What is Data?
Data is an information or fact collected because of
experience, observation or experiment.
It is stored in or used in Computers.
It could be measurement, observation of description of
things. It is statistics collected
during the operation of business. Data
is everywhere.
Data can be measured, collected, reported and analyzed
- into visualization tools graphs / images.
Samples of Data - Employee data, Sales inventory.
There are two types of Data
Qualitative and Quantitative
Quantitative Data:
Can be measured or countable.
Answers - What, who, when, how many and where
Qualitative Data: Acquired through listening to people - observing
situation / recording perception.
On seeing a cup of Coffee -
Qualitative
details
Quantitative Details
1) Robust
Aroma 12
oz
2) Strong 120
degree Fahrenheit
3) Frothy $3:5
4) In
a White cup
How is data Collected –
there are two sources of Data, Primary and Secondary. For eg if I need to run a initiative of hiring
junior level associates for the company, I need data to determine the count to be
hired
Primary Source:
Current demand open for Jr level
Secondary Source: Past what was their absorption and how many
they hired for Jr level in the last quarters/months.
The sources varies for various set of data.
Data are 4 types
- NOIR
Nominal – Data can be organized
into categories, neither a number nor Quantity- Gender / Status / Level
Ordinal – Refer to data
that can be put in an order or rank -
In a running race, we can rank people ignoring the speed and time.
Interval – Data comprised
of consistent interval, here zero exists. Like temperature, pH value
Ratio – Same as
Interval, but if Zero does NOT exist…. Eg Age, Sales .
Why is data important
1) Educate:
It helps you to study and understand the current picture of anything.
2) Prioritize:
Based on this, you will have insights
to improve the
process
3) Evaluate
Progress: Which will
reduce Wastage – Money , Time and man power
Data is more valuable based on
1) Source
of data
2) Clean
/ Rightness
3) Size
4) Age
/ Timeliness
5) Understanding
6) Actionable
Due to the existence of data - we have
various branches of study diverging
Data Analytics –
Process of analyzing raw data, in order to make conclusion about the
information.
Data Mining -
Process of discovering Pattern in large set of data.
Data Science -
process of identifying actionable insights from raw data.
Data Visualization
- process of converting the data into graphical representation
Machine learning /
Cognitive Computing Development – focuses on development
of computer program that can access data and use it to learn for
themselves. EG. Siri uses Advanced Machine
learning technologies to function.
Data Governance
– ensures that the data is consistent and trustworthy and does not get misused.
Data is getting collected constantly
through different sensors. Laptop, smart
phone, IPad.
Data is transformed into AI,
generating various Apps - resulting in more innovation and better user
experience.