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Data Doesn't Sell Itself

Posted by Emmett Kilduff on Jul 1, 2020 1:32:38 PM
Emmett Kilduff
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I left Morgan Stanley in 2012 to start Eagle Alpha. Between 2012 and 2015 only a small number of early adopters were purchasing alternative datasets. Since 2016 spending on alternative datasets has increased substantially from $0.2bn to an estimated $1.7bn by year end 2020 (Figure 1).

analytics_power_2019

 

Source: “Analytics Power 2019” Report by Element22, UBS Asset Management and Greenwich Associates.

*Forecast figure.

The significant increase in spend has attracted new entrants to the alternative data space. Data exchanges have been launched by traditional Wall Street data firms (e.g. Open:Factset and S&P Global), stock exchanges (IEX and Nasdaq/Quandl) and more recently cloud companies (AWS Data Exchange and Snowflake). My sense is that other firms (e.g. Wall Street data firms, stock exchanges, cloud providers, investment banks) are at risk of falling behind if they do not launch data exchanges soon.

There seems to be a correlation between the increase in the number of data exchanges and the number of times I have said (and heard) the phrase “data doesn’t sell itself”! Don’t get me wrong – I want the data to sell itself but I don’t think the alternative data industry is there yet

But that’s just my personal view. To confirm, or deny, my observations I decided to create this blog to ask thought leaders for their views. I reached out to 15 contacts from different parts of the alternative data ecosystem: vendors, buyers, data exchanges and industry experts. And also Eagle Alpha’s head of dataset sales.

I asked each thought leader four questions: 1) whether they agree or disagree with my view?; 2) what are the top two challenges in the dataset sales process?; 3) if they were CEO of a data exchange what would their top two priorities be?; and 4) how will dataset sales be different three years from now?

Apologies up-front for the length of this blog! But if you are truly interested in shortening the dataset sales process I actually think it’s a very interesting read.

Question 1: Data Doesn’t Sell Itself – Agree or Disagree?

Apu Kumar, President at LotaData

  • We agree. Alternative data in itself holds little value.
  • Such data does not sell itself until (a) the value is unearthed and (b) the market knows how to extract and monetize such value with ease.

Benjamin Zweig, CEO at Revelio Labs

  • Data is very difficult to sell. Buyers are heterogeneous in their understanding and familiarity with data, not to mention their different strategies and use-cases.
  • With so much data out there, most of which is useless, you really need to work on building a reputation for having clean and reliable data, that’s properly mapped and validated, which takes a long time.
  • Many clients are interested in seeing the data in action, revealing an insight or identifying an early trend. Working through these examples and tailoring them to different types of buyers is tricky, but ultimately worthwhile.

Darren Voges, Data Strategy Consultant. Prior Firms: Facteus, ADP, Yodlee

  • I agree that data does not sell itself, especially for more complex and valuable datasets.  
  • Having worked with most of the credit and debit card data in the market, this type of data can be complex and not easy to work with.  
  • For more complex datasets it may make sense, for the data vendors, to do the heavy lifting for the potential buyers and create easier to use products. This could include sharing your backtesting results, nuances that are known to exist in the data, where the data is very strong/not as strong and even providing actual algorithms to help them with their testing and normalization.   
  • Card data also requires highly accurate merchant and ticker tagging - this is a very expensive ongoing proposition, but without proper tagging the value is significantly hampered.

Elizabeth Pritchard, Founder of White Rock Data Solutions. Prior Firms: Crux, Goldman Sachs

  • As a like-minded advocate actively working on removing friction in the data adoption process, it is no surprise that I fully agree alternative data doesn’t sell itself to most audiences in finance.
  • Dataset sales are a consultative process with data sales teams bringing the finding, prioritization and product assistance, technical marketing, and orchestration of talent across business, technology, compliance and other stakeholders together to close deals with regulated financial institutions.

James Budd, VP of Research Sales at ETF Global

  • Selling data is no small task and having the right partner is key to success in this space. Feedback is always valued and partners can be great to give an outside perspective in addition to being an extension of your sales pipeline.
  • Data sales is a low barrier to entry but a high barrier to succeed and it’s been our experience that you would want a partner like Eagle Alpha to help complement each step in the process.

Jonathan Hardinges, Strategy Director at GlobalData

  • Answering this question effectively requires awareness that not all data is equal.
  • Firstly, the on-going trend of data commoditisation will continue at pace and in data categories where this applies, barriers to access will be increasingly low, if non-existent. I believe that where this is the case there will be a general shift towards self-serve and pay-as-you-use models.
  • However, in parallel to this, there is increasing focus being placed by corporates and financial institutions on sourcing genuinely unique, high-value information. In this instance, successful sales processes require the vendor to have both technical and domain knowledge.
  • As the value of the data increases - for both the vendor and buyer - it becomes more important to accurately and comprehensively tell the story behind the data (eg provenance, taxonomy, methodologies etc). Also, the value of data increases in-line with the effectiveness of its use and application. This is why it is important that vendors have the expertise to take a solution-orientated approach to sales and on-going customer management, which helps buyers/users maximize the value they extract from specific datasets by understanding how to apply them in the context of their business.

Jonathan Neitzell, Founder of Anduril Partners. Former Role: CDO (Fundmamental Equities) at Goldman Sachs Asset Management

  • Not yet. I think of two quotes in this context, the first by Nobel Prize winner Danny Kahneman suggesting “No one ever made a decision because of a number. They need a story.” As  founding members of behavioral economics, Kahneman and Tversky found that regardless of education, even many experts overlook relevant data and statistics by which they should often be making choices. Knowledge and awareness are not enough until its put into practice. 
  • The second quote “Water, water everywhere, nor any drop to drink” (The Rime of the Ancient Mariner, Samuel Coleridge) reminds me as well that while we may be surrounded by great resources, until inputs (like water) are run through a filtration plant, bottling factory or drinking fountain, it must be made relevant to solving our local need to be useful. 
  • Data becoming useful requires a robust process with hypothesis statements and feedback loops to scale up data applied well, and attract investment for areas requiring additional work. With this roadmap and framework in place, incremental data sets could indeed be identified and sold/scaled quickly. 

Mark Hookey, CEO of DemystData

  • Data does not sell itself within today’s modern stack.
  • Mapping use cases and business problems to relevant, compliant data requires a blend of creativity, domain knowledge, data knowledge, analytics, workflow optimization, and commercial nous. In fact, with the proliferation of emerging new data and platforms, it’s getting harder.
  • Just putting data on a shelf is the easy part. Data doesn’t sell itself.
  • Over time, AI technologies, platforms, and ecosystem players will solve this, but it’s still early days.

Mark Vaughan, Head of Dataset Sales at Eagle Alpha

  • We have been selling alternative datasets for eight years and I definitely agree.
  • I still see some new vendors naively thinking that data sells itself. New vendors need to be smart and thoroughly prepared so they position themselves well and create a great first impression.
  • The best selling alternative datasets have two attributes: 1) a well resourced and proactive sales team. New vendors don’t realize how long the sales cycles can be and how much hand holding buyers need; and 2) quality data in a well-structured offering. In my opinion, Consumer Edge is a great example of an alternative data vendor that has both of these attributes.

Matthew Glickman, VP Data Marketplace, Customer Product Strategy at Snowflake

  • There’s way too much friction and tooling involved to acquire the data from traditional methods, get it into a place in the right format for evaluation.  
  • In addition, data needs to be combined with other datasets before it can be used which adds to the friction.

Mani Mahjouri, CEO/CIO at Blueshift Asset Management

  • The right data does sell itself.
  • But right is not a ubiquitous concept - for us it's when story meets need, in the presence of breadth.
  • Having said that, a compelling story generates need if it is told well. 

Raghav ‘Mady’ Madhavan, Chief Scientist and President of Alto Meta Consulting. Prior Firms: UBS, J.P. Morgan, Morgan Stanley

  • Some datasets get bought, but more often, data needs to be sold. 
  • What I mean is that some datasets are preceded by expectations of value, perhaps due to the richness of source, back-story of the process, or uniqueness of content, so they sell themselves, to the first few clients. Such datasets that manage to live up to the expectations become hot commodities; otherwise, they just become commodity.
  • In the more frequent case, datasets need to prove they are fit for purpose: do they provide insights that are specific to the decision type and style of the intended user? For example, with a buyside client, what is the coverage, timeliness, frequency, quality and informativeness of the data? This is the part where data needs to get sold on its merits - often in an involved multi-stage 'value proof' process.

Ronit Koren, Former Director of Partner Programs at Audit Analytics

  • Agree that data does not sell itself. From an experiential view point, data that has a specific focus, depth of history and complexity can be overlooked without the proper channels to position the value of the data to the buyers (primarily Eagle Alpha). 
  • The need for a data exchange to connect the buyside to the array of available vendors has been proven based on the growth over the past four years.
  • Similar to an agent connecting talent to production studios (i.e. Tom Cruise to Paramount), a data exchange helps to direct the buyer to seller reducing the "noise" and increase so called bankability - or success.

Scott Burns, Head of Data and Research Products at Morningstar

  • I spend a lot of time thinking about the difference between selling data and selling software. With software, you generally find a need: some inefficient workflow or new fangled way to do things and you fill it. And you can go out there and proactively find people with the problem and then sell them on how your product solves it. You get really sophisticated sales and marketing machines and you drive MQLs, to SQLs, to close. Etc.
  • Data, on the other hand can have a much more passive engagement in terms of its sales process. Of course, there are displacement-type data sales and that is true sales work. My product is better than the one you have, be it price, quality or the holy marriage of the two…value.
  • But otherwise, organic use of data first requires someone to come up with a reason to need the data or an investment hypothesis that data can activate or validate. But the data itself is inert and unlike software, doesn’t inherently come with ready-made use cases.
  • Now that I say that, though, once a use-case is discovered, the data can take possession of that use-case and then be sold in the problem solving, idea generating manner that software is sold….hmmm…an interesting conundrum. But the germ of an idea must come first.  
  • And then, I guess in terms of the sales process…the process of proving and extracting the “value” for the data is a bit of a sales process.
  • We see a difference between more transactional sales type efforts…we have an established product, its value is established, the use cases are known and we are just filling out the order form vs. more enterprise type sales with longer sales cycles and debating/circling on value and price.
  • Also, it gets into how you want to semantically parse the word “sales”. If by sales you mean awareness-building…then yes…data does not sell itself. You must endeavor constantly to make sure that if you have data…that people know it and they know where to find it.

Tim Baker, Head of IEX Cloud. Prior firms: Refinitiv, UBS

  • Sadly it doesn’t sell itself in part because of the complexity of the products, and limited self service tools to test and procure the data – you have to have a sales person in the loop help the process along, providing context, supporting trials etc. 
  • You also need to proactively market the product, with proof points around its quality, signal strength etc.

Toby Dayton, CEO at LinkUp

  • Data most definitely does not sell itself. We’ve been selling our job market data to a variety of client segments for over ten years and have yet to encounter an instance where the data sold itself.

Question 2: What Are The Top Two Challenges In The Dataset Sales Process?

Apu Kumar, President at LotaData

  • Discoverability: how easy is it for your buyers to find your dataset?
  • Verification: how can your buyers verify the integrity of your dataset and confirm that it fits their exact needs without a prolonged validation process?
  • Repeatability: will your target customer's needs and use cases necessitate repeat purchase of your dataset?
  • Mark-to-market: does your dataset have comparables to mark the market price? 

Benjamin Zweig, CEO at Revelio Labs

  • The most difficult part of selling data, is going through custom trials. Different buyers get comfortable with the data in different ways. Some buyers like to run back-tests with large quantities of historical data, some like to deeply examine a company they know very well, others like to see some really specific cuts of the data to see if it can answer targeted hypotheses. The list goes on.
  • Selling data is different from selling a ready-made solution. We’re an input into a process, the value of which depends heavily on what else is part of that process. Buyers take different approaches toward data and it’s important to be flexible for how a client is organized internally.

Darren Voges, Data Strategy Consultant. Prior Firms: Facteus, ADP, Yodlee.

  • It’s important to share what steps you’ve taken to curate the data to ensure that it’s clean, has good signal and you aren’t wasting the buyers time. 
  • Once you’ve properly curated the data, getting into the actual testing is the next challenge. Understanding this is key to building the relationship. 

Elizabeth Pritchard, Founder of White Rock Data Solutions. Prior Firms: Crux, Goldman Sachs.

  • Getting found by potential buyers.
  • Demonstrating the value of the dataset – why a customer should pay attention.

James Budd, VP of Research Sales at ETF Global

  • This is an interesting question given the COVID-19 pandemic, which in our opinion exacerbates these challenges. The first hurdle is getting in the door for an introductory conversation. Some firms are more reluctant than others to discuss new data products. Leveraging relationships and finding the right partner and not accepting every potential partner is key.
  • The second challenge is competitive due diligence. The firms we target are inherently secretive and it’s tough to find information on the fund let alone what their feedback on similar products is.

Mark Hookey, CEO of DemystData

  • First, best in breed compliance and privacy is at odds with frictionless dataset testing.
  • Second, understanding datasets at the margin is increasingly a specialised profession that requires domain experts.

Mark Vaughan, Head of Dataset Sales at Eagle Alpha

  • Vendors of alternative data don’t understand the buyers and their specific needs. Buyside firms, PE firms, corporates all have different requirements. I have observed lots of vendors rushing a dataset to market but then losing 6-12 months because the dataset wasn’t productized the way buyers wanted.
  • Most firms that buy data suffer from a lack of resources (people and money) to test and purchase datasets. Firms can’t test every dataset let alone purchase them all.

Matthew Glickman, VP Data Marketplace, Customer Product Strategy at Snowflake

  • Data buyers aren’t typically looking for data. Instead they are looking to answer a question or improve models with data. Investing the significant data engineering time and effort to evaluate data doesn’t often make sense if you don't know if the data’s valuable yet. 
  • Lack of ability for data buyers and data sellers to collaborate live on the data during the evaluation process. Due to the way data is copied, extracted and transformed on the way, it can be completely unrecognizable by the time it’s being evaluated and used by the data buyers.   

Mani Mahjouri, CEO/CIO at Blueshift Asset Management

  • From the perspective of a buyer: establishing sufficient transparency into the origins of the data to satisfy compliance and reliability.

Raghav ‘Mady’ Madhavan, Chief Scientist and President of Alto Meta Consulting. Prior Firms: UBS, J.P. Morgan, Morgan Stanley

  • Showing that the dataset has a unique and discernible value to the user's decision process - one that warrants the costs of acquisition, integration and maintenance of the dataset into the process. Proving uniqueness may involve comparing and contrasting the dataset with competitors - some of which may not be in the same class, others that you have not encountered or have access for comparison. It is shooting in the dark.
  • Creating a tight timeline for getting to a decision is often a hurdle as new dataset acquisition is often a victim of the extended commitments on the time of the client's subject matter experts. SMEs have demands on their time for supporting production processes, and other innovation workstreams with existing data sources. So trials with new data sources get elongated. 

Ronit Koren, Former Director of Partner Programs at Audit Analytics

  • The first challenge is the lack of fit. Funds and vendors could benefit from clearly defining their data needs and offerings. The more a data marketplace grows and develops, the more precise the data needs will be communicated. And yes, one can argue that different pods of the buyside will have different data needs, but saving time and trial resources could benefit from more efficient and comprehensive intakes.
  • The second challenge is the lack of understanding about buyers needs and applications. Buyers have very specific data consumption needs including history, mapping, point in time, lag time and delivery. If a vendor is challenged with fulfilling those needs, the process will be arduous and likely yield unfavorable results.

Scott Burns, Head of Data and Research Products at Morningstar

  • Awareness building.
  • Proving value i.e. price!

Tim Baker, Head of IEX Cloud. Prior firms: Refinitiv, UBS

  • Setting the price, and evolving from a high price product with scarcity, to a more widely consumed product at a lower / more affordable price point.
  • Datasets come with a variety of terms and conditions and are non-standard – it can take months for clients to understand these and get through their legal process. At IEX Cloud, for instance, we've prioritized building a financial data delivery platform with flexible pricing and easy sign-up to break down these barriers.

Toby Dayton, CEO at LinkUp

  • There are a number of challenges in selling alternative data to the capital markets and it’s been really interesting to see the extent to which those challenges have changed over the past 5 years. For the past two years or so, as growth in the alternative data industry has accelerated dramatically, we’ve seen huge growth in the number of firms interested in buying our data which has introduced a much broader range of firms with vastly different levels of experience, different perspectives, different starting points, and different expectations about alternative data.
  • Secondarily, that growth has also significantly increased the depth, breadth, and diversity of the broader alternative data ecosystem which has definitely expanded the market but at the same time, it’s also increased the complexity of our business and forced us to run at a broader spectrum of opportunities.

Question 3: If You Were CEO Of A Data Exchange What Would Your Top Two Priorities Be?

Apu Kumar, President at LotaData

  • Transparency for data suppliers to ensure they have end-to-end visibility as their assets move through the marketplace.
  • Control for both suppliers and buyers to set and negotiate terms while maintaining price parity within the exchange.
  • Discoverability for both data buyers and data suppliers to increase awareness and drive engagement within the marketplace.
  • Compliance: ensuring that data flowing through the exchange exceeds local and regional regulatory requirements.

Benjamin Zweig, CEO at Revelio Labs

  • Data exchanges need to very deeply understand the data they’re selling, which is a big undertaking. There’s a desperate need for category experts who have deep understanding of the nuances of data. When talking to a potential client, it’s critical to address any questions in a clear and transparent way.
  • Helping with mapping is very important. Every data company has trouble getting point-in-time mappings to identifiers, among lots of other issues. There’s real redundancy in the space that can be ameliorated by an effective data exchange.

Darren Voges, Data Strategy Consultant. Prior Firms: Facteus, ADP, Yodlee

  • Ensure that more complex datasets have excellent product managers and sales teams otherwise these products will struggle. 
  • Ensuring great support for the data owners and the buyers - it takes a lot of work to win the rights to sell a dataset so providing the best service and support to the buyers and sellers will go a long way.

Elizabeth Pritchard, Founder of White Rock Data Solutions. Prior Firms: Crux, Goldman Sachs

  • I’d focus on enabling capabilities for business users of the data who have the power in the purchase decision. Equip them with easy search and browse across datasets and the universe of securities.
  • And equip them with easy access to evaluate the data to discover/confirm the value based on investment thesis.

James Budd, VP of Research Sales at ETF Global

  • Onboarding best in class data products is priority number one.
  • Second would be to have the right sales process which is twofold: network to sell the data and having the right team with specialized knowledge rather than generalist. 

Mark Hookey, CEO of DemystData

  • White glove curation of relevant data to each client’s bespoke problems, and increasing focus on data certification and compliance.

Mark Vaughan, Head of Dataset Sales at Eagle Alpha

  • Hire category experts for each alternative data category. Today we have 26 categories in our taxonomy.
  • Make it easier and quicker for buyers to trial datasets.

Matthew Glickman, VP Data Marketplace, Customer Product Strategy at Snowflake

  • Provide measurable value for data sellers by partnering with them to ensure their success on the platform while aligning our combined business interests.
  • Provide consolidated, cross-vendor liquidity of data content from primary and secondary sources on a platform where the data buyers are already running their workloads.

Mani Mahjouri, CEO/CIO at Blueshift Asset Management

  • Streamline the frictions around trial and confidentiality agreements.

Raghav ‘Mady’ Madhavan, Chief Scientist and President of Alto Meta Consulting. Prior Firms: UBS, J.P. Morgan, Morgan Stanley

  • Ensure uniform tagging mechanisms across data providers to extract the power of connected datasets.
  • Provide a mechanism for data buyers to easily and quickly evaluate the 'fit for purpose' of a dataset.

Ronit Koren, Former Director of Partner Programs at Audit Analytics

  • Understanding and prioritizing the demands of the buyer with an understanding and comprehension of the vendor and their dataset. The critical mandate would be to be the most informed intermediary and create win-win opportunities.
  • Facilitating ease of data exchange between buyer and vendor. Often times (again, experientially), the data exchange requires delivery configurations that could take the vendor months to develop. Delivery options or development assistance would help expedite the time to platform accessibility.

Scott Burns, Head of Data and Research Products at Morningstar

  • Mapping.
  • Not to be facetious, but if you have data, but your clients need to expend exorbitant resource to consume it and use it…then you don’t have an exchange of products, you have an exchange of white elephants!

Tim Baker, Head of IEX Cloud. Prior firms: Refinitiv, UBS

  • Ease to contribute data: 1) low-friction access to data to test and the data; and 2) a flexible commercial model.
  • An easy-to-use delivery mechanism.

Toby Dayton, CEO at LinkUp

  • To thoroughly evaluate buyers and sellers of data and provide increasingly rigorous and sophisticated curatorial services for those buyers and sellers.
  • Secondarily, with the massive influx of participants around every aspect of the industry, data exchanges need to be continuously expanding educational opportunities as the industry evolves, matures, and grows increasingly complex and multi-dimensional.

Question 4: How Will Dataset Sales Be Different Three Years From Now?

Apu Kumar, President at LotaData

  • We expect data exchanges will become more specialized. One should expect an entirely new landscape of thriving vertical exchanges that specialize in curated datasets for their industry vertical with AI/ML techniques applied to maximize value from such data.

Benjamin Zweig, CEO at Revelio Labs

  • Data analysts are already transitioning from accounting mindsets, looking for exact numbers from small files, to statistical mindsets, looking for novel insight in a world of big and messy data. Like in other sectors, this trend will likely continue, leading to an increased appetite for and familiarity with non-traditional data.
  • We expect the data provider market to become more consolidated, with fewer high-quality data providers. This will lead to faster sales, more effective onboarding, and, most importantly, more trustworthy analysis.

Darren Voges, Data Strategy Consultant. Prior Firms: Facteus, ADP, Yodlee

  • There will continue to be more entrants into the data monetization arena across the various types of data products. This competition will drive down prices and require an even higher level of sales and ongoing support or you’ll lose out to your competition.

Elizabeth Pritchard, Founder of White Rock Data Solutions. Prior Firms: Crux, Goldman Sachs

  • Dataset sales 3 years from now will move more online. The process will follow similar steps yet the amount of time between discovery of a dataset through the evaluation and licensing process, onboarding and on through to the end use case will be automated and therefore much faster.

James Budd, VP of Research Sales at ETF Global

  • 3 years of additional history for those meticulous quants! 

Jonathan Neitzell, Founder of Anduril Partners. Former role: CDO (Fundmamental Equities) at Goldman Sachs Asset Management

  • If one views Decision Process workflow as an ecosystem, then the components really should tend towards the concept of interconnected standards and frameworks. Data analytics is likely to become a universal language across all industry verticals over the next several years. Whether it is asset management in financial services, or corporate market intelligence teams across public and private enterprise, we all will be looking for key performance indicators (KPIs) to guide our definition of success and alert to risk of failure. 
  • To the extent that data sets and intermediaries can deliver relevant KPIs to business stakeholders, there will be a dramatic increase in demand for data and maturation of the models to digest it. Early technology platforms like ServiceNow, Splunk, and Workday recognized this potential quickly, and incentivized partners to create pre built integrations with their workflow which accelerated customer time to value. It seems reasonable to expect the data vendor landscape to interconnect with relevant workflows and evolve in a similar way. 

Mark Hookey, CEO of DemystData

  • Data testing will be free at scale and there will be an increasing disaggregation of the value chain (data specialists, versus platforms, versus distribution, versus content).

Mark Vaughan, Head of Dataset Sales at Eagle Alpha

  • Purchasing data will become more transactional as adoption rates pick-up.
  • Currently each individual offering needs to be pushed out to the market and the full sales processes needs to be managed every part of the way.
  • Data exchanges that standardise the sales process and data offerings will not only speed up the sales cycle but make it more of a transactional purchase.  

Matthew Glickman, VP Data Marketplace, Customer Product Strategy at Snowflake

  • With the bar lowered with increased access to data buyers on consolidated marketplace platforms, there will be a much longer tail of data sellers successfully selling their datasets.
  • Demand and supply for data enrichment services will be even more important than buying data itself.

Mani Mahjouri, CEO/CIO at Blueshift Asset Management

  • Video will play a much larger role.

Raghav ‘Mady’ Madhavan, Chief Scientist and President of Alto Meta Consulting. Prior Firms: UBS, J.P. Morgan, Morgan Stanley

  • The low-end sales will be self-service, with focus on well understood clusters of data sources that address specific use-cases. This can be facilitated by searchability of data sources by purpose - for example by tags such as ticker aggregates like industry groups, as well as specific economic or business measures such as subscriber growth.
  • High-end sales will remain Proof-of-Value (POV) driven - however with automation that accelerates the process of finding the incremental value each additional new data set will provide.

Ronit Koren, Former Director of Partner Programs at Audit Analytics

  • I believe that vendors can be more accommodating to buyers by adapting and developing products to their needs. 
  • There will be broader applications created to expand opportunities for sales and a deeper focus on what needs to be brought to the table. 
  • Refining of the value proposition will be inevitable as the data space continues to get more crowded.  

Scott Burns, Head of Data and Research Products at Morningstar

  • As datasets become bigger and more complex, the cost of transferring, hosting and the related compute costs will be unsustainable. Buyers will become comfortable with “renting” the data through analytics platforms, rather than requiring that they own and host the data outright.

Tim Baker, Head of IEX Cloud. Prior firms: Refinitiv, UBS

  • The future of data delivery is cloud technology, enabling easier search, delivery and integration into analytics and workflows.

Toby Dayton, CEO at LinkUp

  • Leading data providers in three years will be the providers that have solved mapping, accessibility and deliverability, quality, utility, and value and they will instead have shifted their focus to the depth, breadth, multi-dimensionality, and refinement of not just the data itself but the broader suite of data solutions, applications, product offerings, data combinations, and derived work that allows for and delivers a vastly superior value proposition to an ever-expanding set of buyers around the world.

 

Conclusion

Firstly, based on the views above I think it’s fair to say that data does definitely not sell itself. Some of the ways Eagle Alpha helps vendors monetize their data provides further evidence of this - conferences, webinars, roundtables, roadshows, white papers and podcasts (launching soon).

Secondly, many challenges were identified to improve the dataset sales process including discoverability, evidencing value and streamlining compliance. Regarding discoverability, two examples of how Eagle Alpha is helping vendors: 1) creating independent case studies that demonstrate value; and 2) an alpha capture system that promotes vendors that make successful predictions. Regarding evidencing value, Eagle Alpha has built a proprietary data quality testing tool that helps data buyers speed up the prioritization and trialling processes. Regarding streamlining compliance, FISD is doing great work to create legal and technical standards and Eagle Alpha has incorporated all its published standards into our offerings. I was surprised that no one mentioned that some datasets are frankly not sellable – to solve for this Eagle Alpha’s ‘Discovery Phase’ offering puts a potential dataset in front of a dozen potential buyers in order to obtain blunt feedback and advice.

Thirdly, when the thought leaders were asked what they would do if they ran a data exchange it was interesting to see so many different priorities. Personally, I liked Tim Baker’s “low friction access” comment because I think it encapsulates lots of the points that were made. And because it is consistent with the top priority for Eagle Alpha in H2 2020 which is to get static trial data into the hands of data buyers as seamlessly as possible. The other point that stood out for me was the comment by Scott Burns that mapping is critical otherwise data exchanges will be full of which elephants! I fully agree with this comment but unfortunately, as far as I am aware, there is no silver bullet to solve the mapping issue at scale.

Lastly, it is clear there will be even more data exchanges in the future. Data exchanges are now in an arms race to provide a fast and frictionless solution and integrates into user workflows. But then what – how will all these data exchanges differentiate from each other? In my opinion technology alone can’t solve all the challenges in this blog, relevant solutions and data experts are needed. Which firms will ultimately win the data wars? Here at Eagle Alpha we will continue to focus on solving some of the key challenges our customers (vendors and buyers) have. And we have lots of big ideas for 2021! Ultimately Eagle Alpha wants to be part of the winning team that provides the best solution worldwide.

Thank you to the 15 thought leaders that contributed to this blog.

I welcome your thoughts on this blog.  Please reach out to me direct (emmett.kilduff @eaglealpha.com).

By Emmett Kilduff

Emmett is the founder and CEO of Eagle Alpha. Eagle Alpha is the pioneer connecting the universe of alternative data.