welcome to the databricks solution
accelerator demos
your fast preview on how to apply these
pre-built notebooks based on best
practices to solve common business
problems
today we will be looking at the solution
accelerator for environmental
social and governance better known as
esg analytics
esg is a data and ai challenge the world
of esg is highly unstructured by nature
if we look at the 40 most commonly
disclosed policies only 10 are metrics
and only 10 are hard numbers
the rest are purely policies initiatives
and mainly texts coming from different
systems
so how do we apply ai to quantify the
unquantifiable
and compare organizations in a much more
data-driven way than a subjective score
in a way that feeds into many different
use cases down the line from responsible
investing to supply chain resilience
carbon footprint reduction and even
reputational risk the lake house enables
processing of unstructured data such as
pdfs combined with ai capabilities
in the first series of esg notebooks we
show you how to extract those esg
initiatives
from unstructured pdf documents using ai
a solution that bridges the gap between
what a company says versus what a
company does
using alternative data we apply this
solution on news analytics and social
media to bridge the gap
and create a holistic view of an esg
practice that you can trust and act upon
this first notebook shows practitioners
how to programmatically extract pdf
documents
extract each and every single sentence
every single initiative
and apply natural language processing
and ai to understand what those
statements are all about
learning a vocabulary that is esg
specific with themes such as diversity
and inclusion code of conduct supporting
communities and green energy being able
to machine learn these themes and move
away from lengthy and verbose commonly
discussed policies to machine learned
initiatives that help us to summarize
complex pdf documents into 24 machine
learning policies that we can compare
we can also compare organizations side
by side based on how much they disclose
in each of those categories
if we take an example of a specific
industry or across your investments or
for your different competitors or your
different suppliers
we can look at how much company a says
they're valuing employees compared to
company b
how much more company c invested in
renewable energy compared to company d
and so on this provides an analytical
framework to help better understand
these organizations and how they differ
in their esg activities
by applying this model in news analytics
we show you how to bring 100 million
news articles in real time to understand
not only what a company is saying
but also what a company does
additionally we also find what the
reception was from the media and social
networks
with news and media analysis as a proxy
we show you how to extract
each and every single article across the
e the s and the g
and bring that real-time view of esg
instead of waiting for year-long csr
disclosures
this can be applied for every single
business large or medium companies
financial services or health care and
understanding the influence one
business's esg negative or positive
practices has on another in an
interconnected global market
moreover how can you act on those
different insights using a market risk
calculation looking at this from a
reputational risk or supply chain
resilience perspective
in this example we show you how to
create a simple synthetic portfolio
and tie those esg insights within the
market risk framework
it is no longer about how good a company
looks but also how performant this
company may be
and confirm what we know from research
and literature that shows that companies
with high esg practices tend to have
lower market volatility
we show in the context of value at risk
that an all synthetic portfolio is two
times more volatile due to bad esg
practices
finally we are able to bridge the gap
between the data science and engineering
world into a bi and ai dashboard
combining all those insights into one
platform to understand how much a
company says about e
s and g versus how much a company does
across each of those 24 machine learned
policies
informs us in real time about news
events that may positively or negatively
affect the esg of every single company
and in turn the impact it may have on
market performance
ready to get started on esg analytics
click on the link in the description
below to go right to our full write-up
for this solution and gain a deeper
understanding of how the data bricks
lake house uniquely solves for the
challenges associated with going from
batch to streaming
and bi to ai or visit the databrick
solution accelerator hub
to see all of our available accelerators
as well as keep up to date with new
launches