What is Data Analytics?
Updated: Mar 25, 2022
A basic introduction to data analytics and the role of a data analyst.
First, what is data? Data is a collection of facts or information. And data analysis is how to use data to draw conclusions, and make predictions and decisions.
Data analytics, according to Investopedia, is the science of analyzing raw data to make conclusions about that information. In order words you are collecting data, and with it making informed decisions.
According to Google in its Data Analytics Professional Certificate Program there are 6 steps to analyzing data:
Ask questions and define the problem;
Prepare data by collecting and storing the information;
Process data by cleaning and checking the information;
Analyze data to find patterns, relationships, and trends;
Share data with your audience; and
Act on the data and use the analysis results.
And by using these six steps data can help reveal trends and metrics that would be lost in the mass of information. Especially today when most of our data is digital.
Data analysis helps businesses optimize their performance by making data-drive decisions because they are using facts to guide business strategy. Businesses use these analysis to improve processes, identify opportunities and trends, launch new products, serve customers, and make thoughtful decisions. The process typically begins with a problem that needs to be solved, then a data analyst comes in and uses it to uncover trends, patterns and relationships. From this the analyst suggests a data-drive strategy to solve the problem.
Data analytics is under the discipline of data science, which is defined as creating new ways of modeling and understanding the unknown by using raw data. Generally, there are three discipline: (1) machine learning where you focus on automation and using artificial intelligence; (2) statistics where you make a few important decisions under uncertainty; and (3) analytics where you analyze past data to make suggestions for future decisions, and is filled with ambiguity.
You've most likely analyzed data in your life. If you have a budget for your finances you've looked at past data (your bank statements, previous transactions) and you made a decision based off of it to spend only a certain amount of money. Or if you haven't sat down consciously and looked at past data you've identified patterns and relationships in your life, maybe you know how much sleep you need at night (at least 7 hours for me), or how you feel after eating certain foods, or when you're most productive.
Who are Data Analysts?
Data analysts are the people who analyze the data and help others make data driven decisions. They can work for a variety of different domains but the most common are in: healthcare, technology, and business. Typically data analysts not only have knowledge in mathematics/statistics along with the tools listed below, but they also have business/specific domain knowledge. For instance, a data analyst in the health care has not only statistical knowledge but they also have knowledge of the complex healthcare system, and maybe even a medical background as well.
Skills of Data Analysts
Skills can be broken up into two categories: hard skills and soft skills.
Hard skills refer to a person's knowledge and specific skills in their domain; and
Soft skills are character traits and interpersonal skills that characterize a person's relationship with other people.
Below I will list the hard/technical and soft skills data analysts typically have.
You have at least basic knowledge in these four skills:
Spreadsheets - columns and rows that contain data; common tools: Google Sheets and Microsoft Excel.
Structured Query Language (SQL) - used to store, organize, and analyze data, a language that can communicate with databases; a few of the languages are: MySQL, Microsoft SQL Server
Data Visualization - graphical representation of information; some specific tools are: Tableau, Microsoft Power BI
Programming Language - used for more advanced data analysis; common programming languages for Data Analysts are: R and Python
Soft skills tend to be overlooked in technical/STEM fields but they are just as important.
Problem solving - being able to figure out a problem quickly and efficiently
Teamwork - being able to work with a team to complete projects and tasks
Communication - being able to communicate with others about your findings, whether that be through presenting, emailing or talking with another person one-on-one
Essentially data analysts analyze data to help businesses make informed decisions. This article was also a way for me to practice technical writing, a skill I'm trying to develop. Let me know in the comments below or email me at email@example.com anything else you think I might've forgotten.