Cloudploys - blog poster (1)

Data Analytics – Understand the world around you through “Data”

by BusinessDigitalTechnology

I think from the title, you might have the idea about what we are going to read about. Exactly, we are going to read about Data Analytics. 

What is Data Analytics? It is the process of gathering all the raw data and analyzing it to determine the pattern and making decisions. However, knowing just this is not enough to understand the whole concept. So, let us dive into the entire concept of Data Analytics.

Concept of Data Analytics: Data Analytics is a wide term as any type of information can be used in Data Analytics, it helps determine the taste and trends of the market. Analyzed information can be used to help make better decisions and growth of business. 

For example: 

  1. Manufacturing Company gathers machine run time and down time. Through that information, they can analyze and determine the hours of workload so the machine works at its full capacity.
  2. Marketing companies use Data Analytic to figure out the demand, supply, taste, trends, and season of the market and then help accordingly to introduce products and services in the market.
  3. Gaming companies use data analytics to determine the need to make changes in game. Such as, to reward schedules for players who play for a particular time which keeps them active in the game.

How to Analyze Data? What is the process?

  1. Determine the data requirement according to the group, such as, by age, by education, by experience, etc.
  2. Collect data through different sources such as, online, agency, existing surveys, or personnel questionnaires.
  3. After collecting data, organize it accurately to analyze it.
  4. Then clean up before data analytics. This process helps ensure there is no error or duplicate data. After cleaning up, it is then used to analyze Data.

 Types of Data Analytics:

  1. Descriptive Analytics: This type, describes the data in the particular period and helps compare it to the past. For e.g. – Comparison of sales between last month and present month.
  2. Diagnostic Analytics: This type focuses on why something occurred. For e.g. – Did the winter season affect ice cream sales?
  3. Predictive Analytics: This type focuses on determining what will likely happen in future. For e.g. – How will an IPL match affect the environment of Covid lockdown?
  4. Prescriptive Analytics: This type suggests a course of action. For e.g. – this summer we should take this action and sales will increase by 15%.

What are the benefits of Data Analytics:

  1. Optimize and improve business performance.
  2. Helps reduce cost.
  3. Helps make better decisions.
  4. Helps analyze trends, taste, and demand of the market.

 Who uses Data Analytics?

Several sectors have adopted Data Analytics. But to name a few, almost every travel, marketing, accommodation, and almost every service industry uses Data Analytics. Manufacturing industries also use Data Analytics to grow business. Health care industries use it to determine the number of patients and revenue of the particular financial year to compare with previous years.

 We @Cloudploys provide accurate information for you to understand the concept. We @Cloudploys provide service of Data Analytics. Data Analytics is crucial for every business and helps grow and reduce cost. Now, it is on you to decide whether to acquire such effective technique or not. Stay tuned for more!

Artificial Intelligence VS Machine Learning

Difference between Artificial Intelligence (AI) & Machine Learning (ML)

by BusinessDigital

The two trending buzzwords in the IT industry that have evolved imminently are “Artificial Intelligence” (AI) and “Machine Learning” (ML). More so these two words often seem to be used reciprocally.

However, they are not actually reciprocal, but the perception that they are could lead to false information. Therefore, read this article to make an informed distinction between the two.

AI & ML both appear frequently when the topic under consideration is Big Data, Analytics, and other such tech areas which are popular throughout our world.

To loop it together, Artificial Intelligence incorporates and functions tasks using Machines in a way that we would consider “smart”.

Whereas, Machine Learning is an active application of AI propagated by the idea that we should really just be able to feed machines with data and let them learn for themselves.

But how did this idea evolve in the first place?

AI has been an aged concept even the ancient Greek mythology contained stories of mechanical men designed to mimic our own behavior. Soon after this myth, early European computers were perceived as “logical machines” and by performing capabilities like arithmetic & memory, provoked engineers to create even more smart mechanical brains.

With emergence of tech along with our adeptness in understanding how a human mind works, our concept of what constitutes AI has evolved too. Instead of focusing on increasingly complex calculations, work in the field of AI concentrated on replicating human decision making processes and carrying out tasks in ever more human ways.

Artificial Intelligences = Machines designed to act intelligently 

Are often classified into either of two fundamental groups:

  1. Applied AI – This is far more common, for example systems designed to wisely trade stocks & shares, or action an autonomous vehicle would fall into this category.
  2. General AI – This is less common than the former, these are systems or devices which can in theory handle any task. This has led to the development of Machine Learning, considered as a subset of AI, it can be thought of as the current state-of-the-art and this is where some of the most exciting advancements are happening today. 

Emergence of Machine Learning

This was provoked by two fundamental breakthroughs:

  1. Realization – This concept means that rather than teaching computers everything about the world and how to intelligently carry out tasks, it might be possible to teach them to learn for themselves.
  2. Emergence of the internet – The huge increase in the amount of digital information being generated, stored, and made available for analysis.

With these innovations in place, engineers realized that rather than teaching computers & machines how to do everything, it would be far more efficient to code them to think like human beings, and connect them into the internet to feed all of the information in the world. 

About Neural Networks

This has been the key to teaching computers to think and comprehend the world in a human like manner, while retaining the typical advantages it holds over humans like speed, accuracy and unbiasedness.

A “Neural Network” is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize various aspects like graphics or images and furthermore, classify them according to elements they contain.

It works based on data fed to it upon which it is able to make statements, decisions or predictions with a degree of certainty i.e. Probability. The insertion of a feedback loop enables “learning”, upon sensing whether its determination is correct or not, it modifies its future approach. 

ML Apps can smartly “read” text to analyse if the writer of the text is making a complaint or applauding. It can also “listen” to music and determine if it’s happy or sad music, and find music of the same genre. Furthermore, they are trained to even compose their own music with similar themes, or which they sense is likely to be commended by the listeners of the original piece.

About Natural Language Processing

Another field of AI “Natural Language Processing” (NLP) is another thrilling innovation in this era, and one which is heavily reckoning on ML. Elaborating the idea to different heights, i.e. we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. 

NLP Apps attempt to study natural human interactions, both in written or spoken format, and communicate in return using similar, natural language. ML is used here to help machines cope with vast distinctions in human language, and train it to respond in a way that a particular audience would likely receive from another human.