This is the pilot post of blog post series ‘Data Literacy for Professionals’, this post covers the context, table of content & links to related posts topic-wise. I would like to mention that most topics mentioned here is a field of study in itself (with further sub-fields), what I am trying here is to give you an overview and an approach on every topic. I encourage you to explore these topics further on your own and build an understanding for yourself.
In previous series about ‘Becoming Data-Driven’, I talked/posted about building Data Strategy, exploiting Emerging Technologies, applying Data Governance & building Data-Driven Culture for a business. While all these aspects are important, I feel building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first & foremost thing is to make every employee, every professional data literate.
“In an organization, data in the hands of a few data experts can be powerful but data at the fingertips of almost every professional can be truly transformational.”
I interacted with many professionals who went through my posts but apart from data professionals/executives, not many were able to fully comprehend what I wanted to convey because of their different backgrounds. While I am talking & posting about a thing which has started to impact our daily lives & business, still not everyone is data literate enough to understand, so there is clearly a data literacy gap.
Hence these posts, where I want to explain everything about data from scratch. What is data? How to define data from different viewpoints? How to do basic data analysis using Excel & SQL? What, why & how of Data Models, Data Architecture, Data Science? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem?
I plan to organize my blog posts under following titles:
- Data Basics
- Data Analysis
- Data Models
- Data Architecture (also covered in Modern Data Architecture post)
- Data Science (also covered in Machine Learning Curve post)
- Data Technology
- Data Governance (also covered in Applying Data Governance post)
- Data Strategy (also covered in Building Data Strategy post)
In the next post we will be discussing about ‘Data Basics’, please stay tuned for upcoming posts in this blog post series.