Everything you need to know about Data Analytics

data analytics

What is Data Analytics?

Data Analytics is the science of making sense of raw data in order to draw contusions and solid information. Many techniques of data analytics are automated into mechanical processes and algorithms that work over raw data before human consumption.

Data Analytics finds its importance in finding trends, patterns, and metrics that would be lost if the data is not properly analyzed.

Information of any kind can be subjected to data analytics, for e.g. manufacturing firms analyze data for runtime, downtime, and work queue for various machines and then optimize the machine towards peak capacity.

Data Analytics has different uses in different industries, like in gaming industries, data analytics is used to reward players at the right time to keep them motivated enough to play. Content companies keep reorganizing their data for you to keep clicking and go to the next topic.

Data analysis process

Determine the data requirements and sort the data. Data is usually separated by age, demographics, people, income, or gender. See what is needed, available and how can it be grouped.

The second step is to collect data from computers, online sources, cameras, environmental sources, or personnel.

After the collection, organize it on a spreadsheet or any other software preferable with statistical tools.

The data is to be cleaned and incomplete data is to be filled. This helps in completely accurate analysis. Check for duplications and errors.

Why it matters

Finding loopholes and eccentric findings in data can help a business to add to its top line and bottom line. It can be used to take better business decisions and help analyze consumer trends and satisfaction which leads to new better products and services.

Types of Data Analytics

Descriptive analysis

Descriptive analysis involves going over what has happened over a given period of time. Have the views gone up? Are sales stronger than the last time checked?

Diagnostic analytics

Diagnostic analytics looks for reasons why something has happened. This kind of analysis requires diverse data inputs and hypothesizing. How’s the marketing campaign holding up? Is snow affecting the beer sales?

Predictive analytics

Predictive analytics moves to what is likely to happen in the coming term. What happened to sales when the new government came in. What are the chances that a new government will be coming in?

Prescriptive analytics

Prescriptive analytics will provide answers and a course of action. For e.g. the winters are going to be particularly harsh and the coffee sales will go up, therefore the factory production is to be enhanced with peak capacity and more raw material.

With analytics, one can accurately follow the six sigma program and pinpoint the defects per million.

Travel and hospitality industries have adopted the use of data analytics, turnarounds can be quick in these industries. Healthcare similarly uses data analytics to make quick decisions. Similarly retail uses copious amounts of data to map the customers’ behavior, identify trends, recommend products, and increase profits.

You can check out courses for the topic at Coursera.

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