Turning Data into Information: A Deep Dive into Understanding Your Data

Turning Data into Information: A Deep Dive into Understanding Your Data
  • By Subhadeep Bhattacharjee,
    Published on: Apr 14, 2023
  • Updated on: May 05, 2023
  • Analytics

It will take approximately 16 minutes for you to read this article. By the time you finish reading this write-up – there will be 4.8 million credit card transactions, more than 85000 orders on Amazon, and more than 200 million searches on Google. That's data for you! 

Data - is not just a buzzword; it's a driving force that shapes our daily lives in ways we may not even realize. Data affects every aspect of your life, from the products you buy to the services you use. Eric Schmidt, the former CEO of Google, stunned the world back in 2011 by giving a perspective on the use and importance of data. 

According to Eric, between the dawn of time and 2003, mankind created 5 exabytes of data. By 2010 we were creating the same amount every two days. Between then and now, the time has shrunk to just 40 mins! 

Calling today's world data-driven would be cliche. Everything we do, from buying groceries to paying credit card bills and even enjoying our favorite shows on Netflix, we contribute 2.5 quintillion bytes of data daily. Hence, understanding data is crucial for businesses and individuals alike. It is the most valuable asset for any business, especially in the tech industry. It provides insights into our lives, from customer behavior to intended purchases. However, it can be challenging to make informed decisions without the ability to interpret and analyze data effectively. 

What Is a Database?

The success of any business – from a Fortune 500 company to a local online grocer rests on understanding data is becoming increasingly important, and those who can do it well are in high demand. Here, we cover various aspects of the study and understanding of data, database relationships, and their importance in data mining, Big Data, and data science. 

Your business, big or small, generates a lot of data daily. You deal with tons of data, from sales to inventory records, subscriptions, and more. All that data must be stored, organized, and managed electronically for in-depth analysis and understanding. A database is a structured collection of data. Think of it as a digital filing cabinet that keeps track of different types of information and makes it easily accessible when needed.

Databases typically have one or more tables, each consisting of rows and columns. Each column represents a specific type of data or attribute, while each row represents a unique record or instance of that data. For example, a customer data table might have columns for name, address, phone number, and email address, with each row representing a specific customer.

What Are Database Relationships?

Database relationships refer to how different tables in a database are connected or related to one another. These relationships are essential to creating a well-organized and efficient database. Since you generate lots of data from different marketing and sales channels, understanding database relationships is crucial to making meaningful interpretations. There are three types of relationships in a database - 

  1. One-to-One (1:1) Relationship: In this relationship, one record in a table associates itself with only one record in another table in the database. For example, a person's social security number may be associated with only one driver's license number.
  2. One-to-Many (1:N) Relationship: In this relationship, one record in a table is associated with many records in another table. For example, a customer can have many orders on an e-commerce website.
  3. Many-to-Many (N:N) Relationship: In this relationship, many records in one table are associated with many records in another table. For example, a student can enroll in many courses, and each course can have many students enrolled.

To establish database relationships, tables must have common fields or columns that allow them to be connected. 

What Is Data Science?

Most of us use the term data science and use it interchangeably with data analytics. They share some similarities, but they are not the same. Data analytics is a subgroup of data science that involves analyzing and interpreting data using statistical and computational methods to gain insights and inform decision-making. Data science uses statistical and computational methods to gather insights and valuable knowledge from data. It combines domains of statistics, computer science, and industry knowledge to analyze and interpret complex data sets.

Data understanding in data science is vital to make informed decisions. For example, a data scientist might use machine learning algorithms to analyze customer data for an e-commerce company to identify patterns and preferences in customer behavior. This analysis could help optimize marketing campaigns, improve product recommendations, or develop new products tailored to customer needs. Data understanding is a critical component of the data science process. It involves exploring, cleaning, and preparing data to make it ready for analysis.

What Is Data Mining?

Any business needs to gain a comprehensive view of the data and its characteristics, including its size, quality, structure, and relationships. Data mining is the process of discovering patterns, relationships, and insights from large datasets. It involves data collection, preparation, modeling, and evaluation. It typically involves collecting and exploring the data to understand its features and variables, identifying potential data quality issues, and cleaning the data as needed to ensure that it is accurate and reliable.

Data understanding in data mining is crucial to derive actionable intelligence from it. The insights gained through data mining can inform decision-making, identify trends and patterns, and predict future behavior. For example, a retailer may use data mining to analyze customer purchase history and identify buying behavior patterns to improve marketing strategies and increase sales.

What Is Big Data?

Let us now move to understanding what Big Data is. It refers to extremely large and complex data sets that cannot be effectively processed using traditional data-processing tools and techniques. It may include structured, semi-structured, and unstructured data. Various sources, such as social media platforms, e-commerce websites, scientific research, and IoT devices, may generate these data sets.

For example, social media platforms like Facebook, Twitter, and Instagram generate massive amounts of data daily, including user profiles, posts, comments, likes, and shares. Analyzing this data can provide valuable insights into user behavior, preferences, and trends, which helps in marketing strategies, product development, and business decisions.

How to Turn Data into Information? 

We have already covered the basics of data understanding and the various techniques businesses employ to collect and process data. The route to success is turning data collected from different sources into information. Here you aim to derive meaningful insights and actionable information that lets you make data-driven decisions. Here are the five steps involved in turning data into information:

  1. Define your Objectives: First, you must identify the purpose of the information you want to extract from the data. It will help you to focus on the relevant data and to discard irrelevant information. For example, if you own an e-commerce company and want to increase sales. Your objective could be identifying the products that sell the most and understanding the factors influencing sales.
  2. Collect and organize data: Gather the data you need from different sources. You might gather data from various sources, such as your website, sales reports, and customer feedback. Next, you organize it to make it easy to work with. You may need to clean and transform the data, remove duplicates, and format it in a way that makes sense for your analysis. 
  3. Analyze the data: Once you have collected and organized it, you must analyze it to extract insights and patterns. It can involve applying statistical techniques, machine learning algorithms, or data visualization tools to the data. You can use different tools to analyze your data, including everything from Excel to advanced data analytics software. 
  4. Interpret the results: After analyzing the data, you must interpret the results and draw conclusions to drive your sales and marketing campaigns. It involves understanding what the data tells you and how it relates to your objectives. For example, suppose you discover that certain products have higher sales during specific times of the year and that the number of positive product reviews correlates with sales. In that case, you can focus on increasing the number of product reviews and running targeted marketing campaigns during peak seasons to increase sales.
  5. Communicate the information: You need to present the information in a transparent, concise, and logical manner. You can create charts, graphs, or other visualizations to help convey the insights you have gained from the data. It could also involve writing a report or creating a presentation to explain your findings to others. It will help your team members unlock the opportunities you find in the report. 

Final Thoughts

Understanding your data is a critical step in unlocking its full potential. By analyzing the data, interpreting the results, and communicating your findings effectively, you can turn raw data into actionable insights that can make a real difference in your business outcomes. The possibilities are endless! So, embrace the power of your data, and take the necessary steps to gain a deeper understanding of it. 

Turning data into information can be daunting, but the right approach can unlock valuable insights and opportunities. By delving into your data, you can uncover hidden patterns and trends that can help you make informed decisions and drive success in your business. If you need an expert team to help you in this endeavor, Growth Natives will be your right fit. Our experienced team of data analysts can help your organization turn raw data into actionable intelligence. To discuss this, contact us at info@growthnatives.com or call our toll-free number, +1 855-693-4769.

Author Box

Subhadeep Bhattacharjee

Subhadeep Bhattacharjee is a content writer who follows the world of technology and business. With a passion for both cutting-edge tech and the fascinating world of business, he is on a mission to explore the intersection of these two worlds and share his opinion. When not poring over the latest advancements in AI, marketing automation, and cybersecurity, he is following cricketing action from around the world.


Understanding data structures requires learning their properties, operations, and use cases. It requires training in programming fundamentals and algorithms.

Understanding data is crucial for making informed decisions and identifying patterns, trends, and insights that inform business, research, and policy decisions.

To understand data usage, you can start by identifying the purpose and scope of the data being collected. It’s important to understand the data source, the methods used to collect and process it, and any limitations or biases that may affect its accuracy or reliability.

A database is a system that stores, organizes and manages large amounts of data. At its core, a database is made up of tables or collections of related data organized into rows and columns.

To understand data, you can read Storytelling with Data by Cole Nussbaumer Knaflic, The Signal and the Noise by Nate Silver, and Data Science for Business by Foster Provost and Tom Fawcett

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