The fourth step in the data lifecycle is data
Once we’ve processed our data, we want to
analyze them to create new information that
we can act upon.
There can be many reasons to perform a data
– to provide support for or against decisions
that we need to make,
– to explain observations and behaviors that
we see occurring,
– and to discover new information from patterns
that exist in the data.
There are numerous ways we can analyze data,
We can create reports, which allow us to analyze
both numerical and graphical information.
We can create dashboards, which present key-performance indicators (or KPIs) through
a series of visual widgets.
We can perform interactive data analysis,
using business intelligence tools like Excel,
Power BI, and Tableau.
We can perform data mining, which uses computer
algorithms to find patterns of interest in
large data sets.
We can perform machine learning, which involves
humans training computer algorithms to detect
patterns in new incoming data.
And we can automate data analysis with data-driven
artificial intelligence, which involves machines
teaching themselves how to solve problems
on their own.
There are many ways that we can analyze data,
so it’s important that we choose the right
tool for each type of data analysis we perform.