The Importance of Data Reliability and Quality in Developing Data Products
Data products offer data-driven enterprises innovative ways to leverage their data investments, creating fresh avenues for generating revenue. By analyzing extensive datasets encompassing customer information, operational data, and other relevant sources, these data products extract insights that aid consumers in making informed decisions while addressing unique use-cases for stakeholders.
Global brands like Walmart, Amazon, and others have recognized the transformative power of data, making it their primary competitive differentiator. They understood the significant advantages of accumulating user and consumer data to fuel their proprietary data products. However, the credit for taking the initial step towards big data goes to another well-known company. As early as 1995, retail brand Tesco introduced the Tesco Clubcard, a customer loyalty program.
With the Clubcard, customers could earn redeemable points every time they made a purchase at Tesco stores or through their website. In return, Tesco collected basic information that provided valuable insights into customer travel distances and seasonal preferences. Through targeted campaigns, Clubcard members ended up spending 4% more than non-members. This increase in spending likely resulted in hundreds of millions of additional revenue, contributing to Tesco’s $61 billion in revenue in 2022.
This was all made possible with data. Collecting it intelligently, processing it, analyzing it, and putting it to use as new business opportunities.
In today’s dynamic and fiercely competitive business environment, the significance of data products cannot be overstated. They have emerged as a crucial tool for enterprises seeking to thrive and succeed in the market.
Data products have revolutionized how businesses operate by providing invaluable insights into customer behavior and operational processes. They serve multiple purposes and offer various advantages:
- Enhanced Decision-Making: Data products enable organizations to analyze data from different business areas, such as marketing, sales, supply chain, and operations. This analysis helps identify areas of improvement and optimize performance. For instance, manufacturing companies can utilize data products to identify bottlenecks in production processes, leading to increased efficiency and cost savings.
- Risk Identification and Mitigation: Through sophisticated risk analytics models, data products assist businesses in identifying and mitigating potential operational and financial risks. Financial service providers leverage data products to predict Non-Performing Assets (NPAs) and forecast market performance, enabling proactive risk management.
- Process Automation: Data products play a crucial role in automating various business processes. Manufacturing industries utilize them to optimize factory operations, inventory management, and eliminate bottlenecks. This automation streamlines production and delivery, resulting in faster and smoother operations. Similarly, IT companies rely on data products to gain insights into employee performance, customer satisfaction metrics (CSAT), and marketing strategies.
- Competitive Advantage: Data products form the foundation of an enterprise’s competitive edge. By leveraging accurate customer behavior data, businesses can market products and services more efficiently and precisely. Real-time nudges, point-of-sale discounts, dynamic pricing, and personalized marketing campaigns enable businesses to stay ahead of the competition.
- Regulatory Compliance: Businesses operating in regulated markets must comply with industry and government regulations. For instance, healthcare providers need to ensure the privacy of patient data in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Enterprises within the EU must adhere to the General Data Protection Regulation (GDPR) to protect customer data. Data products play a vital role in monitoring and flagging potential threats to critical data, ensuring compliance with regulatory mandates.
By delivering continuous data that informs decision-making, mitigates risks, and automates processes, the development and optimization of data products has become a primary focus for enterprises seeking to maximize their data investments.
Regardless of the industry or size of a company, businesses are increasingly relying on data products to set themselves apart in the market and gain a competitive edge. This trend is not limited to digital-first entities such as Google and Netflix (learn more about how Netflix does this with Netflix Data Engineering Best Practices). Even players in sectors like healthcare, energy, insurance, banking, and manufacturing are now leveraging data products to enhance their decision-making abilities and streamline their processes.
One familiar example of leveraging data is dynamic pricing, which originated in the airline industry as a means to maximize revenue by filling otherwise empty seats based on demand. Tech giant Amazon adopted a similar approach by introducing price variations determined by factors like urgency, volume, availability, and product requirements. Surprisingly, most people are unaware that Amazon adjusts the prices of its products an astonishing average of 2.5 million times per day!
To develop an effective data product, various factors come into play, with data quality being a crucial consideration.
The 5 Pillars of Effective Data Product
Here are the essential elements that are vital for an efficient data product:
Exceptional Data Quality
Data quality stands as one of the fundamental pillars of any data product. The value of data lies in its accuracy, relevance, completeness, and freedom from errors. Without these qualities, data holds no actionable value for a data product. To ensure data quality, a robust data management system is necessary. This system should offer a comprehensive view of data assets and pipelines, enabling data teams to maintain constant data integrity.
Creating a well-designed, user-centric interface is crucial for developing a data product that meets the needs of its users. Data products should provide a seamless navigation experience, eliminating any unnecessary obstacles. By understanding the target audience and their goals, data teams can achieve product-market fit. A user-centered design approach facilitates intuitive data product usage, thereby increasing user engagement.
As a data product gains popularity and its adoption rate increases, the volume of data it handles grows significantly. Thus, data products must have the capability to handle this gradual surge in data volumes. Teams seeking to build robust data products need a data management system that can manage large data influxes and scale effectively as the product expands.
In the era of data regulatory laws (e.g., GDPR in the EU) and data protection (e.g., CCPA in the United States), data teams are obligated to reinforce data products that handle sensitive user information with robust security measures. Since data handling should be systematic throughout the entire process, data products require a data management solution that monitors and safeguards user data both at rest and in transit.
The fifth pillar of a data product lies in its ability to translate complex data into meaningful and actionable insights through a user-friendly interface. The ultimate goal of a data product is to transform data into valuable insights that drive growth and innovation. Additionally, these insights need to be presented to users in a format that is easy to comprehend, utilizing visual elements such as graphs, metrics, and charts.
Using Data Observability to Build Exceptional Data Products:
While there are various factors involved in building data products, the five pillars mentioned above are crucial in determining their success. They enable data products to deliver value, remain relevant, and provide accurate insights. Data quality, security, and scalability are interconnected, as they rely directly or indirectly on the quality, integrity, and value of the data. To manage these pillars effectively, enterprises should implement an end-to-end data observability platform that grants them complete control over their data assets and pipelines.
Get a demo of the Acceldata Data Observability Platform and learn how you can optimize your data spend, improve operational intelligence, and ensure data reliability.