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10 Tips to Avoid an Alternative Data Hangover

This year’s Battlefin Discovery Day 2018 was held in Miami where global industry leaders shared their extensive knowledge on alternative data to an audience of banks, hedge funds and asset managers.

This year’s Battlefin Discovery Day 2018 was held in Miami where global industry leaders shared their extensive knowledge on alternative data to an audience of banks, hedge funds and asset managers.

The Keynote presentation was delivered by RavenPack´s CEO and Co-Founder, Armando Gonzalez, offering an informative, insightful and thought provoking presentation on the 10 Tips to Avoid an Alternative Data Hangover. He has come up with 10 key questions that financial institutions should be asking when they decide on an alternative data vendor.

1. Structured Data

Buy-side firm´s should be looking for alternative data vendors that pre-process unstructured data to deliver data in a 100% machine readable, structured format, regardless of the data type.

2. Get a Full History

A lot of these alternative data providers are relatively new, consequently, they have only been storing data for a short amount of time, which makes proper backtesting difficult or impossible. Contrary, RavenPack has more than 18 years of backtesting data.

3. Alternative Data Mishaps

The business of alternative data is not a perfect science, sometimes the vendor was not able to store data when it was actually generated. It’s better to be transparent about the gaps or data integrity issues so the consumer can make an informed decision on whether they want to use that part of the data or not.

4. Get Proof of Research

Some of the new vendors have limited or no research demonstrating the value of their data. Consequently, the vendor ends up putting all the burden on the customer to do all the early stage research on their side.

5. Context Matters

When you look at unstructured content such as text, the natural language processing (NLP) engine used needs to understand finance. So much so, vendors can build their own dictionary, as RavenPack has, of financial terms that are only used in this domain.

6. Version Control is Essential

The vendor must ensure to version control their process as either technology improves or their production methods change, else future results are more likely to vary from backtesting performance. Here at RavenPack, we still support 3 older versions in production of our data and technology.

7. Point-in-time Sensitivity

Point-in-time sensitivity is about making sure your analysis only includes information that was relevant and available at any given point-in-time, otherwise you add forward looking biases to your results.

8. Data Maps to Tradable Securities

Most alternative data out there is not about financial securities. Users need to figure out how to relate this information to a tradable security like a stock or bond.

9. Fast and Innovative

Alternative data analytics and AI is a fast moving space, there is a lot of competition amongst companies and technology is changing dramatically every year. To stay innovative and competitive, RavenPack has a dedicated full time Data Science team, working with some of the world’s largest banks including JP Morgan, Deutsche Bank and over 40 academic institutions, to continuously conducting research and development in the analysis of unstructured data.

10. Make Sure the Data is Legal

Both vendors and clients must truly understand where their information comes from and where it’s being sourced, sand ensure it doesn’t violate any laws.

Hopefully data buyers looking at using alternative data in finance find these 10 tips useful. At RavenPack, we ensure data is public, legal, and sourced correctly, we pay royalties to the publishers that give us content, we attribute back to the sources, we make it visible and transparent to clients, we have been an innovator from the start of the alternative data movement, we contribute 80% of our budget to research and development and finally, we have been innovating in NLP, Machine Learning, and the use of Big Data and Cloud technologies.

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