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Data and Analytics

Empowerment Through Data, Analysis and Technology

At the recent Benzinga Fintech Deal Day Conference, Nasdaq’s Head of Alternative Data and Nasdaq Data Link Bill Dague spoke on a panel discussion about empowering investors with data, analytics, and technology. Dague shared how platforms like Nasdaq Data Link and Data Fabric are enabling users to manage, deploy and derive insights to make better investment decisions.  

The following is a transcript of the panel discussion, which has been lightly edited for clarity.  

Moderator Patrick Shaddow [00:00:40] My name is Patrick Shaddow. I’m the President of Syntax Indices. We’re a small but rapidly growing data provider, index provider and index publication company. Today, I’m joined on stage by Bill Dague of Nasdaq and Kyle Braden from iShares. I’ll let them introduce themselves. Bill.  

Bill Dague [00:01:02] Hello. Good morning, everybody. I’m Bill Dague. I lead alternative data and Nasdaq Data Link platform at Nasdaq. I have a background in technology and data science, and I have moved over to kind of run the business to help empower investors with data and insights from the alternative data side of the business. We’re really focused on how we find unique, interesting sources of information that drive better investment decisions and better outcomes. I think things like insurance policy data, credit card receipts, that kind of thing. And then Data Link is our platform for delivering this data. So easy to use APIs discovery through a web portal. The data is easy to consume and easy to integrate with what you have. So, this is a platform we use to deliver our own content, but it’s also a place where we have a third-party marketplace for people to bring content that they want to sell to a broader audience. So that’s something that we’re excited about and want to talk more about.  

Kyle Braden [00:02:03] Hi, I’m Kyle Braden, director of Platform Development and Business Intelligence for the direct channel at iShares. Our group’s purpose at iShares is really to build that connectivity between iShares and the end investor and then also the platforms that serve them. We do that through using a broad look at data and delivering technologies to better serve our clients and ensure that we become the ETF provider of choice for self-directed investors.  

Patrick Shaddow [00:02:32] Great. So, to kick it off, we have a couple of questions that we want to ask our esteemed panel here. And last week, we had a prep call, as you normally do for these types of panels. And one of the angles we discussed in our call last week includes delivering technology and data to address gaps in the market. So let’s start there and talk about why these gaps exist and how they can be filled. Bill, I’d like to start with you.  

Bill Dague [00:02:58] Sure. I think there are there are a number of gaps, obviously. There’s a lot of opportunity to provide value. From a data side, I think it’s probably no surprise to anybody here at this event that that data is becoming way more important in terms of driving investment decisions from retail investors through institutional, and that’s been a trend for the last several years. So there’s always a need for unique, interesting sources of insight that are going to say something new. How do I measure something that’s relevant and important and going to help me be smarter about the world? So that’s what we try to do in the alt data business; we try to service that particular need of finding unique, interesting sources from outside the financial services world. And then the other gap, I think is really, really present, especially in the institutional and investment management side, is how do I leverage technology to bring more data into my process and actually get the value so that that time to value of integrating a new data set, being agile, adapting my investment processes, adapting my approach to integrate more content I think has been a really big struggle. Firms have spent the last few years collecting a lot of different data sets, and now it’s time to figure out, okay, how do I do this efficiently? Scaling up, I think, is a big gap.  

Patrick Shaddow [00:04:17] And also data integration as well. Right. So in terms of, you know, you got one data set here and one data set here, they might not necessarily reconcile with one another. A lot of the gaps could potentially be addressing the inconsistency of the same data, the metric that’s being determined, but through different sources. So, Kyle, I kind of turn the same question to you; what kind of gaps do you see in the market, and what kind of technologies are you using to address those?  

Kyle Braden [00:04:46] Thanks, Patrick. I think one of the biggest gaps we see in the retail space is there’s a lot of tools, and there’s a lot of data that has historically been reserved for the professional investor community, and we’re working to really get that down to the end investor level. And I’m not talking about real-time quotes or charging capabilities that seem to be the industry standard nowadays. But I’m talking about actual tools and data that wealth managers use, that financial advisors use to manage money and ultimately advise their clients. What we challenge ourselves with is how do we bring that down again to the retail level and make that more widely available. And with the understanding that in the last two years, we’ve seen 40 million new self-directed investment accounts opened up globally. That’s 30 million here in the United States. And so these investors are demanding this data, and they’re demanding this technology to put themselves on a level playing field with the professionals out there.  

Patrick Shaddow [00:05:45] Isn’t that a wonderful thing when you have more financial consumers in the market exercising their ability to partake in the financial markets? I mean, the data is everywhere. And we all know that you can get data from this source and this source, and everyone has their data. So, what I kind of want to think about today is, is data in and of itself information, or how do you convert data to information in and of itself? How can you make information more readily available from data sources so that you can make better investment decisions, so can make better analyses that ultimately will empower your clients? What kind of data are you bringing in? And then how are you transferring that to the information?  

Kyle Braden [00:06:30] I would say that that’s a huge pain point right now. With iShares having a global presence and over 1200 ETFs available on different markets and on different continents, we see a broad set of data coming in. And one of the misconceptions is if you have your own data in your organization, you probably understand that it always doesn’t come from one place, right? It comes from, you know, sometimes we have internal resources. Sometimes we get data from the direct platforms themselves, and sometimes it’s a third-party intermediary. And so, how do we consume that data, bring it in, and develop actionable insights that our clients on self-directed platforms can use to better serve their clients as well?  

Patrick Shaddow [00:07:10] Actionable and accessible. Bill?  

Bill Dague [00:07:13]  I think that’s definitely a huge part of it. As Kyle mentioned, the Nasdaq is also a very global business. We have over a billion people who consume Nasdaq data in some form or another around the world. Even on the Data Link platform, we have over 800,000 users, from retail to professional quant hedge funds. When we think about how do we bring products that are going to be a value add, especially for that retail, more self-directed investor, we really have to think about what’s the investment lifecycle, what’s the workflow, and how we can present the data in a way that’s going to be easy to digest. It’s not just having easy-to-use API, Excel plugins, etc., but it’s also putting the data in context and helping the user get there. Creating a product that’s easy to read and easy to understand, that’s intuitive, but also says something meaningful. It’s very easy, I think, to water it down a little too much. So, trying to find that right balance of providing information, providing measurements, but also helping the user get there. Last year we launched a product based on — of course, there was a lot of interest and retail activity in the market — and so we launched a product based basically on public information, look at looking at SIP market data, and we’re parsing that. So we’re really intelligent algorithms to figure out what retail is actually trading on these data feeds. And we distill that down into our retail trading activity tracker, and that gives you a sentiment score. It tells you what our other retail investors doing; are they net buyers and sellers of a particular stock on this day? And I think that helps investors figure out where they sit in the landscape and whether they want to be contrarian or go with the flow. That’s up to them.  

Patrick Shaddow [00:09:03] That’s a very interesting topic; go with the flow. The flow of funds is a major, major advancement in the ability to ascertain who is buying, who’s selling and what types of equity investors are actually doing that. Are they hedge funds? Are they institutional investors? Are they family offices? Are they retail investors? And so what I’ve always been kind of interested in is essentially identifying what I would call the holy grail of market timing. If you could ascertain types of companies that have a common shared attribute that is being traded by a family office to a broker-dealer or a broker-dealer to a family office, what you’re essentially saying is someone’s unloading their position, and they’re unloading a position, or they’re buying into a position based upon some kind of common attribute. So that’s a very interesting data set that you created there. What kind of data sets do you guys use at iShares to really help your end investors?  

Kyle Braden [00:10:04] To your point here on the retail investor and trends that we see is, we look at flows data just like you mentioned earlier. And I think one of the misconceptions is, is that people are trading in and out on self-directed platforms. Well, what we actually see by looking into the data is that retail investors are getting smarter and smarter. We’re seeing two-thirds to three-fourths of our flows this year from self-directed platforms have come to fixed-income products. And you wouldn’t necessarily associate a retail investor with investing in bonds or the fixed-income community as well. And so using that data to debunk some of those misconceptions is definitely something that has opened our eyes here this year at iShares.  

Patrick Shaddow [00:10:45] Maybe each one of you wants to discuss some case studies or talk about trends or tips for our audience members as it relates to data, bringing data in, different types of platforms that you use to analyze your data and then, ultimately, the dissemination distribution portals that you use to get your information and data out.  

Bill Dague [00:11:04] I go back to the gap of agility with how you use data to drive decision-making. Generally, I think this applies even outside of the financial services industry. That’s a challenge that I think any enterprise has to deal with and individuals too but in particular enterprises. And so that’s something that we have our own challenges with how we deal with our own data. And so we’re actually leveraging our Nasdaq Data Link platform to pass data around our organization and make sure that it’s governed and has the right controls, and is logically consistent. And so we’re also taking a look at what we can do to enhance that platform and bring those values, bring that value to other organizations. We have some buy-side institutions also leveraging the platform for that same use case, which is helping them be a lot more agile and helping PMs, for example, on the frontline, say I need to integrate new content into a strategy. Every day I don’t have a strategy and production, I’m losing money. So, if we can get this integrated and deployed more quickly to streamline that workflow and that process, they’re making more money, right? Which is the compelling value proposition. That’s something that we’ve really kind of tried to lean into this idea of thinking about data sets and data content not as some technology asset but really as a finished product. And so, serving up content in a way that’s ready to eat, ready to go for a particular use case, that’s really what we’re building with Data Link, we’re users ourselves, and we’re happy to provide that service to clients too.  

Kyle Braden [00:12:39] Same. We had a client that came to us, and they’re a well-diversified financial institution that came to us and said, ‘Hey, look, we’re doing a great job of acquiring people on our deposit side of the business, and we’re doing a great job of acquiring new customers on the lending side of the business. But what we’re struggling with is cross-selling into the digital investment platform that we have set up.’ And so they shared a bunch of their consumer data with us and having that global look, we apply that and apply some of the best practices where we’ve seen success with what they’re trying to do in other regions around the world and are working with them currently in partnership to develop some new ETF solutions that we’re hoping to launch in the New Year.  

Patrick Shaddow [00:13:14] I love it. It’s almost like a feedback loop, right? So, you have users using your data, and then you start seeing how they’re using your data so that you can better design new systems for that delivery mechanism. Is that something that you experienced in your career here?  

Bill Dague [00:13:30] Absolutely. That’s something that, again, we benefit from having hundreds of data sets on the platform. We get to see what users are investing in and what content is interesting to them. You mentioned flows; flows are a hot topic right now. Everybody’s interested in investor flows, right? So how can we design products and build out a product portfolio that plays to that? I mentioned retail, so being responsive and agile with what kind of products we bring to market is hugely beneficial. I think it’s an advantage for us.  

Patrick Shaddow [00:13:59] And what kind of timing mechanism is that involved in bringing a new product to market in terms of data, and how much scrubbing of data are you doing on a day-in and day-out basis? As we started this panel off, where you have a primary metric, but two different sources might have inconsistent data. So, what do you do when you start thinking about scrubbing data? And then do you taint the data, or do you want to winsorize particular attributes and get rid of outliers? Maybe you can expand upon these topics.  

Kyle Braden [00:14:27] Yeah, it is a huge pain point right now. One of the things that we definitely think about is, do we continue to use internal resources to source and organize and consume the data or do we go external and try to do it as well. I would challenge the leaders in this room to really look at the capabilities of your organization and your team and understand what you’re capable of. And understand that no matter which direction or approach you take, you’re likely going to use internal or external sources regardless.  

Patrick Shaddow [00:14:56] Yeah. And it’s almost like you got data, and then you got your derived data, and then you have the ability to then license your derived data. And a lot of the sticking points that I’ve come across is I consume data, and then I make new data off of that old data. And when I want to start beginning to attribute or provide attribution analysis of how I ultimately came up with a given score or metric or rating, I often infringe or technically almost infringe on the intellectual property of the data that I’m enforcing. So how do you guys kind of get around that?  

Bill Dague [00:15:33] Proper governance is really an important part of the workflow, and that’s something where it’s a fine balance of how do you share your work, which is super important, but also how do you make sure that you’re staying in bounds in terms of what you’re able to do with the content. It’s a fine balance, for sure.  

Patrick Shaddow [00:15:52] Absolutely. All right. Well, that’s it. Ladies and gentlemen, thank you for your time today. 



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