A futuristic digital landscape featuring glowing blue and purple data points interconnected in a wave-like pattern. The abstract visualization represents artificial intelligence, big data, or a digital network, set against a dark background with scattered floating light particles.

Leveraging Data Marketplaces in Asset Management

  • Shubhi Ahluwalia, Olivia Krylov, Sean O'Brien & Chris Senackerib
  • 19 March 2025

As data is increasingly vital for decision-making in asset management, effective data management has become a key competitive strength. In addition to managing traditional data sources like market, portfolio, client, security, and regulatory data, firms must also continually source and manage alternative datasets such as social media, ESG and even specific consumer behavior data from geolocation tracking.
However, this exacerbates the challenge of keeping data organized and available across the firm, rather than trapped in silos and systems. In this article we look at why asset managers should establish a modern data marketplace platform and outline best practices for implementing this type of solution.

Data marketplaces have emerged as an innovative solution to help organizations share data internally or exchange information with different organizations. This is especially relevant in the asset management industry, where legacy systems and data technologies continue to impact operational capabilities and slow down ‘time to market’ for new products and data sources.  

Data marketplaces function as online ‘stores’ that facilitate the exchange of data ‘products’ between data producers and data consumers, for example, through cataloging data to facilitate its discovery, managing who can access the data for what purpose, and by supplying data products.

Key Players in a Data Marketplace  

A circular data ecosystem diagram illustrating the key components of a data-driven infrastructure. At the center, a teal circle contains core elements, including Purpose-built Models, Dashboards & Analysis, APIs, Apps, Data Sources, ML Pipelines & Models, and Conformed Data—each represented by an icon. Surrounding this central hub, a dotted circular connection links five outer grey icons symbolizing various data-related functions such as networking, process automation, certification, integration, and collaboration. The visual represents a structured approach to data management, analytics, and machine learning in asset management or financial services.

Data products collect datasets and process them in ways that make them easier for businesses to use and gain insights. They begin to be created when a data producer or steward uploads a clean dataset to the marketplace and adds defining metadata such as description, date last updated, size, and even sometimes pricing or allowed uses. They can take the form of a managed dataset, a dashboard/report or an analytical model.  

Data consumers can then browse the marketplace on a user-friendly front-end interface, search for a data set or product by keyword, select, request access and extract the data or data product to serve a variety of uses.  

KEY USES FOR ASSET MANAGERS

Creating the right data products and making them easily available through an internal data marketplace generates competitive advantages for asset managers in several core business areas including:1

  • Analyzing market trends. Asset managers leverage market or technical analysis to identify patterns, forecast movements/macroeconomic factors and better understand how the market is moving to support more informed decision making. 
  • Portfolio management. Data analytics help asset managers to understand risk, return and correlations between investments and asset classes. Data tools such as dashboards and correlation matrices can help asset managers to understand portfolio behavior and track key metrics – especially useful when managing multi-asset portfolios and other complex funds. 
  • Risk management. Data analytics can help to pinpoint, measure and manage risks, including modeling market conditions and performing stress tests. 

Across these and other use cases, the benefits of introducing a data marketplace fall into two broad categories: 

  • Improved decision making and innovation. Enhancing data quality and access to a diverse range of information through data marketplaces facilitates more informed and faster decision making. According to the Sixth Annual Gartner Chief Data Officer Survey, respondents who successfully increased data sharing “led D&A [data & analytics] teams that were 1.7 times more effective at showing demonstrable, verifiable value to D&A stakeholders.”2 The influx of digestible information from a data marketplace – including market data, alternative data sources, business intelligence/research and information about economic indicators – increases engagement with data and can also lead to the discovery of new applications and the development of innovative data-based products and services. 
  • Increased efficiency and data security. Data marketplaces help to streamline the data value chain, significantly boosting efficiency as firms work to answer key business questions. By providing a single location for data access, they reduce the time that employees spend searching for and preparing data – cutting costs and boosting productivity – as well as simplifying processes and fostering collaboration across projects, functions and departments. Data marketplaces help the firm to consolidate redundant data and improve data quality. They can be used to automate many data management processes to reduce the time spent on data preparation, cleaning and error correction. Improving the security surrounding data transactions is a huge area of focus for organizations, and a well-designed data marketplace can allow data usage to be monitored as well as ensuring strong data governance and compliance with data and privacy regulations.3

Data Marketplaces: Key Outcomes

A structured infographic outlining the benefits of a data management strategy in three key areas: Simplify Consumption & Accelerate Time to Insights Decreases time to discover and access data. Reduces time spent accessing analytics. Improves visibility to data for a broad group of stakeholders via a centralized solution. Improve ROI & Efficiencies Reduces redundant data by consolidating it into reusable products. Decreases time required to enable and access data. Optimizes investments in data. Insulates consumers from upstream changes. Improve Standards, Quality & Compliance Reduces time spent on data quality resolution. Enhances data conformance, standards, and collaboration. Improves compliance with governance and data use rights. The infographic uses upward and downward arrows to indicate improvements and reductions in various processes, visually emphasizing the impact of efficient data management in asset management or financial services.

BUILDING A DATA MARKETPLACE – FOUR KEY STEPS 

Building and scaling data marketplaces for asset managers requires a structured approach that progresses from defining the scope and engaging stakeholders to full operationalization:

  1. The first step is to identify the critical data sets used throughout the firm and assess their suitability for placing within a data marketplace. In this phase of the process, the firm should also start assessing data quality within each data set. Alongside this initial data assessment, a firm must identify the end users that would benefit most from a data marketplace and examine how they interact with data. The firm will want to create a use case catalog and explore the data products that may need to be included in the data marketplace.  
  2. The firm must then prepare and organize its existing data products by defining and documenting data models, business definitions and taxonomies. This step also involves defining logical data domains and cataloging elements, e.g. with tags and metadata.  
  3. The next step is to design standards and governance to ensure the integrity and soundness of the data marketplace. This involves creating elements such as agreed definitions, standards for metadata, security protocols, definitions and quality benchmarks. The firm must determine the governance standards that data products must meet to enter the marketplace. By testing and refining frameworks, organizations can embed governance, enable collaboration across the firm’s divisions and establish scalable solutions.  
  4. The process culminates in the development phase during which the firm must choose a platform – through buying, building or partnering – and then implement it. The development phase includes adding security measures as well as access and data lineage controls. It is important to ensure the platform is accessible and easy to use, to empower self-service and foster the creation of data-driven insights across the enterprise. Once the development phase is complete, the firm will need to integrate the marketplace with its existing tech stack, launch the data marketplace and train employees on the proper use of the platform.  

The platform needs to be designed so that it can efficiently change and evolve. Firms will need to continue to add data products, scale the platform to handle increasing volumes of data and user demand, and maintain and adapt the platform based on changing business needs. 

Due to the complexity and centrality of data marketplaces, it is critical that firms follow best practices and a well-defined process when it comes to design, implementation and scaling. Furthermore, critical aspects of data management will need to be adopted into the firm’s daily processes. 

A structured table outlining a data governance and marketplace implementation framework, divided into four phases: Define & Scope – Identifying use cases, assessing data models, defining data ownership, and establishing a framework for data products and governance. Test & Learn – Prioritizing data owners, running proof-of-concept (PoC) projects, refining governance models, and embedding metadata and governance standards. Federate & Build – Expanding data frameworks across divisions, upskilling data owners, creating priority enterprise data products, and federating marketplace frameworks. Scale & Mature – Enabling governed data products to be published, socializing data marketplace consumption, creating contextual data products, and fully operationalizing the framework for self-service and oversight. The table categorizes activities under Where (Divisions), Who (Data Owners), What (Data Products), and How (Operationalize) with a step-by-step approach to integrating data governance into an enterprise-wide marketplace.

CONCLUSION – DATA MARKETPLACES ARE ENABLERS

Data products and marketplaces enable and encourage people across the enterprise to leverage data. They democratize access to data and have the potential to enhance data quality and operational efficiency at individual firms and across the industry. 

By enabling asset managers to efficiently acquire and integrate both traditional and alternative data, organizations can foster better investment strategies and risk modeling, improve decision-making and encourage innovation.  

After successfully implementing a data marketplace, asset managers can explore opportunities to expand the framework to unlock additional value from different kinds of data. As the industry continues to evolve, this is likely to prove crucial for asset managers seeking to maintain a competitive edge in an increasingly data-driven environment.  

References 

1 https://www.morningstar.com/business/insights/blog/investment-data/asset-management-analytics  
2 https://www.gartner.com/smarterwithgartner/data-sharing-is-a-business-necessity-to-accelerate-digital-business  
3 https://www.harbrdata.com/blog/internal-data-marketplaces-everything-you-need-to-know  

 

19 March 2025

Authors: Shubhi Ahluwalia, Olivia Krylov, Sean O'Brien & Chris Senackerib

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