Why big data analytics is indispensable for today’s businesses.
Ours is the age of information technology. Progress in IT has been exponential in the 21st century, and one direct consequence is the amount of data generated, consumed, and transferred. There’s no denying that the next step in our technological advancement involves real-life implementations of artificial intelligence technology.
In fact, one could say we are already in the midst of it. And there’s a definitive link between the large amounts of digital information being produced — called Big Data when it exceeds the processing capabilities of traditional database tools — and how new machine learning techniques use that data to assist the development of AI.
However, this isn’t the only application of Big Data even if it has become the most promising. Big data analytics is now a heavily researched field which helps businesses uncover ground-breaking insights from the available data to make better and informed decisions. According to IDC, big data and analytics had market revenue of more than $150 billion worldwide in 2018.
What is the scale of data that we are dealing with today?
- ·It is estimated that there will be 10 billion mobile devices in use by 2020. This is more than the entire world population, and this is not including laptops and desktops.
- We make over 1 billion Google searches every day.
- Around 300 billion emails are sent every day.
- More than 230 million tweets are written every day.
- More than 30 petabytes (that’s 1015 bytes) of user-generated data is stored, accessed and analyzed on Facebook.
- On YouTube alone, 300 hours of video are uploaded every minute.
- In just 5 years, the number of connected smart devices in the world will be more than 50 billion — all of which will collect, create, and share data.
As an aside, in an attempt to impress the potential here, let me state that we analyze less than 1% of all available data. The numbers are staggering!
Before we get to classifying all this data, let us understand the three main characteristics of what makes big data big.
The 3 Vs of Big Data
Volume refers to the amount of data generated through various sources. On social media sites, for example, we have 2 billion Facebook users, 1 billion on YouTube, and 1 billion together on Instagram and Twitter. The massive quantities of data contributed by all these users in terms of images, videos, messages, posts, tweets, etc. have pushed data analysis away from the now incapable excel sheets, databases, and other traditional tools toward big data analytics.
This is the speed at which data is being made available — the rate of transfer over servers and between users has increased to a point where it is impossible to control the information explosion. There is a need to address this with more equipped tools, and this comes under the realm of big data.
There are structured and unstructured data in all the content being generated. Pictures, videos, emails, tweets, posts, messages, etc. are unstructured. Sensor-collected data from the millions of connected devices is what you can call semi-structured while records maintained by businesses for transactions, storage, and analyzed unstructured information are part of structured data.
Classification of Big Data
With the amount of information that is available to us today, it is important to classify and understand the nature of different kinds of data and the requirements that go into the analysis for each.
Human Generated Data
Most human-generated data is unstructured. But this data has the potential to provide deep insights for heavy user-optimization. Product companies, customer service organizations, even political campaigns these days rely heavily on this type of random data to inform themselves of their audience and to target their marketing approach accordingly.
Machine Generated Data
Data created by various sensors, cameras, satellites, bio-informatic and health-care devices, audio and video analyzers, etc. combine to become the biggest source of data today. These can be extremely personalized in nature, or completely random. With the advent of internet-enabled smart devices, propagation of this data has become constant and omnipresent, providing user information with highly useful detail.
Data from Companies and Institutions
Records of finances, transactions, operations planning, demographic information, health-care records, etc. stored in relational databases are more structured and easily readable compared to disorganized online data. This data can be used to understand key performance indicators, estimate demands and shortage, prevalent factors, large-scale consumer mentality, and a lot more. This is the smallest portion of the data market but combined with consumer-centric analysis of unstructured data, can become a very powerful tool for businesses.
What we can do for you
Whether one is seeking a profit advantage or a market edge, carving a niche product or capturing crowd sentiment, developing self-driving cars or facial recognition apps, building a futuristic robot or a military drone, big data is available for all sectors to take their technology to the next level. Bridged is a place where such fruitful experiments in data are being utilized and we are endeavoring to provide assistance to companies who are willing to take advantage of this untapped but currently mandatory investment in big data.3 vs of big data, about big data, AI, big data, bridged, bridged.co, classification of big data, ML, technology, training data