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Full Stack Data Products: Bridging the Insight-to-Action Gap

We’re data-rich, but insight-poor!

Imagine a vast treasure chest overflowing with gold, but without a key. That’s the situation many organizations face today – drowning in data (the gold) but lacking the tools (the key) to unlock its true value.

According to a recent survey, 85% of companies believe that data is one of their most valuable assets, yet only 30% report being able to fully leverage their data for business insights. This stark contrast highlights a critical challenge faced by many enterprises: the gap between data potential and data utilization.

Data products are the key to bridge this gap. They are engineered to deliver a trusted dataset for a particular purpose and make it readily accessible to all business stakeholders. However, it is not easy to build Data products that deliver the intended business value. A 2023 D&A Leadership Executive survey shows that while 92% of data leaders believe their data products deliver business value, only 39% of business leaders agree.

I guess the data here is clear (pun intended!).

Despite several recent advancements on the tech side especially the cloud-based data lakes, data mesh and data fabric architectures and data sharing, the gap between data insights and real-world results persists.

The Data Hurdles

Navigating the journey to effective data management presents several significant hurdles:

Data Silos

Data scattered across various departments and systems, make it challenging to access and integrate valuable information. When data is isolated within specific areas of the organization, it prevents a holistic view, leading to inefficiencies and missed opportunities. This segregation hinders collaboration and creates barriers to a unified data strategy, making it difficult to leverage the full potential of your data assets.

Constant Changes in Source Data

Data sources are dynamic, with frequent updates and modifications that can disrupt data consistency and integrity. This volatility requires robust systems and processes to ensure that data remains accurate and reliable, despite ongoing changes. Adapting to these fluctuations without compromising data quality is a significant challenge for businesses.

Proliferation of Data Sources

From customer interactions to IoT devices, the proliferation of data sources, often leaves businesses struggling to manage and analyze data effectively. The sheer volume and variety of data can be overwhelming, leading to difficulties in data integration and analysis. With data coming from multiple channels and in various formats, organizations need sophisticated tools and strategies to handle this diversity and extract meaningful insights.

Lack of Value Measurement

A critical challenge is tracking how data insights translate into actual business decisions and actions. Without clear metrics and KPIs to measure the impact of data-driven decisions, it’s challenging to justify investments in data initiatives. Organizations often fail to connect the dots between data insights and their influence on business outcomes, resulting in underutilized data potential and missed opportunities for growth and improvement.

Solving Data Hurdles with Full-Stack Data Products

The traditional industry definition of a data product, often limited to the data layer, falls short in my view. A data product, as I see it, is a holistic approach to managing and leveraging your company’s data to drive consumption and actionable insights. It involves applying a product mindset to datasets, treating them as valuable assets, and ensuring they possess the following five qualities to create business value:

Targeted Personas

Targeted Personas: The data product should clearly specify the personas it addresses, focusing on the end users of insights rather than those responsible for generating them.

Business Relevance

Business Relevance: It must address business use cases that are relevant to the chosen domain, ensuring the insights are meaningful and applicable.

Intuitive Interface

Intuitive Interface: A highly intuitive application interface is essential to support the common needs of the personas, making data consumption seamless and effective.


Action-Driven: The data product should not merely provide insights but also drive actions, ensuring that the information leads to tangible business outcomes.

Unified Data Layer

Unified Data Layer: Finally, it should include a unified data layer with modeled data and supporting metadata, enabling a cohesive and comprehensive view of the data.

By ensuring these qualities, data products can truly meet their intended goal of creating significant business value.

Does Your Business Even Need a Data Product Though?

You should invest in a full-stack data product only if you need it! Not every data problem needs a data product. Sometimes, just building the data layer and asking users to create reports is the quickest way to deliver value. So, how do you know if your situation calls for a data product?

Ask yourself three simple questions:

  1. Do you have a clear group of users who will need ongoing decisions or questions answered through data?
  2. Do you know what metrics or outcomes your users value the most?
  3. Are you creating something that will require continuous updates and support?

If you answered ‘yes’ to these questions, a data product might be the right approach.

Getting Tangible Business Outcomes with Data Products – A Real-life Example

We recently collaborated with a leading North American Media Corporation to streamline their advertising planning process and improve the quantification of marketing effectiveness through a custom data product.

  • Data Consolidation: We began by consolidating all their internal first-party data. We then created business applications to calculate advertising costs, measure efficiency, and pinpoint financial waste. These insights enabled media planners and advertisers to optimize campaigns, improve sell-through rates, and reduce revenue leakages.
  • External Data Integration: We integrated this data product with data from their trade partners using a privacy-compliant data clean room solution. This enhanced their CRM with audience data from advertising partners, breaking down data silos and sharpening their focus on target audiences.
  • Audience Insights Hub: We established an audience insights hub on this data platform, merging enriched data with external sources like store sales and visits. This provided a clear view of advertisement relevance and effectiveness for our client and its partners.
  • Unified Data Layer: The data product was built on a unified data layer, complete with modelled data, metadata, and a catalog managed through version control. This setup provided the agility to include additional business use cases as the business evolved.

What began as a solution for a single business challenge has grown to become a crucial part of our client’s daily operations, delivering billions of dollars in additional revenue. This example demonstrates the power of full-stack data products to deliver tangible business outcomes. They not only bridge the data-insights gap but also empower businesses to see a real return on their investment.

Sustaining the Data Product Momentum

Building data products is hard. Sustaining momentum and delivering long-term value is even harder!

You have to think like a product manager—Design patterns, governance, features, releases, technology refreshes, product marketing, and dedicated teams—these are your new best friends.

Don’t let those valuable insights stay hidden. Turn your data into Gold with a data strategy that perfectly aligns with your needs. Talk to us to learn how.

Key Take-Aways

  • Data Overload vs. Utilization: Organizations are often data-rich but insight-poor, struggling to leverage data for meaningful business decisions despite technological advancements.
  • Importance of Data Products: Data products are crucial for bridging the gap between data and actionable insights, ensuring trusted datasets are accessible to all stakeholders.
  • Challenges in Data Management: Key hurdles include data silos, constant changes in source data, the proliferation of data sources, and the lack of value measurement, which hinder effective data utilization and collaboration.
  • Characteristics of Full-Stack Data Products: Full-stack data products focus on end users and their needs, addressing pertinent business use cases with a user-friendly interface. They ensure insights lead to tangible actions and include a cohesive data layer with modelled data and metadata.
  • Determining the Need for a Data Product: Assess the necessity of a data product by considering user groups, value metrics, and the need for continuous updates and support.
  • Real-Life Example: A North American Media Corporation used a custom data product to streamline processes, integrate external data, and achieve significant business outcomes, adding billions in revenue.
  • Sustaining Momentum: Building and maintaining data products requires a product manager mindset, focusing on design patterns, governance, technology refreshes, and dedicated teams.
Shashank Garg


CEO & Co-founder

Shashank is the CEO & Co-founder of Infocepts, recognized among the Top 50 Consulting Firm CEOs by The Consulting Report and the Top Global Data Founders by CDO Magazine. Passionate about all things data & AI, he helps CXOs of Fortune 500 companies transform their businesses using Data & AI.

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