Where Exactly Data Becomes Product: Illustrated Guide

Where exactly data becomes product is a critical question for businesses seeking to leverage their information assets. CONDUCT.EDU.VN offers a comprehensive guide that explores the evolution of data infrastructure and how data products are revolutionizing data management. Discover how to transform your raw data into valuable data assets and derive meaningful insights, enabling data monetization and enhanced decision-making.

1. The Evolution of Data Infrastructure: A Complex Landscape

Like all software systems, data and AI applications rely on intricate underlying infrastructures. However, the data-specific infrastructure has become particularly complex due to the transient nature of data. Data is diverse, dynamic, and often unpredictable.

As challenges arise, we tend to innovate, leading to the addition of new branches to data infrastructures each time data behaves unexpectedly. This results in highly complex data pipelines and outdated structures that are difficult to understand.

The solution lies in integrating data into the data infrastructure itself, rather than merely passing it through pre-built, data-agnostic components. Data must influence the data stack to stabilize it and manage reactive evolution.

2. The Missing Element: Data as an Active Participant

Throughout the evolution of data systems, data has consistently been a passive element, managed by the various blocks and components built around it. It’s time to shift this paradigm and recognize that the rapid obsolescence and complexity of data infrastructures stem from the lack of data integration into the architectures.

But how can data be integrated into the infrastructure when it is neither a tool nor a resource? The answer: Data Products. This article aims to explain the concept and essence of data products, moving beyond superficial definitions to focus on their tangible value. The widespread hype surrounding data products has somewhat diluted their value, but we hope this piece will clarify their significance.

3. Defining Importance: A Cultural Perspective

Before we proceed, let’s revisit an excerpt from a prominent thinker:

Ralph Johnson concluded that “Architecture is about the important stuff. Whatever that is.” While seemingly simplistic, this statement holds profound meaning. It emphasizes that the core of architectural thinking in software is to determine what is important and then dedicate resources to maintaining those architectural elements in optimal condition.

For a developer to transition into an architect, they must be able to identify the critical elements and recognize which elements, if not controlled, are likely to cause significant problems. ~ Martin Fowler

The pervasive technical debt we face today is partly due to prioritization inefficiencies. Determining what is important, though seemingly straightforward, is crucial for any team, individual, or organization.

This principle also applies to data. While it would be ideal to have data power every aspect of our businesses, striving for this is an immediate losing proposition.

4. Importance as a Cultural Metric

While numerous metrics can quantitatively measure priorities and importance, the overall picture of what takes precedence ultimately depends on cultural dynamics.

We must view Data Products through this same lens.

Confining them within strict technical boundaries risks failing to address the problem we aim to solve: reducing data infrastructure and volume overwhelm, rather than adding more cumbersome customizations for each novel query.

Historically, applications have been loosely defined as follows:

(Excerpt from Martin Fowler’s Bliki)

A body of code seen as a single unit by developers

A group of functionalities perceived as a single unit by business customers

An initiative viewed as a single budget by those with financial control

How different are applications and products? While Data Products is a convenient term for summarizing data-related concepts, we don’t need to reinvent the wheel here.

Applications: Used for a purpose
Products: Used to reduce or eliminate effort spent on a purpose

In defining data products, the industry has explored each of these perspectives. Some define data products as a single independent unit of code, data, metadata, and infrastructure resources. Others see them as the fundamental unit for serving a single business purpose. Some combine both perspectives, while others compromise on the definition based on budgetary constraints.

From a high-level perspective, it all boils down to cultural influences.

How do developers operate within the organization? What do they consider a single unit?
How do businesses consume data, and who decides what constitutes a good use case and how?
Who holds financial authority within the organization, and how do they exercise it?

Consider this data developer’s perspective, someone who works with data daily:

Within any organization, the needs and expectations of various consumer groups regarding data availability and timeliness will vary. This variance highlights the importance of Service Level Agreements (SLAs) in defining data product boundaries. SLAs help identify the appropriate data product split within a cluster, ensuring they meet the needs of different data consumers. *~ Ayush Sharma in Understanding the Clear Bounds for Data Products“

However, numerous other data teams approach this problem differently.

5. Accounting for “Importance” as a Metric

Data traverses multiple layers of people, processes, and transformations before reaching the end consumer. Each layer introduces its own cultural bias.

We must never disregard this bias. Ignoring it introduces inefficiencies and frustrations at every level, transforming seemingly perfect theories into painstaking undertakings that significantly impede the data-to-consumption flow.

Products dispersed across layers consider the concerns of users in different layers. In other words, they take into account how analysts and data scientists prefer to ingest and explore data in their workspaces, how the data engineering team operates, or how governance stewards prefer to disseminate policies. What these roles consider important as users = direct feedback for product development.

Let’s examine how the product framework considers both user and operator priorities.

As the sequence shows, products emerge on demand, with the use case or purpose always at the forefront. Let’s break this down further.

First, a conceptual prototype or Data Product Model is developed, closely aligned with the use cases and encompassing all requirements necessary to fulfill ‘n’ use cases. This set of use cases has been predetermined as important by the domain or the broader business.
Based on the conceptual model, analytics or data engineers begin mapping sources to activate it, creating source-aligned data products in the process.
Over time, query patterns and clusters emerge depending on how users interact with source products (what users find important). This allows data developers to create aggregates that can answer queries more efficiently.

Again, the aggregate products depend on how the team operates and manages the constant stream of queries. How and what do data developers prioritize to quench user demands?

Complex source-to-consumption transform code is broken down into more manageable units based on the team’s approach to code and caching. Aggregates emerge when the team realizes that a conglomerate can better serve a broad band of queries compared to direct iteration with source products.

This becomes a new product with its own objectives, resources, SLAs, and greater context given its proximity to downstream consumers. ~Excerpt from another section of this text

These aggregates now directly power the consumer-facing data product (e.g., Sales 360), making it more effective than source products.

Over time, all necessary source and aggregate products are mapped based on consumer usage patterns and data developer operational preferences, resulting in a robust network of reliable data flowing up and down product funnels.

5.1. How Does This Differ From Tiered Architecture?

The key difference is Product Influence, which allows teams to implement a right-to-left or user-to-sources journey instead of the traditional left-to-right data management (where the user receives what upstream teams send their way or consider “important”).

The product framework embedded in the architecture enables teams to drive efforts based on actual business goals and effectively address priority gaps and conflicts across multiple layers. This is because every layer can now practically face the same direction: the user.

6. A Quick View of the Right-to-Left Product Journey

(All diagrams are for representation only)

What use case has the business deemed important?
What are the popular metrics that serve this use case or determine its success?
What measures and dimensions are required to calculate these metrics?
How are these measures and dimensions being served?

The outcome of stage 4 is the product prototype or the consumer-aligned data product model.

6.1. What Happens After The Product Prototype?

Imagine investing significant effort and resources only to discover bugs in how the model addresses the requirements. Iterating to correct these issues multiplies the resource cost and stakeholder frustration. This translates to increased compute bills, extended timelines, and a potentially skeptical CFO.

Therefore, instead of investing in actual data integration and iteration, deploy with simulated or mock data. Several methods exist for creating hyper-realistic data that intelligently reflects the domain’s characteristics.

When deploying the prototype with mock data:

Validate the projections for metrics and dimensions.
Validate metric calculations.
Validate use case delivery.
Incorporate any feedback or gaps and rewire the prototype.

6.2. Start Mapping to Source Products

Assume you already have a pre-existing set of source products from other use cases. Begin exploring the Data Product Marketplace to identify suitable products for mapping to the new data product model. The transforms or SQLs from Source Products to Consumer Products become, as we saw above, potential candidates for Aggregate Data Products.

6.3. Establish Aggregate Data Products (Within Weeks)

Once aggregate products are established, your journey from data to consumer is shortened significantly.

How to convert SQLs to Products:

Add use case context, such as tags that enable querying across wider use cases or metric definitions that facilitate business iteration.
Add SLOs (quality and policies).
Allocate dedicated resources to run the code.

For first-time adopters or those without existing source products:

7. Overview of the Impact

The business directly interacts with the Consumer-Aligned Data Product (CADP) or the logical Data Product Model. Based on evolving requirements, they can create, update, or delete the Data Product. The distance between this CADP and the data progressively diminishes as more intermediate products emerge.

The journey from raw sources is significantly shortened to the distance between an aggregate product and a consumer data product.

All products are independent and serve multiple aggregates or CADPs across different domains and use cases. Each has a lifecycle within the bounds of its limited and well-defined purpose.

This allows higher-level products to comfortably (with high transparency) rely on inputs from lower-level products, reducing complexity and management overwhelm in the path between raw sources and consumer endpoints.

8. Embedding Data into Data Infrastructure

This leads us back to the initial question: How can data be embedded into the infrastructure as an active influence that stabilizes and controls the proliferation of pipelines and model branches?

With a broader understanding of the influence and impact of data products, let’s explore a different perspective that ties together the loose ends.

Data Products cut through the entire data stack, from source to consumption. They are like vertical infrastructure slices (imagine Greek columns supporting a massive pediment) that include the data itself.

Every element in the stack is influenced by data and CONTEXT (DATA ON DATA).

The “Product” approach enables this influence by carrying downstream context all the way up (right-to-left).

Let’s examine the collective influence of data and the product approach from source to consumption (the traditional direction and how it is reversed):

8.1. Sources: Data & Product Influence

Any medium to large organization faces an overwhelming number of data sources. Data Products reduce this complexity by bringing downstream context into the picture.

What has been deemed “important” by downstream functions closer to consumption?
Combined with upstream context on “what else is important” to enable smooth data delivery.

This gives us Source-Aligned Data Products: a combination of context, SLAs, transparent impact, and a clear set of input ports (ingesting only what is in demand).

8.2. Transformations: Data & Product Influence

Based on how the data team operates and what they view as aggregates that serve downstream purposes more easily, complex source-to-consumption transform code is broken down into more manageable units (depending on the team’s approach to code and caching). Aggregate Data Products emerge when the team realizes that a conglomerate is reusable and better able to serve a broad band of queries instead of direct iteration with source products.

For example, source tables like “Sales” and “Orders” might require more complex queries compared to the aggregate “Accounts“. This becomes a new product with its own objectives, SLAs, and richer context due to its closeness to users. The Product is independent, with its own set of infrastructure resources and code, with source products as input ports and isolated from disruption from other product engines.

8.3. SLAs: Data & Product Influence

SLAs are highly dependent on consumption patterns and organizational hierarchies. Who should get access and why? While a bare-bones quality structure works for some, others might require higher quality demands.

The Product slices are built to influence a right-to-left flow of context: from users to source. What are the Level A requirements? How can we boil down the requirements from Level A to different touchpoints of the data across the source-to-consumption stack? Do upstream SLAs conflict with downstream necessities or standards? How can we get a clear picture of these conflicts without corruption from other unassociated tracks (which might be challenging given how pipelines overlap with little isolation of context)?

8.4. Consumption: Data & Product Influence

Products, by nature, are always user-facing. They’re built for a specific purpose. Data Products enable the ability to define consumption while considering the user’s preferences. The user should not bend to the product; the product must bend to the user’s native environment.

The data product can furnish multiple output ports based on the user’s requirements. We call these Experience Ports, which can serve a wide band of demands without additional processing or transformation effort (ejects the same data through different channels).

This may include HTTP, GraphQL, Postgres, Data APIs, LLM Interface, Iris Dashboards, and more for seamless integration with data applications and AI workspaces.

9. Transforming Data into Products: A Practical Guide

To further illustrate where exactly data becomes a product, consider the following steps:

Identify Business Needs: Start by understanding the specific business problems or opportunities that data can address. This involves engaging with stakeholders across different departments to identify their data requirements and pain points.
Data Discovery and Assessment: Next, conduct a thorough assessment of your existing data assets. Identify relevant data sources, assess data quality, and determine the necessary data transformations.
Product Design and Development: Design data products that meet the identified business needs. This involves defining the data product’s functionality, data sources, and delivery mechanisms. Use agile development methodologies to iterate on the design based on user feedback.
Data Integration and Transformation: Integrate data from various sources and transform it into a usable format. This may involve cleaning, standardizing, and enriching the data.
Deployment and Monitoring: Deploy the data product and monitor its performance. Track key metrics such as data quality, usage, and user satisfaction.
Iteration and Improvement: Continuously iterate on the data product based on user feedback and performance data. Add new features, improve data quality, and optimize performance.

10. Data Product Examples

Customer Segmentation Data Product: This data product provides detailed customer segmentation based on demographics, purchase history, and online behavior. It can be used to target marketing campaigns, personalize customer experiences, and improve customer retention.
Sales Forecasting Data Product: This data product predicts future sales based on historical sales data, market trends, and economic indicators. It can be used to optimize inventory levels, allocate resources effectively, and improve sales performance.
Fraud Detection Data Product: This data product identifies fraudulent transactions in real-time based on transaction patterns, user behavior, and anomaly detection algorithms. It can be used to prevent financial losses, protect customer accounts, and improve security.

11. Benefits of Data Products

Data products offer numerous benefits, including:

Improved Decision-Making: Data products provide timely and accurate information that enables better decision-making across the organization.
Increased Efficiency: Data products automate data delivery and reduce the time and effort required to access and analyze data.
Enhanced Innovation: Data products provide a foundation for innovation by enabling users to explore data, experiment with new ideas, and develop data-driven solutions.
Data Monetization: Data products can be monetized by selling them to external customers or using them to create new revenue streams.

12. Data Governance and Ethics

Data governance is essential for ensuring the quality, security, and ethical use of data. Data governance frameworks should define data ownership, data quality standards, data security policies, and data privacy regulations.

Ethical considerations are also crucial in data product development. Ensure that data products are used responsibly and do not discriminate against individuals or groups.

13. CONDUCT.EDU.VN: Your Guide to Data Mastery

Navigating the world of data can be daunting, but CONDUCT.EDU.VN is here to help. We offer a wealth of resources, including articles, tutorials, and case studies, to guide you on your data journey.

Our team of experts can provide personalized advice and support to help you develop and implement effective data strategies.

14. Addressing Common Challenges in Data Transformation

Many organizations face challenges when attempting to transform data into valuable products. Some of these include:

Data Silos: Data is often fragmented across different systems and departments, making it difficult to integrate and analyze.
Data Quality Issues: Data may be incomplete, inaccurate, or inconsistent, leading to unreliable insights.
Lack of Skills: Many organizations lack the necessary skills to develop and manage data products.

CONDUCT.EDU.VN provides solutions to these challenges by offering:

Data Integration Strategies: Guidance on integrating data from diverse sources.
Data Quality Management: Best practices for improving data quality.
Training and Development Programs: Programs designed to upskill your workforce in data-related areas.

15. The Future of Data Products

The future of data products is bright. As data volumes continue to grow and data technologies evolve, data products will become even more valuable.

Emerging trends in data products include:

AI-Powered Data Products: Data products that leverage artificial intelligence and machine learning to automate data analysis, generate insights, and provide personalized recommendations.
Real-Time Data Products: Data products that provide real-time data updates and insights, enabling organizations to respond quickly to changing conditions.
Data Product Marketplaces: Online marketplaces where organizations can buy and sell data products.

16. Frequently Asked Questions (FAQs)

Q1: What is a data product?

A data product is a reusable data asset designed to solve specific business problems or create new opportunities.

Q2: How do I create a data product?

The process involves identifying business needs, assessing data assets, designing the product, integrating and transforming data, deploying the product, and iterating based on feedback.

Q3: What are the benefits of data products?

Benefits include improved decision-making, increased efficiency, enhanced innovation, and data monetization.

Q4: How do I ensure data quality in data products?

Implement data governance frameworks, define data quality standards, and monitor data quality metrics.

Q5: What are the ethical considerations in data product development?

Ensure responsible data use and avoid discriminatory practices.

Q6: How can CONDUCT.EDU.VN help me with data products?

CONDUCT.EDU.VN provides resources, expert advice, and personalized support to help you develop and implement effective data strategies.

Q7: What are the emerging trends in data products?

Emerging trends include AI-powered data products, real-time data products, and data product marketplaces.

Q8: How do I address data silos when creating data products?

Implement data integration strategies to combine data from diverse sources.

Q9: What skills are needed to develop and manage data products?

Skills include data analysis, data engineering, data science, and data governance.

Q10: How do I measure the success of a data product?

Track key metrics such as data quality, usage, user satisfaction, and business impact.

17. Conclusion: Empowering Your Data Journey

Data products are transforming how organizations leverage data to drive business value. By following the guidelines outlined in this article and leveraging the resources available at CONDUCT.EDU.VN, you can unlock the full potential of your data and achieve your business goals.

Ready to embark on your data transformation journey? Visit CONDUCT.EDU.VN today to discover more insights and guidance. Contact us at 100 Ethics Plaza, Guideline City, CA 90210, United States. Reach us via WhatsApp at +1 (707) 555-1234 or visit our website at conduct.edu.vn. Let us help you navigate the complex world of data and turn your data into valuable products.