[The explosion of ETFs and ETF issuers is increasingly creating a marketplace quagmire and a growing quandary for investors. ETFs with similar names or objectives can look alike, but may differ significantly in holdings, sector exposure, geographic exposure, weighting methodology, concentration, income characteristics, and risk. Standardized and extremely granular ETF reference data, along with detailed classifications, have become necessary to help investors make more accurate comparisons and avoid relying only on a fund’s name or broad category.

There are also a host of other important data issues involved, especially in engineering the data quality needed for effective AI usage in research and portfolio construction. Data design and data platforms are becoming important areas for InvestTech innovation.

To better understand the vital need for this data management solution, we spoke with Jack Kimmel, VP, Business Development and Edward Silverstein, Director, Business Development and Co-Head EMEA at ETF Global - a leading independent provider of enterprise-grade ETF reference data and analytics, and host of the semiannual ETP Forum dedicated exclusively to the global Exchange-Traded Products ecosystem. ETF Global’s next ETP Forum will take place in New York City on November 10, 2026.]

 

Hortz: What are the key issues and hidden risks for investors amid the explosion of ETF choices?

Silverstein: With the rush of ETF sponsors and products coming into the market, many ETFs may share similar names, investment objectives, or broad categories while providing investors with significantly different exposures that can ultimately produce very divergent and unexpected portfolio outcomes.

Kimmel: The continued growth of the ETF market has made product selection more complicated, not less. Investors now have access to multiple funds targeting many of the same themes, sectors, asset classes, and strategies. Small differences in investment methodology or ETF portfolio construction can have a meaningful impact over time, making timely and standardized reference data increasingly important for advisors, institutions, researchers, and other market participants

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Hortz: As an ETF data provider, how are you addressing these issues?

Silverstein: As the ETF universe continues to evolve and products become increasingly specialized, having a more granular framework for understanding these differences becomes increasingly important. We see clients compute incredibly selective universes of ETFs, and a granular understanding of fund-level characteristics is paramount to selecting the appropriate vehicle.

ETF Global’s Expanded ETF Taxonomy is specifically designed to provide that deeper level of classification. The taxonomy classifies U.S. Listed ETFs across asset and geographic segmentations, as well as Product Structure, Strategy, Style, and Exposure attributes.

Jack Kimmel: Within the Expanded Taxonomy, we also maintain 1,557 unique classification combinations across eight primary taxonomy categories, helping distinguish products that may appear similar at first glance but provide meaningfully different investment exposures.

 

Hortz: What are other ways that you develop and ensure more granular data?

Silverstein: We source and maintain our data directly from ETF issuers through our Data Consortium. Each night, ETF Global receives direct feeds from U.S.-listed ETF issuers, enabling us to maintain the most accurate, granular, and timely ETF data across the marketplace.

Kimmel: Let me emphasize, though, that a strong ETF data infrastructure requires more than simply collecting quality information. It requires consistent classifications, standardized fields, careful validation, timely updates, and a clear understanding of how different data points should be interpreted.

ETF Global’s work with market participants through its Data Consortium helps support this broader effort by encouraging collaboration and improving the consistency and credibility of ETF information across the industry by building confidence through data standards.

 

Hortz: How does more granular and standardized data support the increasing usage of AI by investment managers in investment research and portfolio construction?

Silverstein: Bottom line, the quality of AI depends on the quality of its data. Artificial intelligence can process large amounts of information quickly, but the reliability of its output remains dependent on the quality of the underlying data.

An AI model analyzing incomplete information may reach a very different conclusion than one working with complete and accurately classified portfolio holdings. As AI becomes more widely integrated into investment research and product comparison, data integrity, timeliness, and granularity will become essential.

 

Hortz: Is there long-term value to reliable ETF data?

Kimmel: The value of ETF data is not limited to a single trade or portfolio decision. Accurate, timely, and detailed information supports product research, risk analysis, portfolio construction, regulatory review, academic research, artificial intelligence applications, and long-term investment oversight.

Maintaining the integrity of that information over time is essential to helping market participants understand what they actually own and how small ETF differences can have a big impact on their portfolio.

Readers interested in exploring these distinctions can request a complimentary trial of ETF Global’s data and analytical capabilities.

Ed Silverstein: One theme that continues to emerge during ETF Global’s biannual ETP Forum is the need for deeper, more dependable ETF analysis and greater visibility into the data below the water line. As products become more sophisticated and investors gain access to an increasing number of seemingly similar choices, in-depth ETF data can be used to uncover hidden impacts on risk and returns.

This conversation will continue during the next ETP Forum on November 10, where ETF investors and industry professionals will examine the trends shaping the next stage of ETF market growth.

 

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