What is Data? Why do we need it?

Did you know? As of 2023, 3.5 quintillion bytes of data is generated every day. In today’s world data is not just important – it’s the new gold. But have you ever wondered why?

Every smallest decision you make is based on some information. Even the dress you choose to pack on your vacation is based on the weather of your destination. You check the weather data, to decide what dress you want to pack. Data is information, and it is used in many things we do every day. Almost every company collects and uses an abundant amount of data to infer things and generate profits. Let’s explore further what are the different types of data which the companies are collecting. 

Table of Content

• What is Data – Understanding with Analogy
• Did you Know? Fun Facts
• Importance
• How to Acquire the Required Data?
• Different Kinds of Data – Explained with Simple Examples
• How To Analyze and Make Sense of Big Data?
• Real Life Use Case in Different Industries
• Why are Companies Analyzing Data?
• Recommended Books and Courses to Learn More

What is Data – Understanding with Analogy

“Without data you’re just another person with an opinion.”

-Edwards Deming, Statistician

Data means any information that can be recorded or analyzed. Here are some examples for better understanding:

• Just like a chef combines ingredients to cook a delicious dish, businesses collect and analyze data to make better decisions.
• Every single piece of data is like an ingredient, which may not be useful on its own but can help to create a useful insight when combined with other data.
• Examples of data include customer information, sales figures, and website traffic.
• Businesses can gain insights into consumer behaviour, market trends, and business performance by analyzing data.
• Data is like the raw material businesses use to make decisions and improve performance.
• Without data, businesses would not know how well they are doing or what customers want.
• By collecting and analyzing data, businesses can create a recipe for success, just as a chef creates a recipe for a delicious meal.

Did you Know? Fun Facts

• 1 GB of data can create 350,000 emails
• 41,666,667 million messages are sent daily on WhatsApp
• Instagram’s users post about 347,222 stories every day
• 1,388,889 Netflix users stream 404,444 hours of video content every day
• Skype has 3 billion minutes of calls per day
• 5 billion Snapchat videos and photos are shared per day
• 333.2 billion emails are sent per day
• People spend $1 million per minute online

Why Companies Analyze Data

The importance of data can be seen in various aspects of decision-making and business operations. Here are some key ways in which data plays a crucial role:

1. Data analysis helps companies make better-informed decisions by providing insights and identifying patterns or trends.
2. Analyzing data allows companies to identify and address inefficiencies, improve processes, and reduce costs.
3. Companies can use insights to identify new business opportunities, such as untapped markets or potential product improvements.
4. Analysis can help companies track user behavior and preferences, which can be used to improve customer experience and satisfaction.
5. Analyzing can also help companies stay competitive by providing information on market trends and competitor activity.
6. Companies can use it to measure the success of marketing campaigns, product launches, and other initiatives, and adjust their strategy accordingly.
7. It helps companies comply with regulations and mitigate risk by identifying potential issues or threats.
8. Analysis can uncover new insights and solutions to complex problems.

How to Acquire the Required Data?

Acquiring data involves a few steps, such as the following

• Identifying the type and format.
• Identifying potential sources such as databases, websites, or surveys.
• Evaluating the quality by checking for accuracy, completeness, and consistency.
• Obtaining necessary permissions and access to different sources.
• Collecting and organizing the data may involve data cleaning, data transformation, and integrating data from multiple sources.
• Storing and securing information to protect it from unauthorized access or misuse.
• Analyzing and visualizing using statistical software, machine learning algorithms, or data visualization tools to gain insights and generate actionable results.

Explore:

6 Types of Data – Explained with Simple Examples

How To Analyze and Make Sense of Big Data?

• Collect from reliable sources
• Organize in an understanding way using tables or graphs
• Clean and preprocess data by removing errors or duplicates to ensure accuracy
• Analyze using methods like statistical analysis or visualization
• Interpret to draw conclusions and gain insights for making informed decisions.

Let’s take the example of Netflix and how they can use these steps to make sense and analyze their user information:

• Collect: Netflix collects user information on what shows or movies are being watched, how long they are being watched for, and when they are being watched.
• Organize: They organize this information in a database, arranging it by the user and show or movie title, with columns for watch time and viewing history.
• Clean: They go through the data to check for any errors or duplicates and remove them to ensure the accuracy.
• Analyze: They use data analytics tools to identify the most popular shows or movies, which ones are being watched the most, and which ones are being binged on.
• Interpret: Based on their analysis, they may decide to produce more content similar to the popular shows or movies, recommend certain shows or movies to users based on their viewing history, or adjust their pricing plans based on how much users are watching.

Real-Life Use Case of Data in Different Industries

Recommended Books and Courses to Learn More

1. Books

There are many great books on Data that can help deepen your understanding of the subject. Here are a few suggestions:

1. “Data Science from Scratch” by Joel Grus
2. “Data Smart: Using Data Science to Transform Information into Insight” by John W. Foreman
3. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
4. “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython” by Wes McKinney

These books can provide a great foundation for understanding data and how to work with it effectively.

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2. Courses

• Master Data Management
• Data Engineering Foundations Specialization
• Python for Data Science and Machine Learning Bootcamp
• Serverless Data Processing with Dataflow: Foundations
• Advanced Statistics for Data Science Specialization
• Data Mining: Theories and Algorithms for Tackling Big Data
• Exploratory Data Analysis with MATLAB
• AI Workflow: Machine Learning, Visual Recognition and NLP
• The Complete Neural Networks Bootcamp: Theory, Applications
• U&P AI – Natural Language Processing (NLP) with Python
• IIM Kozhikode Executive Post Graduate Certificate In Artificial Intelligence & Machine Learning

Conclusion

Data is just information that can be collected from different sources like surveys or sensors. This can help us make better decisions by giving us important information we need to know. It’s crucial to make sure that the data we use is correct, important, and safe. This is because if we use wrong or unsafe data, it can cause problems and lead to wrong conclusions. Techniques like statistics, machine learning, and data imagining can help us understand and make sense of the data we collect. By handling data responsibly and ethically, we can use it as a useful resource to improve our lives.

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Contributed by: Aman Raj