Data Driven Business: How companies create business value with data and AI

Data-driven working is not a new concept. Companies have always used information about customers, products, suppliers, markets or business processes to make decisions. What has changed, however, is the volume and variety of available data as well as the opportunities to use it in business processes with the help of modern data platforms, analytics and artificial intelligence.

And this is where one of today’s key challenges lies: data is often distributed across different systems and business areas, has varying levels of quality or is used without consistent definitions and responsibilities. For companies to derive economic value from it, this data must be reliably available, understandable and integrated into the relevant business processes.

With generative and agentic AI, the requirements for this foundation are increasing further. AI systems use enterprise data for analytics and content creation, provide recommendations, prepare decisions and can increasingly perform tasks independently within business processes. The quality and availability of the underlying data therefore have a direct impact on how reliably these applications can operate.

The importance of a solid foundation is demonstrated by a Gartner survey: companies reporting successful AI initiatives invest up to four times more in fundamental areas such as data quality, governance, AI-ready people and change management than companies whose AI initiatives deliver poor results. A data driven business therefore relies on the interplay of data, people and processes, connected through an appropriate technical and organisational infrastructure.

What does data driven business mean?

A data driven business systematically uses data to make decisions, manage and optimise business processes and identify new business opportunities. Data feeds into both strategic decisions and operational processes.

This can mean, for example, connecting product and customer data to better tailor offers to specific target groups. Supplier and supply chain data can be used to identify risks at an early stage, while product, commerce and marketing data can collectively help companies understand and improve the performance of individual products and channels.

With AI, this principle expands to include automated and increasingly agentic processes. An AI agent can, for example, retrieve data from different systems, analyse relationships and perform a defined task on this basis. At the same time, this increases the requirements for data quality and context, as incorrect, incomplete or outdated information can flow directly into automated processes and AI outputs.

Data, people and processes as the foundation

At its core, a data-driven organisation is based on three closely interconnected elements: reliable data, people who are able to work with this data and processes in which data is actually used. These three areas need to be developed together so that data can be permanently embedded in operational and strategic processes.

Technology provides the necessary infrastructure but is not sufficient as a foundation on its own. Clear responsibilities, shared definitions and employees’ ability to interpret data correctly and use it for their tasks are equally important.

Data: Creating a reliable foundation

Data quality is a key prerequisite for a data driven business. Data must be accurate, complete, consistent and suitable for its intended purpose so that it can be used reliably for decisions, analytics and AI.

Companies use different systems and technologies for this purpose. ERP, CRM, PIM, DAM, MDM, data warehouses, lakehouses, data catalogues and other applications each perform specific tasks within the data landscape. As the number of systems increases, so does the importance of an architecture that makes data usable across these system boundaries.

Companies must also define what specific data means, who is responsible for it, which quality requirements apply and who is permitted to use it for which purpose. Solid data governance defines responsibilities, standards, policies and control mechanisms for working with data, thereby creating an organisational foundation for its reliable use.

With AI, the scope of this governance is expanding. Data is increasingly processed automatically and used for decisions, recommendations or actions, which means that rules for data quality, access, security and responsibilities also need to be aligned with analytics and AI applications.

People: Understanding and using data responsibly

A reliable data foundation can only create value if people are actually able to use it in their daily work and trust it. A data-driven organisation therefore depends on a strong data culture, in which working with data is a natural part of decisions and business processes.

Employees need to understand which data is relevant to their tasks, how it should be interpreted and the limits of what it can tell them. Data literacy therefore becomes a fundamental skill for numerous roles within an organisation.

As the use of AI increases, AI literacy becomes another important skill. Employees need to evaluate AI-generated outputs, recognise uncertainties and assess when human review or decision-making is required. Data literacy and AI literacy are therefore developing into closely interconnected skills that are necessary for the responsible use of data and AI.

Processes: Translating data into business value

The economic value of data emerges through its use in business processes. A complete, high-quality product data record can, for example, help bring products to new markets and channels more quickly, create better product experiences or meet regulatory requirements more efficiently.

The same applies to customer, supplier, location and other enterprise data. Its quality and availability affect operational workflows and can therefore influence process efficiency, customer experience, time-to-market or transparency across the supply chain.

A data driven business therefore closely connects data management and business processes. Companies need to understand which data is required at which points, which dependencies exist between different data domains and which business outcome its use is intended to achieve.

From data silos to a connected data foundation

Enterprise data is typically distributed across numerous systems and business functions. Product data may be held in PIM, customer data in CRM, transaction data in ERP, digital assets in DAM and analytics information in a data warehouse or lakehouse.

This specialisation makes sense because each system performs specific tasks within the data landscape. However, business questions frequently cross these system boundaries and require information from multiple sources. A company seeking to understand why a particular product is selling less successfully may need information about product quality, pricing, product availability, marketing activities, customer reviews, returns and competitors. Connecting these different sources of information creates the context required for a well-founded root-cause analysis.

A data-driven organisation must therefore be able to connect relevant data across system and domain boundaries. Modern data architectures use technologies such as APIs, ETL/ELT, streaming, data virtualisation, data warehouses and lakehouses for this purpose. The technologies used depend on the requirements of the respective use case in terms of data freshness, performance, scalability and governance.

What role does master data management play?

Master data management is an important component of a reliable data foundation. MDM creates consistent master data for core business entities such as products, customers, suppliers or locations and helps identify different versions of the same entity and consolidate information.

Modern MDM encompasses more than removing duplicates and maintaining individual data records. Relationships and business context play a central role because business transactions generally connect multiple data entities.

A supplier, for example, is connected to specific products, locations, contracts and business units. A customer purchases specific products through specific channels and may simultaneously be part of a corporate group. When these relationships are represented transparently, they provide a more comprehensive understanding of the respective business situation.

These contexts become tangible through comprehensive multi-domain MDM. Different master data domains can be managed together and the relationships between them represented. This creates reliable business context that supports people as well as analytics and AI applications in interpreting data.

MDM forms part of a broader data strategy. Analytics, automation and AI additionally require transaction data, unstructured information, metadata and other data types, which means that MDM needs to work together with data governance, data integration, data quality and the wider data architecture.

Data driven business in the age of AI

AI is changing the role of enterprise data. Alongside people and traditional analytics applications, AI systems and AI agents are increasingly accessing enterprise information and using it within operational processes.

AI agents can, for example, retrieve information from different systems, analyse data, interpret results and trigger further actions on this basis. This increases the number of processes in which data is used automatically while simultaneously raising the requirements for its reliability and context.

Current research underlines this development. According to Gartner, an increasing number of organisations are prioritising investments in AI-ready data because insufficient data readiness is one of the key barriers to the successful use of AI. AI-ready data encompasses several dimensions. Data needs to be accessible, understandable, sufficiently up to date and contextualised for the respective AI use case, while being used under appropriate governance rules. Structured and unstructured information needs to be connected depending on the application so that AI systems can access the most comprehensive information base possible.

This is also changing the understanding of a data-driven organisation. People, applications and AI are becoming different consumers of the same data foundation, each requiring reliable access to the information relevant to their respective tasks.

How can the transformation to a data driven business succeed?

The transformation should begin with concrete business objectives. Companies first need to define where data is expected to create measurable value and which decisions or processes need to be improved.

Questions that can help include: Where is relevant information currently missing? Which data quality issues cause costs or delays? Which processes are inefficient because of fragmented data? Which decisions could be improved through a better information base? And which new analytics or AI use cases should be enabled?

Based on this, companies can determine which data, systems and organisational capabilities are required. A sustainable transformation approach takes several closely interconnected dimensions into account:

• Data strategy: Which data is required for which business objectives?
• Data architecture: How is this data stored, integrated and made available?
• Data quality: Is the data sufficiently reliable for the respective use case?
• Data governance: Which rules, responsibilities and access rights apply?
• Data culture: Are employees able and willing to actually use data?
• AI readiness: Are data, technology, governance and the organisation prepared for AI-based processes?
• Business processes: How are data and AI used in practice to achieve measurable outcomes?

These areas should be considered together as part of the transformation. Changes to the data architecture can, for example, affect governance and processes, while new AI applications may introduce additional requirements for data quality, access rights and employee skills.

Conclusion: Being data-driven means translating data into impact

A data driven business uses data systematically to improve decisions, make processes more efficient and unlock new business opportunities. This requires a reliable data foundation, an appropriate architecture, clear governance structures and employees who understand data and can use it responsibly.

With AI, this interplay is becoming even more important. Data is increasingly used directly by AI applications and agents within operational processes, which means that errors or missing context can have a faster impact on downstream results and actions. Data quality, governance, integration, MDM and data culture therefore collectively provide the foundation for the scalable use of analytics and AI.

Our experts consider data strategy, data architecture, data governance, MDM, data quality, integration and AI enablement as interconnected components of a modern data organisation. Together with our clients, they develop data landscapes and processes that reliably provide people, applications and AI with the information they need and make data usable where it creates tangible business value.

Frequently asked questions about data driven business

What does data driven business mean?

Data driven business describes a business approach in which data is systematically used to make decisions, manage and optimise processes and identify new business opportunities. Data is used both for strategic decisions and within operational business processes.

What does a company need to become data-driven?

A data-driven organisation requires reliable and accessible data, an appropriate data architecture, clear governance and responsibilities, as well as employees with sufficient data literacy. Data also needs to be consistently embedded in business processes and decisions so that the available data foundation can create measurable business value.

What role does data quality play in a data driven business?

Data quality influences how reliably data can be used for decisions, analytics, automation and AI. The required level of quality depends on the respective use case, as different applications have different requirements regarding completeness, timeliness, consistency and accuracy.

What role does MDM play in a data driven business?

Master data management creates consistent and trustworthy master data for core business entities such as products, customers, suppliers or locations. Multi-domain MDM can additionally represent relationships between these domains and thereby provide important business context for operational processes, analytics and AI.

Why is data driven business becoming more important because of AI?

AI applications and AI agents increasingly use enterprise data directly to perform analyses, generate content or execute tasks. This raises the requirements for data quality, context, governance and availability because the underlying information directly influences the quality of automated outputs and actions.