Data is very important and al the organizations are running towards data to collect it and store it safely. In today's world "Data Is Gold" and data has the potential to turn businesses into revenue. It can be possible only when if we use data is a perfect manner. Today, there are billions of terabytes of data available this is almost equal to more than 2.7 zettabytes of data that exists in today's digital universe and it is estimated to increase by 180 zettabytes in 2025. To analyze and process data models, machine learning is very important. It involves large dynamic datasets to train itself, test and perform predictive and prescriptive analysis.
Image Source: https://vinodsblog.com
So, this big data or even fraction of this data simply cannot be handled in a traditional manner and requires non-traditional databases, tools, and techniques. As per Gartner, big data is huge in terms of volume, velocity, and variety with information assets demanding innovative platforms for enhanced insights and decision making. Sources of data are everything and it's up to us like how many steps one takes to reach on foot or how much time and how many kilometers one travel to reach their destination by bus/car etc. We can train and learn the consumer behavior by tracking certain patterns and behavioral biometrics. And this will surely give it flying wings. When behavior changes, it raises an alarm and it can detect subtle shifts in the underlying data, and then we can revise algorithms accordingly.
Social media platforms are the biggest inflow data pipes for Global Data Factories (GDF) . The GDF data is the most complex data with variety, speed, and the size which is just humongous. Interestingly, data format is not the same as a data source. The data here could be both structured and unstructured and the insights driving process could be manual or automated. Data Science -adopts/develops appropriate methods to transform data into actionable knowledge, to perform predictions as well as to support and validate decisions. The most dramatic advances in AI are coming from a data-rich or we may call data greedy techniques i.e. machine learning & deep learning.
Image Source: https://vinodsblog.com
Organizing data in particular data framework as a way for businesses to understand their data assets is an easy way to make use of it. It's just recently that companies have begun to analyze data to glean insights that can help improve their businesses. That's why more organizations are seeking professionals who can make sense out of the large volume of data. Today's data has the answer for most of the things if not everything and can be quantified and tracked easily. Machine learning requires lots of data to create test and √?¬Ę??train√?¬Ę?? the AI. So, what is the best direction? The answer lies in the analysis of future technologies developed within the 3GPP framework, FinTech, Artificial Intelligence, and AGI, Machine learning & Deep Learning. This means (though list is endless):
When someone is likely to get married/divorce or even downfall?
When the factory will have a power outage or fire?
What will the temperature be next day or week or even on a particular day in future?
How my followers' trend may look like in the next three months?
How would the health of the person be based on data and environment?
What would be the sales next month?
Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami-like data flow industry of √?¬Ę??Payments√?¬Ę??. FinTech, Insure-Tech, Med-Tech are the major data generating industries involving a massive group of factories. According to a Google source, it shows in the face of increasingly complex reality often characterized by large amounts of data of various types (numeric, ordinal, nominal, symbolic, texts, images, data streams, multi-way, networks etc.) and coming from disparate sources, it is just a matter of practicing your newly-found skills well enough to become proficient.
The art of data analysis right here as big data analysis is about answering questions. It gets generated in millions of gigabytes. Therefore, the biggest challenge it throws is; How to manage it. Data alone is meaningless as it changes fast and comes in different forms that are difficult to manage and process using any relational database management system (SQL or Oracle databases) or any other traditional technologies. So, the technologies which are developed to deal with big data solutions, like Hadoop, Spark, and no SQL are complete if not different, but surely separate from small data solutions like SQL or Oracle databases.