Have you ever wondered how the Google search seems to know what you’re thinking before you even type it in? Did you ever give thought to how Walmart determine how much of a product they should order and when? Are you curious about how Youtube displays the content you love?
It’s all because of Data
Walmart gathers data on everything from customer purchases and economic trends. Uses that information to predict what products will sell and when. So the next time you’re walking down your local Walmart, know that the items on the shelves were placed based on the result of a data analytics process. From recommended products to personalized ads. Every click, every search, every like or share, it’s all being tracked and analyzed to create recommendations for you. And while it can be a little unnerving, it’s also pretty amazing when you think about it. The more data we generate, the smarter the internet gets, and the better the user experience gets.
Data is generated everywhere in our digital world. From Instagram posts to financial transactions, we generate vast amounts of data every day. But what exactly is data?
Let’s discuss what data is, and how it is collected, processed, and utilized to benefit.
The Basic of Data: What It Is and Why You Need It?
In simple terms, data is any information that can be stored and analyzed. It can be in the form of numbers, words, images, or even sounds. Data is generated from many sources like websites, mobile apps, or sensor readings from IoT devices.
Data generated from different sources is collected and processed to extract insights. Insights that can help to make informed decisions and improve operations.
These are a few cases where data can be helpful.
- Data can be utilized to identify patterns, relationships, and trends that can help in decision-making.
- Data can be used to track, monitor and analyze performances over time.
- Data can be used to personalize products and services to better meet the needs of a customer.
- Data can be used to identify new opportunities and develop innovative products and services.
Data can come from a variety of different sources and need not be in the same format as all the sources. It can be in the form of text, videos, images, or anything. This is where data classification comes in!
Data Classification: Understanding the Different Types of Data
It is the process of categorizing data into different types based on its characteristics and attributes. It involves identifying the type of data, such as structured, unstructured, or semi-structured. The process helps to better understand the nature and value of the data, which in turn enables us to manage it more effectively.
Data is organized and stored in a specific format, such as a database table. It has a well-defined schema that allows access, analysis, and process attributes easily. Examples: Customer Information, Numerical Data in Sheets.
Semi-structured data is a combination of structured and unstructured data. It has some organizational structure but is not fully defined, like Json Files. Semi-structured data can include data that is not easily captured in a structured format, such as social media data, sensor data, and log files.
Data that has no predefined format or structure, make it more difficult to process and analyze. It can include data such as images, videos, audio files, emails, and social media posts. It is often generated in real time, making it difficult to capture and store in a structured format.
From Gathering to Insight: How Data Is Collected and Transformed into Meaningful Information
Data collection is like gathering puzzle pieces. Each piece represents a small part of a larger picture. The goal is to collect enough pieces to form a complete image. In the same way, data collection involves gathering pieces of information from different sources, such as surveys, sensors, social media, and more.
Think of it this way: When you want to bake a cake, you need to gather all the necessary ingredients before you can start mixing them together. In data collection, you need to identify the relevant sources and gather the necessary data to analyze and draw insights from them.
Once data has been collected, the next step is data processing. Data processing involves transforming raw data into a more useful format. This can involve cleaning and organizing data to make it easier to analyze.
A Few of the steps involved as part of data processing are:
Data cleaning involves removing or correcting inaccurate or irrelevant data. This involves removing duplicate data, correcting spelling errors, and dealing with missing data.
Data transformation involves converting data into a more useful format. Converting data from one data type to another, normalizing data to a common scale, or aggregating data into meaningful groups.
Involves combining data from multiple sources into a single, unified dataset. This can involve resolving data conflicts and merging data with different structures.
Data analysis involves using statistical and machine learning techniques to extract insights and knowledge from data. This can involve identifying patterns, relationships, and trends in the data.
So, now that we’ve got the basics down of how data gets collected, processed, and analyzed, you will see things in a whole new light!
Thanks for reading! I hope you found this post informative and helpful. If you have any questions or comments, please feel free to leave them below.
If you’re interested in learning more about this topic, be sure to check out our next blog post.