Advanced Analytics and AI Readiness with Fusion by J.P. Morgan

Read J.P. Morgan's report, The key to effective data management:

Institutional investors are adopting increasingly complex investment strategies that require more data and advanced analytics. This has driven investors toward AI-readiness for data management, which can accelerate their ability to obtain actionable intelligence.  The rise of ESG strategies and the structural convergence of public and private assets are additional factors that are complicating data workflows.

The top data challenges

Data access, consumption and integration are major challenges facing asset managers, asset owners and corporates. Only a quarter of financial organizations can source and manage all their data inputs effectively, according to a report from Worldwide Business Research (WBR) Insights and Rimes.

For example, data discovery and distribution often present obstacles for organizations, due to information sources often being siloed. Plus, each investment process relies on data feeds from various vendors and formatting can be inconsistent; this requires data teams to spend a significant amount of time on standardization.

Manually addressing these complexities and reconciling data does not fit smoothly into existing workflows. Firms use multiple data providers, with many data feeds and vendor connectivity nuances. There’s a dependency on methods of data transfer, like FTP, that are disconnected from the processes and this further complicates error handling, often leading to limiting data strategy scale.

Against the backdrop of these challenges, there is an increasing need to make data available for data science and machine learning – and the data must be in a state that’s ready for use, while ensuring there are suitable controls around access and how it’s used.  

Jammed data, stored in a variety of sources

Financial institutions use many data sources and vendors, but data is typically structured differently across sources and is often incomplete, with differing formats and identifiers. Entire teams are required to clean data manually. This process can be time-consuming and expensive, thus limiting data accessibility. Institutional investors who are facing pressure to get their data management AI-ready are confronted with another challenge: the data itself isn’t ready, lacking a common semantic layer to model and normalize it across multiple providers, sources, types and structures. 

As data demands continue to grow, different teams have varying data needs. In addition, financial institutions underutilize the data they currently have, leading to wasted money and resources. Data from many sources must be extracted manually. Financial institutions end up polling servers for updates instead of using event-driven notifications and API. These queries are wasteful, unnecessarily consuming bandwidth and leading to processing delays. Plus, the disparate data isn’t unified.  

Accessing usable data efficiently is a widespread issue for institutional investors given the need for manual extraction and pervasive errors. Firms are increasingly investing in more sophisticated data management strategies, with 60% of investment management firms polled telling Broadridge they plan to increase spending on digital and data analytics in the next two years. 

A better way to manage data

Given the pressing challenges financial institutions face in accessing and managing their data at scale data for both public and private assets, investing in cloud-native capabilities can serve as a ready solution for consuming and integrating data.

Data Mesh, part of the Fusion solution, enables investors to access data across sources through modern distribution channels including API, Jupyter Notebook and cloud-native channels such as Snowflake and Databricks. Through Data Mesh, investors can simplify their consumption models and accelerate their analytic outcomes and subsequent strategic decisions. 

As a leading asset servicing provider, J.P. Morgan uses its expertise and deep understanding of complex client challenges to offer a better way to manage data. Fusion by J.P. Morgan, a data technology solution for institutional investors, provides end-to-end data management, analytics and reporting across the investment lifecycle. The platform seamlessly integrates and combines data from multiple sources into a single data model that delivers the benefits of scale and reduced costs, along with the ability to more easily unlock timely analysis and insights. 

Addressing key challenges and solving data access issues across a firm’s data landscape requires the right technology and expertise. This is where Fusion Data Mesh can help firms take advantage of the cloud’s elasticity and tap into the growth and rapid development in analytics.