G2

Arkajit D.

Chief Technology Officer

Information Technology and Services

Mid-Market (51-1000 emp.)

5/14/2026

"Powerful Data Unification, but Implementation and Tuning Take Real Effort"

4/5

What do you like best about SAP Datasphere?

What I like best about SAP Datasphere is how effectively it helps unify fragmented enterprise data without forcing organizations to completely rebuild existing data landscapes.

In our case, we were dealing with operational and analytics data spread across ERP systems, finance platforms, reporting databases, cloud applications, and custom operational tools supporting fintech workflows. One of the biggest challenges was maintaining a consistent view of business data across teams because every department was working from slightly different datasets and reporting logic. SAP Datasphere helped create a more centralized and governed data layer without disrupting existing operational systems.

What stood out immediately was the balance between integration flexibility and enterprise governance. The platform made it easier to connect SAP and non-SAP environments while maintaining better control over data consistency, lineage, and business context.

From a UI/UX perspective, the platform felt more business-oriented compared to traditional data engineering-heavy environments. Analysts and operational stakeholders could collaborate more effectively with data teams because the data modeling and access workflows were easier to understand.

Another strong point was performance for enterprise-scale analytics workloads. Even with large operational datasets and cross-system reporting requirements, query handling and data accessibility remained reliable for most business intelligence workflows.

Integrations were also a major advantage since the platform connected well with analytics ecosystems and reporting tools already being used internally. That reduced migration friction and improved adoption across teams. Review collected by and hosted on G2.com.

What do you dislike about SAP Datasphere?

One thing I disliked about SAP Datasphere is that while it is extremely capable for enterprise-scale data unification, the implementation and operational setup can become complex very quickly in real production environments like ours.

At work, we use multiple operational systems across finance workflows, reporting platforms, customer analytics, and internally developed fintech applications. Bringing all of that data into SAP Datasphere required much more planning and governance alignment than we initially anticipated. A large part of the effort was not just technical integration, but also standardizing business definitions, reconciling conflicting datasets, and ensuring reporting consistency across teams.

For example, transaction reporting, reconciliation dashboards, and operational KPIs were originally being calculated differently by finance, operations, and analytics teams. While SAP Datasphere ultimately helped centralize and govern those datasets effectively, building clean semantic models and optimized reporting layers took significant collaboration between data engineering and business stakeholders.

Another challenge we experienced directly was performance tuning for complex analytics workloads. Standard dashboards and operational reporting worked well, but as teams started running cross-system analytics queries combining ERP data, operational metrics, and customer activity datasets, maintaining fast and consistent query performance required additional optimization work. Review collected by and hosted on G2.com.

What problems is SAP Datasphere solving and how is that benefiting you?

SAP Datasphere solved one of the biggest operational challenges we were facing internally: fragmented and inconsistent data spread across multiple business systems.

At work, our finance data, operational KPIs, customer reporting, compliance records, and product analytics were distributed across ERP platforms, cloud databases, BI environments, and internally developed fintech systems. Different teams often relied on separate reporting logic and disconnected datasets, which regularly created inconsistencies during operational reviews and leadership reporting discussions.

One issue we repeatedly faced was during monthly reconciliation and compliance reporting cycles. Finance, operations, and analytics teams were pulling numbers from different systems, and even small mismatches between reports would trigger long manual validation exercises before decisions could move forward. After implementing SAP Datasphere, we were able to centralize and standardize those datasets into a more governed reporting layer, which significantly improved consistency and trust in the data being shared across departments.

Another major improvement was visibility across teams. Product, operations, and leadership teams could access unified analytics dashboards without constantly depending on spreadsheet consolidation or engineering teams to manually prepare reports. That reduced reporting delays and made operational decision-making much faster.

In day-to-day workflows, the platform helped create a more reliable data foundation for everything from transaction reporting and operational monitoring to executive-level business reviews, which had a direct impact on efficiency and cross-functional alignment. Review collected by and hosted on G2.com.