of their organisation’s data. Only 7% reported being very confident.
Meanwhile, 80% said the extent of data duplication was challenging, while 89% said data being in silos prevented real-time decision-making.
Furthermore, 82% said data management issues would slow the adoption of AI technologies, while 73% expressed concerns about compliance.