Presented by SAP
There’s no question the future of business data and decision-making is driven by AI. And with steady advancments in AI, organizations across industries feel the pressure to drive innovation across the end-to-end process. But the foundational challenge to achieve success with AI is data fragmentation.
“The reality for most of our customers is that every part of their business is deeply connected, but when they need to make decisions based on insights, that’s when data feels like a fragmented experience,” says Tony Truong, director of product marketing, data and analytics at SAP.
The misalignment between IT and business is due to inconsistencies in how they view the business – each has quite differing approaches to the balance between data agility and data governance. Bringing these together is a time-consuming task for IT because when data is extracted from its source,the business context — an understanding of that data in relationship to the processes it was originally associated with — is completely wiped out. For the data to be usable, all of the metadata and logic must be rebuilt from scratch. And by the time that lengthy, redundant process is completed, the data is already getting stale.
There’s also a mismatch in data definitions across different departments or systems — each department may look at the same data point in a different way. For instance, what the sales team considers a “customer” might differ from the marketing team’s definition. The discrepancy in semantics can affect how business leaders view the impact of a marketing campaign for the business development team. The inconsistency can lead to significant inefficiencies and delays decision-making, Truong says.
“This fragmented experience leads to missed opportunities and disconnects between integrated solutions,” Truong says. “Managing data and applications across different platforms is complex, requiring specialized skills and tooling that could increase the operational and complexity costs if not done right. And when data is shipped to users without the context necessary for it to be useful, collaboration becomes significantly limited, and the organization loses the power of shared decision-making insights and collective expertise.”
Without context, large language models (LLMs), other applications downstream and business users aren’t working with enough domain-specific knowledge to deliver the business insights organizations are chasing. And to ensure data consumers can leverage this data thoroughly, organizations need to prioritize business semantics, data literacy and self-service capabilities.
The importance of business semantics
As organizations integrate data across multiple business processes, they need a new way to maintain the accuracy of that data. That’s where business semantics comes in.
AI models and applications require semantically rich data in order to produce reliable business outputs. A semantic layer is an abstraction layer between underlying data storage and analytics tools. It translates metadata (or business context) into natural language so that users can interact using terms they understand, and hides complex underlying data infrastructure, which dramatically simplifies data exploration and analysis.
This provides business users with a way to discover and understand relationships between data, enabling them to answer complex questions and uncover hidden insights that traditional databases might miss. It also offers secure, truly self-service access to data and analytics, which is a major step forward for business decision-making. When teams have streamlined access to the same contextual data, it takes far less time and effort to generate insights, dramatically speeding up data-powered decision-making for users at every level and in every department.
“When data products are enriched with domain-specific knowledge and are accessible, this gives ownership of the data back to the business and makes them infinitely usable across the organization, since the value of a data asset is proportional to its usage,” Truong says.
How business data fabric unlocks business semantics and self-service
A business data fabric is key to delivering an integrated, semantically-rich data layer over underlying data landscapes. It’s a data management architecture that provides seamless and scalable access to data without duplication, differing from a standard data fabric in that it keeps the business context and logic intact.
It creates a single source of truth, offering agile self-service access to trusted data, and accelerated, accurate decisions, real-time data for in-the-moment insights and flexibility and a simplified data landscape. That maximizes the potential of your data and current infrastructure investments, while comprehensive data governance ensures every stakeholder that private data stays private.
IT can federate access and security so that teams have self-service access without needing to rebuild systems and processes or make offline copies, and the data is secured from unauthorized access. The data modeling and semantic layer creates a common language for data across systems by creating a model that describes the data, and a semantic layer that offers a business-friendly interface to data consumers.
“When business processes are integrated, you can take advantage of your existing investments and future investments,” Truong says. “Data is harmonized and ready to use. All your lines of business can have a single system to power their cross-organizational decision-making systems.”
Dig deeper: Learn more about how a business data fabric can transform your AI capabilities.
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