Authored by 20 AI + Data Leaders

Modern Data + AI Integration:Strategies and Architectures

Free Download

Data Product Management Best Practices & Principles

In today’s data-centric landscape, managing data like a tangible product might be a new concept for many. However, the approach of organizing data as a data product to streamline the product management processes has been successfully adopted by industry leaders such as Instacart and Poshmark

This brings us to the emerging discipline of data product management based on the approach of producing and consuming data products. Let’s first define what a data product is before explaining how it serves as the modern cornerstone for data product management, a development very important for data product managers interested in streamlining their processes.

What is a data product?

A data product is a cohesive assembly of data, metadata, description, samples, and policies that is ready for exchange and collaboration among stakeholders who produce and consume data. By transforming data from various data sources into a common data product format enriched with metadata, organizations can have a consistent structure across all the data sources and applications that they can rely on.

Focusing on data products rather than raw datasets enables product managers, data engineers, and business users to collaboratively design and maintain data flows that directly support business operations. This approach requires more than just executing business logic—it’s about creating collaborative environments where stakeholders can safely and efficiently interact with data.

If you are interested in learning more about data products, read this article, which is dedicated to explaining them in detail. You can also learn more about how Nexla has created NexSets as the data product building block for its data integration platform to deliver both flexibility and collaboration in how integrations are managed.  

Transforming data into data products with Nexla’s NexSets (source)

Data products in action

Data Products derive their power from decoupling the production and use of data between a Data Producer and a Data Consumer. Imagine a bustling hospital environment. Let’s say real-time bed availability from the central hospital management system is available via a real-time API. This API source helps create a Data Product, say “Bed Availability.” This Data Product has been tested, validated, documented, and made available to approved users. Now, the Emergency Department systems must show Bed Availability on a dashboard. The dashboard creator can now find the Bed Availability Data Product, get access to it, and then have the Data Product deliver data in a format and system that feeds into the dashboard.

This integration bypasses the need for a joint project and tight integration across teams. Instead, the output of one system was published and readily available for another team to discover and use in a system and format of their choice.

Is your Data Integration ready to be Metadata-driven?

Download Free Guide

By structuring and exposing only the essential data, these products ensure that teams are working with the most relevant information, thereby optimizing workflows and potentially saving lives. This example shows how effective data product management can propel a data product from being a nice-to-have to truly outstanding. 

In the following sections, we’ll explore the core principles and best practices in this domain. The table below provides a summarized view of the core principles and how they support data product management.

Data product management core principles and best practices

Core principle  Data product management best practice  How the best practice relates to the core principle
User-centric design of the data operations platform Engage with users to gather insights and address pain points. Ensures that the data operations platform and the data products within them meet the user requirements.
Data quality and integrity Utilize automated and manual checks to validate data. Guarantees trustworthy and reliable data.
Scalability Structure the product to enable it to be updated and expanded. Automatically manage versioning of changes. Allows the data product to evolve and adapt while tracking those changes
Cross-functional Collaboration Implement a cohesive data governance strategy that incorporates data fabric and data mesh principles.  Enhances the utility and acceptance of the product across teams.
Iterative development and documentation Use agile methodologies to ensure quick iterations. Keep up-to-date guides, metrics definitions, and FAQs for various stakeholders and data engineers.  Enables quick adaptation to market needs. Facilitates easier troubleshooting and onboarding.
Data security and compliance Stick to regulations and use encryption to safeguard data. Ensures that data is stored and used responsibly.
Monitoring and analytics Utilize real-time metrics and dashboards to monitor performance. Allows for continual assessment and improvement.

User-centric design of the data operations platform

Ensuring a user-centric design of the data operations platform in data product management goes beyond focusing on the mere functionality of the tool. User-centric design encompasses the entire experience of the end-user, from the initial interaction through ongoing usage. The data operation platform should fully understand the user’s specific needs, behaviors, and constraints. 

Here are some strategies you can use to ensure that your data operations platform has a user-centric design

  • Engage with users: Conduct in-depth research through surveys, interviews, and usability testing to gain insights into user needs and preferences. For instance, surveying data engineers about their challenges in data integration can lead to prioritizing engineering investments in developing integrations to new data sources. Find out what data products are difficult to work with and why, and use the information to prioritize the implementation of automation and monitoring features. 
  • Address pain points: Identify and analyze users’ challenges and devise solutions to deal with them. An example could be identifying users struggling with collecting data from a GraphQL API, which might focus your engineering investments on solving that problem by adding more data sources to your platform. 
  • Simplify user interaction: Allow for personalization and customization so users can tailor the data operations platform to their specific requirements. Create internal guides or quick reference materials to help users navigate the setup of data integrations and transformations. Encourage the use of existing intuitive features and tools to reduce the learning curve for new users.

The screenshot below shows what such an interface could look like, where key features are visible at a glance and consistent across all data sources.

Intuitive user interface for managing data products (Source: Nexla)

Intuitive user interface for managing data products (Source: Nexla)

Data quality and integrity

In data product management, it’s imperative to keep your data accurate, consistent, and up to date. After all, decisions made based on data products are only as good as the data they contain. The trick is to have a mix of automated alerting with manual human oversight. For instance, when an alert pops up for missing data, your data engineers can proactively dive in to fix any issues on the spot. This combined approach keeps your data products reliable and trustworthy, ensuring that you make the right decisions based on tip-top data. 

Here are some strategies you can use for effective data quality management of your data products: 

  • Implement automated data checks: Set up automated alerts to flag data anomalies such as null values, duplicates, inconsistency, misalignment, missing data, or invalid data types. Develop real-time data validation scripts, ensuring immediate detection of data quality issues. 
  • Incorporate manual checks: Data engineers should conduct regular audits when automated alerts are triggered. The alerts will cue the data engineers to investigate anomalies for complex issues that the automated alerting did not catch. 

You can set up automated alerts for data quality issues in several ways. You can leverage data operations platforms such as Nexla and avoid the coding obstacle course, or you can develop scripts to perform these tasks for you. 

Guide to Metadata-Driven Integration


Learn how to overcome constraints in the evolving data integration landscape

Shift data architecture fundamentals to a metadata-driven design

Implement metadata in your data flows to deliver data at time-of-use

Leveraging data operations platforms for automated data quality processes 

Using Nexla for automated data quality alerts is a cinch. Nexla integrates data quality processes into the data integration workflow, automatically monitoring and validating data in real time. Here’s how you can leverage data operations platforms like Nexla for this purpose: 

  • Real-time data monitoring and validation: Nexla automatically monitors data as it flows, integrating metadata intelligence and applying smart validations. This includes checking for values, patterns, types, and required attributes​​.
  • Customizable validation rules: When transferring data, you can set specific rules for output validation. For instance, you can ensure that customer data contains a valid email and an IP address in the correct IPv4 or IPv6 format. These validation rules can be added, edited, or deleted without any coding and are automatically applied as data flows into the destination​​.

Screenshot of output validation rules (Source: Nexla)

Screenshot of output validation rules (Source: Nexla)

  • Notifications and alerts: Nexla provides notifications detailing the processes and steps where errors occurred and automatically quarantines affected data without interrupting the data flow. You can customize these notifications based on your specific quality criteria​​.
  • Error tracking and annotations: Under the “flows” overview tab, Nexla allows you to check for error labels in any dataset and make annotations, offering a clear view of data lineage to pinpoint where errors occurred​​.

Screenshot of data flows dashboard (Source: Nexla)

Screenshot of data flows dashboard (Source: Nexla)

  • Error inspection and resolution: In Nexla’s dashboard, you can view detailed lists of errors in data flow summaries. Each error report directs you to the specific location of the error, enabling easy inspection and resolution with just a few clicks​​.

Opt for a unified solution with built-in quality validation

Nexla’s approach incorporates data quality management at multiple points in the data solution, ensuring that data is ready to use in any format. This unified platform with a low-code and no-code interface significantly reduces engineering costs and ensures efficient operations and analytics built on high-quality data​​.

Nexla’s no-code UI and low-code Python and Javascript code snippets make it easy to transform, filter, and join Nexsets to create new Nexsets. (source)

Nexla’s no-code UI and low-code Python and Javascript code snippets make it easy to transform, filter, and join Nexsets to create new Nexsets. (source)

The DIY path: Scripting your data quality checks

If you’re thinking of bypassing the data operations platform route, you can go down the scripting path. Setting up data quality checks this way is doable, but fair warning: It’s not exactly a walk in the park, and you’ll need good coding skills to pull it off. Maintaining these quality rules over time is as important as creating them initially. Here’s a simple example of a Python and SQL script using pandas that flags various data anomalies like null values, duplicates, inconsistency, misalignment, missing data, or invalid data types.

Python script using Pandas

import pandas as pd

# Assuming you have a DataFrame ’df’ loaded from your data source
# Replace ’your_data_source.csv’ with your actual data source
df = pd.read_csv(’your_data_source.csv’)

# Check for null values
null_values = df.isnull().sum()

# Check for duplicates
duplicates = df[df.duplicated()]

# Check for a specific invalid data type, e.g., non-numeric in a numeric column
# Replace ’numeric_column’ with your actual column name
invalid_data_types = df[pd.to_numeric(df[’numeric_column’], errors=’coerce’).isnull()]

print("Null Values:\n", null_values)
print("\nDuplicates:\n", duplicates)
print("\nInvalid Data Types in ’numeric_column’:\n", invalid_data_types)

SQL query script

-- Assuming you have a table named ’your_table’
-- Replace ’your_table’ and column names with your actual table and column names

-- Check for null values in all columns
SELECT COUNT(*) AS null_count, column_name
FROM your_table
GROUP BY column_name
HAVING null_count > 0;

-- Check for duplicates
SELECT column_name, COUNT(*)
FROM your_table
GROUP BY column_name

-- Check for invalid data types, assuming ’column_name’ should be numeric
FROM your_table
WHERE NOT REGEXP_LIKE(column_name, ’^[0-9]+(\.[0-9]+)?$’);
What is the impact of GenAI on Data



Designing data products to handle increasing workloads involves storing data products in a database and developing data operations pipelines for data transformation and end-user access via a user interface or API. This requires building your data products with an architecture that’s flexible and can scale up without a hitch.

Think of a retail company that initially serves a local market, using a data product to track inventory, sales, and customer preferences. As the company grows, so does its data product. This could involve handling increased data volumes from multiple store locations and a broader range of customer data. 

A scalable architecture typically employs a cloud service provider like Amazon Web Services (AWS), Azure, or Google Cloud for long-term data storage and computing needs, dynamically allocating resources as demand increases. This scalability allows the platform to add new features, like personalized recommendations or real-time inventory updates, without slowing down. 

However, developing a platform to host data products using Elastic Compute Cloud (EC2), AWS Lambda, Amazon RDS, and Amazon S3 requires significant engineering effort. That’s why platforms like Nexla provide embedded storage for data products and infinite scaling capabilities to avoid the need for lengthy software development and maintenance.

Cross-functional collaboration

Effective teamwork across all departments is critical to leverage the full potential of the scalability discussed above. This synergy ensures that data products are aligned with an organization’s diverse and evolving business needs. 

In data product management, you can successfully integrate data meshes and data fabrics to foster effective cross-functional collaboration within an organization. For instance, data mesh architecture empowers individual domains to manage their data for smoother interdepartmental collaboration, while data fabrics offer a unified view of data across multiple sources, enabling seamless access for all departments. 

This interconnectedness of data meshes and data fabrics harmonizes domain-specific management with organization-wide data accessibility. This combination cultivates a data-informed culture and drives organizational collaboration, transforming data into a shared asset, in turn, catalyzing collaboration. 

Nexla’s data fabric architecture promotes universal and central access to its Nexset data products

Nexla’s data fabric architecture promotes universal and central access to its Nexset data products

Iterative development and documentation

Agile methodologies and rigorous documentation practices are essential components of data product management. In the agile framework, iterative development and thorough documentation go hand-in-hand. 

Agile methodologies emphasize short, adaptive development cycles, allowing teams to quickly respond to changes in user needs and market trends. This approach ensures continuous improvement and flexibility in product development. Here’s how: 

  • Quick iterations for responsiveness: Agile’s iterative cycles facilitate rapid adjustments and enhancements, ensuring that data products remain relevant and effective.
  • Concurrent documentation: Up-to-date documentation captures evolving features, changes, and decisions, serving as a vital reference for the team and stakeholders.
  • Synergy of development and documentation: This combined approach ensures that while the product evolves through iterative development, the documentation accurately reflects the current state of the product, preserving knowledge and facilitating clear communication.

Here’s a table with key tools for agile development and documentation:

Tool Purpose Key Features
Jira Agile project management
  • Progress tracking through sprints 
  • Effective task management 
  • Customizable workflows
Confluence Documentation and collaboration
  • Collaborative space for documentation 
  • Integration with Jira
  • Page templates and rich content editing
Trello Kanban-style project management
  • Visual boards for task tracking
  • Easy-to-use interface 
  • Real-time collaboration features
Asana Task and workflow management
  • Timeline view for project planning.
  • Workload management
  • Automated workflows
Slack Communication and collaboration
  • Channels for team discussions
  • Integration with project management tools
  • File sharing and real-time messaging

Data security and compliance

Prioritizing data security and compliance is crucial in data management. Adhering to regulatory standards and implementing robust encryption practices are essential steps to protect sensitive data. This approach not only ensures compliance with legal requirements but also builds trust by demonstrating a commitment to responsible data handling.

By proactively securing data and aligning with industry regulations, organizations can safeguard their data assets and maintain their integrity, which is crucial for sustained business operations and customer confidence.

Data Integration platforms like Nexla, with built-in Data Operations,  facilitate data security and compliance. Nexla offers features to monitor and ensure data compliance with regulatory standards. You can even set up automatic alerts for non-compliance issues. 

Monitoring and analytics

The use of real-time monitoring and analytics is pivotal in data product management. Continuous performance tracking through real-time metrics and intuitive dashboards ensures the reliability and quality of your data products. This capability allows for ongoing assessment and targeted improvements. 

With tools like Nexla, organizations can monitor key performance indicators and leverage advanced analytics to gain deeper insights. Such features facilitate timely decision-making and ensure that data products are consistently aligned with business objectives and user needs, driving continual optimization and enhancement of data-driven strategies.

Nexla’s data monitoring feature (Source: Nexla)

Nexla’s data monitoring feature (Source: Nexla)

Powering data engineering automation



Data Extraction

Data Warehousing

No-Code Automation

Auto-Generated Connectors

Data as a Product

Multi-Speed Data Integration







Mastering data product management 

In this article, we delved into what makes a data product click, including user-centric design, ensuring top-notch data quality, scalability, and the importance of teamwork across functions. Additionally, the role of agile practices in keeping things moving smoothly, alongside rigorous documentation, can’t be overstated.

In this data-driven age, tools like Nexla are game-changers, simplifying complex data tasks, ensuring compliance, and keeping a keen eye on analytics. It’s clear that managing data products is much more than handling datasets: It’s about creating valuable, secure, and adaptable data solutions that align with evolving business and user needs. This is the essence of data product management: blending technical prowess with a keen understanding of the bigger picture.

Like this article?

Subscribe to our LinkedIn Newsletter to receive more educational content

Subscribe now