Just data collection is not enough! It is what you do with the data that counts.
Every organization wants to to know what's happening in their company. How best to tell that if not by collecting data, but just data collection is not enough, it doesn't end at data collection. Data in itself 'doesn't say anything', its just meaningless. However, this doesn't make data collection any less significant. As it happens, there is no data analysis or even data science without data, and there is no data without data collection. This makes data collection a very critical stage in data analysis as the accuracy of data collection dictates the accuracy of the output after accurate analysis.
That established, if an organization or say a business is stunted, or if there is need to spark growth to a growing business, then we are talking data analysis.
What is data analysis?
'Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making.' (Guru99)
Before we can perform any kind of analysis, we need to understand the two types of data we are likely to come across. We will however not go into greater details.
i. Qualitative data - This is data that is addressed in either a verbal or story structure.
ii. Quantitative data - This is data expressed in numerical terms/variables.
Fun fact : )
The best thing about data analysis is that everyone can learn to use data analysis in their work, even those without a background in Mathematics and/ or Statistics. (Of course it will require a bit of investment of time, effort and exercise), but that's about the reason I write this blog, with you every step till together we achieve, yay!
In data analysis, there are key points we need consider.
Purpose >>> what we do and why.
Professionally, you don't just wake up on a good day and decide you want to do analysis on data. One must carefully analyze the purpose/ the aim of conducting the analysis. There is also a need to decide on what type of data analysis is most suitable or simply preferred.
Data interpretation
After data analysis, there is need to communicate/ interpret the results and in doing so we use the results of data analysis. Without data interpretation analysis is just useless, or incomplete if otherwise to say. It is after data has been interpreted that we can make decisions on the best course of actions to take.
Data visualization.
Everyone will appreciate visual data. Data visualization is basically just a graphical representation of the analyzed data in a map or graph. Data visualization makes it easier for the human brain to understand both big and small data.
Data analysis is made easier and more fun by data analysis tools. These tools make it possible for analysts to manipulate data and process data. Some of the commonly used tools are
*R *SQL
*SAS *Python
*Matlab *Tableau
Those into computer science also prefer to use Java for analysis. But as I said, these are just some of the tool used, many more tools are used, SPSS,Stata, and so many more equally helpful tools. The most important thing though, according to me is not the tools one uses for analysis but the results one is able to get.
I think that's enough basic knowledge to get us started on analysis. Excited about our first data analysis? So am I : ). In the next blog we will try to use python to perform some basic manipulations on data.
Love pandas? I know you will
References
Introduction to data analysis handbook - Eric
Data science tutorial https://www.guru99.com/what-is-data-analysis.html