Introduction to Standardized Data Models

What is a common or standard data model? How does it help marketers and the business achieve growth using a CDP?

Introduction to Standardized Data Models
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What is a Standardized Data Model?

A standardized data model is essentially a framework used to organize and manage data consistently. In the context of customer experience data, it's like having a universal language that enables different systems and applications to understand and use the data effectively. Think of it as the grammatical rules that guide languages, ensuring that communication is clear and structured.

The Importance of a Standardized Data Model

Why is this important in marketing? It's simple: clarity, efficiency, and interoperability. With a standardized model, data from various sources can be integrated seamlessly, enabling marketers to gain a holistic view of customer interactions and behaviors across different channels and platforms. This integration is crucial for making informed decisions and creating targeted marketing strategies.

System Overview of Data Models

Standardization and Interoperability

The key goals behind a Real-Time Customer Data Platform (RT CDP) are standardization and interoperability. Standardization refers to the process of bringing different forms and formats of data into a unified format. Interoperability is the ability of different systems and organizations to work together (inter-operate). If a CDP can't start you off with a common or standardized data model for integration then you should look at a different vendor as this is a red flag. Please note that a data layer on the client is not a data model. If the vendor promotes their data layer technology as a standard be wary of this claim and move on to a more modern platform.

Automated Schema Definition

Schemas are like blueprints for data; they define its structure and format. An automated approach to defining schemas helps in managing customer data more effectively, ensuring that the data collected from various sources is consistent and usable for customer experience management.

Focusing on Specific Verticals

Open Source Management

For specific industries or verticals, managing the data model through open-source platforms like GitHub ensures transparency and community-driven improvements. Open-source management allows for public documentation and the collaborative evolution of the data model, tailored to the needs of specific industries. If the vendor does not publish their model be wary of this vendor and seek only vendors that have open, published and transparent models you can leverage and customize to your needs.

Common Structures and Definitions

A standardized data model provides common structures and definitions that any application within the platform can use. This universality ensures that all micro-services within the platform can communicate effectively, sharing and interpreting data in a consistent manner.

Examples of Common Data Models

  1. Microsoft’s Common Data Model (CDM): Microsoft's CDM is a standardized, modular collection of data schemas published by Microsoft and its partners. It serves as a unifying layer for data across applications, enabling users to build and extend applications with the same data model. Link to Microsoft CDM
  2. Adobe Experience Data Model (XDM): Adobe's XDM system is designed to improve the power of digital experiences through a standardized framework for customer experience data. It helps in organizing data across different channels and systems to provide a comprehensive view of the customer journey. Link to Adobe XDM
  3. Salesforce Data Model: Salesforce offers a comprehensive data model that allows organizations to manage and analyze customer information effectively. It integrates various aspects of customer data to provide a unified view of the customer for personalization and strategic decision-making. Link to Salesforce Data Model

These models are exemplary in how they facilitate the organization and use of customer data for enhanced customer experiences. While some vendors will promote you do not need a model this premise can be confusing. In the real world collecting all data regardless of format is not a good strategy. The model helps the marketer downstream to draft campaigns and assign the right data to the right campaign, having clear naming conventions for analysis and insight, and for future-proofing to use AI technologies not yet released that can leverage the model for more accurate results. You will read more about these below.

Adopting a Common Data Model Standard

Integrated Customer Experience Data

By adopting a common data model standard, organizations can incorporate diverse customer experience data into a unified representation. This integration speeds up the process of gaining insights and allows for more cohesive and comprehensive data analysis.

Insights and Personalization

Through this model, marketers can derive valuable insights from customer actions, define customer segments, and personalize communications effectively. This means delivering the right message to the right person through the right channel at the perfect moment, increasing the effectiveness of marketing efforts.

Deeper Dive into Technical Aspects

Data Modeling Techniques

Data modeling involves various techniques to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. These techniques ensure that data is stored efficiently and can be retrieved and manipulated effectively for business needs.

Data Integration and Quality

Standardized data models play a crucial role in data integration - combining data from different sources into a unified view. They also ensure data quality, which is paramount in making accurate and reliable business decisions.

Benefits of Standardized Data Models

  1. Enhanced Data Consistency: By standardizing the format and structure of data, inconsistencies are minimized, leading to more reliable data.
  2. Improved Data Quality: Standardized models help in maintaining high data quality by reducing errors and discrepancies in data collection and processing.
  3. Efficient Data Management: They facilitate better organization and management of data, making it easier for marketers to access and use the data they need.
  4. Scalability: Standardized models are scalable, allowing businesses to manage increasing volumes of data effectively as they grow.

Challenges in Implementing Standardized Data Models

  1. Data Silos: Breaking down data silos, where data is isolated in different departments or systems, is a significant challenge in standardizing data models.
  2. Change Management: Implementing a new data model requires changes in existing systems and processes, which can be difficult to manage.
  3. Training and Skill Development: Employees need to be trained to understand and work with the new standardized data model.

Practical Applications in Marketing


Marketers can use standardized data models to create detailed customer profiles, enabling highly personalized marketing strategies. This personalization can significantly enhance customer engagement and conversion rates.

Customer Segmentation

With a unified view of customer data, marketers can segment customers more effectively based on various criteria, leading to more targeted and effective marketing campaigns.

Predictive Analytics

Standardized data models facilitate predictive analytics by providing a comprehensive dataset for analysis. This can help marketers anticipate customer needs and behaviors, allowing for proactive strategy development.

Real-World Scenarios and Impact

  1. Omnichannel Marketing: Standardized data models are essential in managing omnichannel marketing strategies where consistency across different channels is key.
  2. Customer Journey Mapping: They help in mapping the customer journey more accurately by providing a unified view of the customer across different touchpoints.
  3. Market Analysis and Strategy Development: By providing a comprehensive view of the market and customer data, these models assist in developing more informed and effective marketing strategies.

Conclusion and Future Outlook

The role of standardized data models in marketing is becoming increasingly significant as the volume of data and the need for data-driven decision-making continue to grow. These models not only enhance the efficiency and effectiveness of marketing strategies but also play a crucial role in improving the overall customer experience.

Looking forward, the evolution of these data models will likely incorporate more advanced technologies like artificial intelligence and machine learning, further enhancing their capabilities. As the digital landscape evolves, so too will the ways in which standardized data models are used in marketing, opening up new opportunities for innovation and strategic growth.

In conclusion, understanding and effectively utilizing standardized data models is crucial for any marketing team aiming to leverage data for strategic advantage. This comprehensive overview provides a foundation for marketers to appreciate the importance of these models and to consider how they can be integrated into their own marketing strategies and operations.

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