What is a Good Example of Data?

In today's digital landscape, data comes in countless forms, from neatly organized spreadsheets to unstructured text in emails and social media posts. But what exactly makes for "good" data? What are exemplary instances of data that deliver tangible business value? This comprehensive guide explores high-quality data examples across different types, formats, and applications to help your organization leverage its data assets most effectively.

Understanding Data Types: The Foundation of Good Data Examples

Before diving into specific examples, it's essential to understand the primary categories of data that organizations work with today. Each type has unique characteristics, uses, and requirements for being considered "good quality."

Structured Data

Structured data follows a predefined format or schema, making it easily searchable, analyzable, and organized. It's typically stored in relational databases with clear relationships between data elements. Think of it as data that fits neatly into tables with rows and columns.

Good structured data examples include:

• Customer information in a CRM system (name, contact details, purchase history)
• Financial transaction records
• Inventory management data
• Employee records
• Product catalogs with clearly defined attributes

Unstructured Data

Unstructured data doesn't fit neatly into predefined models or schemas. It's more free-form and requires specialized tools for analysis. Despite being more challenging to process, unstructured data often contains rich insights that structured data alone cannot provide.

Good unstructured data examples include:

• Customer reviews and feedback
• Social media content
• Email communications
• Support tickets and chatbot conversations
• Videos, images, and audio recordings
• Scientific research papers

Semi-Structured Data

Semi-structured data falls between structured and unstructured. While it doesn't conform to strict database structures, it contains organizational properties (tags or other markers) that make it easier to analyze than purely unstructured data.

Good semi-structured data examples include:

• JSON and XML files
• Email messages (structured headers with unstructured body content)
• Log files from applications and servers
• HTML web pages
• Electronic Data Interchange (EDI) transactions

Characteristics of Good Data: Quality Matters

Regardless of data type, truly good examples of data share certain fundamental qualities that make them valuable and actionable. Let's examine these characteristics with real-world examples.

Accuracy

Good data accurately represents reality without errors or distortions. This is perhaps the most fundamental quality of good data.

Example: A retailer's inventory management system that provides real-time, accurate counts of products across all locations, enabling confident decision-making about restocking, promotions, and logistics.

Completeness

Complete data contains all the necessary values and attributes required for its intended use, without significant gaps.

Example: A healthcare provider's electronic medical record (EMR) system that captures comprehensive patient information—demographics, medical history, medications, allergies, lab results, and treatment plans—providing a complete picture for informed care decisions.

Timeliness

Good data is available when needed, particularly for time-sensitive applications where outdated information can lead to poor decisions.

Example: Financial market data feeds that deliver stock prices, trading volumes, and other metrics with millisecond latency, enabling algorithmic trading systems to make informed decisions based on current market conditions.

Consistency

Consistent data maintains the same format, structure, and values across systems and over time, avoiding contradictions or irregularities.

Example: A multinational company's customer database that standardizes address formats, currency notations, and naming conventions across all global regions, enabling unified analytics and reporting.

Relevance

Relevant data directly supports specific business objectives, analytical needs, or decision-making processes.

Example: An e-commerce platform that collects and analyzes customer browsing patterns, purchase history, and abandoned cart data specifically to improve product recommendations and increase conversion rates.

Exemplary Data Examples Across Industries

Let's explore standout examples of good data across various sectors, highlighting how they deliver value through different characteristics and applications.

1. Retail and E-commerce

Customer 360 Data: Leading retailers create comprehensive customer profiles that integrate online and offline behavior, purchase history, support interactions, social media engagement, and demographic information. This unified view enables personalized marketing, inventory planning, and product development.

What makes it good: Integration of structured transaction data with semi-structured and unstructured behavioral data creates a holistic picture of customer relationships. When properly implemented, this data is consistent across touchpoints, regularly updated, and directly relevant to multiple business functions.

Real-world impact: A major home improvement retailer used integrated customer data to identify DIY enthusiasts who purchased certain tools but not required accessories, creating targeted campaigns that increased attachment rates by 23% and improved customer satisfaction.

2. Manufacturing

IoT Sensor Data: Modern manufacturing facilities deploy networks of Internet of Things (IoT) sensors that continuously monitor equipment performance, environmental conditions, and production metrics in real-time.

What makes it good: This data excels in timeliness (real-time streams), accuracy (calibrated sensors), completeness (monitoring all critical parameters), and direct relevance to operational efficiency. The best implementations integrate this structured time-series data with contextual information like maintenance records and quality inspection results.

Real-world impact: An automotive manufacturer implemented predictive maintenance using sensor data from assembly line robots, reducing unplanned downtime by 38% and extending equipment lifespan by identifying patterns that preceded failures before they affected production.

3. Healthcare

Electronic Health Records (EHR): Comprehensive, longitudinal patient records that integrate clinical notes, lab results, medication lists, vital signs, imaging data, and treatment histories.

What makes it good: Strong governance ensures accuracy and completeness of patient information. Standardized terminology and coding systems (like SNOMED CT or ICD-10) provide consistency. Integration with pharmacy systems, lab systems, and imaging applications creates a holistic view of patient health.

Real-world impact: A hospital network using integrated EHR data developed an early warning system for sepsis that analyzes vital signs, lab values, medication data, and clinical notes, reducing mortality rates by identifying at-risk patients an average of 18 hours earlier than traditional methods.

4. Financial Services

Transaction and Risk Data: Financial institutions compile comprehensive datasets that include customer transactions, credit histories, market movements, and risk indicators.

What makes it good: This data combines extreme accuracy requirements with near real-time availability. The best implementations maintain consistent formats despite originating from diverse systems, and include contextual information like geolocation, device details, and behavioral patterns.

Real-world impact: A credit card company integrated transaction data with location information, spending patterns, and merchant categories to develop a fraud detection system that reduced false positives by 62% while improving actual fraud detection rates by 31%.

5. Transportation and Logistics

Fleet and Shipment Tracking Data: Integrated datasets that combine GPS location, vehicle diagnostics, driver behavior, traffic conditions, weather data, and delivery status information.

What makes it good: This data excels through its timeliness (real-time updates), integration of multiple data types (structured GPS coordinates with unstructured traffic and weather information), and direct relevance to operational efficiency.

Real-world impact: A logistics company implemented dynamic route optimization using integrated tracking and external data sources, reducing fuel consumption by 17%, increasing on-time deliveries by 22%, and improving vehicle utilization through more accurate prediction of delivery windows.

Emerging Data Types: New Frontiers of "Good Data"

As technology evolves, new types of data are emerging as valuable business assets. These cutting-edge examples showcase how the definition of "good data" continues to expand.

Synthetic Data

Artificially generated data that mimics the statistical properties of real data without containing sensitive information has become increasingly valuable for testing, training AI systems, and overcoming data privacy constraints.

Example: A healthcare AI developer created synthetic patient datasets that maintain the same statistical relationships and edge cases as real medical data but contain no actual patient information. This allowed them to develop and validate diagnostic algorithms without privacy concerns, accelerating development by 40%.

Graph Data

Data that focuses on relationships and connections between entities rather than just the entities themselves is proving valuable for understanding complex networks and ecosystems.

Example: A pharmaceutical company built a knowledge graph integrating research papers, clinical trials, molecular data, and drug interactions. This network representation revealed previously unseen relationships between compounds and biological pathways, accelerating drug discovery for challenging disease targets.

Real-time Event Streams

Continuous flows of time-stamped data representing activities, signals, or observations as they occur are enabling more responsive and adaptive business processes.

Example: A smart city implementation integrates traffic sensors, public transportation signals, emergency service locations, and event schedules into a real-time event stream that dynamically adjusts traffic light timing, public transportation schedules, and emergency response routing based on current conditions rather than static plans.

Characteristics of Poor Data Examples (Avoid These)

Understanding what makes data "bad" can help organizations recognize and address issues in their own data assets. Here are common examples of problematic data:

• Duplicate records: Customer databases with multiple entries for the same individual due to varied name spellings or multiple sign-ups
• Inconsistent formats: Date fields stored as MM/DD/YYYY in one system and DD/MM/YYYY in another, leading to analysis errors
• Missing values: Healthcare records with incomplete allergy information, creating potential patient safety risks
• Outdated information: Inventory systems that don't reflect recent returns or transfers, resulting in stock discrepancies
• Irrelevant data collection: Gathering extensive demographic information for simple newsletter subscriptions, creating unnecessary privacy concerns and deterring sign-ups

Best Practices for Creating Good Data Examples in Your Organization

To ensure your organization generates and maintains exemplary data assets, consider implementing these best practices:

1. Establish Clear Data Governance

Create well-defined policies, standards, and responsibilities for data management across its lifecycle. Designate data stewards for critical domains and implement quality monitoring routines.

2. Design with Purpose

Before collecting data, clearly define the business questions it should answer and decisions it should support. Align data structures and attributes with these specific objectives.

3. Implement Validation at Collection

Build input validation rules to catch errors at their source—when data is first entered or captured. This is far more efficient than cleaning data after collection.

4. Document Metadata Thoroughly

Maintain comprehensive information about your data—its sources, transformations, business definitions, quality metrics, and usage guidelines—to ensure consistent understanding across the organization.

5. Establish Feedback Loops

Create mechanisms for data users to report issues and contribute to quality improvements. Some of the best data quality enhancements come from those who work with the data regularly.

Conclusion: The Payoff of Good Data

High-quality data examples aren't just technical achievements—they're business assets that deliver tangible returns. Organizations with exemplary data foundations consistently outperform their peers in:

• Decision-making speed and accuracy: Reliable data enables confident, timely choices without extensive validation efforts
• Operational efficiency: Good data reduces errors, rework, and reconciliation efforts across business processes
• Customer experience: Accurate, complete customer data enables personalized interactions and consistent service across touchpoints
• Innovation capacity: When teams trust their data foundation, they can focus on developing new insights and capabilities rather than questioning basic facts
• Regulatory compliance: Well-governed data streamlines reporting requirements and reduces compliance risks

As you evaluate and enhance the quality of your organization's data assets, focus not just on technical metrics but on the business value each dataset delivers. The best examples of data aren't just clean and well-structured—they directly enable better business outcomes and competitive advantages in your specific context.

Learn more about how DataMinds Services can help you build high-quality data assets