Man Pondering Alternative Data

TripleBlind at Work: Alternative Data

Alternative data is increasingly valuable as many ​​banks and credit unions work with Alt Data like geolocation, IOT, satellite imagery, and ESG data to make informed investing decisions. But, with stricter data regulations, enterprises need to ensure that the data being used will not cause any increase in risk and liability. 

Use Case: How TripleBlind helps firms work with alternative data

TripleBlind works with investment companies to enhance the role of data in their service. With our Virtual Private Data Sharing Solution, investors can process data as they do today, without human analysts seeing it. There’s no additional risk or liability for either party involved; plus, investors are more likely to get access to the most impactful datasets.

Unlike synthetic data or differential privacy, models and algorithms operate on real data, resulting in a more accurate analysis without bias. Unlike manual masking, expensive anonymization steps can be skipped before processing data. The implication is that data like credit card transactions, geolocation, email receipts, point-of-sale transactions, web usage, and mobile app usage data can be used seamlessly and with the utmost privacy built in.

Alt Data Diagram

With TripleBlind, investors become equipped with better investing insights and gain access to new data previously unavailable due to privacy and trust concerns.

 

Related news: TripleBlind Accelerates Data Sharing Capabilities of Alternative Data Platform Eagle Alpha through New Partnership 

Reach out to us at contact@tripleblind.ai for any questions or a free demo! Follow us on Twitter and LinkedIn to be updated on our Use Case Blog Series. 

 

Read more:
TripleBlind At Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data

EKG resolving in TripleBlind logo

TripleBlind at Work: Mayo Clinic

Nearly a year ago, we announced our collaboration with Mayo Clinic and first demonstrated the potential of applying TripleBlind’s solution to accelerate how we develop, test, and deploy AI solutions in healthcare. 

Today, AI model developers at Mayo Clinic are working with eight hospitals across Europe, India and Israel using EKG data subject to various data privacy laws ranging in strictness. They have found that TripleBlind makes collaboration faster, easier and much cheaper, as no Personal Identifiable Information (PII) is exposed, and no manual de-identification is needed outside of TripleBlind’s automatic Blind De-Identification. The process of integrating TripleBlind with Mayo Clinic’s technology was seamless, as IT departments only needed to establish one connection with TripleBlind for each counterparty.

The collaboration utilizes TripleBlind’s novel approach to distributed artificial intelligence model training and inference, called Blind Learning. This technology was invented to allow models to be trained on datasets distributed across multiple parties who are unwilling or unable (due to privacy laws) to send the data to one centralized location where the model training occurs.

The new solution enables all the data being used for training to never be moved, yet it is still usable by data scientists, while enforcing the appropriate privacy regulations, including HIPAA, Europe’s General Data Protection Regulation (GDPR), and the strictest of data localization policies. Through this collaboration with Mayo Clinic, we’ve proven that TripleBlind allows multiple parties to collaborate in real time using real data without incurring significant speed or computational costs.

When data can be used without moving it or exposing the raw underlying information, the process of training AI models on distributed datasets becomes much simpler, faster, and cheaper. From saving intense data cleaning steps, to reduced legal process burden and liability, the benefits extend beyond making current processes easier to unlocking new opportunities to use globally sourced data.

Our collaboration with Mayo Clinic is just the start. For more information about how TripleBlind serves the healthcare industry, watch this short video. If you’re interested in TripleBlind, email us at contact@tripleblind.ai to learn how we can unlock privacy for you and schedule a free demo.

 

Follow us on Twitter and LinkedIn to be updated on our Use Case Blog Series! In the meantime, check out our other use case blogs.

TripleBlind At Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data 

 

Hero Image Leadership headshots of Siddhartha Banerjee, Jay Smilyk, Tim Massey, and Suraj Kapa

TripleBlind Expands Leadership Team as its Private Data Sharing Solution Achieves Commercial Success

Follows recent announcements of $24 million funding and being named a Gartner “Cool Vendor”

KANSAS CITY, MO – December 9, 2021 – TripleBlind, the private data sharing solution that allows enterprises to collaborate via its Blind Virtual Exchange API which ensures raw data is never moved or exposed, has announced new hires to its leadership team. The following new hires bring expertise to the company that will expand partnership, collaboration and business growth for TripleBlind across industries.

These appointments follow several recent TripleBlind announcements, including raising $24 million in an oversubscribed Series A funding round led by General Catalyst and Mayo Clinic and being acclaimed as a “Cool Vendor” in Data Privacy by industry analyst firm Gartner.  

Jay Smilyk joins TripleBlind as Chief Revenue Officer (CRO). He has held C-level and leadership positions at multiple software and security companies, including Sepio Systems, Vectra Networks, Safend/Supercom and Cynet, and has more than 20 years of sales management experience. At TripleBlind, Jay will oversee company revenue growth and management, sales and marketing. 

Suraj Kapa is TripleBlind’s new Senior Vice President of Healthcare, joining after serving for more than eight years in several senior positions at Mayo Clinic, most recently as Medical Director, AI for Knowledge Management and Delivery. He brings more than 10 years of experience practicing as a cardiac electrophysiologist and working with AI for healthcare. In his new position, he will oversee all healthcare business unit operations and own the processes and functions related to the TripleBlind Healthcare Business Unit.

Tim Massey joins TripleBlind as Vice President of Product & Customer Success. Leveraging more than three decades of experience in technology working with large enterprises and startup companies, Tim will oversee the development and execution of TripleBlind’s product strategy and ensure that customers realize maximum value through their use of TripleBlind solutions. He is highly regarded as a thought leader in the industry through his time holding leadership positions at multiple technology companies, including successful Kansas City startups EyeVerify and Handmark.  

Siddhartha Banerjee has been named TripleBlind’s Director of Business Development, focusing on financial services and capital markets. He previously held senior sales positions at Databricks, Wipro and Compact Solutions (acquired by Informatica). Sid brings more than 25 years of experience in global sales, consulting and leadership to the team.

“These strategic hires will allow TripleBlind to accelerate the commercial success our private data sharing solution is achieving today. Each executive brings extensive experience that will propel us to unlock new opportunities in financial services and healthcare, as well as additional markets,” said Riddhiman Das, CEO and co-founder of TripleBlind. 

 

About TripleBlind

TripleBlind offers proprietary cryptographically-enforced privacy for data and algorithms, allowing institutions to collaborate around the most private and sensitive data without it ever being decrypted or leaving their firewall. TripleBlind provides one-way encryption and allows only authorized operations on any type of data, any algorithm, computable by third parties in real-time. TripleBlind never hosts or accesses shared data.  

TripleBlind’s Private Data Sharing Solution unlocks the estimated 43ZB of data that are not commercialized today. The company’s patented breakthroughs in advanced mathematics enable organizations to secure larger and more diverse data sets for innovating enhanced algorithms for medical diagnoses and improved anti-fraud initiatives in financial services. TripleBlind enforces international and regional data privacy standards, including HIPAA, GDPR, PDPR, and CCPA.

 

Helpful links: 

TripleBlind Two Minute Overview Video
Background on Competing Solutions

TripleBlind technology significantly differs from existing solutions and is not based on homomorphic encryption, secure enclaves, tokenization/masking/hashing and differential privacy, synthetic data, federated learning and blockchain. For an overview, a live demo, or a one-hour hands-on workshop, contact@tripleblind.ai.

 

Contact

Victoria Guimarin
UPRAISE Marketing + Public Relations for TripleBlind
tripleblind@upraisepr.com
415.397.7600

Stylized illustration of a neural net in the shape of a brain enclosed in a protective sphere. Neurips

TripleBlind Pleased to Announce Acceptance into the NeurIPS 2021, Demo Track

We are excited to announce that our tool demonstration paper has been accepted to NeurIPS 2021, Demo Track. The paper is entitled: TripleBlind: A Privacy-Preserving Framework for Decentralized Data and Algorithms.”

The paper sheds light on the trilemma of accuracy-privacy-communication challenges for training and inference using decentralized data and algorithms. In particular, we demonstrate two major innovations of TripleBlind: Blind Learning and Privophy. Blind Learning is our training paradigm for neural networks. Privophy is a set of cryptographic protocols that enable efficient privacy-preserving processes; e.g., secure inference-as-a-service. Both innovations enable AI practitioners from all domains (i.e., physicians) to run real-time decentralized training and inference using multi-institutional datasets via a set of semi-automated, easy-to-use APIs.

Our demonstration will focus on three major concerns when it comes to privacy-preserving AI using decentralized data and algorithms: accuracy, privacy, and communication cost. We illustrate how Blind Learning can match the accuracy of centrally trained models without having to transfer any raw data outside the owner’s organization while being more communication efficient than rival solutions, including Federated Learning and Split Learning. We also demonstrate in real-time the efficiency and ease of use of our secure multi-party computation inference protocol.

The audience will also get a chance to interact with our toolset via Jupyter notebooks. The notebooks will be placed on cloud compute nodes that play the role of realistic organizations with datasets and models.

We invite you to join our demo session on Friday, Dec. 10 at 11:20-11:35 a.m. CT

For more information, visit TripleBlind’s webpage at the NeurIPS website. You can also read our tool demo proposal

 

————————————————

The audience can use our APIs to train deep models using these datasets and also perform secure inference in real-time. We will provide five types of notebooks during our demo presentation that allow the audience to try the following tasks:

  • Training an image classifier using CIFAR-10 images distributed over three clients
  • Training a classifier using tabular data distributed over two clients
  • Training a multi-modal classifier using data of different types (text and images) distributed over two clients
  • A private set intersection to find common IDs between two clients using secure MPC
  • A secure MPC inference using a model and data owned by two different parties without compromising the privacy of neither the model nor the data
Money 2020 Recap Hero

Recap – Money 20/20 Europe

This fall, in person events were back (with the proper precautions, of course). TripleBlind recently sent COO Greg Storm, Director Partnerships Sam Abadir, and Manager of Marketing and Business Development Mitchell Roberts to Amsterdam for the largest financial services event in Europe: Money 20/20.

There, the team set up shop in this beautifully TripleBlind-branded booth (see picture below), where they met with financial services organizations dealing with data sharing issues that are either halting their current goals or preventing the pursuit of value-adding, revenue generating projects.

TripleBlind's Money 2020 Booth

At the center of many of these discussions was Greg Storm, who co-founded TripleBlind just under two years ago to address these exact issues. When he wasn’t hosting thought-provoking conversations exploring the in-and-outs of data privacy, Greg interviewed with Open Banking Excellence, a community of Open Banking and Open Finance pioneers, giving his expert opinion on what it will take to allow consumers to gain control over their financial information. You can watch the interview here.

Greg was also invited to participate in a private think tank with industry leaders. The attendees and panelists were hand-picked to discuss the topic: “The future is trustless. What do we need instead?”

Taking place in a cozy speakeasy complete with leather couches, an open bar, and a refreshing absence of glowing screens, the session resembled the Prohibition-era hidden bars in the United States, which attracted people with provisions of banned libations, but sparked incredible discussions on modern issues, as patrons were shielded for just a moment from the distractions of the outside world. 

The session was moderated by Dave Birch (Global Ambassador, Consult Hyperion), a well-known thought leader in the field of fintech. Dave asked the panelists difficult questions on the what and the how of trust as it relates to personal data. 

Co-panelists Louise Maynard Atem, Research Lead, Women in Identity; Felix Gerlach, CPO & Co-Founder, Passbase; and Katryna Dow, CEO and Founder, Meeco contributed their knowledge and experience gained from varying backgrounds to the future-facing discussion. A report documenting their discussion can be found here.

Greg Storm Talking at Money 2020

The learnings were encouraging. The group agreed that something must change to give end-consumers more control over their data. At one point Greg exclaimed, “It bothers me that, in order to view my Uber rating, which is information that should be owned by me, I have to log on to Uber to see it – and if I want to share it with someone else, Uber controls that, too.”

Other parts of the discussion focused on questioning the role of identity verification in the complex modern world. Is proving your identity sufficient or even relevant? In many cases, it is not. Sticking with the bar example, consider a bar looking to avoid serving alcohol to underage patrons. The bartenders do not need to know or care about the identity of the individual, but rather they need to know for certain that the individual is either above or below the age threshold for being served an alcoholic beverage. There are many such examples, begging the question, why is proving identity more important than proving characteristics or facts about oneself? Of course, identity verification still plays a vital role in certain scenarios, but clearly, there is work to be done in this area.

The mechanisms for achieving those results, though, are where things get interesting. How do we give data controls back to individuals without halting progress and innovation? If people have the ability to choose where their data goes and how it is used, won’t they just choose to keep it locked down and private? How do people prove characteristics about themselves without first telling you who they are? That sounds like magic.

That’s where TripleBlind comes in. Our solution allows for data to be “shared” without being transmitted, meaning insights can be gleaned from data that sits comfortably and safely behind a firewall, in the cloud or on premise. In the past, it was impossible to compute over encrypted data without having access to a secret key that decrypts the data. TripleBlind’s breakthroughs mean that the secret key is never even created, and the data is still computable! It’s not magic – it’s mathematically verified one-way encryption. And, it’s the future of data sharing.

By integrating with TripleBlind, platforms designed to allow consumer-control over their data will be equipped with the privacy-preserving tools they need to execute data operations, approved by the consumer, without transmitting any of their data to a third party. Individual features can be proven the same way an identity is proven, in a way that eliminates the trust requirement. Innovation continues, or even accelerates. Privacy is enforced.

It is clear that these problems must be solved in the near future, as the data ecosystem reaches an inflection point on the plot of innovation versus privacy. Listening to experts who tackle aspects of these issues in their daily ventures reinforces that A. the right people are putting their heads together and remaining open-minded about potential solutions, and B. we are getting very close to solving these issues.

We’re excited to be critically involved in this important moment for financial services, and can’t wait for the next opportunity to discuss data privacy with other thought leaders around the world. 

If you are interested in further discussing these topics, please reach out to us here!

TripleBlind Product Interface Example

TripleBlind Updates Virtual Private Data Sharing Solution to Enhance Performance and Simplify the User Experience

Releases Solution Upgrade to Address Use Cases in Financial Services, Healthcare and Life Sciences

KANSAS CITY, MO., Dec. 6, 2021 – TripleBlind, the virtual private data sharing solution that accelerates the ability of enterprises to commercialize data while preserving privacy and enforcing compliance with all worldwide data privacy regulations, has updated and expanded its solution offering to address additional use cases.

With TripleBlind, financial institutions can share data to create a comprehensive view of consumers and create more effective anti-fraud and anti-money laundering strategies. Healthcare organizations can build more diverse patient data sets enabling development of highly accurate diagnostic algorithms, develop better treatments and drugs. 

TripleBlind’s latest upgrade ​​is built to allow data projects to progress as usual, but with an added layer of privacy. The main focus of the solution design is the user experience; the end user should never have to be an expert in the underlying cryptographic primitives in order to use the technology.

Since Python and R are the chosen languages for the majority of machine learning and data scientist users, TripleBlind’s Blind Data Tools and Blind AI Tools support these languages and the powerful data analysis and machine learning libraries made available through each. Using TripleBlind’s Blind Data Tools, the user is able to perform all the typical tasks involved in the data pre-processing pipeline, as well as perform tasks including logical aggregations, redactions, and Blind Sampling to ensure the quality of the data before usage.

TripleBlind’s Blind AI Tools support training a wide range of AI network architectures so that any desired architecture can be built on nearly any data type, without needing to “see” the data. TripleBlind users can also run Blind Inferences on trained models without ever physically exchanging the data or the model, allowing deployment of AI models across the globe.

With TripleBlind’s Blind Compute functionality, data and algorithms stay private and remain usable on automatically de-identified data. The company’s novel cryptographic method of splitting calculations in parts that can be run in parallel saves storage capacity and time, while further boosting privacy. Since TripleBlind’s APIs present themselves as similar to well-known frameworks such as Pandas, PyTorch, Scikit-Learn and Tensorflow, any number of them can be easily implemented and used by third parties. Only a few lines of code are needed to add privacy to an enterprise’s existing infrastructure. 

With TripleBlind’s Blind Query, enterprises can bring together the data they need without privacy risk to them or their customers. Blind Query provides several ways to search, join and analyze data from disparate sources, while TripleBlind’s Blind Algorithm Tools enable the easy distribution of algorithms while maintaining strict security and theft prevention, enabling greater and safer collaboration.

For even greater accessibility, TripleBlind’s updated web user interface now allows enterprises to define and run a no-code drag-and-drop version of Blind Join. It logically aggregates two or more datasets for analysis in real time, without the user needing to look at a single line of code with all the same privacy benefits applied. 

Our Blind Virtual Exchange API is a simple code snippet that ensures raw data is never moved or exposed. Our API enables collaboration between entities, allowing operation on every detail of that data without ever transmitting unencrypted or non-aggregate information, while also enforcing permissions and providing an audit trail of use,” said Steve Penrod, Vice President of Product Development at TripleBlind. “Our Blind Algorithm Tools will empower financial and healthcare enterprises to easily distribute algorithms while maintaining strict security and theft prevention.”

TripleBlind Virtual Private Data Sharing Solution product architecture

TripleBlind’s upgrade comes on the heels of the company announcing $24 million in Series A funding that was oversubscribed and being named a 2021 Gartner® Cool Vendor in Privacy

 

About TripleBlind

The TripleBlind Private Data Sharing Solution unlocks the estimated 43ZB of data stored by enterprises today that are inaccessible and not commercialized due to privacy concerns, operational complexity and regulations. The company’s patented breakthroughs in advanced mathematics enable organizations to secure larger and more diverse data sets for innovating enhanced algorithms for medical diagnoses and improved anti-fraud initiatives in financial services. TripleBlind enforces international and regional data privacy standards, including HIPAA, GDPR, PDPR, and CCPA.

TripleBlind is superior to existing solutions such as homomorphic encryption (slows compute performance), secure enclaves (siloes data), tokenization/masking/hashing and differential privacy (reduces accuracy), synthetic data (not real data), federated learning (limited use for algorithms), confidential computing (requires data centralization) and blockchain (not interoperable). Innovators including Accenture, the Mayo Clinic, and Snowflake trust TripleBlind to protect sensitive data. For an overview, a live demo or a one-hour hands on workshop, contact@tripleblind.ai

 

Media Contacts
Victoria Guimarin
UPRAISE Marketing + Public Relations for TripleBlind
tripleblind@upraisepr.com
415.397.7600

Webinar: The Data Monetization Opportunity (and How to do it Securely!)

In this webinar TripleBlind, the company that offers a proprietary cryptographically-enforced data privacy solution, and Eagle Alpha, the leading external data aggregators, will discuss why and how corporates should externally consider monetizing their data.

The topics to be covered include:

  • An overview of the external data market and opportunities (also known as alternative data)
  • First-hand accounts of successful data monetization strategies
  • The importance of data security & compliance procedures when monetizing data
  • How TripleBlind helps firms safely monetize data
  • How the combination of Eagle Alpha and TripleBlind can supercharge your external data monetization efforts

For this webinar we will also be joined by Toby Dayton, CEO of employment data vendor LinkUp, and Enrique Garcia-Ayesta, CEO of telco data vendor Apeiron Insight.

 

Begin Transcript:

Chris Barnett:

All right. We’ve got a good group of attendees now. And even though a few more people are joining, we’ll go ahead and get started. So, I’m Chris Barnett from TripleBlind. I’m here with my co-host today, Caroline Grace, also from TripleBlind. We’re here to present The Data Monetization Opportunity and How to do it Securely and Privately. We’ve got a great panel today. I’m to just going to do a quick introduction of everybody, and then we’ll hand it over to my friend, Ronan, to get us started. So, Ronan Crosson is the director of data strategy and analytics at Eagle Alpha. He’s here also with Natalie Aitken, who’s head of business development at Eagle Alpha. Some of our guests are Sam Abadir, who’s director of partnerships at TripleBlind. We’ve got Enrique Garcia-Ayesta, who’s the co-founder and CEO of Apeiron Insight, and also Toby Dayton, CEO of LinkUp. So, we’re very pleased to have this group. Ronan, we’ll turn it over to you to get us going.

Ronan Crosson:

Hey, thank you very much, Chris, and thank you very much to TripleBlind for inviting us and having us here today to talk around the wonderful world of data and alternative data. As Chris mentioned, I’m director of data strategy and analytics here at Eagle Alpha. We’re delighted to kick off this partnership with TripleBlind, and hopefully this will be the first of many joint webinars and joint initiatives we do together. So, just for the agenda for today, so I’m going to… Obviously, Chris has done the introductions already. I’m going to give a brief overview of the alternative data market, what exactly alternative data is, and what growth were seeing there. Then we’re delighted to have a CEO panel discussion, as Chris mentioned, with the CEOs of Apeiron Insight and LinkUp. Then we’ll talk a little about legal and compliance. It’s an important topic when considering monetizing your data. And then we’ll get into the meat of how to supercharge your external data monetization opportunity and the benefits of the combination of Eagle Alpha and TripleBlind. We promise we’ll have this all wrapped up within 60 minutes.

Ronan Crosson:

So, Eagle Alpha, so that’s me. We don’t need to do that again. So we are, Eagle Alpha, a global business. We’re headquartered here in Dublin, Ireland, but we have offices all over the world, in New York, L.A., London, Madrid. We’re a global team. Our client base is global as well, with clients in the U.S., Canada, Europe, UK, Asia. We’re well renowned for our alternative data conferences, which we host globally across multiple cities. Our team is structured like many of the data teams that we see at our client firms, particularly when it comes to hedge funds and asset managers. So we have data scientists. We have engineers. We have sourcing experts, and then we have data strategy experts. And then on our data vendor partners, similar to our clients, our data vendors are also global with vendors based in Americas, Europe and Asia.

Ronan Crosson:

Just briefly to talk about what we do just to give you some context, so we are an alternative data aggregation platform and advisor. So, when we talk about alternative data, it’s a term that’s most frequently used in the investment world by hedge funds and asset managers really to mean new, interesting data that wasn’t traditionally used. Within the corporate world, it’s typically referred to as external data that differentiate from data that’s internal within the business. But essentially, we’re talking about new and interesting data sets. And then I’ll highlight some of the categories.

Ronan Crosson:

But what we do, we position ourselves at the center of that ecosystem, where we’re helping the owners of really interesting data sets get that data into market and get in front of key buyers. So asset managers is a key vertical that we service. Much of the conversation today we’ll talk about is selling data into hedge funds or asset managers, but private equity and corporates and other categories of buyer are also within our ecosystem. But essentially, what we’re doing is offering a platform where the owners of the data can find new buyers and get in front of buyers with really targeted messaging. On the buyer side, then similarly, we’re helping them narrow down the massive opportunity of data to find the right data source for their specific needs.

Ronan Crosson:

But anyway, that’s enough about Eagle Alpha. Do reach out if you have any questions on any of that, but just get into the alternative data market and just a little bit of an overview for anyone who’s new to the topic. So, alternative data, there’s multiple ways it can be describe, but we like to think of it as non-traditional data that could be used in the decision-making process. So we categorize it across 26 categories of data. So you can see those in front of us, geo-location, internet of things, consumer credit, consumer transaction, et cetera, et cetera. So these are the data set categories that when we think about alternative data, and it’s often easiest to think of the industry across the industry from those categories.

Ronan Crosson:

So, how much has been spent? So, based on market estimates, there’s about 2.4 billion being spent on alternative data. That’s purely from that asset manager side because they have been the early movers. About 2.4 billion spent in 2021. That’s growing very rapidly. Estimates that that will grow at an almost 60% CAGR out to 2028. So the market estimate, their estimates from Grand View Research suggested that this market could be worth upwards of 68 billion by 2028, so really is a really massive industry and a massive growth industry. When we look at the hedge fund market and look at the buyers there, even though there is over two, three billion being spent, the penetration rate is still relatively low. So we see from analysis from Bank of America, we can see that over half the market still is not working with alternative data and 55% of funds are not using alternative data. So that speaks to the opportunity in terms of the growth, but we are seeing the number of buyers are growing rapidly.

Ronan Crosson:

And so, this is Eagle Alpha’s own intel based on our internal SCRM. What we can see is that the number of buyers 2020 versus 2019 grew by about 45% year over year globally. We’re actually running this same analysis for 2021 as we speak. The asset manager grows. That grew by about 30% in that period, but actually the faster growing area is the non-asset manager. So it’s corporate. It’s private equity. We can actually see that. When we look at the market breakdown in the bottom of this slide, we can see that in 2019, about two-thirds of the market have spend on alternative data was from asset managers. That balance is shifting. In 2020, it was about two-thirds of the market, still the majority, but two-thirds is where it fell by the end of 2020.

Ronan Crosson:

So, we’re seeing that rapid growth, particularly when it comes to the spend from private equity and corporate buyers of alternative data. So, that’s the demand side. When we look at the supply side, similarly, we’re seeing rapid growth in the supply of alternative data. So, this is looking, again, year over year 2019 to 2020. What we saw was actually acceleration of the supply of data from 2019 to 2020, from a 35% growth rate to 45% growth rate, and we expect that to continue to accelerate. The demand is pulling more data. Also, the supply, the growth of technologies, more data being collected is also pushing that supply.

Ronan Crosson:

In terms of where are we seeing demand at the moment in terms of data, because that’s today’s conversation is all about monetizing your data, I’d call out four areas where we are seeing particular demand for data at the moment. First is ESG, environmental, social, and governance. So this really is the single biggest trend in the fund management industry right now, partly driven by demand by their end clients, partly driven by regulations, but really any data can provide perspective on the ESG credentials of business, such as the emissions data or gender wage gap. These kind of metrics are really in high demand at the moment. The next category I’d highlight is consumer transaction. So, consumer transaction data is the most popular data category by spend. It’s accounting for approximately a quarter of all spend on alternative data is in consumer transaction category. So, this is an area where we always see demand and definitely an area where we continue to focus on.

Ronan Crosson:

B2B data, so any data can provide an insight into B2B businesses, such as SME, software, or an industrial sector. That’s in high demand because there’s such a sparsity of data in that area. There’s some overlap there with the transaction data. Any sort of B2B transaction data is the most highly sought after at the moment within that B2B space. There’s a lot of data on the consumer side, less so on the B2B side. And then the fourth category I’d call out is healthcare data. So, this is really data that can provide an insight in the sector, such as pharma, medical devices, biotech, and so on. That’s an area where we see really high demand for that for data and definitely we could match any providers of data. We could match them with the longest of buyers.

Ronan Crosson:

So, that’s the background. What I’m going to do now, as we mentioned, we’re delighted to have an expert panel of CEOs from data vendors. We’re delighted to have Toby Dayton, who’s the CEO of LinkUp, and also Enrique Garcia-Ayesta, who’s the co-founder and CEO of Apeiron Insight. Toby, Enrique, can you hear me okay?

Toby Dayton:

Yep.

Ronan Crosson:

Great. I think you’re on mute, Enrique.

Enrique Garcia-Ayesta:

Yeah. Yeah. Yeah. I can hear you perfectly. Yep.

Ronan Crosson:

Perfect. Great. Well, thank you very much for joining us today. Just for some background, LinkUp and Apeiron are among the most successful and most interesting data vendors that we work with in the alternative data space. Both of them, their backgrounds are slightly different. They have different applications and sector of coverage, but I’m sure we’ll find some commonalities in their story. On that point, maybe, Toby, just to start with you on the point of stories, do you want to maybe just give us a little bit of background on where your data monetization journey started?

Toby Dayton:

Sure. So just as a little bit of background, just give you a quick overview of LinkUp, so we are the leader in delivering job market data to the capital market. So we’ve been in the human capital management space for about 20 years, and we’ve been in the data business for about 10-plus years or so. What we do is we’re indexing job listings directly from company websites around the world every day. So, we’ve got a historical database of job openings, again, sourced directly from the corporations that are doing the hiring. So we go out to right now about 60,000 company websites. We’re indexing about six million jobs every day and updating those on a daily basis. Then we’ve developed a range of data products and solutions that are delivering insights into labor demand at a company level, geographic sector, and all the way up to a macro level.

Toby Dayton:

So, it’s a little bit of background on the data source itself, the data set. So, we really started… As I said, we’ve been in the data business for about 10-plus years. Initially, what we were doing is selling the job openings to companies in and around the human capital management space. So for about the first five years, we were powering job sites, job boards, delivering analytics, and job listings to a wide range of companies in and around the human capital management space. We started seeing that there were some very significant predictive attributes around our data. So we actually started to build some models back in about 2010, ’11 to forecast non-firm payrolls. And because the job opening is a signal of an intent to fill a job, fill a role with a new hire, there’s some very strong, predictive attributes and we found very high correlations between our data and job growth of future periods.

Toby Dayton:

So, that really started to pique our interest in starting to move into the capital markets and started working with Eagle Alpha actually very early in about 2014, 2015. It’s been a fantastic partnership. And that’s really when we started working on monetizing our data in and around the capital markets and started out with a large, what we call LinkUp raw, raw data set for large global systematic quants that has since expanded into quantimental and fundamental other asset classes, private equity, credit, all kinds of different asset classes around that, and then most recently moving into corporate buyers there. We’ve been at this for quite some time, and it certainly has been an interesting monetization journey, for sure.

Ronan Crosson:

Yeah, really interesting journey. It’s interesting to hear how you pivoted over time from one focus to another. Enrique, maybe if you want to share a little bit on the background of the data monetization journey for Apeiron Insight.

Enrique Garcia-Ayesta:

Sure. I don’t know if this is a similar part to the one Toby has described, but we are or we have been a traditional telecom service provider, basically what you see flows of phone calls and messaging between operators. By gathering the historical information, you can see the evolution of markets. You can see how people might move from AT&T to Verizon, or whichever flow of customers might be happening due to the flows and the changes between the telecom services happening. We build models that help us predict how these changes are going to be affecting the growth or shrinkage of specific operators. We also map it to fiber deployments and to new technology changes.

Enrique Garcia-Ayesta:

Over time, what was and has been traditional telco service provider offering that we still deliver to our telco customers has become also a deep data analytics knowledge on how markets evolved based on the Azure service dynamics in the telco market and that has been the evolution, which like Toby was explaining, it’s a new market area for us, and hence having a guiding hand, like Eagle Alpha has been providing us over time, has been a great help because it’s a very different industry to our traditional one, and hence understanding which are the ticky dynamics, what is the way to monetize the data, where are the pitfalls, how to approach the stakeholders is a completely new area for us, and that’s been very good in terms of helping us monetize that data.

Ronan Crosson:

Yeah, it is fantastic. Apeiron are a great example of the corporate entity monetizing exhaust data and have been very successful at that. But you talked a little bit about the nuances of selling data. Can you talk maybe a little bit more about the greatest challenges you faced as you look to monetize your data?

Enrique Garcia-Ayesta:

I would say the biggest challenge is understanding the nature of the capital markets industry is nowhere close to what we have experienced as telecom service providers. It’s a bit of a black box industry. You can throw lots of ideas, lots of services to them. It’s hypercompetitive, which means that whatever formula any of the portfolio managers decides to use as an investment strategy, it’s a formula they will want to teach secretly, and hence for us to be able to understand whether our services were working or not was a bit of a black box. We would just know how things were evolving based on renewal of contracts, but it doesn’t help you improve the services because we always compare ourselves to being kind of a farmer. We have good raw products. We understand our product very well, and I think that is a key variable that companies need to look at.

Enrique Garcia-Ayesta:

We could supply this products in a perfect format to the chefs, and the chefs are the capital markets professionals who will then cook their own sauces and will make their own excellent dishes that deliver them the alpha and the incremental value that they’re going to be able to make more money, but they won’t tell us how they build all the sauces. So, we have been enough to deliver very specific data sets, but not in the way that will help us improve it from that point onwards. And that’s one of the key challenges that we face is black box principle in which we see the industry operates.

Ronan Crosson:

Yeah. Understood. Thanks for that, Enrique. A reminder to attendees, if you have any questions for Enrique or Toby, please do feel free to drop those into the Q&A, and we’ll put them to the two of them. It’s a great opportunity to speak to two people who’ve been there and done that and have been very successful. Toby, I was worried we’d lost you there for a second. Your screen went black, but you’re back with us.

Toby Dayton:

Yeah. Sorry about that.

Ronan Crosson:

No, it’s fine. It’s fine. The greatest challenges you faced with monetizing your data, what would you highlight?

Toby Dayton:

Yeah, there have been… And let me know, my internet is not great, unfortunately so I don’t know if you can hear me okay.

Ronan Crosson:

No, you’re loud been clear. Yeah, we can hear you.

Toby Dayton:

Okay. Sorry about that. So, the challenges have been pretty significant. Early on, as I mentioned, we were one of the earliest entrants into this alternative data landscape. We had to do a lot of price discovery and productization of our data set early on, which presented some challenges. I think the other big challenge more recently is that the industry itself continues to expand very, very rapidly, as you indicated, Ronan, in your opening remarks about the backdrop of the industry. It is changing very rapidly and-

Ronan Crosson:

Maybe, Enrique, coming back to the point, so you talked a lot about the challenges you faced and the nuances of selling into the capital markets. From that experience, is there a specific advice-

Toby Dayton:

I’m sorry about that, Ronan. I don’t know if you can hear me now.

Ronan Crosson:

We can. We can. We can hear you now. Are you still with us, Toby? We might just come back to you, Enrique, while Toby’s getting his connection fixed. But the advice, based on your vast experience, what advice would you have for corporates who are considering monetizing their data?

Enrique Garcia-Ayesta:

To me, a key line of thought has to be, this is about the marathon. This is about building value over the long run or experiencing working with the capital market companies that they are very serious in what they do and they’re very professional. The data, there is not a PowerPoint sell. These people understand the data probably as well as we do, or on data, and hence, it’s all about providing them the tools for them to work through the data. I think that, therefore, it requires a good level of investment in terms of commitment over the long run from the company.

Enrique Garcia-Ayesta:

There are quite a few friction points, and I think removing friction points will accelerate the road to money. Having parties that are able to address those friction points would be, I think, a key element to success. In this case, the parties that we are here in discussion are probably… They’ve shown to us at least to be capable of removing those friction points, which is about access to the data, our security around compliance, which are changing the landscapes on a regular basis. Hence, I think having those partnerships that help remove those friction points is a key component to success in this market.

Ronan Crosson:

Yeah. Actually, we might come back to that point on the data compliance. Clearly, it’s a key topic for today’s webinar, given the expertise and solution that TripleBlind offer. Toby, hopefully you’re back with us now. Are you? Nope. No. We’ve lost Toby again. On that point, Enrique, what have you seen in terms of the requirements of buyers from a legal compliance perspective and how is that complicated and the sales process?

Enrique Garcia-Ayesta:

Well, I mean, if you look at it from a… There are two main components, at least in what we see, which is one about, is there personal data involved and is it material non-public information involved within the businesses that are being provided by the vendor? So, the personal data aspect is a changing one. In Europe, we have had GDPR for some time. California also has had its own personal data laws being applied or released. I believe there are different states in the U.S., which are also implementing their own privacy data principles. China has done the same. So, it’s a changing landscaping, in which every week or every month, there’s some news that need to be taken into consideration.

Enrique Garcia-Ayesta:

Also, the SEC, the U.S. financial services authority is taking positions on this. So, you need to be quite on top of the compliant landscape to be able to keep within the limit, so what is good to do or not that was talking about the information. The same applies to material non-public information, which is you have to make sure that you have the rights to supply that data, and that data is not in any way private to the specific parties, but actually can be shared with different parties.

Ronan Crosson:

Mm-hmm (affirmative). Yeah. Actually, it’s a great point. It’s actually the next topic I’ll talk to is the legal and compliance focus, because it is such an important area. Toby, can you hear us okay?

Toby Dayton:

Sorry, Ronan. Yeah. Can you hear me now?

Ronan Crosson:

Yes. Yeah. So I think you were talking to the challenges you faced, monetizing your data. Unfortunately, I don’t think Toby’s internet is going to behave for us today. But I think we’re probably at about time anyway, and we can move on to our next session, because Enrique, you did very kindly give me a segue and lead up into the next topic. And so, thank you very much, Enrique, for joining us today. Thank you for sharing your insights.

Enrique Garcia-Ayesta:

Thank you.

Ronan Crosson:

Thank you to Toby as well. Apologies, attendees, for Toby’s technical problems, but moving on. So, as Enrique highlighted, the legal and compliance is a major factor and major consideration when it comes to monetizing your data, and there’s a couple of reasons for that. First and foremost, for many buyers’ alternative data or external data is the first time they will potentially be exposed to confidential information, to non-public information in investment world, or for personally identifiable information. These types of data sets may not be data sets that they’re exposed to on a day-to-day basis in their internal business.

Ronan Crosson:

Certainly, in the investment world, they’re traditionally using which working with publicly available data that’s issued by an exchange or by the companies themselves. But external data, alternative data, it starts bringing them into this field of having to think about this confidential data and MNPI or material non-public information, or PII, personally identifiable information. I guess we all know, these types of data are heavily regulated with heavy penalties for breaches. And so, it’s something that buyers and both the providers and the consumers of this data are very much aware of.

Ronan Crosson:

And then another point to highlight is the investment industry is amongst the most heavily regulated industries. And so, operators in the space, they place significant emphasis on compliance. I guess sometimes there’s the assumption that data is flowing freely and being used in an ad hoc or in a non-regulated way within the industry, but that’s not the case. They’re hypervigilant, hyper-paranoid about any sensitive information, MNPI or PII coming into their businesses. These are things that the industry and anyone selling into the industry needs to be aware of.

Ronan Crosson:

On the left, we just included a couple of logos of data vendors or data providers who have had some legal compliance issues over time. So, App Annie is the most news. App Annie is an app data provider, the most successful app data provider selling into hedge funds and other areas of capital markets. In September, the SEC charged at App Annie with securities fraud related to how they were presenting their data. They were collecting data from the app providers. They were meant to be anonymizing that data, and then selling aggregated insights based on the anonymized data to the investment world. But what they were accused of doing was actually not using anonymized data and actually using the data in raw formats to calculate their final estimates. This was a breach of duty to both the buyer and a seller, and the SEC characterize this as securities fraud. So, they faced a very large $10 million fine, as well as other penalties on individuals in the business.

Ronan Crosson:

So, that’s an example of how the improper processing of very sensitive data for an app vendor resulted in very heavy penalties. Other examples, The Weather Channel, the city of L.A. brought a case against The Weather Channel that they were improperly collecting and using data. That case was settled out of court. Envestnet Yodlee, I mentioned consumer transaction data, they are probably the most successful consumer transaction data. Similar to The Weather Channel case, there’s an investigation, ongoing FTC investigation as to whether they are properly disclosing to the owners of the data that they’re collecting the data and whether the data has been processed in a reasonable and compliant way. These are very recent and topical, and in the case of Envestnet Yodlee, an ongoing case around the legal and compliance sensitivities of data.

Chris Barnett:

Hey, Ronan.

Ronan Crosson:

Yes.

Chris Barnett:

We got a question from the audience. I thought this might be a good time to drop it in, if that’s okay.

Ronan Crosson:

Sure.

Chris Barnett:

Yeah. The question is how much is a typical data set worth once somebody makes a decision to monetize it?

Ronan Crosson:

Yeah, it’s very hard to generalize. Data pricing can vary from $10,000 to seven figures. Consumer transaction data, financial data, you can see deals of seven figures. There would be a small number of deals at very high price. On average, the pricing is somewhere between 40 and 6,000 per data set. Again, that can vary depending on who you’re selling the data to. So, the quantity of funds who consume lots and lots of data, their average price plan would be between 75 and $100,000. Whereas a fundamental fund who are using smaller subsets of the data, that might get the lower end, around 40,000 us, but thanks for the question. Again, attendees do, please, keep the questions coming.

Ronan Crosson:

Just on the compliance considerations, and these are points that Enrique brought up and I mentioned briefly, but these are the kind of questions that providers of data need to be thinking about, particularly when selling into the investment world. These are considerations that we work with data vendors to get comfort around. So, material non-public information, MNPI, we talked around. Personally identifiable information, PII, we’ve discussed that as well.

Ronan Crosson:

SEC examination, so the Securities and Exchange Commission, you need to be prepared for potential examinations and what information you’re collecting and be ready to answer those questions. Again, there is the template to that and there are approaches that we can talk to. The kind of question you get asked around, exclusive sales arrangements, whether it’s okay to sell data exclusively to a single buyer. Again, in the capital markets, that can be challenging and can cause some challenges. In some markets, such as the UK, it’s thought to be illegal or not permitted. In U.S., I think it is permitted, but it can cause extra scrutiny.

Ronan Crosson:

Reputation risk is something you absolutely you think about, and we need to be upfront. Are you comfortable with your data being sold and used by hedge funds? Are hedge funds comfortable that if the market, if the media understood or the wider consumers understood how they’re collecting, how they’re consuming data, even if it’s all above board and legal, are they comfortable with the potential reputational risks? Again, there are ways around that. There’s white labeling. There’s private labeling. There’s various channel partners that can help protect from some of those risks.

Ronan Crosson:

Continuity of service is something that the buyers are always thinking about. Will this data set be available in this current form for a foreseeable future? They don’t want to build an investment process around the data set that then, for some reason, could get discontinued, that this source of their data could get pulled. So, these are all things that buyers are thinking about, and therefore providers of data need to think about when selling the data. So, what I’m going to do, I’m going to maybe pass it back to Chris to introduce the next session.

Chris Barnett:

Okay. Great.

Ronan Crosson:

I’m sorry. Yeah.

Chris Barnett:

Go ahead, Ronan.

Ronan Crosson:

No, it’s you. I thought it was going to be Sam, but it is you. Chris.

Chris Barnett:

Okay. Sure. So, Sam, why don’t you take it away?

Sam Abadir:

Yeah. Thank you. If you want to go to the next slide real quick, I think this is… What we’re going to talk about are some of the challenges that happen during the journey of monetization, right? We heard earlier that it just takes a long time. There’s a lot of effort. We’ve heard about compliance. We’ve heard about legal. There’s a lot of things that actually happen. Natalie is going to join us. She’s going to pick up these first slides to talk about some of the challenges that exist in there. And then I’ll pick it up and talk about how TripleBlind is actually solving some of the issues, actually many of the issues of this journey, cutting down your time and making you get to market faster with your data. Natalie?

Natalie Aitken:

Great. Thanks so much. Yes. So just in regard to where we at Eagle Alpha and working with the vendors that have been on previously is there needs to be a real mainly board level conversations in regard to monetizing exhaust data. I think that that really starts with thinking about the financial services landscape, understanding where the value of the data set is, what type of fund, whether it’s public or private investor, whether it’s quant or fundamental firm, and also what has use for the data, understand the competitive landscape, understand from a pricing perspective. I’ve got a slide on that in a moment, so go into just a little bit more detail. And then as Ronan has indicated, the myriad of compliance, where as Enrique said, just doesn’t really exist to the same level when you are thinking about selling data to either a corporate buyer or actually thinking about it from a B2B perspective.

Natalie Aitken:

We’ve all seen some great movies about the use of alternative data within hedge funds. If you really want to get a good education, I do recommend watching Billions. That gives you a good indication of how data is used within the industry, but then having a realistic estimate of the market opportunity. As Toby has mentioned and Ronan previously, this market from a supply perspective has increased exponentially over the last few years. So, a lot of people underestimate, sorry, overestimate the market opportunity, and which can lead to considerably awkward conversations when you get to your first year sales targets. But I think where there is the opportunity where the market or the data fits, it is about understanding how that application can be used by the buyers, so really getting detailed feedback on buyers. There’s very sophisticated hedge funds and asset managers out there who have been working with data extensively for 20, 30 years, so getting early feedback on the value of that data, the limitations of that data, and actually how that fits into data mosaics and complex data science techniques that these funds have implemented.

Natalie Aitken:

And then we move down the track of all going well for the first three. We then start thinking about productization and what does that actually mean. What does that mean from a documentation perspective, from a compliance perspective? What does it mean from a trial, from legal and compliance, obviously? And then what do you have to do to be able to get your data physically ready to be able to used by the investment community? Tickerization for the public markets, obviously being able to apply your data to companies that are trading, thinking about from a private market perspective how are you tagging to companies, for example, is very important.

Natalie Aitken:

And then once that’s all completed, then you go through the next stage of getting beta trials, taking your data and providing it to the sophisticated buyers in the market, getting feedback as much as you can. As Enrique said, it can be a very black box industry, but really understanding the application of that. Educating your sales teams, if you’ve got them internally or partnering with someone that could take you to market and doing that in a way that you are building, say, a pipeline and a revenue line that allows you to either grow the products through time with further and further insights, or actually think of about having a wider application, whether it’s the quantitative funds that will use the data as inputs to algorithms, or a user interface, for example, that might be used by the fundamental firms.

Natalie Aitken:

So, generally, we see the monetization journey taking place over a year, 18 months. Certainly, in the past, we’ve seen some corporates taking three or four years to go to market, and that’s why we here at Eagle Alpha are really excited about the partnership with TripleBlind because that really does reduce… It takes away loads and loads of the friction, but it enables really exciting data come to market, where in the past, it just hasn’t even been possible or from a technical perspective or a compliance perspective. So, just moving on to the next slide.

Natalie Aitken:

Ronan and I had some very interesting conversations with corporates in regards to the value of data. Certainly, working, as I have before, in an exhaust company, in a data company, a lot of the time we think our data or our insights that we have internally is incredibly valuable, but that value is really determined by a lot of these aspects here. How timely is it? Is it intraday? Is it daily? Is it weekly? Is it monthly? Is it annually? What’s the history of it? How extraordinary and unique is it? Does it have broad coverages from the number of tickers, for example? Some funds will only work with data that has over a thousand companies or the Russell 1000 or Russell 3000. Obviously, legal and compliance, how close to the sun is it? How close to the truth is it? How close is it to the revenue number, for example, or the KPI that could provide alpha?

Natalie Aitken:

And then, obviously, we’ve talked out ticker mapping and data provenance. And then how thorough and granular is it? The number one question is how much money can be made off it? A lot of the time, as a corporate, you’ll have no idea of what that is, because the funds won’t come and tell you, but it’s about working with the organizations that can give you some lead indicators about the value of that. So, all of that comes into an overall value. Typically, hedge funds need a 10X return of the subscription to the value that they can make using that data. It gets very, very complicated when you’re thinking about quant firms, for example, but generally that’s a rule of thumb. So just to the next question, I think that’s back to you, Sam, about how you might be able to solve some of these issues.

Chris Barnett:

Hey, Natalie.

Sam Abadir:

Yeah. Thank you very much.

Chris Barnett:

Sorry, Sam.

Natalie Aitken:

Oh, sorry.

Chris Barnett:

We had an audience question here that I was just going to squeeze in before we switch speakers, if that’s okay.

Natalie Aitken:

Oh, cool.

Sam Abadir:

Yep.

Chris Barnett:

Because I think this might be one that Natalie wants to address. So, the question from the audience is, “If I start today or January, when will I start to see results or some payout from this activity?”

Natalie Aitken:

Yeah. As I mentioned previously, it can be a horribly long drawn out process over years if you don’t have the right internal coverage, of the right internal resources, but also you don’t understand where the market is. We’ve seen three, four years, sometimes taking… We’ve seen other corporates who monetize very, very quickly because they understand the elements that I talked about before. How do you productionize the data? How do you get it ready from a legal and compliance perspective? How do you make sure that the data that you’ve got is available for sale from a MNPI and PII perspective? How do you actually get the data from its raw form internally at an organization and get that ready to be able to be tickerized? And even how do you encourage the organization to understand that historical data, for example, doesn’t have the same value for a hedge fund as it does for a corporate?

Natalie Aitken:

So, there’s all of these things to bear in mind, but we’ve seen data come to market very quickly if it’s done correctly or it’s a horribly long drawn out process, which nobody is satisfied at the end. We have seen that where back testing of data may take years to go through. We’re hoping in the couple of quarters, two quarters seeing revenue by Q3, Q4. That’s really the ideal timeframe.

Sam Abadir:

Well, hopefully the technology that we’re going to be talking about next, TripleBlind, will significantly shorten this whole data productization process. That’s what we’re really going to spend the next couple minutes talking about, right? Our privacy preserving data solution. What TripleBlind has done is we’ve developed this cryptographic approach to allowing data owners to let their product development teams and other parties perform transactions on that data without ever seeing the data, or without ever touching the data or losing any type of efficacy of the data. We’ve developed this cryptographic approach that allows calculations without having to go through the process of hashing or masking or anything like that. So, it’s a short preview of how we’ve actually solved a lot of these problems.

Sam Abadir:

Now, the founders of our company actually had this problem itself with the different company that they started a long time ago. What they needed to figure out was a way that they could use data without moving it across boundaries or across borders, stay compliant, remove the legal and IT costs. There’s a whole host of problems that happen once they had to share data across borders and attempt to share data with different parties. It didn’t work as well as they’d hoped. They sold that company. They started this one here, and this is actually the solution to that. So if we can go to the next page, we’ll get a little bit deeper into that.

Sam Abadir:

Actually, before we get deeper to that, I want to talk about a couple of misconceptions. A lot of people think that GDPR and HIPAA, and if you go in Canada, PIPEDA, or the different… There’s dozens and dozens of these different privacy laws around the world. The common misconceptions says is that you cannot do anything with this data, where really there is about an out in every single one of these law. These outs say that if you cannot take the data and tie it back to an individual person, then what happens is it’s no longer personally identifiable information.

Sam Abadir:

So, that PII we’ve been talking about and the hedge funds who’ve been looking for the healthcare data to do their analysis, they’re looking for data, which often would have PHI or personal health information in there. Traditionally, those have been too difficult to de-identify or impossible to de-identify, or after you’ve de-identified them, you’ve lost all the value that sits in there, and that’s where this misnomer comes in. We’re working within these rules to make sure that you can actually use the data without ever being able to tie it back to somebody else. Let’s go to the next page.

Sam Abadir:

It’s not just us saying this. We proudly, happily just announced a Series A a number of weeks ago. We have a whole slew of very big name investors. Accenture is a global consulting company. I’m sure many people here have probably heard of them. Mayo Clinic is one of the leading hospital systems in the world. They actually had the problem. They became an investor because we solved this problem for them. They wanted to use healthcare data from around the world to create different models from… And they wanted to use their own data, I mean, as part of the Mayo hospital systems around the world. They couldn’t use their own data because of GDPR and other types of laws until they started using TripleBlind. So, if you want to know anything more about some of this stuff, feel free to reach out to me. We can dive a little bit deeper into that. Let’s go to the next page.

Sam Abadir:

I’m going to tell you a quick story about how this works and feel free to put some questions in the question section. Whenever I do this one on one with somebody, it always turns into a conversation with some questions. So, in this scenario, hopefully this is a scenario that everybody on the phone is familiar, with everybody in the Zoom is familiar with, where you have a pile of data. Your organization has a pile of data, and you have a product development team. This is the team that does those steps that you saw in Natalie’s first slide, those five steps they go through and talk to their compliance. They evaluate the market, but then they work to prepare the data and create their products, and that’s the piece that Natalie was saying was taking nine months-plus to actually go do.

Sam Abadir:

In this scenario, we have both of those parties. The vendor product development team wants to create a new product. They actually want to do something using realtime data that could leverage personal information or that material non-public information. Those two would make it so that the realtime piece is basically a non-starter. So anyway, but that’s what they want to do. The data prep takes too long. The compliance issues takes too long. Repeating this over and over would be practically impossible because you’d have to go through that process over and over and over again. So, the short is they couldn’t do anything like that. Using TripleBlind, we’ve actually made that, so it’s actually a real process that can be performed about as frequently as you want.

Sam Abadir:

So, in the first scenario, the data product of the vendor product team, they’re going to use their instance at TripleBlind and they’re going to virtually knock on the door of the data owner, right? And that’s probably somebody who sits down the hallway, but if you’re a global company, maybe it’s somebody who sits in a different continent. It doesn’t matter. When they knock on the door, they’re going to ask permission to use that data set. And then the person who owns the data is going to say, “Yes, you can use that data set.” But prior to this point, what they’ve gone through and done is said, “This is private information. This is private information. This is MNPI.” We got to make all those hidden, that note that the vendor guy can use, but can never see. So they can use the data. They can never see the data.

Sam Abadir:

So, in this scenario, maybe they want to join three sets of data sets that they have from different applications. They want to see what’s the profile of somebody who uses a movie app, plus a shopping app, plus a food service app, right? What’s that profile that person look like? They can actually use the name in there and join all those names together to create that profile without ever exposing the names, right? So, in session three, that third line down, that’s exactly what happens is we start that process. The query, the product that the vendor team wants to create, that gets one-way encrypted. The data sets, those also get one-way encrypted. What I mean by one-way encrypted, I mean, there’s no key. There’s no way to ever undo this.

Sam Abadir:

Then, in line four, those encrypted bits of data, which you can never tie back to an individual, so therefore it’s no longer PII, are used to create the calculation. This is the peaks that always has people scratching their heads. They’re like, “How do you do that?” Well, that’s the advanced math that we’ve done, that we created. That’s the nature of our product. Out of the end, in the scenario which I had, you actually get out that profile of that person using all three of those data sets that joined on the people’s names without ever exposing the people. And then at the very end, you just have that product. You have the product. The neat thing about that product is I don’t have to go through compliance every time. You might have to go through compliance the first time just to get them comfortable, but after that, when they’re comfortable, that no PII or PHI or anything is ever being used or exposed to create your product, they’ll let you run that over and over and over again. Let’s go to the next slide.

Sam Abadir:

I’m going to pause for a second in a case anybody does have a question. This is a more complicated version of what was shown earlier by Natalie, right? It shows parts of that data process and how that data system is broken today, where if somebody is going to request the data, then you have to go through legal, you have to go through compliance, and then you have to scrub that. It’s that part that takes nine months. Now, if I can remove all of that nine months-plus of time, I’ve cut my cost and I’ve increased my margins, and I actually have something that I can get to market a little bit faster. At the very end, there’s one last piece, which I didn’t focus enough on. A lot of times, if you want to use that data using legacy system, using legacy approaches, I might to move that data over borders, right?

Sam Abadir:

I sit in the United States and I want to use some data in Europe, and I want to use some data from Canada. Laws pretty much make that impossible today without doing all that prep and scrub, but then let’s go to the next page. TripleBlind actually solves all that. What happens is you make your agreement with what you can and cannot see with the data owner. The data owner will set the permissions. And then the product management person, the product developer, they can use that data anytime they want to because the permissions are set. All that work that you did once upfront with legal and compliance is done. Over and over and over again, you can recreate this model every day, three times a day, three times a month, quarterly, whatever your data buyer finds to be valuable. And that’s a huge, huge difference from what we could do before. I think that’s my last slide.

Natalie Aitken:

Thanks, Sam. Oh, I’ll just-

Sam Abadir:

I’m just making sure there weren’t any questions. I was going to ping Chris-

Natalie Aitken:

Oh, sorry.

Sam Abadir:

… if he’ll pop in with a question or anything.

Chris Barnett:

So, Sam, the question here from the audience is, “Does this mean that I don’t have to aggregate the data that has PII to monetize it? You didn’t mention aggregation.”

Sam Abadir:

If you mean by aggregation collect the data, put it all into one place, and then run that data all in one place, I don’t have to physically aggregate that data anymore. What we’re doing is virtually aggregating that data. All the data owners, no matter where they live, the data stays in their database. It stays in their data lake. It stays in their tables. So, I don’t have to put all this together. Sorry, I didn’t make that more clear. If I’m using multiple data sets from different places around the world, or if your app that you have has data residency requirements, and you’ve got an Amazon warehouse in Europe and one in China and one in South America, no problem at all. No problem at all. Just TripleBlind will aggregate virtually all that data and let you use it all at the same time. Hopefully that answered your question.

Chris Barnett:

Yeah, that sounds like a good answer to the audience question. Thank you.

Sam Abadir:

Natalie, back to you.

Natalie Aitken:

Oh, thanks, Sam. Thanks. Yeah. So just to recap some of the key messages when we are thinking about going to market, I think Ronan mentioned the size of the prize, where we are thinking 56% compound growth each year up to 2028. It’s a substantial amount of capital that’s going to be put into this industry, both from an investment perspective, as you see with organizations like TripleBlind getting great funding, as we see corporates wanting to monetize and get revenue out of it. I think we can see that coming from the amount of funds that aren’t using alternative data, as Ronan mentioned before. There is a small number of very, very sophisticated, mature funds out there who are looking at 300-plus data sets a year, thinking about buying a hundred, well, between 30 and a hundred data sets over the next couple of years. There’s significant amount of resources being allocated at the fund level of all types.

Natalie Aitken:

Obviously, the quants, their lifeblood is data, where we think about fundamental firms really trying to become quantimental, where data is at the very, very heart of investment opportunities. I think COVID accelerated that, where changes of consumer behavior, for example, has fundamentally changed certain industries and investors need to stay on top of that. We know about 700 funds who are considerably transacting in this market. It’s about tapping into those very early on, tapping into that expertise from a pricing perspective, really understanding how do you build your internal business case and communicate that to a board level to talk about how you can take advantage of this and monetize your data, but keep bear in mind it’s incredibly competitive. So it’s really understanding is your data valuable? Is it unique? And how does it stack up? What kind of additional value can it bring to certain parts of the industry?

Natalie Aitken:

So, we look forward to working with you and TripleBlind, bringing some of this really exciting stuff to market. But just as my very last slide, we’ve spoken about all of these. The key to success with which Enrique and Toby talked about is just this market is very unique. It has its own difficulties in regard to it’s very opaque, but it’s really understanding all of this industry, being able to discuss the differences of how a quant works versus a fundamental firm works, how the legal and compliance, how do you identify people, and then the complexity of buying. There’s not been any industries in the world where you have to give over your entire history of data for someone for free for three months. And then at the end of that time, you might not get any feedback about the value of it. So, how do you change your even internal processes to be able to allow the buying process to take place? Yeah. So, that’s the end of me. So, thanks very much for listening. I’ll head back to Sam for the wrap-up slides.

Sam Abadir:

Thank you. These are the panelists that we have today, right? If you want to reach out to any one of us, feel free. Our contact information is right here and we’d be excited… In fact, we had a conversation for half an hour before this. We were all very excited about hopefully having conversations with you guys about the things that we talked about today.

Enrique Garcia-Ayesta:

Sorry, Sam. Could I just add something very fast?

Sam Abadir:

Yes, sir.

Enrique Garcia-Ayesta:

This is Enrique. I mean, it’s been a thorough of discussion, and I think in some aspects probably sounded a bit defensive. We’ve been talking about a lot of the challenges and I think something which is important here is to explain that the returns can be high and that once someone’s data or insights become part of a investment strategy and machinery, then the stickiness of the contracts tend to be good and long term, and hence the initial big effort pays off over not a long period of time, because once you are in and you keep on delivering a good consistent service, then those contracts will probably be renewed over time. So, I think that’s an important positive also.

Sam Abadir:

Good comment. Very good comment.

Ronan Crosson:

Yeah, I think that’s everything. I’m not sure, Chris, if you had any closing comments. But certainly, from the Eagle Alpha team, thank you very much TripleBlind for inviting us. Thank you very much to all the attendees today. Special thank you to Toby and Enrique for sharing their insights. Do reach out to any of us that are email addresses, you can see here, or through your contact, your Eagle Alpha or TripleBlind contact. Chris, was there any closing comments from you?

Chris Barnett:

Yeah. Thanks, everybody, especially attendees. We will be following up on email with a recording of this and also the slides and just getting you the contact information one more time for everybody. So, thanks everybody and have a great day.

Sam Abadir:

Thank you very much.

Ronan Crosson:

Thank you all.