What is the difference between data and information?

What is the Difference Between Data and Information? An In-Depth Look

Data and information – we hear these terms used interchangeably all the time. But while they are related concepts, there are some key differences between the two that are important to understand, especially in a business context.

Put simply, data refers to raw, unprocessed facts and figures, while information is data that has been processed, organized, and presented in a meaningful context. Information is used to make decisions, while data on its own doesn‘t tell us much of a story.

As businesses become increasingly data-driven, it‘s more critical than ever to understand the nuances of data vs information, how data gets transformed into information through analytics, and data management best practices. Getting this right allows organizations to gain valuable insights, make smarter decisions, and ultimately gain a competitive advantage.

So let‘s dive deeper into the world of data and information, and explore what you need to know to become a data-savvy business professional in 2023 and beyond.

What is Data?

First, let‘s define what exactly we mean by data. Data is essentially raw, unprocessed facts, figures, and details. It can include numbers, text, images, audio, and other types of files.

Some common examples of data in a business setting:

  • Sales transactions and revenue figures
  • Website traffic and click-through rates
  • Customer contact details and demographic information
  • Social media mentions and engagement metrics
  • Sensor readings from manufacturing equipment
  • Employee timesheets and performance reviews

On its own, data doesn‘t have much meaning or significance. It needs to be processed, analyzed, and put into context to derive any valuable insights from it. That‘s where information comes into play, which we‘ll come to shortly.

Data can be categorized into different types, each with their own characteristics:

Structured vs Unstructured Data

  • Structured data is highly organized and formatted data that is easily searchable, usually stored in databases (e.g. names, dates, addresses).
  • Unstructured data is not organized in a pre-defined way and is more difficult to search and analyze (e.g. social media posts, audio/video files, images).

Qualitative vs Quantitative Data

  • Qualitative data is descriptive, expressed in language rather than numbers (e.g. customer feedback comments, employee survey responses).
  • Quantitative data is measurable, expressed in numbers and values (e.g. sales figures, website traffic).

What we consider "big data" today is typically a combination of all of the above – large volumes of structured and unstructured data, both qualitative and quantitative in nature. Making sense of big data is a key challenge and opportunity for modern organizations.

What is Information?

So how does data turn into information? Information is essentially data that has been processed, organized, structured, and presented in a meaningful context.

Think of data as the raw ingredients, and information as the finished dish. The ingredients on their own don‘t mean much, but once prepared, combined, and plated by a chef, they convey meaning and provide value.

Some examples of information:

  • A chart showing revenue numbers over time to depict business performance
  • A map with color-coded sales territories to help optimally allocate resources
  • A graph comparing website traffic sources to understand where visitors are coming from
  • A spreadsheet that calculates profit margins across product lines

The process of turning data into information typically involves:

  1. Data collection and acquisition from various source systems
  2. Storing the data in databases or data warehouses and processing it to get it into a usable state
  3. Analyzing and querying the data to identify patterns, trends, and insights
  4. Interpreting the analysis and presenting the findings in a meaningful, understandable way, often through visualization

This last step of interpretation and presentation is key. The exact same dataset could be used to produce two entirely different sets of information, based on how it is sliced, diced, and conveyed to the audience.

As an example, let‘s say we have data on employee performance reviews across departments. One analyst could use descriptive statistics to show the average review score for each department. Another analyst could dig deeper to identify which specific factors most influence high or low scores. Both deliver information, but the latter is arguably more actionable and meaningful.

Key Differences Between Data and Information

So in summary, what are the key differences between data and information?


  • Raw, unprocessed facts and figures
  • No inherent meaning on its own
  • Must be processed and analyzed to be useful
  • Structured or unstructured format
  • Qualitative or quantitative


  • Data that has been processed, organized, and given meaning
  • Provides context and tells a story
  • Answers questions and supports decision making
  • Typically presented through reports, dashboards, and data visualizations
  • Requires human interpretation and insight

The Data Lifecycle

The process of turning data into information, insight, and action typically involves several key stages, known as the data lifecycle:

  1. Data collection and acquisition: Gathering data from various source systems and importing into a data repository. Ensuring data accuracy and consistency is key.

  2. Data storage and processing: Storing raw data in databases or data warehouses and processing it into a standardized, usable format, applying data quality checks.

  3. Data analysis and querying: Using statistical techniques and algorithms to identify patterns, trends, and outliers in the data. Generating reports and building dashboards.

  4. Data interpretation and visualization: Using human judgment to interpret what the data analysis is telling us and presenting it in an engaging, meaningful way through data visualization.

  5. Taking action: Applying the insights generated to make data-driven business decisions and take action. Continuously measuring results and acquiring more data to refine and improve.

Data Quality and Governance Best Practices

One of the biggest challenges in turning data into meaningful information is ensuring data quality and consistency. Without quality data, any information and insights will be flawed or misleading.

Some key data quality dimensions to consider:

  • Accuracy: Is the data correct and reliable?
  • Completeness: Is all relevant data included, with no gaps?
  • Timeliness: Is the data up-to-date and available when needed?
  • Consistency: Is the data formatted and defined consistently across systems?

Maintaining data quality requires having the right people, processes, and tools in place, and a robust data governance framework. This involves:

  • Identifying data owners and stewards responsible for each dataset
  • Establishing data quality KPIs and thresholds
  • Instituting data profiling and cleansing processes to identify and fix issues
  • Creating a business glossary of common data terms and definitions
  • Ensuring regulatory compliance and data privacy/security

Examples and Use Cases

Let‘s bring this to life with some practical examples of how different business functions are using data and information to drive results:

Marketing and Sales:

  • Analyzing customer demographic, psychographic, and behavioral data to build detailed buyer personas
  • Using predictive analytics to score leads based on likelihood to convert
  • A/B testing marketing messages and offers and measuring conversion rates
  • Visualizing sales pipeline velocity and identifying bottlenecks

Finance and Accounting:

  • Aggregating and reconciling financial data across global business units
  • Analyzing profitability across products, customers, and channels
  • Forecasting and budgeting based on historical data and growth assumptions
  • Detecting anomalies and potential fraud through pattern analysis

Operations and Supply Chain:

  • Tracking inventory levels and optimizing stock based on demand forecasts
  • Analyzing production line data to identify quality issues and improve efficiency
  • Measuring on-time delivery and order fulfillment KPIs
  • Evaluating supplier performance and risk levels

HR and People Analytics:

  • Identifying factors that correlate with high employee engagement and retention
  • Analyzing workforce demographics and skills to inform talent strategies
  • Measuring diversity and pay equity across the organization
  • Predicting which high-performing employees are at risk of leaving

The Importance of Being Data-Driven

In today‘s fast-paced, constantly-evolving business environment, organizations can‘t afford to make critical decisions based solely on gut feel, anecdotes, or "the way we‘ve always done it."

Data provides a foundation for making sound, evidence-based decisions. It enables business leaders to:

  • Gain a deeper understanding of customers, employees, and market trends
  • Test hypotheses and experiment to see what works and what doesn‘t
  • Forecast more accurately and plan for the future based on predictive models
  • Monitor ongoing performance and KPIs and course-correct tactics in near real-time
  • Identify opportunities for process improvement and automation
  • Benchmark against industry peers and understand competitive positioning

Organizations that embed data and analytics into their culture and ways of working will be best positioned to stay agile, innovative, and successful in the long run.

The Future of Data and Analytics

As we look ahead, it‘s clear that data will only continue to grow in volume, variety, and velocity. At the same time, continual advances in technologies and techniques will make it easier to harness the power of this data. Some key trends shaping the future:

Artificial Intelligence and Machine Learning: AI and ML techniques like deep learning and natural language processing will increasingly be used to automate the process of extracting insights from data. Intelligent algorithms can rapidly identify patterns and anomalies that would be near impossible for humans to see.

Automation and Real-Time Analytics: Automated data pipelines and ETL processes will enable data to flow seamlessly across systems in real-time, allowing businesses to gain up-to-the-minute insights. Robotic process automation (RPA) will be used to streamline data entry and reporting.

Self-Service Business Intelligence: Advances in self-service BI and data visualization tools will enable business users to interact with data directly to get the answers they need, without having to go through IT or data analysts. Natural language querying will make data accessible to all.

Cloud Computing and Data-as-a-Service: Organizations will increasingly store and process their data in the cloud to enable greater scalability, flexibility, and cost efficiency. The rise of data-as-a-service will give companies access to a wider variety of external datasets to enrich their own data.

Augmented Analytics: A new paradigm that uses machine learning to automate data preparation, insight discovery and sharing. This will enable businesses to go from data to insights and actions faster than ever before.


Data and information may seem like interchangeable terms, but it‘s important to understand the distinction. Data is the raw material, while information is the finished product that enables decision making and action.

As data continues to proliferate, organizations that can effectively harness it will gain a significant competitive edge. This requires robust data management and governance, a culture of experimentation and critical thinking, and staying on top of the latest tools and techniques.

By 2025, IDC predicts that the global datasphere will grow to 175 zettabytes. With the right strategies and capabilities, this explosion of data can be an incredible asset, not a liability. As an old adage puts it, "information is power." Or to update it for the 21st century, those who can turn data into information and insight will be the ones with power.