From data burden to business advantage: Building data products that drive growth

Bringing product thinking to data: A practical guide for developers, data scientists, business users, and executives.

In today’s digital landscape, organizations are drowning in data while simultaneously thirsting for insights. We have heard often-quoted statistics like, “Companies use less than 50% of their collected data for decision-making, with the average employee spending nearly 30% of their workweek searching for information.” This paradox exists because most organizations treat data as a byproduct rather than as a strategic asset designed for consumption.

In our previous articles in this series, we explored how data should flow effortlessly through an organization and serve as a foundation for business intelligence. Now we arrive at the transformative concept that can reshape the entire approach to data management: treating data as a product rather than a project.

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Think about your favorite software product or online service. What makes it valuable? It likely solves a specific problem, is easy to use, and delivers a clear benefit. Now, imagine applying that same product thinking to an organization’s data assets. This shift in perspective—from viewing data as raw material to seeing it as carefully crafted products ready for consumption—is what “Data as a Service” (DaaS) is all about.

Let’s explore how this product mindset can transform data from a technical challenge into a strategic business advantage that drives growth and innovation throughout an organization.

Managing data through a product lens – “Data as a Service” (DaaS)

To see what excellence looks like in practice, let’s use a typical product framework and contrast it to traditional data management approaches.

In conventional organizations, data is siloed, difficult to access, and requires significant effort to integrate and prepare for analysis. This creates bottlenecks, slows down decision-making, and prevents teams from leveraging the full potential of their information assets.

DaaS steps in as the solution by offering data in a consumable and on-demand format. Imagine it as a curated catalog of data products, each designed to meet specific needs. Instead of wrestling with raw databases and complex pipelines, users can simply “subscribe” to the data they need, when they need it.

The key “features” of DaaS

Thinking in product terms, what are the key “features” that make DaaS attractive?

• Accessibility: Just like a user-friendly app, DaaS provides easy access to data through standardized interfaces (APIs, data marketplaces, etc.). No more digging through disparate systems or relying on specialized technical teams for every data request.
  
• Usability: A well-designed product is intuitive. DaaS aims to provide data that is clean, well-documented, and readily usable for its intended purpose. Think of it as data that comes with clear instructions and is already formatted for your analysis tools.
  
• Scalability: Just as a successful product can handle a growing user base, DaaS solutions are designed to scale with your data needs. Whether you need a little data for a small project or vast datasets for enterprise-wide analysis, the infrastructure can adapt.
  
• Maintainability: Behind every great product is a team ensuring its reliability and upkeep. With DaaS, the underlying data infrastructure, quality checks, and updates are typically managed by the data product teams, freeing up the rest of the teams to focus on extracting insights rather than managing access and understanding the lexicon to navigate.
  
• Value proposition: Ultimately, a product must deliver value. DaaS offers several key benefits, including faster time-to-insights, reduced operational costs associated with data management, improved data quality, and the ability to innovate more quickly by leveraging readily available information.

“Customer” satisfaction: Who benefits from DaaS?

Just like different products cater to different customer segments, DaaS benefits a wide range of users within an organization:

• Data scientists and analysts: They gain access to clean and prepared data, allowing them to focus on building models and generating insights instead of data wrangling.
  
• Business users: They can access relevant data to inform their decisions without needing deep technical expertise. Think of self-service analytics powered by readily available data products.
  
• Application developers: They can easily integrate data into their applications through APIs, creating data-driven features and enhancing user experiences.
  
• Executive leadership: With consistent, reliable data products supporting organizational metrics, leaders gain confidence in their decision-making and can more effectively measure strategic initiatives against established KPIs.

The evolution: From data projects to data products

Many organizations traditionally approach data initiatives as projects with defined start and end dates, specific deliverables, and often a narrow scope tied to a particular business need. While this approach can yield short-term results, it often leads to data silos, duplicated efforts, and a lack of a cohesive data strategy. To truly embrace the power of concepts like DaaS, companies need to evolve towards a product mindset in their data management practices.

This shift isn’t always easy and requires a fundamental change in how organizations view and manage their data assets. Here’s a look at how companies can navigate this graduation:

1. Shifting focus from deliverables to outcomes

• Project mindset: The primary focus is on delivering the defined outputs within the project scope, timeline, and budget. Success is often measured by whether the project was completed on time and within budget.
  
• Product mindset: The emphasis shifts to the ongoing value and impact of the data assets. Success is measured by how effectively the data products are being used, the insights they generate, and their contribution to business goals over time. This requires continuous monitoring, iteration, and improvement based on user feedback and evolving needs.

2. Embracing continuous improvement and iteration

• Project mindset: Projects typically follow a linear lifecycle with distinct phases. Once the project is “complete,” significant changes or enhancements often require a new project.
  
• Product mindset: Data products are viewed as living entities that require continuous improvement and iteration. Regular feedback loops are established with data consumers to identify areas for enhancement, address pain points, and adapt to changing business requirements. Think of it like releasing new versions of a software product with added features and bug fixes.

3. Understanding and serving the “Data customer”

• Project mindset: The focus is often on fulfilling the requirements of the specific stakeholders who initiated the project. The needs of other potential data consumers might not be fully considered.
  
• Product mindset: A key aspect of the product approach is identifying and understanding the various “customer” segments for data within the organization (e.g., analysts, business users, application developers). Data products are then designed and managed to meet the diverse needs of these users, ensuring broad adoption and value creation.

4. Investing in data infrastructure as a platform

• Project mindset: Data infrastructure is often built on an ad-hoc basis to support specific project needs, leading to a fragmented and complex landscape.
  
• Product mindset: Companies start to view their data infrastructure as a foundational platform that enables the creation and delivery of various data products. This involves investing in scalable, reliable, and well-governed infrastructure that supports self-service access and efficient data management.

5. Fostering collaboration and cross-functional teams

• Project mindset: Data projects are often driven by specific teams (e.g., IT or a particular business unit) with limited collaboration across other departments.
  
• Product mindset: Building and managing successful data products requires collaboration across different functional teams, including data engineers, data scientists, business analysts, and domain experts. A core data product area team facilitates the collaboration and communication flow – this ensures that the data products are aligned with business needs and are usable by a wider audience.

6. Measuring success beyond project metrics

• Project mindset: Success is primarily measured by on-time delivery and budget adherence.
  
• Product mindset: Success is measured by metrics such as data product adoption rates, user satisfaction, time-to-insight, the impact of data-driven decisions, and the overall contribution of data to business outcomes.

Practical challenges to consider

The journey from project to product thinking isn’t without obstacles. Organizations typically encounter several challenges:

Resource allocation: Traditional budgeting processes often favor defined projects over ongoing product development and maintenance. This is changing as more organizations adopt a product mindset for their overall development and data should not be left behind.

Organizational structure: Siloed teams can impede the cross-functional collaboration essential for effective data products. Especially for data, establishing dedicated data product teams with representation from both technical and business domains will create a flywheel effect.

Skill gaps: Building and maintaining data products requires a blend of technical expertise and business acumen – upskilling team members and potentially hiring specialists with product management experience.

Cultural resistance: Longtime practitioners may resist the shift from project-based work to ongoing product support. The tone for data products must be set top down to break through organizational inertia.

The transition path

The journey from a project to a product mindset in data management is a gradual one. It often involves:

• Getting the basics right: Starting with a data product inventory and a standard framework defining what constitutes a data product in the organization are table stakes.
  
• Identifying early wins: Identifying data sets or capabilities that can be treated as initial “data products” with clear users and value propositions.
  
• Establishing feedback loops: Implement mechanisms for data consumers to provide feedback on the usability and value of data assets.
  
• Investing in enabling technologies: Adopt tools and platforms that support self-service data access, data catalogs, and data quality management.
  
• Promoting data literacy: Educate users about the available data products and how to leverage them effectively.
  
• Iterating and learning: Continuously refine the approach based on experiences and feedback.
  
• Communicating value continuously: Track usage metrics and business outcomes tied to data products, and regularly share success stories to build momentum.

Conclusion: From burden to strategic asset

The shift from viewing data as a technical byproduct to treating it as a carefully crafted product represents one of the most significant opportunities for organizations to extract value from their information assets. By applying product thinking principles to data management, companies can transform data from a burden that requires constant maintenance into a strategic asset that drives growth, innovation, and competitive advantage.

Consider: Are we building data projects that will require rework with each new business need, or are we creating enduring data products that drive innovation, efficiency, and better decision-making continuously across the enterprise? The answer to this question may well determine the organization’s ability to thrive in an increasingly data-driven business landscape.

Additional resources

• Reimagining data as a service for enterprise (Rocket Source)
• Shifting mindsets: why you should treat data as a product (Thoughtworks)
• From project to product mindset: How to make the change (ProdPad)

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