Table of contents
No headings in the article.
Hello again! 👋🏻
If you are new to this page, then Hi. In that case, I am Sourajita Dewasi. I recently started studying Data Analytics from the famous Google Analytics Course from Coursera. I decided to record my footprints of learning digitally. And my last blog was a Basic Introduction To Data and People Analytics. So, let's begin!!!
Like everything on earth, for a process to be complete, there are multiple small steps or phases one must go through. Similarly, in Data Analysis, we go through the six stages, which are termed as:
- Ask: Questions and Define the problem. This is usually a discussion with the stakeholders regarding the issue they want us to solve. Getting a big picture of things and where the data analysis fits helps deliver better. This is an important step given that if the data analysis is not aligned with the business interest, the time and money investment will go to drain.
- Define issue to be solved
- What is a successful result?
- Prepare: Data by collecting and storing the information: This usually involves preparing a timeline of the analysis process and starting the collection of data from multiple sources. For example, if external data can fill up for the lack of data or if internal data is enough. This is a crucial step to get an overview of the process and set ourselves up for the future. Technically, this step will involve:
- Build a timeline
- Collect data with inclusive employee surveys
- Process: Data by cleaning and checking the information: This involved Data Cleaning to reduce errors. Even though no dataset is perfect, that doesn't mean we can't format it to the best possible case. Removing minor inconsistencies like typos and big problems like inconsistent format while merging two datasets from different sources.
- Clean data to make it complete, correct, relevant and free of errors or outliers
- Analyze: Data to find patterns, relationships and trends: The supposedly most crucial step is to answer the questions asked through analysis and querying the data. It also involves sharing findings and recommendations with team leaders while partnering up with business domain experts or subject matter experts to better understand what the data actually projected!
- Share: Data with your audience: As simple as it sounds, this can be pretty difficult. Most of the time, the audience is not supposed to know or understand data from a massive number of cells without proper visualization. Hence, it is essential to bring insights from data in a slideshow or report that the audience understands and feels comfortable interacting with!
- Act: On the data and use the analysis results
And that's how one of your data analysis project is done and dusted.
In the data analysis, we saw a term called the Subject Matter Experts. So, who are they? You aren't supposed to know all the business you will be analysing as a data analyst! Knowing one or two types of business or industry subjects is excellent, but all is impossible. So, we need subject matter experts.
Subject Matter Experts
- They are needed to help include insights from people familiar with the business problem.
- They can look at the result of data analysis and identify inconsistencies, make sense of grey areas and validate the choices being made.
Another popular job role that data analysts work closely with is Data Scientists! Sometimes they are even termed the same as that Data Analysts. Still, Data Scientists are supposed to create new ways of modelling and understanding the unknown by raw data and create further questions using data. While Data Analysts try to find answers to existing questions by creating insights from data sources.
This is all for today. Coming up, we will discuss various Life Cycles of Data. See you soon!
Thanks for reading! ❤️
If you are studying the same course, let me know in the comments. Also, connect with me on my social: LinkedIn, Twitter or Github!