Migrating and managing German software giant SAP's S/4HANA data is a big challenge. Organizations in the midst of transitioning to platforms lack a comprehensive data strategy, have responsibilities spread across departments, lack skills, lack access to data, and have data management issues leading to artificial intelligence. (AI) use is reportedly lagging.
These are the key findings from a survey of 52 senior decision-makers from SAP user organizations in the UK and Ireland about the challenges of migrating to S/4HANA for data management consultancy Syniti.
S/4HANA is SAP's latest generation of enterprise resource planning software that runs across cloud and on-premises data centers. The innovation of S/4 was to abandon previous dependence on the Oracle database and run applications on SAP's own HANA in-memory database.
However, S/4HANA was born in 2015, and migrating customers to S/4HANA is a long process, with data management and migration issues being major obstacles to a smooth transition.
Nearly four-fifths (77%) reported data management challenges when migrating from SAP ECC 6.0 to S/4HANA. Only 7% found it not challenging.
Syniti's research found that only a minority (12%) of respondents' organizational data strategy covers the entire organization. Almost a third (31%) cover most of the organization and 21% cover some part of the organization. Almost a quarter (23%) are still in the planning stages of their data strategy.
Chris Gorton, EMEA managing director and senior vice president at Syniti, said customers often lack a comprehensive data strategy to plan for their desired data outcomes. “Customers cannot control their own destiny when it comes to data,” he said. “They have habits and attitudes towards data that simply don't fit in today's market. Many companies still have the same approach they had 20 years ago, relying on untrained talent and using Excel We use it to build and migrate datasets.
What approach should customers take? “Data comes first,” Gorton said. “That means you need to start 12 to 18 months in advance and keep in mind the end goal of what you want to get out of your data. Use the knowledge of what you want from the end state as you design your project. We want to be able to transfer, enrich, and validate data.”
Responsibility for an organization's data strategy was found to be split between IT (62%), data analytics (23%), CEO and CFO (3%), and 5% have a dedicated transformation team I reported.
An astonishing 70% of those questioned said they don't have the right business or data skills to effectively leverage all their data. Just under 1 in 4 (23%) said they did so.
When asked, the majority said they were either not very confident (34%) or somewhat confident (54%) about the quality and accessibility of their organization's data. Only 7% reported being very confident.
Meanwhile, 80% said the degree of data duplication makes it difficult, and 89% said data in silos hinders real-time decision-making.
Furthermore, 82% said data management issues would delay the adoption of AI technology, and 73% expressed concerns about compliance.