High-quality business partner data is fundamental for enterprise success today. More than just accuracy, it's about having consistent, up-to-date information on customers and suppliers to make important decisions. Quality business partner data enables organizations to find new opportunities, build stronger relationships, and boost their market presence.
I am Kai Hüner, responsible for the SAP-endorsed app CDQ First Time Right at CDQ AG. In this blog post, I will discuss the importance of trusted business partner data. I will also highlight the dualistic role of artificial intelligence (AI) which both relies on and amplifies the potential of this trusted data. Additionally, I will explain the effective integration and utilization of this data in customers' SAP environments. For further insights, please refer to my blog post First Time Right with Trusted Business Partner Data in SAP Master Data Governance.
What is trusted business partner data?
Trusted business partner data is crucial information about customers and vendors, defined by its source, accuracy, and timeliness. Let's examine how different external data types contribute to this trust:
- Open Data: This is data everyone can access and republish without restrictions. Common sources include governments, NGOs, and certain businesses. When looking at business partners, databases listing registered entities, like their official names, addresses, and industries, are important. For instance, to verify a vendor's address, a national postal service's database might be the go-to resource. Many, like trade registers, are seen as reliable because they follow strict rules and, often, legal requirements, such as tax number registers.
- Commercial Data: These datasets come from specialized vendors at a cost, often highlighting their accuracy and updated nature. Consider databases offering deep insights into potential customers or vendors – from their financial status to their past activities. Companies like Dun & Bradstreet or Bureau van Dijk give detailed company profiles. However, trust levels in these providers can differ. Some companies might trust the data based on positive past experiences, while others might be more cautious.
- Shared Data: This is data that's exchanged in dedicated platforms or communities. An example is the CDQ Data Sharing community; if one member company updates information about a common business partner, every member gains from this new information. The combined checking by many companies can boost trust. On a related note, the SAP Business Network provides a platform for a range of data sharing and business network activities, making it another important reference in this context. Still, each company has its own level of trust in this shared data.
- Web Data: Company information found online, be it on official websites, social media, or news sites, offers a wealth of insights. A comment on a social media platform or a news release can provide key details. But the vast and changing nature of the internet means trust in this data often varies. Some might trust data from a well-known company site, while others might double-check it against other sources.
To conclude, trust in business partner data varies. Some sources, like trade registers, are widely trusted, but others depend on individual judgment. Companies need to navigate open, commercial, shared, and web data to create their own understanding of trust, aiming to get a complete and correct view of their business partners.
How does trusted business partner data create value?
Trusted business partner data serves multiple purposes across various domains of a business. Its role in optimizing processes, managing risks, and informing strategies is crucial. The entire lifecycle of such data, from its source to its application in business strategies, contributes significantly to a company's success. The following figure showcases this data value chain and the related data value formula that translates data into tangible business outcomes.
Utilizing this formula as a reference, let's further examine how trusted business partner data contributes to diverse business value:
- Digitalization of business processes: Efficient processes in areas like procurement, supply chain, marketing, and sales rely on accurate business partner data. Errors due to poor data can increase operational costs. According to the Tenfold Rule, the cost of rectifying poor data quality can be ten times higher than managing it correctly from the start.
- Risk management and compliance: Precise and consistent business partner data ensures that companies can effectively screen against sanction or embargo lists and adhere to key regulations. Quality data also helps in identifying potential financial and operational risks early on.
- Driving business insights and leveraging AI: Having a harmonized view of business partner data across the company sets the stage for deeper analyses. This includes evaluations such as assessing procurement volumes to refine sourcing strategies. Moreover, for AI-driven methods like customer segmentation or optimizing supply chains, quality master data is crucial, especially when integrated with other data sources.
- Integration with external data sources: Merging business partner data with other data types can deepen insights. For instance, merging supplier data with market insights might unveil hidden market opportunities.
- Data quality as an ROI (return on investment) multiplier: Reliable business partner data ensures that insights derived are dependable, leading to effective strategies. When marketing campaigns are built on accurate data, the returns are optimized.
- Empowering digital strategies: Quality vendor data can streamline e-commerce processes. Similarly, dependable customer data improves personalization, enhancing customer loyalty.
- Fostering collaboration and innovation: When businesses have mutual trust in their shared data's accuracy, they are more likely to collaborate, leading to innovative solutions and offerings.
How does trusted business partner data stand out in the age of AI?
In the current landscape where AI plays a prominent role, the significance of trusted business partner data becomes even more pronounced. AI systems, particularly machine learning models, operate at their peak efficiency when fed with accurate and reliable data. Trusted business partner data offers just that – a foundation of accuracy that these models can rely on.
For applications that require predictions, such as forecasting market movements or discerning customer preferences, the quality of the data being used becomes paramount. A machine learning model powered by detailed, high-quality business partner data is more likely to yield accurate results than one using less reliable data.
But trust in data extends beyond its accuracy. It is also about the origin of the data and its adherence to certain standards. Data that is sourced from credible origins, meets legal requirements, and aligns with ethical considerations holds a different value. When organizations base their decisions on such data, they are ensuring a level of responsibility and integrity in their actions.
Furthermore, as businesses become more interconnected and data-driven, the ability to verify the authenticity and ethical collection of data becomes vital. AI, for all its capabilities, is not equipped to evaluate these aspects of data on its own. While it can sift through vast amounts of information and identify patterns, it can't inherently determine if a piece of data was ethically sourced or if its origin is authentic.
This limitation underlines the importance of human involvement. Even in an era where AI is poised to take on an increasing number of tasks, the process of verifying and trusting data remains a distinctly human responsibility. As we rely more on AI for decision-making, ensuring the trustworthiness of the data we provide it becomes crucial. The partnership between humans and AI is strengthened by the quality and integrity of the data at its core, emphasizing the unique and continuing role humans play in the data validation process.
How to use trusted business partner data in an SAP landscape?
Within an SAP environment, using trusted business partner data is essential for enhancing operations and refining decision-making. SAP Master Data Governance (MDG) plays a key role in this approach, ensuring data remains consistent, unified, and accessible throughout the organization.
SAP MDG offers a distinct advantage through its data provider integration feature. This allows for the immediate incorporation of trusted external business partner data sources. Such a feature ensures timely access to top-tier data, minimizing errors and reinforcing the stability of operational processes.
For detailed guidance on configuring CDQ as a data provider for SAP MDG, please refer to my blog post First Time Right with Trusted Business Partner Data in SAP Master Data Governance.
Key takeaways
Trusted business partner data is about having essential, accurate, and timely information on customers and vendors. Its trust levels can vary based on the source, spanning open, paid, shared, and web data. This data is pivotal for optimizing business processes, managing risks, and ensuring compliance. Its significance isn't confined to its accuracy but extends to its capability to create tangible business outcomes.
Transparency in information origin and the availability of trusted sources are pivotal in an environment significantly influenced by AI. Ensuring that provenance information is accessible, and that data comes from reliable origins is fundamental, not only for AI operations but also for strategic decision-making. Such clear, trustworthy data maximizes the benefits and reliability of AI analyses and predictive models, enhancing their application across various business activities.
Within SAP environments, trusted business partner data stands out as a critical element. SAP MDG guarantees that data remains consistent and unified across the organization, and its data provider integration feature ensures access to quality data in real-time.
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