Transition into information age

The transition into the information age has brought a massive proliferation of data, but fortunately, at the same time there has been rapid innovation in tools to analyze this data. In past, the focus of most large scale data analysis solutions has been on metrics that are easily measured – the number of visitors to a webpage, what products visitors purchase, how many ‘likes’ certain posts get. However focusing only on things that are easy to measure can mean missing the most important data.

Data Analysis

New methodologies in data analysis seek to change that to tap the potential of a much wider selection of data sources. One of these data analysis technique is Text analytics, also known as text mining, a way of transforming raw, unstructured text into structured data, which can then be measured and analyzed scientifically. It seeks to quantify the sprawling masses of text such as product reviews, customer service interactions, or comments on a product page, and turn it into measurable data, indentifying the “who,” “what,” “when,” “where,” “why,” as well as the emotional tone of conversations.

However, there is a limit to how much text reading can be automated. Having a program go over the comments section on a business’s Facebook page will never be the same reading it personally.

1. one
2. two
3. three
   1. hopes
   2. nexts
4. four
   • sadf
   • asdfa
   • sd

sdfasdf

asd

f

asd

f

sd

f

asd

f

asd

fa

sdf

a

sdf

dsfasdf

asdf

a

sdf

sdf