Why Your Data Product Isn’t Delivering—and What You Forgot to Include

In the contemporary business landscape, organizations invest heavily in data products to gain insights and drive strategic decisions.

However, many find these tools fall short of expectations. A significant reason for this underperformance is the reliance on traditional dashboards as the core Data Product, which often lack the dynamic capabilities of modern Data Applications (Data Apps). These advanced tools integrate Artificial Intelligence (AI), Machine Learning (ML), writeback functionalities, and add seamless connections to other systems.

What Is a Data Product?

A Data Product is a tool or application that delivers data for consumption to meet specific business objectives. It encompasses various components—such as data sources, pipelines, models, and interfaces—that work together to leverage data for actionable insights and solutions.

Examples include analytics dashboards, predictive models, and Data Apps. Note that Data as a Product (DaaP) is separate and should be approached differently.

Why Your Data Product Isn’t Delivering

The most common blockers to success in Data Products are:

1. Application Fatigue: Disconnected tools and fragmented workflows force users to switch between multiple applications to complete tasks.
2. High Application Cost: Multiple redundant licenses for infrequently used applications or features increase operational expenses.
3. Slow Decision-Making: Delayed access to relevant insights due to siloed data, poor integration, or reliance on manual reporting processes.
4. Analytics Lag: Inefficient data pipelines, outdated infrastructure, and excessive query times slow down data processing.
5. Static Data Representation: Dashboards typically offer a snapshot of data at a specific point in time, which can quickly become outdated in fast-paced environments.
6. Lack of Interactivity: Users often cannot interact with the data beyond viewing it, limiting their ability to perform deeper analyses.
7. Isolated Systems: Traditional dashboards may not integrate seamlessly with other tools and systems, leading to data silos and inefficiencies.

What You Forgot to Include

To truly empower your organization, consider incorporating the following elements:

1. Integrated Workflows: By embedding analytics directly into daily tools and processes, you minimize the need to switch between applications, enhancing productivity.
2. Unified Licensing: Implementing a unified data platform reduces the necessity for multiple application licenses, leading to significant cost savings.
3. Real-Time Decision Support: Providing up-to-the-minute data insights enables swift, informed decisions, keeping your business agile.
4. Interactivity: Offering interactive reports with drill-down capabilities allows users to explore data deeply, uncovering insights that static reports miss.

By incorporating these advanced capabilities, your data product evolves into a powerful solution that enhances efficiency and fosters a data-driven culture. These features are the defining elements of a Data App.

The Emergence of Data Apps

Data Apps represent an evolution in data product design, offering dynamic and interactive experiences that overcome the constraints of traditional dashboards. Key features of Data Apps include:

• Advanced Analytics Integration: By incorporating AI and ML algorithms, Data Apps provide predictive analytics and deeper insights.
• Enhanced Interactivity: Users can engage with data through interactive elements, such as drill-downs, filters, and the ability to input data directly.
• Seamless Integration: Data Apps integrate with various tools and platforms, enabling a unified data environment and reducing the need for multiple applications.

For example, platforms like Astrato empower users to transform static dashboards into interactive Data Apps, enabling real-time data interaction.

Addressing App Overload and Over-licensing

We live in an era where there’s an app for everything—There’s an app for that, as Apple famously said. While specialized tools seem like a boon, they often lead to confusion and productivity loss due to context switching. Instead of empowering users, app overload creates fragmented workflows and mental fatigue. A Data App reverses this trend by being the app ‘that already does it’, integrating multiple functions and simplifying the user experience. Data Apps mitigate application abundance by:

• All-in-One Functionality: Data Apps reverse the need for many different applications by being a comprehensive solution that integrates multiple functionalities.
• Centralizing Functions: By consolidating needs—such as data analysis, reporting, and input—into a single platform, Data Apps streamline workflows.
• Enhancing User Experience: A unified interface allows users to perform tasks intuitively, enhancing satisfaction and engagement.

By integrating features like writeback and AI-driven analytics, Data Apps provide a cohesive solution that keeps users productive.

Why Data Mesh Is Important for Modern Data Products

Data mesh helps organizations overcome inefficiencies of centralized data management by decentralizing ownership and promoting scalability and agility. It is built upon four core principles:

1. Domain Teams Own Data: Empower domain teams to own and manage their data, fostering accountability.
2. Data as a Product: Encourage teams to treat their data as products, focusing on quality and usability.
3. Self-Serve Data Tools: Provide platforms and tools that enable teams to manage their data autonomously, reducing dependencies.
4. Federated Governance: Establish governance frameworks that allow decentralized data management while ensuring compliance.

Data mesh complements the shift towards Data Apps by making data accessible and usable across teams. Data Apps provide an interface for accessing decentralized data, turning data mesh principles into practical solutions.

The Business Impact of Transitioning to Data Apps

Adopting Data Apps can lead to significant business benefits:

• Accelerated Decision-Making: Real-time data access and advanced analytics enable quicker, informed decisions.
• Increased Agility: Organizations can rapidly adapt to market changes through dynamic data interactions.
• Enhanced Collaboration: A unified data environment fosters better collaboration, with stakeholders having access to consistent information.

By moving beyond traditional dashboards to Data Apps and adopting a data mesh architecture, businesses can unlock the full potential of their data assets.

Reassess Your Data Approach for Greater Impact

If your data product isn’t delivering the expected value, it may be time to reassess your approach. Transitioning from static dashboards to dynamic Data Apps with AI, ML, writeback capabilities, and seamless integrations can transform how your organization interacts with data. Embracing a data mesh architecture ensures data ownership, quality, and usability are embedded into every part of the organization, enhancing engagement and fostering agile business decisions.