What Is Data? A Data Analyst's Perspective
As a seasoned data analyst, I like to think of data in simple terms: data are just pieces of information we collect and examine. In practical life, that could be anything measurable – say, a student's test score, the color of a car, or the temperature of a city – each is a data point. Essentially, data are facts or figures we record to understand the world.
Understanding Data: From Raw Inputs to Information
Technically, data are the raw inputs computers work with: as Tutorialspoint puts it, data are "the systematic record of specified characters, quantity or symbols on which operations are performed by a computer". In other words, behind the scenes all data are stored as binary bits (0s and 1s) or coded symbols.
When organized (for example, put into tables or graphs), data becomes information that we can use to make decisions.
Key Insight
Data transforms into actionable information through organization and analysis
Types of Data: Structured, Unstructured, and More
Data comes in many shapes and formats. In data science we often categorize it:
Structured Data
This is neatly organized, like a spreadsheet or database with a fixed schema (rows and columns). Think of customer names and phone numbers in a table. Structured data "has a fixed schema and fits neatly into rows and columns", making it easy to query with SQL or spreadsheet tools.
Unstructured Data
This is free-form information with no predefined format. Examples include emails, photos, videos, social media posts or audio files. Unstructured data "has no fixed schema" and can be quite complex to analyze. Techniques like natural language processing or computer vision are often used to extract insights from unstructured data.
Semi-Structured Data
Between the other two is semi-structured data. It doesn't live in a strict table, but it has tags or markers (like JSON or XML files, or emails with headers) that give it some organization. This makes it easier to search than pure text, yet still flexible for things like web APIs or log files.
Qualitative vs. Quantitative Data
Another way to think about data is qualitative (categorical) vs. quantitative (numeric). Qualitative data are descriptive labels (like "red", "excellent", or country names), whereas quantitative data are measurable numbers (like height in cm or sales in dollars).
Qualitative Data
A car's color, a survey response ("Good", "Average"), etc. are qualitative.
- Descriptive labels
- Categories and classifications
- Country names, colors, ratings
Quantitative Data
A test score (85/100), temperature (28°C) and any continuous value are quantitative.
- Measurable numbers
- Numeric values
- Height, sales, temperature, scores
Both types matter in analysis – qualitative data often gets summarized with categories or charts, and quantitative data with statistics or plots.
Data in the Real World: Business, Healthcare, Social Media
Data powers insights in every field today. A few examples:
1
Business and Retail
Companies mine data to make smarter decisions. For instance, Walmart uses historical sales and logistics data to optimize inventory. Their AI-driven system analyzes past sales, online searches, and even weather and demographic data to predict holiday demand. By "leveraging historical data and predictive analytics," Walmart can strategically place products in stores and warehouses and ensure efficient delivery. In short, retail sales data and web traffic data help businesses forecast demand, manage stock, and personalize marketing.
2
Healthcare
Patient data – from medical records to wearables – is used to improve care. Hospitals use analytics to forecast patient demand and manage staffing and resources. By analyzing clinical data and trends, healthcare providers can predict disease outbreaks or identify high-risk patients (enabling early intervention). For example, data models can flag which patients are most likely to develop chronic conditions so doctors can adjust care plans. In essence, analyzing medical and operational data leads to better outcomes and efficiency in patient care.
3
Social Media and Tech
Social platforms live on data from user activity. Every like, share, comment or click is data that algorithms use to personalize content. Social media algorithms "control content visibility, sequence and recommendations based on user data like actions, behaviors and interests". If you interact with certain topics (say, basketball or cooking), the platform will show you more of that content. This data-driven personalization keeps users engaged and drives advertising targeting. In sum, social apps turn massive logs of user interactions into recommendations, feeds, and ad targeting through data analysis.
Business Analytics in Action: The Walmart Example
"Leveraging historical data and predictive analytics," Walmart can strategically place products in stores and warehouses and ensure efficient delivery.
Historical Sales Data
Past sales patterns and trends
Online Searches
Customer interest signals
Weather & Demographics
External factors analysis
Demand Prediction
Optimized inventory placement
A Surprising Insight: Dark Data and Big Data Growth
Beyond these basics, there are some hidden corners of data that even tech-savvy people find interesting:
55%
Dark Data
Surprisingly, organizations collect far more data than they actually use. A Splunk survey found 55% of an organization's data is "dark" – meaning it's stored but never analyzed. This could be old logs, untagged videos, or unexamined sensor streams. In practice, that means a company might only be leveraging 45% of its information! The idea of dark data highlights that collecting data isn't enough; knowing what you have and how to analyze it is critical.
175
Zettabytes by 2025
We're living in the era of "big data". IDC projects that by 2025 the world will have about 175 zettabytes of data (that's 175 trillion gigabytes). To put it in perspective, storing that much data is like millions of times all the books in libraries, but in digital form. Remember, all of it boils down to bits (0s and 1s) in computers. This staggering volume matters for things like cloud storage, privacy, and how we mine data effectively.
Getting Started: Build Your Data Literacy
If you're new to data, the best way to learn is by playing with data yourself. Try using common tools (even a spreadsheet or free R/Python libraries) to explore a simple dataset.
01
Choose Your Tools
There are plenty of beginner-friendly analytics tools: Excel or Google Sheets for starters, and free options like Python's pandas, R, or visualization platforms like Tableau Public.
02
Develop Data Literacy
I encourage you to take an interest in data literacy – that means understanding how data is collected, cleaned, and interpreted. Even basic skills (like making charts or writing a bit of SQL) can open your eyes to the power of data.
03
Practice Makes Perfect
In today's world, data skills are as useful as knowing how to read or write.
Ready to Go From Data to Decisions?
If you want to move beyond theory and actually use data to drive strategic decisions, this is where most beginners — and even experienced analysts — get stuck. Understanding what data is is only the first step. The real leverage comes from knowing how to question data, guide analysis, and turn insights into executive-level decisions.
That’s exactly why I created Applied AI for Strategic Data-Driven Decisioning.
In this course, I teach how to combine data analytics, prompt engineering, and generative AI to accelerate analysis, improve reasoning, and communicate insights clearly — without needing to be a hardcore programmer.
In this course, I teach how to combine data analytics, prompt engineering, and generative AI to accelerate analysis, improve reasoning, and communicate insights clearly — without needing to be a hardcore programmer.
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You’ll learn how to:
- Use AI as a thinking partner for data analysis, not just a code generator
- Apply prompt engineering to SQL, Python, data exploration, and validation
- Transform raw data into strategic narratives executives actually understand
- Make better, faster decisions using AI-augmented analytical workflows
If you’re starting your journey in data — or if you already work with data and want to stand out — this course was designed to give you practical, real-world skills you can apply immediately.
Data alone doesn’t create value. Decisions do.
And the future belongs to professionals who know how to connect the two.
And the future belongs to professionals who know how to connect the two.