SAP Netherlands: strong data management of great value for business AI success

The effectiveness of business AI applications depends heavily on the quality of the underlying data structures. Solid data management is crucial, says SAP Netherlands. The company provides tips and an action plan for companies that want to get maximum business value from this technology.

Business AI is gaining business ground. A logical development: since the breakthrough of genAI tools such as ChatGPT, more and more technology suppliers are equipping their core solutions with AI assistants. SAP does this itself, for example by integrating Joule into all its core solutions. This integration removes an important barrier for many companies.

Not one size fits all

Yet successful use of AI cannot be taken for granted. Many companies want to get started with AI, but don’t know where to start. A structured approach is important to create a good basis. That is why SAP Netherlands gives the following tips:

1. Define the use case

“Start with the end in mind. Ask yourself what you want to improve or accelerate with AI,” says Niels Van der Kam, Solution Advisor at SAP Netherlands. This step requires a good understanding of the business challenges, the associated processes and the role AI can play in solving them.

“Good governance on AI starts with this inventory. Not only make an inventory of the opportunities and possibilities that this technology offers, but also make a clear analysis of any risks and, for example, the extent to which this case fits within legislation, regulations and ethical frameworks.”

2. Identify, collect and organize the necessary data

It is then crucial to determine which data is required. “Not all data is equal. Identifying the right data sets is essential for the success of an AI project. Make an inventory of what data you need to achieve your goals,” Van der Kam adds.

Van der Kam also emphasizes the importance of good data integration: “Data is often located in silos, spread throughout the organization. Bringing them together into a coherent whole is a condition for success.” To deliver reliable, relevant and responsible results, the data must at least be of good quality. The data must also be representative of the intended purpose.

3. Choose the right algorithms and tools

The choice of specific algorithms and tools depends on the objective and the available data. There are a wide range of AI tools available, but the trick lies in choosing the right tool. It is an advantage if the AI ​​tool is well integrated into the core solutions used.

In addition, there are various so-called ‘Large Language Models’ (LLMs) available, from Google or OpenAI, for example. These models are trained on massive amounts of text data and are capable of performing complex language tasks, such as generating and understanding natural language. Each of these models has its own specific strengths and weaknesses. Take an inventory of these and determine which one best suits the use case.

Value of data management platform

It is then important to offer the company data to those AI solutions in a controlled manner. A data management platform is of great value,” says Marcel de Bruin, Data Architect at SAP. According to him, there are a number of reasons for this:

1. Integrated data approach

Companies generate enormous amounts of data from various sources. Think of internal systems, external databases, sensors, social media and more. These data flows can quickly result in data silos. That complicates a holistic view of business operations. AI algorithms also suffer from this.

“You want to have a clear understanding in advance of which data you need to answer which question. You centralize the necessary data with a data management platform. This way, AI models have access to all relevant information they need to make accurate analyzes and predictions,” says De Bruin.

2. Data contextualization

For many AI applications, business context is just as important as the data itself. Without that context, even the most accurate data sets can lead to misleading analyzes and suboptimal decisions. Data management platforms provide powerful tools to contextualize data, allowing AI models to better understand the meaning and relevance of data.

3. Data quality and purity

A good data management platform ensures the quality and consistency of the data. It helps identify and correct errors, inconsistencies and duplicates in the data. Data management platforms also offer advanced tools for data cleaning, validation and normalization. Without good quality data, AI output is also suboptimal.

4. Scalability and flexibility

Companies do not operate in a vacuum, but in an environment where data is constantly growing and changing. Data management platforms are scalable and flexible, making them easy to adapt to changing data volumes and new data sources. “This ensures that the AI ​​infrastructure can grow with the needs of the business, without causing costly downtime or inefficiencies,” says De Bruin.

Crucial role

Implementing AI requires more than just technological innovation; it requires a strategic approach. One where the focus is on business needs and efficient data management. A data platform plays a crucial role in this. “With a data management platform, you maximize the potential of your data and lay a solid foundation for AI applications,” he concludes.