Login

Lost your password?
Don't have an account? Sign Up

Understanding Big Data

“Big data” this term is used quite frequently whether it is in business or industries. But irrespective of the fact that you belong to the tech industry or not, the term big data is the future of every business. So here’s our beginner’s guide to understand what exactly big data stands for, how it is used across organizations, what good it does and how it can be the future of your company as well.

What is Big Data?

Big data refers to any kind of information or data sets whose size or type is beyond the ability of a traditional relational database to capture, manage and process the data with low latency. That is traditional computers or regular systems and tools fail to process or store such data sets. 

Big data is also about the technology that helps collection, processing and organising information which may be structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.The characteristics of big data usually includes high volume, high velocity and high variety. A high volume of data may be gathered from many sources; data is often processed with great velocity, even in real-time, to provide the most valuable analysis; and, the data can come from a wide range of sources, even in a variety of formats.Recently, two more Vs have been added – veracity and value. Is the data consistent and complete, or can its veracity be trusted? And just because it’s classified as big data, does its analysis bring value to an organization?

Where does this data come from?

The data sets are usually derived from the Internet of things (IoT), mobile devices, sensors, video/audio, networks, log files,  social media, transactional applications which are generated in real time on a very large scale. 

Think of the data an individual creates on a day-to-day basis. Data is generated every time any person opens the browser to search something, every time a customer buys something online or even browses a shopping platform.  You generate data every time you go online and do a search on your computer; every time you use your GPS on your smartphone; every time you interact with a social media platform; the list goes on. Every digital activity leaves a digital footprint behind, and the organizations with which you interact collects and analyzes that data.

Here are some other examples where data is collected in massive amounts:

  • Huge retail chains like Walmart and Costco store customer transactions.
  • Social media sites like Facebook and Twitter store and access user data.
  • Amazon analyzes customer data to provide product recommendations.
  • Spam filters go through millions of emails every day.
  • Mobile phones generate information through calls, texts and browsing, as well as GPS data.
  • Satellite technology stores and collects thousands of images every day.

So what massive data is collected and information is processed?

The analysis of the data is where the real gain lies in collecting and gathering all this data. The analysis helps businesses predict outcomes and form guidelines for decision making to avoid errors. 

Here are a few examples of how data is used:

With the help of AI farmers are succeeding at yielding healthier crops, controlling pests,

monitoring soil, and growing conditions, organizing data and improving a wide range of agriculture-related tasks and creating optimized plans to be followed for best results.

AI is not only helping farmers automate the tasks but is assisting them in cultivating higher yield with fewer resources. With AI and technological advancements in the future this sector will be well-equipped to deal with food production issues for the growing population.

Cities can gather data from the traffic sensors and better organise the traffic flow to avoid jams and delays. In a similar fashion, law enforcement can use data analysis to determine areas that need increased policing, for instance, or to prepare for major events.

But what are the challenges faced? 

Here are some, to begin with

Security: When collecting data, sometimes personal, a lot of security concerns arise. Data encryption becomes a must for all the organisations. The access to such information should remain in the hands of a few who need this data for decision making. Your security news can however be fulfilled by Mego, one such data analytical decision twin that comes with restrictions, authentications in places to create a safe data environment.

Storage: The more the size of the data expands the further the storage requirements go. To ensure proper information (enter the feature of mego that enables storage)

Competence: Many AI based decision support strategies require a lot of rundown time, hands on training and large computers to drive compatibility and bring in efficiency. But Mego is AI driven combinations of optimiser and re-optimisers that can help to quickly narrow down to the next best optimal scenario on a basic computer without hours of running time. 

As we learn more about big data through use and experiments the possibilities to leverage the power of AI will increase despite the challenges, So if your organization truly wants to explore get yourself the AI that works for you. AI that fulfills all your strategic IT KRAs, in ways that work wonders with one platform that enables, empowers and adds value to your organization for several decades to come. Mego is a Decision Support System built to assist your thinking and empower you by de-leveraging tech-support scaffoldings. To be able to reimagine thinking and unleash the true potential of your Enterprise Data Repository. To know more: www.m76analytics.com

Leave a Comment

Your email address will not be published. Required fields are marked *

*
*