Identifying the Challenges Faced by Organizations

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, or inaccessible in real time. This fragmentation hinders agility, clouds decision-making, and slows 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Their data contains errors, is missing data, or includes out-of-date information.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 that they can rely on to build AI applications.

SAP applications are based on different technologies. This means that extracting data is not always straightforward.

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 software might be needed to convert the data and harmonize so it is ready to use in analytics.

Special skills might be needed to build and operate the different data flows, so this might mean finding skilled people in the organization might be an issue.

Data pipelines can become complex to build and operate. The data fragments can get lost along the way leading to incomplete data sets.

SAP applications often provide the same object, such as customer, product or sales revenue, but are defined differently. This means that it is impossible to get a clear picture of business performance when you need to consider all variations of the object.

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.
• Meta Data: 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 lost and must be rebuilt in the target system. This is not only wasted effort but leads to errors. SAP applications generate extremely valuable semantic information that it tightly bound to the business data. The semantics information should be extracted with the business data and land in the target systems. This means that powerful and trusted analytics and AI can be created immediately on the data that is already enriched with semantics.

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

Customers want to use all their SAP 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?

SAP provides tools for customers to build analytics on their SAP data. But business users want out-of-the-box analytics that they can use right away to immediately gain insight using their data.

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

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.

Customers' data storage costs are escalating as they generate data from more applications than even before.

Customers need low cost storage that is scalable and 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 making 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 might not be using the latest version of data sets to train machine learning models
• Storing multiple copies of data is expensive
• 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 to be able to view the data lineage so they can trace where the data originated and where it has landed and all the points along the way where data might be stored