Data Analytics 101 — Basics of Data Analytics for Beginners
Did you know that 2.5 quintillion bytes of data is created every day? If not, this is just the tip of the iceberg. We are producing data at an accelerated pace each passing year. Data has become the new wealth in today’s Big Data era.
Its not the one who has the better technology but the one who has better data that wins the race.
All the companies in the world are now competing against each other to safeguard and make the most out of the data they have gathered. This is because large amounts of data show patterns and trends which can drive business decisions. This answers the question of why Data Analysts are one of the most sort out roles in the market. Data Analytics can be applied anywhere and everywhere. It unlocks potential right from healthcare to cosmology.
If you're new to Data Analytics, which everybody is at some point, then you’ll get to know the basics of it and the various phases it involves. I will be going into detail about all the phases of the data analytics process in my upcoming articles.
Let’s get started!
What is Data Analytics?
Data analytics is the process of examining data sets in order to find trends and draw conclusions about the information they contain.
For example, the data from consumers of an e-commerce store might indicate the products in which the customers are interested. This conclusion from the customer data might help the organization to increase the stock of that product or make an important business decision.
Before we begin, the very first step of the Data Analytics process is to define the problem you’re trying to solve. This is a critical step before we begin since it points us in the right direction for the upcoming phases.
Defining a problem can be challenging. Especially when you have multiple aspects under consideration for the analysis. But defining the problem solves half of it. A simple way to address this is to ask yourself a couple of questions
1. What is my current position?
2. What is my goal?
3. What is stopping me from achieving my goal?
By asking these questions, you should be able to define the problem statement.
For example, for a company facing high attrition, the HR personnel might want to find out the reason. So they answer these questions
1. What is my current position? — All time high resignations
2. What is my goal? — Achieving employee satisfaction and good employee retention percentage
3. What is stopping me from achieving my goal? — Employee dissatisfaction
So by the end of these 3 questions, we can define our problem statement for our analysis which is
What is causing employee dissatisfaction?
We use this problem statement as a base to proceed with our analysis, in this example, an employee survey.
Phases of Data Analytics
There are no fixed steps of data analytics for every problem statement. But according to the Google Data Analytics course, the below 6 steps should address most problem statements.
Let's go through each one briefly
This phase includes talking to stakeholders, process owners, and management to get a fair idea of the problem to solve.
In our example, the data analyst might talk to the HR department, Project Managers, and the Management to enquire about the employee dissatisfaction and help define the problem statement.
The prepare phase of the analysis deals with gathering data from various data sources.
Going back to our example, the data analyst would create an employee survey and circulate it throughout the organization.
The real-world data is not as clean as we think. This phase of the data analytics process deals with cleaning, transforming, and bringing the data to a useful and analysis-ready state.
Common tasks include formatting, addressing blank values, typos, etc.
The actual analysis phase starts here. Once the data is ready for analysis, data analysts work their magic on the data set and uncover patterns/trends. More importantly, the answer to the problem statement is found in this stage.
Note that some additional formatting, sorting, and filtering are done during the analysis phase to view a better dimension of the data set.
In our example, the reason for attrition such as compensation, projects, management, etc. is uncovered during this phase.
The findings from the previous stages are conveyed to the management in the form of visualization like dashboards, graphs, charts, etc. This phase holds importance since there is a need to hold the management's attention to present our findings.
Going back to our example, issues such as compensation, projects, management, etc. is presented to management through dashboards by comparing the above metrics with market standards and verticals.
what's the use of these findings if they aren't acted upon? The final phase of the data analytics process is to act on the findings and resolve the issues causing the problem statement which is the high attrition rate in our example.
Data Analytics is a powerful way to help businesses grow. Hope you have an understanding of basic concepts and how they impact the overall analysis process.
Check out my other articles on Blockchain and Machine Learning/Deep Learning. Let me know about any other topics to cover in the future!
Catch my previous article here 👇