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I N T R O D U C T I O N

Historically, the financial services industry predominantly relied on readily available public-security and company-level data (i.e., regulatory filings, broker research, pricing data) to make investment decisions.

FIN A NCI A L IN V ES TING AC TI V ITIES MOV ED BE YOND SINGLE

A S SE T- CL A S S FO CUSED S TR ATEGIES while still relying on siloed security-

level database architecture, introducing complexities in aggregating content

across instruments and entities to identify exposure. The shortcomings of this model were exposed as the 2008 financial crisis highlighted the fact that firms and global

regulators did not have the necessary metadata or robust data connections

available to effectively define and measure counterparty risk exposures.

THE IN TR ODU C TION OF BIG DATA A ND A DVA NCEMEN T S IN

TECHNOLO GY highlighted the growing importance of organizing and connecting diverse and

disparate data intelligently. The availability of alternative datasets, which include unstructured content and the need to

visualize connectivity, has expanded data-driven strategies beyond typical security-level reference and market data to unique entity-,

individual-, and company-level content. To exploit the benefits of big data, organizations must transform operationally-driven siloed

databases into structured data models.

The industry depended on security-level identifiers and siloed databases to consume and manage this content, making it a challenge to aggregate and connect data in meaningful ways.

Over time, two major shifts have drastically changed the investment landscape:

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The financial industry is in a data connectivity “arms race.”

Using this data governance framework, machine learning (ML) and artificial intelligence (AI) can drastically expand financial analysis and modeling capabilities. Some examples of this include leveraging ML and AI to recommend investment opportunities, automate trade decisions, enhance quantitative algorithmic trading models, and apply natural language processing to analyze earnings calls and other corporate events for sentiment analysis.

Given the increased sophistication of these trends, the underlying database infrastructure and identifiers used to represent securities, business entities, funds, and their corresponding relationships have failed to keep pace. The financial industry is in a data connectivity “arms race.” The financial institutions that can effectively leverage and connect conventional market and reference data with alternative and unstructured content assets—and aggregate these datasets at an enterprise level—will be at a significant advantage compared to their peers. The successful execution of a data and technology strategy that exploits connectivity and uncovers alpha generation opportunities provides more accurate risk exposure analysis and can ease compliance by effortlessly supporting regulatory reporting requirements. For these reasons, the use of connected content sets, or “smart data,” can result in a significant competitive advantage.

I N C O R P O R AT I N G A N E N T E R P R I S E - W I D E D ATA M A N A G E M E N T S Y S T E M

As a growing amount of information becomes available in the market, managing and organizing multiple sources of data has become increasingly challenging. Data silos introduce redundancies and inconsistencies that limit productivity and increase cost. Implementing an enterprise-wide smart data solution allows for increased efficiency and scalability while providing flexibility. Firms need a solution that can be deployed across the back, middle, and front office to assist in the linking and aggregation of various content sources and satisfy the needs of multiple workflows.

Financial firms globally are challenged to aggregate financial, operational, and third-party content at an enterprise level. However, relying solely on security-based architecture limits a firm’s ability to support and connect business entities, funds, people, and securities. An effective data management strategy requires moving to an entity-based architecture.

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In this model, the primary key is entity-centric, which allows the full scope of a firm’s content to connect with a single master entity table and be deployed on an enterprise-wide basis. Firms need to leverage symbology as well as unstructured and alternative data. Here are some of the key primary datasets that provide the foundation for a financial entity database:

• Entity structure/hierarchy relationships

• Debt and equity capital structure

• Credit parent relationships

• Supply chain relationships (shipping transactions)

• Competitors

• Investments (private equity, venture capital, funds, ETFs)

• Ownership (equity and fixed income)

• Sanctions lists

• Corporate governance and activism

• People (corporate executives, financial professionals)

• Transactions (M&A, PIPEs, share repurchases)

• Credit ratings

• Sector/Industry taxonomies

Firms need to leverage symbology as well as unstructured and alternative data.

Pricing Fundamentals Estimates

Company Documents (filings, transcripts, research reports)

Bond Issuance/Terms and Conditions

This data needs to be connected to datasets that will help to uncover unique parent/subsidiary/partner relationships. The incorporation of this additional data allows for limitless levels within each entity hierarchy, creating significant flexibility in aggregating content. These relationships must be updated daily as global-investment activities, mergers and acquisitions, and corporate-action events are constantly taking place. Firms should cross-reference their single-entity data with these relationship types to accurately evaluate risk exposure:

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Here are some examples of how connecting multiple datasets at an entity level can help an organization mitigate risk:

• Leverage geocoding to fully understand the geographical distribution of an entity’s organizational structure

• Analyze security-to-issuer mapping to accurately analyze credit and risk, specifically where debt associated with an entity is overlooked and not aggregated under the ultimate parent

• Examine single name exposure and risk aggregation. Scan all your portfolios, or a subset, to quantify your exposure to a single security, issuer, industry, country, or a specific set of securities

At the core of the master entity table is a global identifier mapping to link these disparate datasets. The implementation of an enterprise-wide entity master eliminates data redundancies and discrepancies, leading to cleaner and more accurate data. At a high level, concordance is the connection of two or more disparate concepts based on the agreement of relevant characteristics. Concordance can also be used to describe the reconciliation of a concept to entity and security identifiers. This can be accomplished through known identifier mapping or an implied match based on a collection of the same or similar attributes.

There are several options here, including freely available systems such as the Legal Entity Identifier (LEI)1 system as well as vendor-specific and in-house proprietary systems. The LEI was developed after the 2008 global financial crisis to provide a global standard for entity data management2. With the post-crisis implementation of new financial industry reporting regulations, including MiFID II, Dodd-Frank, and GDPR, the LEI was intended to ease the burden of data management. While adoption of the LEI has not been as widespread as expected due to a lack of regulatory mandates, the growing cost of integrating outside and home-grown systems underscores the importance of an industry standard.

Three market trends make having a data management solution critical for financial services firms:

Rapid growth in private markets

Explosion of data vendors and associated integration challenges

Proliferation of content, absent a known identifier

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1 About LEI: https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei

2 “Entity Data Management Handbook – Fifth Edition.” A-Team Insight. 2019. https://a-teaminsight.com/guides/entity-data-management-handbook-fifth-edition/?brand=ati

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Firms require a broad coverage of securities and entities, exhaustive mapping capabilities, and product flexibility that will allow the solution to meet firms’ ever-expanding needs.

Content and Technology Demands:

Gartner highlighted the growth of ML and AI in augmented data management as one of the top data and analytics technology trends in 20193. Using ML and AI to “make data management categories including data quality, master data management, metadata management, data integration, as well as database management systems (DBMSs) self-configuring and self-tuning” will allow firms to automate many manual tasks. This, in turn, will free up resources to work on more value-added tasks.

Superior entity resolution best practices will continue to yield smart datasets that continue to extend alpha generation, reporting, and compliance at financial institutions globally.

3 Ross, Andrew. “Gartner: Top 10 Data and Analytics Technology Trends for 2019.” Information Age. February 18, 2019. https://www.information-age.com/gartner-data-and-analytics-technology-trends-123479234

Whether choosing to buy or build a data management solution, investments in content and technology are key.

Sourcing and Creating New Entities and Entity Types

Enhancement and Cleansing of Entity Metadata to Facilitate

More Accurate Matches

Coverage and Expansion of Supported Identifier Types

Achieving Scale Through Advanced, Automated Mappings to Reduce Manual Dependency

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TA P P I N G I N T O N E W A N D U N I Q U E D ATA S O U R C E S

More and more asset managers are looking beyond traditional datasets in search of alpha-generating signals. To get an edge on the market and competitors, looking beyond structured data into the world of unstructured data is no longer an option but a must for systematic and quantitative investors.

The new datasets being demanded by the financial industry are voluminous and complex.

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The new datasets being demanded by the financial industry are voluminous and complex. This ever-increasing volume of data challenges the industry to move as quickly as possible to make innovation happen. The availability of alternative datasets has expanded data-driven strategies beyond typical security-level reference and market data to unique entity-, individual-, and company-level content.

Much like it sounds, alternative data offers different sources of data (images, billing statements, heat maps, social media interactions), often at a higher frequency than the traditional filing and price data. Satellite data, for example, can offer real-time insight on the foot traffic or business a specific corporate location is attracting. If a private satellite can see that there are 80 spots open in a food chain’s parking lot and only four are filled, by aggregating information about all the parking lots that connect to that restaurant, you’d have the data to make an educated guess on how it’s performing. Access to this information offers a competitive advantage to those in the investment community who can see it. Since these satellites are usually privately owned, brokering deals with the firms collecting that data is becoming ever more critical to success.

While alternative data was a field once dominated by hedge funds, it is quickly being adopted by more traditional asset managers4 who are looking to these new data sources to feed their investment strategies. Alternative data increases transparency, which can help clients evaluate existing positions on companies they cover. Today, we’re seeing the adoption of alternative data not just for alpha generation but also for the risk management use case. Deeper company insights are helping investors spot opportunities as well as risks.

Satellite data, for example, can offer real-time insight on the foot traffic or business a specific corporate location is attracting.

4 Insight. “How Can Alternative Data Drive Success for Active Manager?” November 30, 2017. https://insight.factset.com/how-can-alternative-data-drive-success-for-active-managers

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Machine learning and artificial intelligence can help firms go through large volumes of data and look for trends, drastically expanding financial analysis and modeling capabilities.

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However, as investment managers are evaluating new and unique sources of data, they must also make sure the same strong vetting and due diligence processes are in place as with traditional equity data. Taking a methodical approach to incorporating alternative data into your workflow is critical. The key is to identify what types of data feeds will supplement and improve what you already have in your investment process. Alternative datasets need to add value to the existing foundation of entity-level data that firms are already using.

In addition to alternative data feeds, AI’s role in helping firms incorporate novel data and technologies is on the rise. ML and AI can help firms go through large volumes of data and look for trends, drastically expanding financial analysis and modeling capabilities. Some examples include leveraging ML and AI to recommend investment opportunities, automate trade decisions, enhance quantitative algorithmic trading models, and apply natural language processing to analyze earnings calls and other corporate events for sentiment analysis.

The key to extracting meaningful insights from these analytics is for the data scientists and business leaders to work together in defining the desired outcome. This link between analytics and business operations—with data updated in real time to allow a firm to react to events as they occur—is what Gartner calls “continuous intelligence5.” More and more firms are moving to this model, using real-time data and analytics to make smarter decisions.

Taking a methodical approach to incorporating alternative data into your workflow is critical.

5 Ross, Andrew. “Gartner: Top 10 Data and Analytics Technology Trends for 2019.” Information Age. February 18, 2019. https://www.information-age.com/gartner-data-and-analytics-technology-trends-123479234/

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G R O W T H I N C L O U D + O P E N S O U R C E T E C H N O L O G I E S

Adding linkages and keys to vast amounts of content does not mean creating a giant, inflexible data warehouse that is costly to maintain. On the contrary, we are moving to a world where all kinds of data can be stored in location as is, with the scheme created on demand, thus reducing storage costs. This is the very concept of a data lake6, a pool of structured and unstructured data stored without a specific purpose in mind. Data is loaded into the lake directly from source systems and transformations only occur to fulfill the needs of each query. Therefore, data lakes are highly agile and adapt easily to changes.

In addition, many companies are moving their data to the cloud, gaining flexibility and scalability with lower costs. Storing data on the cloud enables data to be accessed and distributed easier, both internally and externally. It also facilitates the incorporation of open-sourced business analytical tools. Tableau’s James Eiloart says7, “The concept of data gravity suggests that services and applications are pulled in the direction of where the data resides. So as data moves to the cloud at an accelerated rate, analytics will naturally follow.”

“ The concept of data gravity suggests that services and applications are pulled in the direction of where the data resides. So as data moves to the cloud at an accelerated rate, analytics will naturally follow.”

- J A M E S E I L O A R T, TA B L E A U

C O N C L U S I O N

Today, we find ourselves in a much different marketplace than we saw a decade ago. There has been a shift in focus to deploy enterprise-level database architectures to introduce efficiencies, realize cost savings, and satisfy regulatory reporting requirements. Investment strategies that cross multiple asset classes are becoming the rule rather than the exception. The creation of smart datasets to fulfill the promise of “big data” analytics is becoming increasingly important as organizations look to optimize their content resources to gain long-term value.

This evolution requires the deployment and execution of an effective data management strategy and framework that includes extended identification schemes to support and connect business entities, funds, people, and securities on an enterprise-wide basis. Financial firms recognize the need to organize and manage their content assets in a centralized way, as well as to fully exploit the promise of alternative datasets, ML, and AI to generate alpha.

6 Dennis, Amber Lee. “Date Lakes 101: An Overview.” Dataversity. August 25, 2016. https://www.dataversity.net/data-lakes-101-overview/

7 Baxter, Michael. “Top Five Business Analytics Intelligence Trends for 2019.” Information Age. December 2, 2018. https://www.information-age.com/business-analytics-intelligence-123477004/

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