Hi everyone, I'm Bryan Lenker,
and I'm the Financial Services Industry CTO at Snowflake.
Today I'll be demonstrating how a hypothetical investment manager
can seamlessly incorporate ESG data into their investment planning workflow using Snowflake
The momentum to adopt and incorporate ESG into financial services workflows has never been higher.
With factors such as the COVID pandemic, continued climate change, and heightened
social unrest, investors and regulators are now expecting financial professionals to identify
and properly evaluate investment opportunities and risk against ESG measures.
Fortunately, the financial services industry is well positioned to be a leader in this space.
For this demo, I will show how to discover and source new sets of third-party ESG data;
how to integrate and apply these new data sets into an existing database of in-house ESG metrics;
how to compare proprietary ESG scores with third-party ESG data; and then apply this data to our portfolios.
By the end of this workflow, we will be able to help investment managers identify any
holdings with deteriorating ESG scores, screen for alternative investment opportunities, and quickly
rebalance their portfolio to meet the needs of their clients.
As you can see here, we have a set of JSON files sitting on object storage.
For our clients, this could be something like S3, Azure Blob
or Google Cloud Storage. For this demo, we have an internal object storage stage that I have
loaded this to, and as you can see, we have these compressed files that include our daily portfolio data.
By just clearing the data as it lies on the internal stage before we've even loaded it into a
proper table in Snowflake, we see our JSON data showing our portfolio--the holdings, the shares,
as well as the in-house ESG score. Later in this demo, we will show how our portfolio ranks against
this proprietary score that our analysts have derived, and how it compares to ESG scores from
the Snowflake Marketplace. So diving in and showing some Snowflake feature functions, here you can
create a table containing a special variant column to load that JSON into. Now let's load the data.
We'll go ahead and scale our warehouse up. Load that data using a JSON file format. Grabbing it
from the stage, putting it in our new table, and then immediately scale that warehouse back down.
With those JSON files loaded, we can go ahead and just take a look at that raw table.
Next, using Snowflake's notation, you can click into our portfolio,
see our holdings--the ticker symbols, the name of the securities,
any trades that have been made in the previous day, and our in-house ESG scores.
i can turn this into a view so it looks more like a normal table
and will be easier for us to refer to later on in the demo. As an example, I can quickly return
this view and look at all of our positions for Tesla across our various portfolios.
Now I'm going to show you how to bring in Factset's ESG data. What you're seeing here is
Snowflake Data Marketplace. We're going to pull from Factset's TruValue Labs SASB Scores Datafeed,
which you can see more info on here, including things like coverage, history, and documentation.
We'd normally click on this request data button, but we've already handled the agreements
for the purpose of this demo. We've received this data from Factset through a share
and harmonized it with our in-house ESG scores. We will focus on materiality, as well as their
“All Categories Insights and Rankings” scores, all of which you can see here in the results pane.
Promoting this as a view, you get to your analytics layer where BI users and tools
will actually report out of. And we can begin to query this view,
looking at our internal ESG score as well as Factset's "all categories" ESG score.
For this next query, we'll look at anything newer than March of last year, and we're looking at the
average internal score by day compared to the average Factset True Value score.
Here, you can see on our results pane that our average internal score is a little
higher than what Factset is providing. We can make this a little easier to see by
visualizing the results quickly with a simple line chart comparing the two metrics over time.
This scoring differential is something that may trigger a
portfolio manager to examine and re-evaluate.
Something that our internal ESG data lacks is scoring around benchmarks.
By utilizing the Marketplace again, we can access this data from Factset.
Here you can see the results of this query returning ESG scores at the benchmark level.
By bringing this data in, our portfolio managers can now benchmark their portfolios against
these different indices. Now, using the Snow Site functionality, we can visualize this in a bar graph.
And you can see that we have been able to quickly go from tabular data to some visuals
to grab insights from, and that, specifically, our portfolio seems to be performing on an
aggregate ESG scoring higher than the S&P 500, but lower than the S&P Total U.S. Stock Market.
With Factset's ESG data merged with our in-house ESG scores, we can also see any constituents that
have dropped in their ESG scores month over month, as this query is showing us.
This insight will help us determine and influence how we construct and rebalance our portfolios.
We can even dig further into specific holdings, as the next query shows,
and understand more about why specific attributes might be declining.
And maybe, in this instance, removing a holding completely from our portfolio.
Finally, we have a dashboard to pull all this information together. We can see our market values
from our portfolios, our internal ESG scores, plus the scores we brought in from Factset through the
Marketplace, as well as the benchmark values. And finally, our top decliners and advancers in our
portfolio based on those combined scores.
Wrapping up, we were able to highlight a number of topics:
Reduce time in data discovery and data acquisition by leveraging the Snowflake Data Marketplace,
lower overall total cost of data ownership with reduced or no ETL, with data onboarding
from on-prem or S3 storage, ease of data management, fast query times within Snowflake, a more efficient
and secure way to share data across internal teams and external counterparties, using Snowflake Data,
sharing capabilities, and an effective dashboard to highlight the effectiveness of all the KPIs.
This overview of incorporating ESG into an investment manager's portfolio construction
and rebalancing workflow is just a quick example of one of many different use cases that can be
enabled by Snowflake and the Financial Services Data Cloud.
Whether you're in asset management, banking, or insurance,
Snowflake powers business-critical workflows by enabling and streamlining
data access, collaboration, and security--and with near unlimited scalability.
and multi-cluster compute, different teams across different lines of business can run
workloads across the front, middle, and back office without contention.
Thank you for joining us today.
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