The Rise of Data and Analytics
No one can argue that data is big today. Really big. And data continues growing every day -- at
an exponential rate.
According to the nonprofit organization The Conversation, it is estimated that each day we
generate 500 million tweets, 294 billion emails, 4 million gigabytes of Facebook data, 65 billion
WhatsApp messages and 720,000 hours of new content added daily on YouTube.
The Conversation also recorded the following astonishing numbers:
In 2018 alone, the total
amount of data
created, captured,
copied, and consumed
in the world was 33
zettabytes (ZB) – the
equivalent of 33 trillion
gigabytes.
In just two years
(2020), that number
almost doubled to
59ZB. (That's 59 billion
typical 1TB computer
storage drives.)
And by the year 2025,
it is predicted to reach
a mind-boggling 175ZB.
(Keep in mind that one
zettabyte is equal to
8,000,000,000,000,00
0,000,000 bits.)
Having access to all that data is one thing. But the ability to ingest, digest and extrapolate
that data is something entirely different. The human brain -- as powerful as it is -- has a limit
on the amount of data and information it can process at any one time. It is increasingly
difficult for humans to keep up with this torrential flood of data that drives today's
competitive world.
One could argue statistical analytics and data analytics have been around for a very long time.
But it wasn't until the 1940's and 1950's -- with the introduction of the computer and the
advent of the digital age -- those analytics began a whole new transformation. The ability to
capture and synthesize a host of data enabled major advances in planning, production, and
decision-making. Since then, simple statistical models have morphed into sophisticated
algorithms, neural networks, and artificial intelligence.
This paper will look at how these newer analytic models are impacting risk management and
business operations across the enterprise.
LEVERAGING DATA | 2
