Commercializing Alternative Data

Commercializing
Alternative
Data

Alternative Data Strategy
Overview
Copyright © 2021 S&P Global Market Intelligence Inc. All rights reserved
Commercializing Alternative Data
Productionizing Alternative Data

What is Alternative Data?
4
Alternative data is
defined by the beholder.
Newly collected, derived
or formatted data are all
considerations.
Someone’s traditional
data may become
someone else’s
alternative data. For
example, something as
simple as news has been
around for years, but by
structuring and delivering
it in a machine readable
format, it opens up the
opportunity for a different
set of users to analyze.
Reviews
&
Ratings
Geo-
Location
Web
Crawled
Trade
Sentimen
t
Estimate
s
Employm
ent
B2B
Online
Search
Satellite
&
Weather
Social
Media
Key Devs
Pricing
ESG
Mobile
App
Usage
Event
Detection
Internet
of Things
Economi
c
Business
Insights
Expert
View
Financial
s
Open
Data
Public
Sector
Data
Aggregators

Top Three Trends and Challenges For Alternative Data
3
Data
Availability
1
Data
Storage
2
Data
Processing
Power
3
Lack of clarity
on what data
exists
1
Data cleansing and
linking requires
significant
technology and
resources
2
Unproven value
and quality of
content
Challenges
Trends

Clients spend up to 10x more than the cost of the initial data
purchase on processes to derive value from data
Sourcing
Scrubbing
Standardizing
Costs in the Client Workflow
Linking
Storing
Analyzing
Size of workflow cost
10X the cost after initial data purchase
Pain Points

Develop, structure and link best-in-class data, enabling our clients to unlock valuable
insights through the convergence of alternative and traditional data.
Proven Value: Quality Over Quantity
S&P takes the heavy lifting out of Alternative Data. Our product
development process ensure datasets go through significant vetting to
ensure the data provides new and valuable insights.
Linked Content & Seamless Delivery
S&P’s ability to link complex content sets, often with point-in-time history,
enables our clients to more quickly integrate cutting edge content and
insights into their workflows.
Datasets to Meet Different Levels of Sophistication
S&P develops datasets that allow clients to ingest structured raw data, as
well as, derived analytics to enable insights across a variety of use cases
and client types.
Alternative Data
Available
Product
Development

Textual Data Suite
80% of today’s data is unstructured, much in the form of text. The rise of Artificial Intelligence and Machine
Learning has led to a growing adoption of Natural Language Processing to help uncover additional insights
from textual data.
SCRIPTS Asia Transcripts
Japanese transcripts from often closed, invite-only financial calls and briefings of 1,000+ Asia Pacific companies, both translated and
transcribed to English available exclusively via Xpressfeed, our powerful data feed solution, and Snowflake’s cloud data platform.
Global Machine Readable Transcripts
Textual transcripts data from earnings calls of 11,000+ companies globally delivered in a machine-readable format with metadata
tagging.
Textual Data Analytics
Sentiment scores and behavioral metrics delivered intra-day derived from applying Natural Language Processing to historical
earnings call transcripts of over 11,000 active companies dating back to 2004.
Global Machine Readable Filings
Textual portions of public filings, inclusive of both EDGAR-filed 10Ks/10Qs and ex-US annual/quarterly filings, broken down into
the various sections, such as Management Discussions & Analysis, Risks, Competition, and Intellectual Property for over 50,000+
active and inactive companies globally.

Global Machine Readable Filings
Value Proposition
► Access over 1.5MM aggregated Annual, Interim, and
Supplemental filings from our global public company
coverage
► Focus on higher added-value textual analysis with our highly
structured format that removes several steps required before
natural language processing (NLP), including sourcing,
downloading, reformatting, parsing, and cleansing documents
Use Cases
► Replicate fundamental analyst workflows across millions of
documents to increase breadth of analysis and identify
documents and sections of highest importance
► Systematically identify themes, trends, and major changes
within a company’s reporting of material qualitative information
► Leverage NLP to quickly and comprehensively find material
changes and uncover unique insights
Dataset Structure
More information available on Marketplace
Each section identified, reformatted, parsed, & cleansed
Machine Readable Textual Data extracted from Annual, Interim, and Supplemental Filings

Textual Data Analytics (TDA)
Sentiment Scores and Behavioral Metrics
Derived from Earnings Call Transcripts
Key Features
Number of Sentences
& Words
Language Complexity
Caller & Analyst
Engagement
Positive/Negative
Sentiment Measures at
Sentence, Section and
Transcript Level

Productizing Alternative Data

Alternative Data Productizing Principles
3
Provide
consistence user
experience across
data products
1
Reduce time
to market
2
Increase
throughput
4
Enhance
value of data
5
Create
derivative
products

Data Science / ML / Analytics
Workbench
S&P Global Marketplace
Analytics Platform Client
Feeds
Cross-div
data
Core
data
Alt.
Data
Raw Curated Consumer
XF Loader
Client DB
APIs
Internal Applications / Tools
Client
Data
BI Tools
Tableau,
Power BI
etc.
Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Global Market Intelligence.
Machine
Readable
Filings
Textual
Data
Analytics
Linking More…
Apps /
Workflows

Marketplace Design Pattern Benefits
A unified data analytics platform – Databricks at its core
Delivered more than 10 alternative data sets in 4 months
Robust data pipeline
Golden copy of data, serving flexible delivery
Acceleration of dataset evaluation through Workbench

Commercializing Alternative Data

S&P Global Marketplace – Go To Market Strategy
Easy Exploration
Clients can explore the full catalog of data
and solutions from S&P Global including sample data and
data dictionaries
Research Papers and Thought Leadership
We develop strong proof statements, research, and white
papers alongside our datasets to help clients understand
insights and analysis that can be derived from each
offering
Sandbox Environment
Robust library of pre-built notebooks that flatten the
learning curve and provide use cases to help demonstrate
dataset value
marketplace.spglobal.com
Focused on Reducing Time to Value

Easy Exploration & Thought Leadership
Below are a few examples of this multichannel execution with alternative data
Storefront Catalog Exploration Webinars & Research

Marketplace Workbench – Powered by Databricks
Pre-built notebooks help users gain an understanding of the data and show insights that can
be derived from the data
Key Features
18
Pre-built notebooks
to show use cases
Collaboration tools
Populated with
S&P Global data
Multi-language
support appeals to
user’s coding
preference

Commercializing Alternative Data

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