3 pillars of Data Analytics

What is Data Analytics?

Data Analytics 

takes the raw data you have, 
removes useless records, 
extracts important details, 
converts it to layman readable information,
and summarizes the data into a report or a chart 

You can do this manually, but with data analytics, you are able to trace all the way back to the raw data. It will not forget any step you have taken.

And the beauty of data analytics is that it can repeat the entire process instantly when you update your raw data. It is like automation without programing codes.

But before you progress further, you need to know that data is not limited to numbers. Data is a collection of:

What is data?

But before you progress further, you need to know that data is not limited to numbers. Data is a collection of:

and Audios.

It could be written on papers, computer, electronic devices, inside a person’s mind, etc.

Now for the serious definition of data analytics 

This is from investopedia.com: Data Analytics is the science of analyzing raw data in order to make conclusions about that information.

www.mastersindatascience.org says : Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends.

www.cio.com: Data analytics is a discipline focused on extracting insights from data.

Types of Business Data

Customer Data – Name, ID No, Gender, Age, DOB, Education Level, Job, Salary

Sales Data – Product Name, Product Code, Size, Colour, Qty, Sales Amount, COGS,
Manufacturing Location, Invoice Date

Accounting Data – Sales Revenue, Sales Cost, Expenses, Equipment Costs, Cash,
Investments, Shares, Billing Date

IT Data – Computer Brand, Serial Numbers, Seller, Owner Name, Configuration

HR Data – Employee Name, Employee No, Date of Employment, Department, Salary,
Age, Gender, Education Level

Market Data – Market Potential, Penetration Rate, Market Share, Location

Marketing Data – Campaigns, Campaign Date, Channels (Online and Offline, Retail),
Product Name, Product ID, Budgets, Profile of Customers, Markets, Country

Supply Chain Data – Shipment Data, Order Date, Quantity, Order Amount, Delivery
Data, Origin, Ordered Amount, Order Date

Product (BOM) Data – Material Name, Material Code, Colour, Source, Quantity, Cost,

Admin Data – Office Rent, Equipment cost, Furniture Cost, Utility Cost, Pantry Cost