CDM in Action: Creating Reusable Data Products

Ineffective data management costs organizations around 15-20% of revenue loss, even after investing heavily in artificial intelligence (AI). Shifting to reusable and scalable data products from ad-hoc data projects ensures long-term business value.

However, with consistent, long-term value, data products also encounter challenges, such as becoming stale and inconsistent across disparate systems. Worse, these problems can often go undetected, making data products unreliable for scaling business operations or for use in downstream applications. This lack of reliability makes them counterproductive.

This is where the common data model (CDM) comes into play. CDM addresses these challenges by standardizing and structuring raw data from disparate sources into reliable and reusable assets, ensuring data consistency and integrity from the get-go.

This article will explore the importance, applications, and challenges of data products. It will also demonstrate how a CDM can overcome these challenges, ensuring data is standardized, scalable, and ready for modern business demands.

Data Products in the Context of CDM

Data products are tools and applications that use data to deliver insights and predictions. A CDM provides an organized way of structuring data, ensuring accuracy and consistency across systems to establish a reliable data foundation. For example, a CDM provides a standardized language for data, eliminating inconsistencies that arise from different systems.

Characteristics of Data Products

To fully understand data products, it’s important to acknowledge their key characteristics.

• Reusability: Once created, data products can be used for multiple projects by various teams, reducing redundancy and extra effort.
• Discoverability: Data products are stored in central repositories, enabling consumers to easily find and access them.
• Standardizability: Data products are formatted to provide high-quality, structured data, regardless of their sources, to maintain quality and reliability.
• Security: With proper access controls, encryption, and continuous monitoring, data products achieve necessary security.
• Interoperability: They are designed to enable seamless integration across platforms, providing users with consistent insights across platforms.

Importance of Data Products

To accelerate business outcomes, data products can have a major impact as they help in quick decision-making, enhancing operational efficiency, and recognizing new profit-making opportunities. Their role can be observed in the following areas:

• Accelerated Reusability and Efficiency: To reduce the time, manual work, and cost required to prepare data for AI and analytics, data products provide access to pre-built, reusable assets, which also eliminate redundant data wrangling.
• Seamless Integration for Interoperability: Without having to re-engineer, deployments and integrations are made easier by data products, which reduces the amount of redundant data wrangling and technical debt associated with custom connectors.
• Empowering Cross-Team Collaboration: Cross-team collaboration among data engineers, domain experts, and other stakeholders is facilitated by sharing templates and definitions that convey semantic clarity. This is possible due to the aspects of accessibility, integrability, and discoverability.

Pain Points of Data Products

Meanwhile, data products have many advantages, but their implementation can cause some issues. Therefore, it is essential to acknowledge those pain points to maintain long-term value.

• Cloud Migration Challenges: Migrating data products to cloud infrastructure introduces several complications. Data products require large-scale storage and intensive compute resources, and pay-as-you-go models can exponentially increase costs if not managed carefully. Moreover, as the data needs grow, data pipelines become difficult to maintain and scale..
• Distributed Teams and Local Performance Needs: Organizations with global teams face different issues. Ensuring low-latency access to a centralized system is challenging, particularly while meeting the diverse compliance regulations of each region. It can be a significant obstacle to achieving a system that keeps performance and compliance in balance.
• Inconsistent Schemas: When data is stored from disparate systems without a proper unified structure, it can create integration gaps without a standard to follow. This, in turn, can create false or misleading insights.
• Source-Specific Pipelines: Each source for a data product may require its own connector and specific requirements, necessitating a custom-built ETL pipeline. This leads to an increase in maintenance costs, leading to slow analytics..
• Metadata and Documentation Management: The metadata surrounding data products can become stale or incomplete if not regularly maintained and updated. However, management requires defining clear ownership and implementing automated processes, which can be challenging to build as data volumes grow.
• Poor Data Quality: Users may come across integration risks, broken dashboards, and reports if the data lacks proper quality and contains incorrect, missing, or outdated values. Furthermore, the resultant analytics would be misleading, leading to erosion of trust in data products.

How CDM Helps

Data products, with all their benefits, often fall short in consistency and scalability. To overcome these critical issues, CDM provides a powerful solution by standardizing data.

CDM establishes proper and formatted standardized structures, or datasets, for common business entities such as consumers, transactions, and products, satisfying business needs. By introducing the concept of predefined schema templates and a centralised system, CDM decouples pipelines from source-specific constraints, encouraging generality and agility, facilitating easy maintenance and quick onboarding.

CDM not only provides the format but also enriches it with metadata and documentation for easy adoption, understability, and data discoverability. It tracks lineage and embeds metadata as part of the process, ensuring governance, transparency, and auditability, along with easy debugging and management.

It ensures that proper rules and protocols are followed by enforcing standardized verification rules across the board, including schema validation, data contracts, and standardized documentation. This retains the quality of the data and the trust of the user.

Applications of CDM

CDM has many applications in different domains where it aims to achieve consistency and reliability:

• Collaboration: CDM creates a pathway for seamless integration with validation for all teams, as they have access to the same centralised system when working on cross-functional projects.
• Streamlined Data Operations: CDM manages operational tasks as it reduces data silos with interoperability and aligns data structures, helping in operational integrations.
• AI and Analytics: CDM translates raw data sources into governed, consumable products along with metadata and entity definitions by enabling ETL processes. Then, machine learning modeling and data analytics are powered by AI-ready data products.
• Industry-Specific Applications: In addition to its core competencies, CDM provides tangible value to industries by catering to their domain-specific requirements and data management needs. For instance:
  • In retail, it provides real-time inventory tracking and personalized marketing
  • It helps healthcare professionals in maintaining patient safety through robust monitoring and real-time trial oversight
  • It facilitates compliance reporting and performance analysis in financial asset management

Powering Business Values with Data Products and CDM

For your business to grow, you need to move beyond isolated datasets and adopt a unified approach. A common data model (CDM) provides this foundation by ensuring that your data is organized, governed, and ready to use.

Building AI-ready data products on top of this reliable foundation enables teams to make faster decisions and to create a scalable and future-proof data environment for sustainable growth.

Get Nexla’s CDM for building robust data products to unleash the power of your data assets.