Alternative Data Newsletter #99 – December 20th, 2018

  • Eagle Alpha Survey: we held surveys at our NYC event on November 29th, 2018 at full capacity. Results from some of the selected polls are presented below. Contact us to learn about other takeaways – enquiries@eaglealpha.com.
  • What will be the biggest challenges working with alternative data in 2019?
    • Budget.
    • Cleaning.
    • Compliance.
    • Conversion.
    • Mapping.
    • Onboarding.
    • Prioritization.
  • Spend on alternative datasets in 2019?
    • 11% said <$100k.
    • 11% said $100-$500k.
    • 44% said $500k-$1m.
    • 0% said $1m-$2m.
    • 22% said $2m-$5m.
  • How many people will you add to your data team in 2019?
    • 13% said 1.
    • 0% said 2.
    • 25% said 3.
    • 63% said >4.

Events You Should Attend

  • 2019 Calendar: Singapore in March, NYC in May, London in October, New York in December. Contact us for further information – events@eaglealpha.com.

What Datasets Are Getting Traction?

  • Quant: this vendor provides a tool to monitor the sustainability of over 7,000 of the world’s largest corporations. The company uses AI to systematically combine over 200 environmental, social and governance metrics with news signals from over 50,000 sources across 15 languages. Our Data Sourcing clients can view the full profile here.
  • Discretionary: this company tracks 10,000 aircraft operators and service companies as well as over 12,000,000 passengers with global flight tracking solutions. Our Data Sourcing clients can view the full profile here.
  • New: this vendor delivers company information, negative news, credit data and lawsuit data for more than 50 million companies in China. The dataset go backs to January 2013. Our Data Sourcing clients can view the full profile here.

Data Science Lab

  • Open Sourcing this weekAdaNet – a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.
  • What we’re reading this weekFrom RankNet to LambdaRank to LambdaMART – Microsoft report on successful algorithms for solving real world ranking problems.

Legal & Compliance, Efficiency Improvements & Best Practice

  • Dataset Costs/Pricing: in January 2019, we will launch a working group for buyside firms to have a better understanding and transparency regarding dataset costs.
  • Standardized DDQ: the first step is for Lowenstein Sandler and Simmons & Simmons to produce the draft documented which then will be reviewed by our Data Forum members.
  • Efficiency Improvements – Standardized Metadata: contact dataforum@eaglealpha.com to get access to the first document standardizing the metadata.

Updates For Alternative Data Vendors

  • Monthly Vendor Solutions Webinars: yesterday we ran our first webinar covering the topics of datasets in demand, dataset pricing and mapping. Contact us to attend the January webinar – dataenquiries@eaglealpha.com.
  • Data Monetization Seminars: we are planning seminars to educate vendors, VCs and PEs on how to successfully monetize data. Seminars will take place in the US (Los Angeles, San Francisco, Boston, NYC) and in EMEA (Amsterdam, Frankfurt, Paris, London). To register your interest please email enquiries@eaglealpha.com.
  • 2019 Events Calendar: Singapore in March; New York in May, London in October, New York in December. Contact us for further information – enquiries@eaglealpha.com.
  • 2019 Roadshows: now is the time to start planning your roadshows for 2019. Roadshows yield higher conversion rates than any event. According to our roadshows in 2017 and 2018, the conversion rates to trials are over 60%. Contact us to discuss this: enquiries@eaglealpha.com.

Altdata.tv

  • Lombard Odier – watch video.
  • Data Capital Management – watch video.

Notable News in the Alternative Data Space