Identifying the Challenges Faced by Organizations

Before we dive into SAP Business Data Cloud, let's first consider the data management problems that organizations are currently facing.

In today’s business environment, organizations are navigating an overwhelming surge of data coming from every direction: internal systems, customer interactions, supply chains, and external markets. While data holds immense potential, the reality is that much of it remains scattered across silos, locked in incompatible formats, and inaccessible in real-time.

This fragmentation hinders agility, clouds decision-making, and slows AI innovation.

Customers are eager to get started with their AI development projects but they soon realize that their data foundation that they have built cannot be trusted and is likely to generate unreliable results.

Their data contains errors, is missing data, or includes out-of-date information. This can be caused by complex extraction processes that could corrupt data or miss data validation.

This means AI is working on a data set that cannot be trusted. It's no surprise that AI generates unreliable results.

Customers want a data set they can trust to build AI applications.

SAP applications are based on different technologies. This means that extracting data can be complex.

There might need to be special middleware to extract, prepare and load the data to a consolidated data platform before it is ready to use in analytics. Extra hardware and specialized software might be needed to convert the data and harmonize it so it is ready to use in analytics an AI.

Special skills and languages might be needed to build and operate the different data flows. Finding and retaining skilled people to build and run the data flows might be an issue.

Data pipelines can become complex. Data fragments can get lost along the way, leading to incomplete data sets.

SAP customers want the data from their SAP applications combined and delivered in a ready-to-consume format without the need to build and run complex extraction processes.

Different applications often provide common objects, such as customer, product or sales revenue, but with different definitions. For example, one application might define product as a character string with length 14. Whereas another application might define product as an integer with length 17. Apart from the technical incompatibilities, it might also be a problem of meaning. For example, one application defines products as the finished product, but another application defines product as a sub-component in the assembly of the finished product. This means that it is impossible to get a clear picture of business performance when you need to consider all variations of the object from different source applications.

Business users want a single definition of each business object regardless of how the object is defined in the source application so that the business user does not have to work with multiple definitions.

Source applications not only generate the business data but also the rich business semantics that give meaning to the data.

Here are some examples of semantics:

• Relationship Information: Account manager Fernando has taken over responsibility for customer A from Pierre.
• Facts: Supplier B did not deliver any orders on time in Q1 and we emailed customer C in April to let them know of a delay in deliveries for Q2 while we look for alternative suppliers.
• Data rules: The warehouse number and zone must always be specified when referring to a bin location.
• Region-specific Information: The currency Yen is used for invoicing in Region A. Product A-212 is known as 'Chargeur' in France and 'Charger' in UK.

When data is extracted from source applications, the semantics are left behind and must be rebuilt in the target system. This is not only wasted effort but leads to errors when the semantics are not rebuilt accurately. SAP applications generate extremely valuable semantic information that it tightly bound to the business data. The semantics information should always be extracted with the business data. This means that powerful and trusted analytics and AI can be created immediately on the data that is already enriched with semantics.

Organizations do not expect to lose valuable semantics during data extraction.

The SAP application portfolio is very large with more than 100 applications. SAP customers usually run multiple SAP applications. For example, SAP S/4HANA, SuccessFactors, Concur, and Fieldglass.

Organizations want to use all their data for analytics to ensure they have the big picture.

Imagine providing the sales data from S/4HANA to the Customer Success team, but leaving behind the valuable customer feedback that you carefully captured in a separate system?

Business users and AI agents expect to work on a complete data set.

SAP have always provided tools for customers to build analytics on their SAP data. But business users want out-of-the-box analytics for each line-of-business (LoB) that they can use right away to immediately gain insight using their data.

Opportunities are missed when the business has to wait for the development of analytics. IT are already under pressure with many projects and may not have the capacity or skills to develop sophisticated analytical applications.

Business users do not want to wait for IT to build dashboards and analytical apps that have lengthy development cycles.

Data storage costs are escalating as applications generate more data than ever before. Analytics and AI needs a lot of data to generate reliable results. Many organizations are storing huge amounts of data in high-cost, high-maintenance repositories, such as disk-based data bases, that were not designed for the needs of modern scaling.

Organizations need low-cost storage that is scalable, secure and can handle any type of data: structured, semi-structured and unstructured.

An organizations' data is typically spread across multiple data platforms. These platforms can be on-premise, cloud-based or a mix of both (hybrid). In addition, developers and business users are provided with powerful tools that facilitate the poor practice of generating multiple copies of data sets stored in local folders and repositories.

Keeping track of where data is and who has access to it has become one of the biggest data management headaches. It creates multiple problems including:

• The risk that data scientists or AI agents might not be using the latest version of data sets to train models
• Storing multiple copies of data leads to high storage costs
• Organizations can be financially penalized if they fail to comply with local and global data management regulations. Organizations need to be able to quickly identify who has access to which data, why access to the data is needed, and for how long they have access. As data is processed and passed through data pipelines, organizations need clear visibility of the data lineage. They need to quickly trace where the data originated, where the data has landed, and all the points along the way where data might be stored.

Organizations need a data platform with built-in data governance at the core.

• Organizations do not trust their data to use in AI development.

• Organizations want access to their SAP data but extraction can be complex and costly.

• Different SAP applications often provide the same business entity but with different definitions. Business users want to work with only one definition of a business entity.

• Business semantics are lost when business data is extracted.

• SAP customers want to use data from all of their SAP applications to build analytics.

• Building analytical applications from scratch takes too much time and opportunities are missed.

• Customers are collecting huge amounts of data, but storing data at scale is expensive.

• Keeping track of where data is and who has access to it has become one of the biggest data management headaches and can lead to financial penalties for non-compliance.