From the data source to the 360° profile
What you need to know about modern customer data management
Customer data management, CDP, DMP and data architecture: we explain the differences, show the connections and help you to successfully develop your data strategy. For personalised marketing, better decisions and a future-proof database.
Orientation in the data ecosystem
What does a Customer Data Platform actually stand for? What distinguishes it from a data management platform? And how does all this relate to proper customer data management and a well thought-out data architecture? This page provides an overview, categorisation and links to in-depth blog articles.
Data architecture
Data architecture is the strategic and technical foundation that makes customer data management possible in the first place. It defines how data is structured, linked and processed across systems in the company - including data models, interfaces, identity logic and governance structures.
Exemplary data architecture questions:
- What does our central customer profile look like?- How are CRM, web and e-mail technically connected?- What rules apply to data quality and identity resolution?
Customer Data Management
Customer data management encompasses the collection, maintenance, structuring and utilisation of customer data across systems. The aim is a 360° customer view based on consistent, GDPR-compliant data. It forms the basis for personalisation, segmentation and data-driven marketing - for example in CDPs or CRM systems.
Data Management Platform (DMP)
DMPs primarily collect anonymous third-party data, usually via cookies, and are used for targeted online advertising. Unlike CDPs, they are not identity-based and do not store any permanent customer profiles. Due to data protection and the loss of cookies, DMPs are losing relevance - CDPs with a first-party focus are gaining in importance.
What is a Customer Data Platform (CDP)?
A CDP is a platform that centralises first-party customer data from various sources, consolidates it into standardised, identifiable customer profiles and makes it usable for marketing, sales or service in real time. It enables cross-channel personalisation and automation - provided that the underlying data management is properly set up.
This is how everything comes together: Your data ecosystem at a glance
This pyramid shows how your central data systems interact:
CRM systems record and maintain customer data along direct touchpoints, for example in sales or service. They provide important first-party data, but are limited to operational use.
DMPs supplement anonymised data from cookies and third-party sources for digital advertising. They help to address target groups in the upper funnel, but are less effective in a cookie-free environment.
CDPs (Customer Data Platforms) are at the heart of a future-proof data strategy: they consolidate first-party data from all sources, link it to identified customer profiles and make it usable for personalised campaigns.
For this to succeed, two basic requirements must be met: a scalable data architecture that structures systems and data flows and consistent customer data management that ensures quality, completeness and availability.
This is how everything comes together: Your data ecosystem at a glance
This pyramid shows how your central data systems interact:
CRM systems record and maintain customer data along direct touchpoints, for example in sales or service. They provide important first-party data, but are limited to operational use.
DMPs supplement anonymised data from cookies and third-party sources for digital advertising. They help to address target groups in the upper funnel, but are less effective in a cookie-free environment.
CDPs (Customer Data Platforms) are at the heart of a future-proof data strategy: they consolidate first-party data from all sources, link it to identified customer profiles and make it usable for personalised campaigns.
For this to succeed, two basic requirements must be met: a scalable data architecture that structures systems and data flows and consistent customer data management that ensures quality, completeness and availability.
4 steps to an effective customer data strategy
1. Understand the data situation
Carry out an inventory: Which customer data is located where? In what state? What data silos and gaps exist?
Typical sources: CRM, e-mail, web tracking, e-commerce, support
2. Define goals and use cases
Determine what you want to achieve with the data - such as better segmentation, personalisation or automation.
Important: prioritise realistic use cases, define measurable KPIs
3. Build architecture & data management
Plan how data from different systems is merged and maintained - e.g. with CDP, interfaces or data layers.
Data quality, identity resolution & governance are also included here
4. Use & continuously optimise data
Activate your data in campaigns, CRM, analysis or personalisation - and continuously improve processes.
Success factor: regular review loops & cross-functional alignment
4 steps to an effective customer data strategy
1. Understand the data situation
Carry out an inventory: Which customer data is located where? In what state? What data silos and gaps exist?
Typical sources: CRM, e-mail, web tracking, e-commerce, support
2. Define goals and use cases
Determine what you want to achieve with the data - such as better segmentation, personalisation or automation.
Important: prioritise realistic use cases, define measurable KPIs
3. Build architecture & data management
Plan how data from different systems is merged and maintained - e.g. with CDP, interfaces or data layers.
Data quality, identity resolution & governance are also included here
4. Use & continuously optimise data
Activate your data in campaigns, CRM, analysis or personalisation - and continuously improve processes.
Success factor: regular review loops & cross-functional alignment
Whoever owns the data owns the customer relationship
The requirements for customer data are changing rapidly: third-party cookies are disappearing, data protection regulations are becoming stricter and customers expect personalised communication in real time - but without giving up their privacy.
In this environment, it is clear that companies that can draw on their own high-quality data will secure long-term access to their customers.
Customer data management is therefore not a purely operational process, but a strategic success factor. Those who do not actively structure, maintain and utilise first-party data risk dependence on platforms, falling conversion rates - and in the worst case, the loss of the direct customer relationship.
Our perspective: Customer data management is the operating system for successful customer loyalty in a data-driven world.
Whoever owns the data owns the customer relationship
The requirements for customer data are changing rapidly: third-party cookies are disappearing, data protection regulations are becoming stricter and customers expect personalised communication in real time - but without giving up their privacy.
In this environment, it is clear that companies that can draw on their own high-quality data will secure long-term access to their customers.
Customer data management is therefore not a purely operational process, but a strategic success factor. Those who do not actively structure, maintain and utilise first-party data risk dependence on platforms, falling conversion rates - and in the worst case, the loss of the direct customer relationship.
Our perspective: Customer data management is the operating system for successful customer loyalty in a data-driven world.
Data management & customer experience: how to interlink technologies effectively
Today, successful data management is much more than just managing customer data; it is the key to implementing a modern customer experience (CX) strategy. However, many companies face the challenge of dovetailing their data architecture, systems and teams. After all, real added value can only be created if data management technologies are strategically aligned with CX goals.
The most important success factors for future-proof data management are:
Holistic data integration
Customer data from CRM, web tracking, email, support or e-commerce must be brought together centrally - ideally in a Customer Data Platform (CDP) that enables a 360° view of the customer.
Actively manage data protection, consent and compliance
With GDPR & Co., data protection is mandatory in data management. Anyone using customer data needs transparent opt-in processes and a good consent management system. This maintains user trust - and guarantees legal security.
High data quality = better customer insights
Data maintenance is a central part of successful customer data management. Duplicates, gaps or outdated entries not only disrupt analyses, but also the customer experience. Good data quality management ensures clean, reliable data: the basis for precise targeting and automated campaigns.
Real-time data for quick decisions and personalisation
The best data is useless if it arrives too late. Real-time data integration makes it possible to analyse user behaviour immediately and display suitable content or offers directly, e.g. on the website, in the newsletter or in the customer portal.
AI & automation: generating added value from data
With artificial intelligence (AI) and machine learning, companies can utilise their data even better, e.g. for predictive analytics, lead scoring, product recommendations or dynamic content. This makes data management a real growth driver.
GDPR in practice: using customer data effectively and in compliance with the law
The use of personal data is central to data-driven marketing and personalised customer experience - but only if it is done in a legally compliant manner. This practical article from OMR Reviews shows how companies can set up GDPR-compliant processes and at the same time exploit the full potential for customer loyalty and personalisation.
Go to articleThe use of personal data is central to data-driven marketing and personalised customer experience - but only if it is done in a legally compliant manner. This practical article from OMR Reviews shows how companies can set up GDPR-compliant processes and at the same time exploit the full potential for customer loyalty and personalisation.
Gamechanger AI: using customer data correctly
Data is the raw material, but only artificial intelligence (AI) can turn it into real added value. Companies that want to remain competitive today use AI not only to analyse, but also to actively shape the customer experience. AI brings structure to the flood of data, recognises patterns, makes predictions and thus enables a highly personalised, automated customer approach in real time.
Recognising behavioural patterns at an early stage with predictive analytics
AI-supported algorithms analyse historical and current customer data, identify purchase probabilities, churn trends and cross-selling potential. This allows you to make data-based decisions before the customer takes action - a decisive advantage for sales and marketing.
Hyper-personalisation for a better customer experience
With AI, you can customise content, offers and communication channels automatically and in real time. Whether it's email marketing, product recommendations or website personalisation: every customer experiences a tailored customer journey based on their preferences and behaviour.
Automated decisions for greater efficiency and scalability
AI not only helps you to understand, but also to act: it provides specific recommendations, prioritises leads, manages campaigns and optimises processes. This turns big data into smart decisions and turns your customer data strategy into a scalable growth driver.
Data democratisation: How specialist departments can finally use customer data themselves
Traditional data management is often heavily dependent on IT - data queries take a long time, analyses delay decisions and a lot of potential in marketing, sales or customer service remains untapped. This is precisely where the trend towards data democratisation comes in: Data should no longer be exclusively controlled by data teams, but should be widely usable within the company.
No-code and low-code tools as well as modern self-service platforms make it possible for specialist departments to analyse, segment and activate customer data independently, without programming knowledge or complex data queries.
This development brings clear advantages for companies:
🟠 Faster campaigns through independent data work in marketing
🟠 More relevant customer approach through targeted segmentation in real time
🟠 Relief for the IT department through fewer operational enquiries
🟠 Better basis for decision-making through direct data access in specialist departments
It is important that companies align their data strategy and architecture accordingly: Standardised data models, intuitive user interfaces and clear governance rules create the basis for trustworthy, scalable self-service use.
Data democratisation is therefore not a trend, but a real game changer for greater agility, customer proximity and data-based decisions throughout the entire company.
Data democratisation: How specialist departments can finally use customer data themselves
Traditional data management is often heavily dependent on IT - data queries take a long time, analyses delay decisions and a lot of potential in marketing, sales or customer service remains untapped. This is precisely where the trend towards data democratisation comes in: Data should no longer be exclusively controlled by data teams, but should be widely usable within the company.
No-code and low-code tools as well as modern self-service platforms make it possible for specialist departments to analyse, segment and activate customer data independently, without programming knowledge or complex data queries.
This development brings clear advantages for companies:
🟠 Faster campaigns through independent data work in marketing
🟠 More relevant customer approach through targeted segmentation in real time
🟠 Relief for the IT department through fewer operational enquiries
🟠 Better basis for decision-making through direct data access in specialist departments
It is important that companies align their data strategy and architecture accordingly: Standardised data models, intuitive user interfaces and clear governance rules create the basis for trustworthy, scalable self-service use.
Data democratisation is therefore not a trend, but a real game changer for greater agility, customer proximity and data-based decisions throughout the entire company.
Your contact
Martin Brudek
Director Marketing Technologies & Marketing Technology Management | Marketing Automation
+49 9131 9712 2173Frequently asked questions about data management (FAQ)
What is Customer Data Management (CDM)?
Customer data management refers to the systematic collection, structuring, maintenance and utilisation of customer data across various channels and systems. The aim is to create a complete, consistent and usable 360° customer view as the basis for personalised communication, data-driven marketing and better decisions.
Why is good data management so important?
Effective data management improves the customer experience, increases the efficiency of marketing and sales processes and ensures compliance with data protection regulations. It prevents data silos, reduces wastage and enables targeted personalisation.
What is the difference between CDP, CRM and DMP?
CRM systems manage customer data along direct touchpoints, for example in sales or customer service.
Customer data platforms (CDPs) collate first-party data from all sources, consolidate customer profiles and activate this data in real time.
Data management platforms (DMPs) usually collect anonymous third-party data for advertising purposes - but are losing relevance due to data protection guidelines and cookie losses.
What is first-party data and why is it so valuable?
First-party data is information that a company collects directly from its customers, e.g. through purchasing behaviour, website interactions or newsletter registrations. It complies with data protection regulations, is particularly reliable and offers the best basis for long-term customer loyalty and a personalised approach.
How can I merge data from different systems in a meaningful way?
A key challenge in customer data management is data integration. Platforms such as CDPs, which combine data from CRM, web tracking, e-commerce and support systems, are suitable for this. Interfaces (APIs), standardised data models and a clear governance structure are important here.
What does data governance mean in the context of customer data?
Data governance encompasses all rules, processes and roles for the secure, standardised and legally compliant use of data. It is essential for ensuring data quality, complying with data protection regulations (e.g. GDPR) and building trust with customers.
What role does artificial intelligence play in data management?
Artificial intelligence (AI) and machine learning help to recognise patterns in large amounts of data, automatically form customer segments or make predictions (e.g. churn, purchase probability). They enable personalised customer experiences in real time, across all touchpoints.
What is data democratisation and why is it relevant?
Data democratisation means that not only the IT department has access to customer data, but also specialist departments such as marketing, sales or product development. Thanks to self-service tools and no/low-code platforms, they can analyse and use data independently and without prior technical knowledge.
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