Lessons from SQL for Data Analysis
Reviewing specific lessons derived from the book; SQL for Data Analysis
Advanced Techniques for Transforming Data into Insights by Cathy Tanimura
The lessons will only be extracted from Chapter 1.
Chapter 1: Analysis with SQL
There are various lessons shared by the author highlighting the significance of data analysis, predominantly influenced by the rapid generation of data, availability of computing tools and storage.
Key lessons I derived from reading the first chapter include:
· Data analysis is composed of three core elements: data discovery, data interpretation and data communication.
· The driving force of conducting data analysis is improving decision-making for humans and machines via automation.
· The centre-piece of data analysis is anchored on answering “why” humans and businesses behave in a certain way — discovering patterns and anomalies present in a given dataset.
· Data analysis can be conducted on data generated from business activities such as sales transactions and analytical processes such as user interaction tracking on websites and mobile applications.
· Almost every industry has been affected by data analytics — finance, retail, telecommunications, manufacturing and education.
· Every business department; marketing, human resource, sales, finance, product development and logistics can adopt data analysis in its processes.
· Emerging career fields such as data engineering and data science have been brought about by the combination of techniques, applications, and computing power.
· The main fodder for data analysis is historical data, hence the time-travel element of data analysis. Analyzing historical data enables a company discover opportunities, weaknesses, and gaps in processes and predict a range of possibilities.
· There is a caveat linked to the backward-looking nature of data analysis; past events do not necessarily/ accurately forecast the future. The world is constantly changing. Business landscape factors such as competition and products evolve. Macroeconomic and socio-political elements also change, this affecting predicted outcomes derived from data analysis.
· Reducing weaknesses in business processes reduces business risks such as fraud.
· Unlocking new opportunities can be in the form of new product developments and optimization of customer experience.
· Organizations that are anti-data analysis are majorly driven by the cost-to-value ratio argument, whereby the cost attached to collecting, processing and analyzing data is greater than the return value expected from the process.
· Secondary reason for companies being adamant to invest in data analysis is ethical considerations associated with data. Ethical considerations linked to collection, storage, usage and disposals of data are mandatory.
· The author reiterates the importance of viewing data analysis as a journey, a lifestyle whose benefits require patience and constant generation of new questions seeking to solve specific business problems.
· It is akin to rinsing outputs of questions answered, and repeating the analysis process based on new questions asked by the analyst.
· Communicating results is an essential skill for any data analyst.
· Persuasive presentation based on simple analysis is more effective than complicated analysis presented poorly.
· The end-product of data analysis is execution of recommendations derived from data analysis process. The social glue that makes it possible for organizations to adopt data-influenced recommendations is partnerships, thus the importance of building meaningful relationships with key decision makers and partnerships with other colleagues who champion for implementing data-driven insights.
The author focuses on Structured Query Language (SQL) technology as a tool for conducting data analysis. SQL is the language used to communicate with databases. The overarching objective of the author involves describing benefits of adopting SQL as a data analysis tool, how SQL fits into the data analysis workflow and various types of data analysis that can be executed using SQL.
The first chapter is just a tip of the iceberg of data analysis using SQL. The subsequent chapters are a must read for any novice or experience data analysts aiming to unlock the magic wand known as SQL in data analytics. The book has 9 chapters only.
If you enjoyed reading the snippet lessons derived from the first chapter that set the tone for the whole book, feel free to grab a copy of the book on Amazon using the link below:
https://www.amazon.com/SQL-Data-Analysis-Techniques-Transforming/dp/1492088781
Disclaimer: I am not promoting the book on behalf of the author. The above review is based on personal opinions.
I do not get paid any commission if you purchase the book. I simply believe every writer deserves monetary gain linked to his or her work.
Feel free to look for free versions of the book.
