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Printable Handouts
Navigable Slide Index
- Introduction
- What is big data
- Four types of big data
- Transaction data
- Customer data
- External data
- Social media data
- Big data applications in corporate finance
- Financial forecasting (goal and benefits)
- Financial forecasting (suggested data)
- Cost of capital (goal and benefits)
- Cost of capital (suggested data)
- Goal of corporate finance
- Risk management
- Mergers & acquisitions
- Thank you
This material is restricted to subscribers.
Topics Covered
- What is big data
- Types of big data
- Financial forecasting and modelling
- Cost of capital estimation
- Risk management
- Mergers and acquisitions
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Bite-size Case Studies:
Talk Citation
Puleo, M. (2017, January 31). Big data in corporate finance [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved October 14, 2024, from https://doi.org/10.69645/PVII3069.Export Citation (RIS)
Publication History
Other Talks in the Series: Business Intelligence, Big Data, and Applications in Industry
Transcript
Please wait while the transcript is being prepared...
0:00
Hi, my name is Mike Puleo.
I'm an Assistant Professor
of Finance.
And today, I'm gonna be
talking about
"Big Data and its Applications
in Corporate Finance".
0:08
Okay, so first of all,
to get started,
what is big data?
You hear this all the time.
It's kind of a buzzword.
There's a lot of talk about it.
But let's be clear and kind of
provide some background
about what we're actually
talking about.
So people would usually
characterize big data
with at least these
three characteristics,
some people add a fourth. But,
first I'm gonna say, the volume.
So there's so much more data now
than ever was before.
I mean, every year
there's more new data created
than it existed
in the entire history
until 1997, right?
So we have so much data now
from so many different sources
that it's really, historically,
it would have been a problem,
right?
Handling and analyzing this data
becomes an issue
in and of itself
because there's just so much of it.
We're talking about
billions of data points.
So that's number one, volume.
And also, the second point
would be, velocity.
So not only is there
an incredible amount of data
more than there was in the past,
it's also coming in very quickly.
So think about how often,
data streams
are always coming in.
There's new data all the time,
Twitter feeds, RFID tags,
sensors, and smart metering.
So this data is not only
an incredible volume,
but it's coming in very quickly.
And again, so accommodating
this speed and amount of data
has historically been
a big challenge.
And it's something
that the people
are working on now
and there's some
exciting developments there.
And the last characteristic
would be, variety.
So there's so many
different kinds of data now.
So with the Internet
and the proliferation of the Internet,
so we have everything
from structured, numeric data
that comes in,
you know, in database form,
rows and columns.
And that's what we like,
and it's easy to analyze,
all the way down to e-mails
and qualitative data.
And just things like
that that have to be processed.
And somebody has to go back
and sort of take from that
what actually can be
used and measured.
So the volume, velocity,
and variety of data,
this is what generally
refers to big data.