“Why would data analytics teams spend most of their time preparing data for analysis versus actually analyzing it? Data often exists in fragmented silos. Replicating, transforming, validating and governing data is complex, leaving limited time for analysis.
In today’s environment where global uncertainty effects enterprises and real time insights are crucial – this blog explains how SAP supports enterprises in tackling this challenge with Business Data Cloud.”
Anindita Mukherjee
Whether it is finance, procurement or IT, business leaders need insights at the right time to be able to make informed decisions and course correct with agility. For example, understanding how the global supply chain disruptions are affecting spend, working capital and risk profile. Could there be capability gaps in functions critical to the company’s growth? Achieving this level of predictive insights requires intelligent and connected technology. This is still not commonplace.
Reality is a lack of actionable insights as relevant data is often trapped in rather inaccessible systems. AI produces poor results without the right data foundation. The data divide makes the already complex work of leaders more challenging.
SAP’s strategy for intelligent and connected IT
To overcome these challenges SAP developed a strategy to ensure your IT is intelligent and connected. This strategy focuses across three layers:
- Making it possible for applications within your IT landscape, both SAP and non-SAP, to be interconnected.
- The wealth of data from SAP suite of applications is prepared, contextualized and translated into valuable real-time insights.
- With the vision that AI agents leverage rich ERP data to enable seamless workflows and processes.
Truly intelligent and connected AI, insights and workflows can only exist when all three layers are met. Otherwise, it ends up with a landscape where systems do not communicate, data remains disconnected, and AI cannot function properly.
SAP’s Business Suite of cloud applications fulfills the application layer, and Generative AI applications, such as Joule and AI agents, across different SAP solutions contribute to the AI layer. However, the data layer has historically been a challenge. Especially now, when AI agents are highly dependent on the data they use. If that data is incomplete or disorganized, so will be the AI’s output.
The 80/20 rule strikes again
What makes contextualizing data so difficult is the complexity of data within SAP. This is one of the main contributors to the data divide: data preparation takes more time than the actual analysis. As IDC, a global market intelligence provider, found: “The breakdown of time spent on data preparation versus data analytics is woefully lopsided; less than 20% of time is spent analyzing data, while 82% of the time is spent collectively on searching for, preparing, and governing the appropriate data”.
We see this in practice as well, especially when companies have legacy systems. A leading telecom company launched a three-month campaign, and an entire month passed before getting concrete insights into sales performance, costing it valuable time to react and make necessary changes. This was despite external data analysts, in addition to the Telco’s existing set-up of data warehousing and BI teams. If a company lacks an existing set-up, the time lag could go even up to 6 months, as per a Dutch data scientist in the telecom domain.
It is not uncommon for the large Dutch companies to have teams of 100 to 200 people for data analytics. 20 to 50 percent of that can be attributed to managing the complexity of reconstructing ERP data context, outside of applications in a third-party analytics platform.
The following image illustrates this complexity: a schematic representation of a data model within SAP.
No wonder it takes many analysts weeks or even months to arrange data in a way that valuable insights can be derived from it.
SAP Business Data Cloud
We tackled this issue head-on. Our solution for this challenge: SAP Business Data Cloud. This is a fully managed SaaS solution that unifies and governs all SAP data and seamlessly connects with third-party data analytics platforms such as Databricks, without the need to recreate the data context, in a 0-copy, real-time architecture. The solution provides leaders with the context to make even more impactful decisions.
Also McKinsey states:
- “New business use cases can be delivered as much as 90 percent faster.
- Total cost of ownership, including technology, development, and maintenance costs, can decline by 30 percent.
- The risk and data governance burden can be reduced.”
Discover how your organization can gain smarter insights from data. Join us at the SAP Data Summit Netherlands on June 24 in Utrecht. Register now.
Sources:
For the creation of this blog, the following sources were consulted.
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Equalizing Time Spent on Data Management vs. Analytics | IDC Blog
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Manage data like a product to unlock full value | McKinsey
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SAP and industry expert inputs; Insights from SAP customers
