Deciphering Data to Uncover Hidden Insights — Understanding the Data

Is This Article Series for Me?

This article is meant for everyone! This includes students who just want to familiarize with general concepts, professional data analysts who want to learn new ways to analyze data, and business decision makers who want to know how to get better insights from business data. If you are not familiar with big data and analytics, you should browse for our free e-learning classes on this subject, and try the Alibaba Cloud Apsara Cloud Certifications to consolidate your knowledge.


This article covers the overall process of deciphering data from conceptual, practical, and best practice perspectives. Anyone with valid data can use this article as a guide to get insights from data with the help of open-source technologies. However, if you are doing data analytics for business intelligence, I strongly recommend using Alibaba Cloud QuickBI.

  1. Create Account in Alibaba Cloud.
  2. Add a valid Payment Method to your account.
  3. Enroll yourself for free trial of QuickBI Pro in your console.

Overview of the Article

For this article, we are going to be looking at:

  1. Domain — BFSI (Banking, Financial Services, and Insurance)
  2. Modules — From Understanding Data to Visual Stories
  3. Use cases — ATM Analytics, Customer 360
  1. Understanding the data
  2. Wrangling the data according to your business scenario (if needed)
  3. Ingesting the data
  4. Modelling the data
  5. Visualizing the data

Understanding the Data (Conceptual)

When it comes to big data, more data isn’t necessarily better. Your data is only as good as your ability to understand and communicate it, which is why understanding the data is so essential.

  1. What do you do with it?
  2. What should you look for?
  3. Which tools should you use?

What Do You Do with It?

We should analyze the data to understand the domain it belongs to. With the domain in mind we should ask right questions against the data to get insights out of it. For example, if the data shows ATM location details, transaction type, number of transactions, and transaction amount, it clearly depicts the data belongs to the BFSI domain.

What Should You Look For?

We should look for some “interesting” insights. As we discussed earlier, we need to ask right questions against the data to understand it better and decipher insights.

What Tools Should You Use?

We need to choose the right tool to wrangle, process, visualize the data effectively. There are lot of tools available in market, all of them with their own unique strengths.

  1. Quick BI allows you to perform data analytics, exploration, and reporting on mass data with drag-and-drop features and a rich variety of visuals.
  2. Quick BI enables users to perform data analytics, exploration, and reporting, and empowers enterprise users to view and explore data and make informed, data-driven decisions.

Understanding the Data (Practical)

As we discussed earlier, we are going to understand the data better with real use cases.

UseCase-1: ATM Analytics

Here we will use the data from ATM Dataset.

  1. no_of_withdrawals
  2. no_of_cub_card_withdrawals
  3. no_of_other_card_withdrawals
  4. total_amount_withdrawn
  5. amount_withdrawn_cub_card
  6. amount_withdrawn_other_card
  1. atm_name
  2. weekday
  3. festival_religion
  4. working_day
  5. holiday_sequence
  1. Total number of transactions
  2. Total transaction amount
  3. Top 5 ATMs by transaction volume
  4. Top 5 ATMs by transaction amount
  5. Lowest 5 ATMs by transaction volume
  6. Lowest 5 ATMs by transaction amount
  7. Number of different transactions by ATM

UseCase-2: Customer 360

Here we will use the data from Customer360.

  1. Balance
  2. Duration
  3. Campaign
  4. Pdays
  5. Previous
  1. Age
  2. Job
  3. Marital status
  4. Education
  5. Default
  6. Housing
  7. Loan
  8. Contact
  9. Day
  10. Month
  11. Poutcome
  12. Deposit
  1. Balance by job
  2. Balance by marital status
  3. Loan by age
  4. Loan by job

Understanding the Data (Best Practices)

Here are some of the best practices when trying to make sense out of data, particularly data relating to the two use cases above.

  1. Determine the appropriate domain, and understand the domain basics.
  2. Always ask right questions about the data
  3. Which ATMs fall under the Transaction Volume Benchmark?
  4. Which ATMs fall under Transaction Amount Benchmark?
  5. Which ATMs fall under Hit Rate Benchmark?
  6. Which ATMs perform well irrespective of External Influences?
  7. Top Violators
  8. Income or Profitability of ATMs
  9. Have a clear understanding of Facts and Dimensions.
  10. Name the columns meaningfully.
  11. “Job” as “Job Category”
  12. “Marital” as “Marital Status”
  13. “pdays” as “Previous Days”
  14. “poutcome” as “Previous Outcome”
  15. Name the columns in sentence case and always use space instead of underscore
  16. “Job_Category” as “Job Category”


I hope that this article gives you a better grasp of the basic principles on data analytics, specifically on understanding your data. If you want to know more about big data and analytics, I highly recommend the Alibaba Cloud Apsara Cloud Certifications. You can advance your skills by learning, and even earn official Alibaba Cloud certifications to demonstrate your professional competency.



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