Why Central Data Storage Is Becoming Mandatory in B2B

Today, companies want to address their customers across many channels in an automated, personalized and legally compliant manner: Website, online store, marketplaces, email, social media, self-service portals, field service apps and, increasingly, AI-supported assistants. The basis for this is always the same: clean, consistent data about customers, products and interactions.

Many B2B companies still fail to achieve this. Important information is scattered across ERP systems, CRM, Excel lists, store systems and various data islands. This costs time, generates errors and makes automation, AI use and reporting unnecessarily complicated.

Centralized data storage - in the form of master data management, PIM, DAM or customer data systems - is now a prerequisite for scalable, efficient and compliant work.

Maintained Twice - An Old Problem in a New Guise

Master data is often maintained in several systems in parallel:

• Customer data in ERP and CRM
• Product data in ERP and store
• Media data on file servers, SharePoint or in marketing tools

There are also new sources such as webinar platforms, marketplaces or IoT systems.

The Consequences:

• Contradictory information on prices, specifications or availability
• Lost potential in segmentation and personalization
• High manual effort for consistent campaigns, catalogs or product sheets
• Risks for data protection & compliance

In short: the requirements are more complex, many systems have grown historically and are still not consolidated.

Consolidation Instead of Chaos: From Tool Zoo to Data Backbone

The solution lies in a clear data architecture - often referred to as a "data backbone" or "single source of truth":

• Fewer operational systems, clearly defined roles (ERP for commercial processes, CRM for customer relationships, PIM/MDM for product data, DAM for media)
• Central data models and governance: Who is responsible for which data? Which fields are mandatory? Which quality rules apply?
• Integration strategy: APIs, event streaming and integration platforms instead of wildly grown point-to-point interfaces

Central data management is not just an IT issue today, but strategically relevant for sales, marketing, service and product management - and a prerequisite for the successful use of AI.

Record Master Data Centrally - And Use It Correctly

Customer, supplier, product and machine master data is a company's critical infrastructure. Advantages of centralized data management:

• Sales & marketing: consistent customer image across all touchpoints
• Product management: faster introduction of new products
• Service & after-sales: reliable allocation of spare parts, maintenance contracts or SLAs
• Reporting & analytics: reliable basis for forecasts and AI-supported recommendations

Modern master data management solutions combine governance, workflows and automation: data is not only stored centrally, but also checked, enriched and versioned.

Centralized Product Information: From Data Status to Experience

Products in B2B usually require a lot of explanation: technical specifications, variants, standards, certificates, accessories, prices, delivery times - often in several languages.

With a central PIM, this information can be maintained once in a structured manner and displayed in all channels:

• Online stores, marketplaces and partner portals
• PDF data sheets, catalogs, price lists
• CPQ solutions (Configure-Price-Quote) and product configurators
• Apps for field sales and service

The focus today is on consistent experiences across all touchpoints, including self-service and AI-supported advice

Digital Asset Management 2025: More Than Just Media Storage

A DAM system centrally manages images, videos, 3D data, CAD files, presentations, certificates and assembly instructions. Advantages:

• Central metadata maintenance: rights, languages, topics, product references, channels
• Automatic adaptation for channels: Web, social media, previews, video transcoding
• AI-supported tagging: assets are automatically recognized, tagged and found more quickly

This is how a DAM reduces effort, errors and the need for coordination, especially in complex B2B structures with many regions, markets and partners.

Interfaces as Structured Data Pilots

Today, data flows between systems take place via:

• APIs, webhooks and event streams instead of rigid CSV exports
• Integration platforms (iPaaS) or middleware in the cloud
• Near real-time synchronization for prices, inventory and personalized experiences

Important: Each system must have clear responsibilities so that changes are propagated consistently

Omnichannel & Composable Commerce

B2B customers today expect B2C-like experiences:

• Information on website, portal or marketplace
• Online configuration of products
• Digital quote request and electronic signature
• Ordering via store, e-procurement or field service
• Self-service for status, invoices and returns

Without central data storage, new silos are created. It turns system diversity into a competitive advantage: data is consistent, channels are flexible and target group-oriented.

Data Protection, Regulation & AI

Central data storage also supports compliance:

• Transparency: where is personal data stored?
• Accountability: information, correction, deletion practically possible
• Consent & preference management can be clearly mapped

Central data is also the basis for AI scenarios: personalized recommendations, intelligent search, chatbots or sales co-pilots.

Conclusion: Central Data Management Is a Business Program

Central data management in B2B is not a nice-to-have, but a hygiene factor:

• Enabler for sales growth: better leads, higher conversion, cross/upselling
• Catalyst for efficiency: less manual maintenance, fewer errors, faster time-to-market
• Basis for compliance: data protection, reporting, governance
• Prerequisite for AI use: sales, marketing, service

How to get there: clear data strategy, clear target image of the system landscape and pragmatic implementation. Starting now lays the foundation for efficient, scalable and future-proof business processes.

Getting started with centralized data management does not begin with a tool, but with clarity: a clear target image of your own data architecture is crucial: which data is business-critical, which systems are leading - and how should they interact in the future? Companies that answer these questions in a structured way now create the basis for scaling, compliance and the sensible use of AI.