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Here are the book will cover:

  • Why you need to master data analysis skill
  • Basic concepts of data analysis not many people know
  • One powerful data processing tool you need to master
  • Basic techniques of data analysis you need to learn
  • Practical and detailed guide on how to analyze data from scratch

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Why You Need To Master Data Analysis Skill

Data is everywhere.

Almost everyday you see data, whatever your profession is.

Especially right now, when data is growing so fast in terms of volume, variety and velocity.

Yes, we are now in the big data era.

It’s that much, that not many people have this skill to get insight from the overwhelming amount of data.

No need to search for data source from outside, the data available in your workplace seems like it hasn’t been optimally utilized.

In my workplace, the employee who have data analysis skill have a better opportunity in their career. Especially if the result of this data analysis is used as presentation material by the head of the division. His career skyrocketed.

This is one real example. The management is in dire need of insight that can give a better change in managing the company’s activities to increase profitability. The upper management won’t hesitate to promote the employee who provide that insight. The opportunity of promotion for the employees with good data analysis skills is better than the average employees.



Want to get the insight than can answer the problems in your field of profession and open your best career opportunity?

Master data analysis skill.

Basic Concept of Data Analysis

I already know what data analysis is, no need to explain again.”

Maybe that is what comes to your mind when reading the title in this chapter.

It’s true, reading theories are not too exciting. You want to jump right into the quick steps chapter.

Go ahead. Use the menu in the table of contents and click to the chapter you want to read.

But, I still recommend you to read this chapter until the end.

You need to know the basic concept of data analysis to make it easier for you to master this skill.

What is Data Analysis

So, what exactly is data analysis?

Data analysis is a process of knowing, understanding, sorting, solving, detailing, outlining data using various ways or techniques such as manipulation, visualization, grouping, comparison, or other techniques so you can get a useful information that can answer a problem and also help in decision making.

Yes, the goal of data analysis is for better decision making. A decision based on information from data analysis not just taken instinctively. A hugely crucial decision that can make a great company turn into a bankrupt company or vice versa.

This makes data analysis an essential skill in almost all fields.

Here are some examples how we use data analysis in business:

Introduction of Starbucks latest coffee product

Starbucks introduced their latest coffee product by watching if the customers love that new product. In the morning when the product start selling, Starbucks monitored various blogs, Twitter, discussion forums and groups about coffee to see the reactions. In the afternoon, Starbucks found out that even though the customers like the taste of the coffee, they think the price is too expensive. Starbucks then immediately reduced the price. In the evening, all negative perceptions about the new coffee product is gone.

A fast response approach like this of course is better compared to traditional approach by waiting daily sales report to come in first which in the end they can see the sales is disappointing. The next outdated approach is by finding out why the sale is disappointing by doing a focus group discussion. And then, a few weeks after, Starbucks then find out that the price is too expensive and then they reduce they price.

Amazon recommendation system

Amazon uses sales data, frequently seen product, and behavior and also other customer preferences to make a recommendation system. This system helps Amazon increase their sales by 29%. If you visit Amazon, in the bottom of the page you will see some recommendation products, the products you’ve seen before, and the best selling product and other interesting product offered by the system.

Chevron’s efficiency in drilling activity

The cost of each drilling in the Gulf of Mexico is estimated to reach $100 million. It’s a huge deficit if there is no crude oil found. To increase their chances of finding an oil source, Chevron analyzed 50 terrabyte of seismic data. With the support of sophisticated computers, storage capacity, and an established analysis team, they increased their success rate to find an oil source from 1 in 5 drillings to 1 in 3.

Data Analysis Process and Phase

To gain information and insight that can result to a better decision making, you must know the processes and steps of data analysis. The steps of data analysis consists of 6 steps.

  1. Define Your Goal
  2. Decide the Measuring Method
  3. Data Collecting
  4. Data Cleansing and Shaping
  5. Data Analysis
  6. Data Interpretation and Communication

1. Define Your Goal

This is the first phase of data analysis process. In your professional field, you must define the goal of the problem that you want to solve starting from the right question. This phase is so vital because a wrong question will yield the wrong information or insight as well.

“Why this month’s total sales is less than the previous month?”

“Why 7 out of 10 on going projects is behind schedule?”

“How to prevent our customers to move to our competitors?”

Those are examples of problems that need to be solved.

By determining the right question, you can focus more in reaching the goal and also more effectively solve the problem.

The data source that will be used later also depends on determining the goal and the right question from this phase.

2. Decide the Measuring Method

You have determined your goal in the first phase, the next is what needs to be compared with and how you measure it.

For example:

“Why this month’s total sales is less than the previous month?”

From this problem, you can determine that the thing that need to be measured is sales. And then, you determine again the metric of sales, amount of goods or amount of money or both.

  • Amount of goods metric: piece, dozen, score, box, portion, etc.
  • Amount of money metric: hundred thousand rupiahs, million rupiahs, thousand dollars, etc.

Measuring can also be compared by the period. Compared to the previous month or the previous year in the same month.

Other than that, you can also think about the possible cause of the problem that can be used as additional measurement. For example in sales, factors like number of buyer, promotion, discount, location, price, can also be used as additional measuring method.

Make sure this measurement is done correctly because later it will affect the quality of the analysis result and decision making.

3. Data Collecting

You have determined the goal and also the measuring method in the previous phase. The next one is to look for and gather the relevant data. Data source can be from anywhere. Internet, internal database, survey, interview, etc.

The example problem from the previous phase. Sales data, promotion, number of customers, can be collected internally. For customer satisfaction survey can be done by questionnaire or by direct interview.

The one thing to remember is you must gather all those data structurally so it can be processed immediately.

4. Data Cleansing and Shaping

From all the phases of data analysis process, this phase is the most time consuming. When you open the data you collected from the third phase, you will find a data structure that is not readily processed. It’s a mess. Duplicates, typo, special characters, uppercase and lowercase letters, empty data or NA, different date/time format, and others are some problems that can be found in a database.

To make it easier to clean and shape this data, you can use a familiar tool, Microsoft Excel. You can also use other data processing software you can use, for example SPSS, SAS, STATA, Python, R or others.

Don’t underestimate this phase. Just make and shape your data as good as possible so you can immediately analyze the data. If the data is not cleaned, no matter how good you are at analyzing data, the result won’t be optimal. Remember, if the input is garbage, the output is also garbage.

5. Data Analysis

After you clean and shape the data, the next important phase is data analysis. The goal of this phase is to understand the data deeper with all the related variables. There is some basic data analysis techniques you can use.

  • Exploration Technique
  • Visualization Technique

Exploration Technique

As the name implies, explore to understand the data. You start by finding out how many rows and columns from the database, and then you see the variable type: character, numeric, or categorical. Followed by summarizing the data so it can show the important information, like the variable or category with the most frequency, highest numbers, average, and other information.

The point is you need to use the functions from the analysis tools to understand the data. Sometimes you need to re-do the process from the previous phase, which is the data cleansing and then come back to this phase. Yes, phase 4 and 5 are iterative processes. This is done so you can easily understand the information contained within the data.

Visualization Technique

You can understand the data easily by representing it in a form of graph like a bar chart, line chart, scattered chart, histogram, and other infographics. The visualization technique is the easiest, fastest, and the most effective technique in showing the information about the data.

Other than that, the result from this visualization technique can be used as a tool to communicate the information and insight.

If there is one data analysis technique that I recommend someone to master for the first time, this visualization technique is the one you need to learn as soon as possible.

6. Data Interpretation and Communication

The analysis result you made must be interpreted. The goal of the interpretation is to convert the analysis result in a technical form into a discovery, information, or insight that can be understood by anyone.

This interpretation result should answer the questions in the first phase of process.

Create conclusion, even better if you can make a recommendation and steps needed to solve the problem.

No matter how good you are at data analysis, it all will be useless if you don’t communicate, recommend, inform, and give insight to your superior, management, client, customer, society, or your stakeholder. This doesn’t mean that data analysis technique is not important at all, but by interpretation and communicating the analysis result, the impact efficiency in giving a solution to a problem will be felt more.

Communication media also have many alternative. You can make your data analysis result in a form of infographic (image file), a writing in a pdf format, presentation slide, spreadsheet, dashboard, and also articles in a blog.

By communicating the data analysis result, the door to opportunity is open wide, inviting your best career in.

3 Steps to Data Analysis Mastery

You already know about the basic concept of data analysis that becomes the foundation and your base in learning data analysis skill in the previous chapter. That concept is an important thing to learn so you can master data analysis skill as soon as possible.

In the next chapter we will discuss how to learn and apply the data analysis technique. You will be guided step by step in how you can quickly master the data analysis skill with these 3 steps.