How Data Platforms Turn Raw Data into Revenue and Drive Growth

Modern technology is revolutionizing data, transforming it from a passive resource into a dynamic, revenue-generating product. The shift to Data as a Product (DaaP) is no longer just a trend; it's a strategic necessity. In 2023, businesses worldwide spent over $220 billion on data and analytics, highlighting the race to harness data's full potential. Yet, despite this investment, only 29% of companies are feeling truly data-driven. 

The gap lies in how data is used—not just collected. By adopting DaaP, businesses can break free from outdated, siloed data practices, turning raw information into powerful, scalable products that drive growth, innovation, and a competitive edge. This article explores the DaaP model, its benefits, best practices, and how companies like Acceldata are leading the way in this transformation.

Nowadays, data has evolved from a passive resource into a dynamic, revenue-generating asset. This transformation, known as Data as a Product (DaaP), is reshaping industries worldwide. By 2023, businesses spent over $220 billion on data and analytics, but only 29% reported being truly data-driven. The key to bridging this gap lies in how data is utilized which is beyond simple collection, DaaP turns data into actionable, scalable products that drive growth and innovation.

This article goes into what Data as a Product means, its benefits, best practices for adoption, and how top companies like Acceldata are leading this transformation.

What Is DaaP?  

Data as a Product (DaaP) involves treating data as a primary business asset, similar to physical products. Unlike traditional data management, which focuses primarily on storage and retrieval, DaaP revolves around refining, packaging, and delivering data solutions that directly address specific business challenges.

For example, Salesforce's Customer 360 platform integrates data from multiple departments—sales, marketing, and customer service—into a unified view. This data product helps businesses personalize customer experiences, leading to improved service and increased sales.

Traditional data management vs. DaaP: What’s the difference?  

Traditional data management focuses on centralized storage and ensures accessibility. It often limits effective data sharing across departments. DaaP addresses this by enabling user-friendly, reusable, and scalable data products that promote seamless collaboration.

For example, you can take Capital One’s Eno, an AI-driven assistant that utilizes customer transaction data to offer real-time fraud alerts, spending insights, and savings opportunities. This illustrates how DaaP turns raw data into a customer-centric product that adds real value.

Core Principles That Drive DaaP Excellence  

For DaaP to be successful, it must be based on solid concepts that transform raw data into strategic assets. These essential features ensure that data solutions stay scalable, user-friendly, and adaptable, allowing organizations to use data efficiently.

1. User-centric design: The development of any data product should begin with understanding the needs of its end-users. Data products must be intuitive, easy to interpret, and serve a clear purpose. Scania’s Connected Analytics Platform provides predictive maintenance insights, helping customers reduce vehicle downtime and showing how user-centric design in DaaP improves operational efficiency.
   
   
2. Data quality: Without reliable data, the foundation of any data product collapses.
   Robust data quality protocols ensure that insights derived from data products are accurate and trustworthy, reducing the risk of incorrect business decisions. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. Businesses can reduce these costs by prioritizing data quality in their DaaP strategies.
   
   
3. Scalability: Data products should be designed to grow alongside the business. This means they must handle increasing volumes of data without a decline in performance. For example, PayPal uses scalable data models to monitor and analyze millions of transactions per day. It identifies fraudulent activities and ensures secure processing.
   
   
4. Continuous Innovation: Successful data products integrate continuous feedback loops, which ensures they adapt and improve over time. For example, Netflix's recommendation engine continuously refines its algorithms based on user viewing data to improve content suggestions.

Why Business Leaders Should Adopt Data as a Product (DaaP)  

Adopting DaaP enables businesses to turn raw data into strategic assets, improving decision-making, driving innovation, and generating new revenue streams. Let’s look at some of the key benefits:

• Improved decision-making: With centralized data insights, companies make faster and more informed decisions. For instance, Starbucks uses data analytics to adjust product offerings based on regional sales trends, weather patterns, and customer preferences.
  
  
• Faster time-to-Insights: Data products streamline the analysis process and enable teams to gather insights quickly and act promptly. Retail giant Walmart uses real-time inventory data to optimize supply chains, ensuring that shelves are stocked efficiently and reducing the need for costly last-minute logistics.
  
  
• Higher ROI on data investments: Instead of just storing data, companies can monetize it, creating new revenue streams. For example, Rolls-Royce uses data from its aircraft engines to offer a “Power-by-the-Hour” service, where customers pay based on engine usage rather than purchasing the engines outright. This service is underpinned by continuous data monitoring and predictive analytics.

Best Practices for Adopting DaaP  

To fully utilize DaaP, businesses must go beyond traditional data management and adopt a strategic approach. Let’s look at the key practices:

1. Promote a data-driven culture: You should encourage teams across the organization to use data in their decision-making processes. Regular workshops and training can bridge knowledge gaps and promote the adoption of data products. Companies like Procter & Gamble have embedded a culture of data-driven innovation by integrating data science into every part of their product development process. For a mid-sized manufacturer, introducing data-based decision-making could transform supply chain management, using predictive analytics to optimize production schedules and cut down waste, ultimately saving costs.
   
   
2. Invest in robust data quality measures: You should establish data governance protocols that ensure data accuracy, consistency, and compliance. For example, banks collaborating on fraud detection rely on shared, high-quality data to improve models. Standardized data formats and validation rules can greatly improve data reliability. An e-commerce startup can avoid stockouts and lost sales by implementing automated data validation, ensuring accurate inventory levels and smoother operations.
   
   
3. Design with a user-centric approach: You should develop data products with the end user in mind, focusing on solving specific problems rather than overwhelming users with unnecessary data. The New York Times, for instance, offers readers personalized content recommendations, enhancing engagement. Similarly, a software company could boost dashboard adoption by streamlining the interface to show only relevant metrics tailored to each client’s needs.
   
   
4. Utilize Advanced Analytics: Use AI, ML, and predictive analytics to drive innovation. Predictive maintenance, as seen with Scania’s connected vehicles, helps businesses avoid disruptions and optimize operations. Likewise, a logistics firm using real-time data to predict truck maintenance needs can significantly reduce downtime and ensure timely deliveries, improving overall efficiency.

Business Use Cases of Data As a Product  

Data as a Product (DaaP) transforms how companies across industries utilize data, turning it into strategic, actionable insights. By adopting DaaP, businesses can drive growth, optimize operations, and create personalized experiences that enhance customer loyalty. Let’s look at how DaaP is transforming industries:

• Retail: Target uses DaaP to revolutionize its promotional strategies. By analyzing vast datasets, including purchase history, in-store behavior, and online browsing patterns, Target creates highly personalized offers that resonate with individual shoppers. This tailored approach has led to a 20% increase in customer engagement, driving higher conversion rates and building long-term customer loyalty. For instance, Target's data platform can predict a customer’s future purchases, even identifying major life events like pregnancy, allowing for timely and relevant marketing that keeps customers coming back.
  
  
• Healthcare: Health insurance companies like Humana utilize data products to transform patient care. By aggregating and analyzing patient health records, claims data, and wearable device information, Humana provides personalized wellness plans that encourage preventive care. This proactive approach has reduced hospital admissions by up to 30%, leading to better patient outcomes and lowering overall healthcare costs. Additionally, by monitoring real-time patient data, Humana can offer targeted interventions, such as reminders for medication adherence, further enhancing patient health and satisfaction.
  
  
• Automotive: For example, Scania’s Connected Analytics Platform utilizes DaaP to drive innovation in the automotive sector. Aggregating data from over 600,000 connected vehicles, Scania uses predictive analytics to forecast maintenance needs, reducing vehicle downtime by more than 30%. This proactive maintenance has saved fleet operators thousands of dollars in repair costs and ensured that vehicles remain operational, enhancing overall efficiency. For companies managing large fleets, this approach can translate into millions of dollars saved annually, making Scania’s platform a critical tool for optimizing logistics and transport operations.
  
  
• Finance: JPMorgan Chase uses DaaP to enhance fraud detection and customer service. By analyzing transaction patterns across millions of accounts, their data products identify unusual activities in real time, flagging potential fraud within seconds. This has significantly reduced fraud losses, protecting customers while maintaining trust. Additionally, their data platform provides personalized financial advice, analyzing customer spending habits to suggest savings plans and investment opportunities, leading to a 15% increase in client engagement.

Common Barriers to DaaP Adoption  

Data as a Product (DaaP) transforms how companies across industries use data, turning it into strategic, actionable insights. By adopting DaaP, businesses can drive growth, optimize operations, and create personalized experiences that enhance customer loyalty. Below are powerful examples of DaaP in action across different sectors:

1. Cultural resistance: Shifting to a DaaP approach requires a change in mindset. Traditional teams may not want to move from familiar data practices to adopting a new, integrated model. For example, consider a manufacturing company where departments have long been operating. When asked to integrate data for a comprehensive product analytics tool, some team members might hesitate, worrying that sharing data will lead to increased scrutiny or changes in their workflow. To overcome this, it requires clear communication about the benefits of DaaP and leadership buy-in.
   
   
2. Isolated data systems: Legacy systems and departmental data can hinder data integration, making it difficult to build effective data products. Imagine a financial services firm that wants to create a unified customer profile by integrating data from loans, credit cards, and investments. Each department has its database, and the data structures are incompatible. Without a centralized platform to merge this information seamlessly, the company risks missing out on critical cross-selling opportunities.
   
   
3. Aligning data products with business goals: Without a clear alignment between data strategies and business objectives, data products may fail to deliver tangible value. For example, a retail chain might develop a data product that tracks foot traffic patterns in stores but neglect to link these insights to inventory management. As a result, they gather data without translating it into actions that improve sales. Effective communication between data teams and business units can ensure that data initiatives are driven by clear, strategic goals.

Essential Tools for Implementing DaaP

Effective DaaP relies on robust tools for integration, governance, and observability. Platforms like Snowflake and Databricks enable scalable data management, while tools such as Alation and Acceldata ensure data quality, security, and seamless operation.

• Data platforms like Snowflake and Databricks offer scalable solutions for data integration and analysis. Databricks’ Lakehouse Platform has been adopted by companies like Shell and Comcast to unify their data management strategies.
• Data governance tools ensure data security, compliance, and accessibility. Alation is also popular for its data cataloging features, helping businesses discover and manage data products effectively.
• Data observability platforms like Acceldata monitor data quality, reliability, and performance, ensuring the seamless functioning of data products. This level of oversight is critical for enterprises that rely on data to make strategic decisions.

Leading Providers of Data as a Product Solutions  

As businesses increasingly adopt Data as a Product (DaaP) to transform raw data into strategic assets, the demand for robust platforms that support data quality, scalability, and efficient management has increased. Several leading providers have emerged, offering specialized solutions that cater to different aspects of DaaP, from data observability to cloud-based data warehousing. Let’s look at some top players in this:

• Acceldata is a leader in data observability. Acceldata ensures data quality and reliability, helping companies build robust data products. Our platform is used by companies like Hershey's and Dun & Bradstreet to streamline data operations.
• Databricks is known for its unified data analytics platform, which facilitates scalable and collaborative data product development. Databricks supports organizations like Regeneron in accelerating drug discovery.
• Snowflake offers cloud-based data warehousing, enabling seamless data sharing and collaboration. Capital One utilizes Snowflake to power its enterprise data platform, allowing scalable and secure data management.
• Alation specializes in data cataloging, helping businesses discover and manage data products effectively. Alation's platform has been pivotal for Pfizer in optimizing its research data.

Emerging Trends in Data as a Product

Data as a Product (DaaP) is quickly expanding, with new trends that enable better and more profitable data utilization. Businesses can now use and profit from their data in a variety of ways, from AI-driven data products to personalized experiences and data marketplaces.

• AI-driven data products: AI will further automate data analysis and enable faster and more accurate predictive insights. Amazon uses AI to optimize its supply chain, saving billions in logistics costs annually.
  
  
• Personalized data experiences: Data products will become more customized to individual users, ensuring insights are relevant and actionable. Spotify’s music recommendation system is a classic example of personalized data experiences that boost user engagement.
  
  
• Rise of data marketplaces: Businesses are increasingly exploring data marketplaces to monetize their data products, offering new revenue opportunities. For instance, Equifax sells aggregated credit data to businesses for risk assessment purposes, creating a lucrative revenue stream.

Achieve Data Excellence with Acceldata  

Acceldata is helping businesses utilize DaaP by providing robust data observability and management solutions. Their platform ensures that data is accurate, reliable, and readily available, facilitating the development of high-quality data products. Acceldata's comprehensive approach empowers organizations to navigate the complexities of data integration, making it easier to build, deploy, and scale data products effectively. For example, by monitoring data pipelines in real time, businesses can quickly identify and resolve data quality issues, ensuring consistent performance. To explore how your organization can use DaaP, book a demo with Acceldata today.

Summary  

Adopting Data as a Product empowers businesses to unlock new growth opportunities, optimize operations, and deliver personalized customer experiences. By using the right tools, promoting a data-driven culture, and aligning data strategies with business goals, companies can turn raw data into valuable, revenue-generating products.