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Steilvorlagen 2020:
Keynote by Emmet Kilduff,
Founder and CEO of Eagle Alpha


How Innovative Corporate Use External Data
to Enhance Decision Making

Emmett_Kilduff
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About Eagle Alpha (Chart 2). Eagle Alpha provides data solutions to the buyside, private equity, corporates and data companies whose source of revenue is data monetization.

Established in 2012, Eagle Alpha was one of the first companies to recognize the value of data (non-traditional data). Now Eagle Alpha is the pioneer connecting the universe of data (both non-traditional and traditional). Its clients view Eagle Alpha as a strategic data partner with trusted relationships on both sides of the marketplace. The firm’s data solutions are used by data buyers (buyside firms, private equity and corporates) to make data-driven investment and business decisions and data vendors to gain access to qualified buyers of data.
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The External Data Revolution
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The first major technological shift forward was the Agricultural Revolution (1775 - 1800). Next, in the Industrial Revolution of the late eighteenth and early nineteenth centuries, economic productivity doubled in about 150–200 years (1800 - 1960). Productivity skyrocketed in the Computer Revolution, with a three-fold gain in the half century starting in the 1960s (1950-2010). Now the world may be entering a period of even more rapid productivity gains thanks to the remarkable power which can be gained from data 2010 /2010 - Beyond). According to J.P. Morgan the data revolution is being driven by (1) developments in computing, e.g. the cloud and cheap memory), (2) advances in methods of analysis, e.g. machine learning, artificial intelligence and deep learning; (3) new datasets (data companies and "exhaust" data).

External Data. External data is any data generated from outside an organization. There are already thousands of external datasets that are relevant to corporates. External datasets include categories such as social media, satellite, consumer transactions, geo-location and employment. These datasets are from two primary sources: data companies that are set up to monetize data, and companies that have ‘exhaust’ data to monetize.
Data Volume. The volume of data being created, shared, and stored is increasing at an exponential pace. According to J.P. Morgan’s 2017 paper on Big Data and AI Strategies, “with the amount of published information and collected data rising exponentially, it is now estimated that 90% of the data in the world has been created in the past two years alone" .

Mining External Data for Insights is increasingly important. Only relying on internally generated information can leave gaps. Many syndicated data providers can be an excellent and reliable source of this standardized content to enrich, augment, enhance and even outsource internal data governance efforts. This can include basic company data for business relationships (customer, vendor, partner and prospects), but there are opportunities across other domains such as brand, location and media. Structured content works harder than unstructured content and as Taylor mentioned, “a big benefit of these external sources is they can provide corporates with a well-governed and objective structural framework for entries, hierarchies, segments and geographies.”
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How Corporates use External Data.
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As Adena Friedman, CEO of Nasdaq, noted in February 2019: “I think corporates can use it for competitive analysis. They could use it for researching the next thing that they want to try to build or create. They could understand foundational drivers in the economy to understand how much R&D they should be putting behind new things. For example, alternative data sets could provide insights for clothing companies considering what direction to take their fashion line, or the next type of sauce a big food company should put money behind researching.”

External datasets can be leveraged by several departments within corporates to improve business outcomes (Chart 10).

Corporates refer to alternative data as external data and together with internal data it is an essential element in quality decision-making for various departments. Companies that use more internal and external data sources possess a greater range of inputs and analysis for decision making, leading to improved results. Corporates will be the largest buyers of external data which was emphasized by FactSet’s Chief Executive Philip Snow in an October 2019 Financial Times article when he stated their “fastest growing segment is corporates, business development and investor relations”.

According to Deloitte, external data sources are helping businesses personalize marketing offers, improve HR decisions, gain new revenue streams by launching new products or services, enhance risk visibility and mitigation, and better anticipate shifts in den demand for their products and services,

Figure 4: How Corporates Have Used External Data

External data uses
Examples of relevant external data
A credit union personalized marketing offers for members based on customer profiles developed with the aid of external data
• Traditional consumer demographic data - age, gender, address, etc.
• Social media data
• Geolocation data
A digital media company used external data to better predict and improve employee retention rates
• Postings from job websites
• Government economic and labor sources
• Social media data
A logistics company uses external data to predict disruptions to clients’ supply chains
• Social media data
• Online news postings
• Data from suppliers
An agricultural giant launched a new service to help farmers predict and optimize crop yields
• Geolocation data
• Weather data
• Internet of Things (IoT) data
A grocer uses external data to improve demand forecasting and reduce stockouts
• Weather data
• Data from suppliers
• Economic data and forecasts
Source: Deloitte

The use cases for external data in the corporate space are endless and often unique to each business, industry or corporate department. For example, insights can be obtained regarding competitive intelligence, manufacturing, revenue, customers, people, mobile strategy and R&D.
Departments who rely purely on internal data are reactionary in nature, as internal data is lagged. Utilizing external data to mine insights gives corporate departments the ability to detect changes to market conditions in real-time. Jorn Lyseggen, in his book Outside Insight, outlines how external data will change decision-making in three key ways: (1) it adds forward-looking insights, (2) decisions happen in real-time, and (3) companies measure progress and plan for the future by benchmarking against their competitors.

The table below outlines the types of external data that can be used by corporate departments and the insights that can be gained.

Figure 5: Example Use Cases By Corporate Departments

Purpose
Relevant Department
Example Categories And Use Cases
Customer Insights
Marketing
• Social media data – analyse brand perception.
• Online search data – analysis into customer behaviour.
Market / Competitive Intelligence
Product, Sales
• Satellite – analyse activity at a competitor manufacturing plant.
• Web crawling – crawl competitor websites for pricing data.
Product Development
Product
• Patent data – how much should we be investing in R&D?
• Geo-location data – where should we launch new stores?
Supply Chain Management
Procurement
• Shipping data – monitor output of supplier using HS (Harmonized Shipping) codes.
• Credit data – track account receivables of supplier.
Macro Environment
Board, Finance / Treasury
• Shipping data – insights into FX by analyzing shipping between countries.
• Credit data – track credit levels by sector, state and
country.
People Insights
Human Resources
• Employment data – Natural Language Processing (NLP) analysis of employee comments.
• Employment data – analysis of employment and hiring trends at competitors.
Acquisitions
Board / M&A Team
• Web traffic data – track visitors to‘order page’ of e-commerce site.
• Employment data – analyze sentiment of staff at target.
See What Investors See
Investor Relations
• Consumer transaction data - understand revenue predictions.
• Pricing data – insights into price points and trends.
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As of February 1st, 2020, there were 1,200 datasets in our database that were spread across 28 categories: 24 non-traditional data categories (dark blue) and 2 traditional categories (light blue). We forecast there being 5,000 datasets by the end of 2024, which several of our clients believe is conservative. US-based datasets represent 55% of the database, with the remainder split between EMEA and APAC. Considerable growth in China has been seen of late, with approximately 200 datasets from China-based vendors alone.
Next Part: Case Studies

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