Decentralized data means choppy seas
Data challenges are holding back institutional investors from reaching advanced analytics, Artificial Intelligence (AI) and Machine Learning (ML) goals to improve operational efficiency and portfolio performance. Savvy AI- and ML-readiness requires clean, standardized data to expedite insights-driven decisions across the investment journey.
But to power investment decisions currently, investors too often rely on rapidly increasing volumes of unstandardized data from an array of sources, often delivered in different formats and structures that lack consistent identifiers or complete reference data information. Managing this decentralized, inconsistent and incomplete data and extracting value is time- and resource-intensive, friction-filled and hard for institutional investors to scale across their organizations and within their operating models. In fact, companies lose 20% to 30% of revenue each year as a result of inefficiencies introduced by siloed data, according to a study from market research firm IDC.1
Just as moving from non-standard wooden cargo cases to standard shipping containers revolutionized the freight transportation industry in the 1950s, modernizing investment data management to include stackable, neatly delivered data in standardized virtual containers can also introduce invaluable efficiencies and cost savings while boosting the amount of what can be transferred.
This ability to access and utilize standardized data in a digital container with a uniform look and feel, despite provenance from various providers and sources, is paramount for investors to combine datasets and switch between portfolios and views. It can also ensure that their securities services data is modeled, enriched and harmonized to manage asset allocation and accelerate risk and trade decisions. Especially as the sophistication of investor modeling grows to combat market headwinds and operational costs, the ability to make fleet-footed decisions from data is increasingly crucial, placing a premium on clean, consistent, trustworthy data.
Companies can lose 20% to 30% of revenue annually because of siloed data.
Source: Research firm IDC
Smooth sailing: A uniform data container
Multiple data sources and feeds are plaguing the ability for institutional investors to integrate and manage data at scale.
Typically, data is delivered in different formats and structures from data and analytics vendors. Managing these disparate feeds slows time to market, adds operating costs and makes scaling a challenge. Institutional investors certainly can’t rely on outmoded incumbent methods to normalize data that are expensive and resource-draining, squandering the precious time of data and engineering teams who have to service data requests manually instead of deploying those resources to more strategic tasks.
The challenge of contending with the resulting erroneous or inaccessible data is mounting: data downtime is doubling year over year, and average resolution time is increasing 166%, per a Wakefield Research data quality survey.2 The economic consequences are grave, too: more than half of businesses believe data quality issues affected 25% or more of their revenue, according to Wakefield.3 In fact, the average impacted revenue jumped from 26% in 2022 to 31% in 2023.4
Graphic showing the cost of slow data. Erroneous or inaccessible data is causing downtime to double year-over-year. The average resolution time is increasing 166%. The average impacted revenue from data quality issues increased from 26% in 2022 to 31$ in 2023. Sources: Wakefield Research data quality survey and Ibid.
AI-readiness, which would automate the data normalization process, requires organizing complex data in a pristine fashion to then allow speedy, insights-driven decisions on performance analytics, exposure analysis and market risk, investment compliance, ESG analytics and liquidity needs. As such, there’s a natural evolution within the data industry – normalized, consolidated and complete investment data from a series of sources with all accounts and composites included.
Another major pain point: datasets tend to have missing reference data, account information and portfolio details, leading to time wasted completing the data before analysis or reconciliation can take place. The organizers need to apply a common semantic, a standardized set of rules to make the data easily usable with structures and identifiers that are the same. Missing country codes, account names, categories and identifiers, for instance, complicate analysis between datasets.
Oftentimes, data coverage is also incomplete, as some asset types aren’t modeled, like private assets, over-the-counter (OTC) derivatives and bank loans. As portfolios grow in complexity, new instruments and asset classes are constantly added to investment strategies. Linking and mapping each new asset type requires modeling expertise before data can be used. Chief technology officers or chief data officers may want to be able to ingest and normalize data around orange juice futures on the InterContinental Exchange (ICE) and Gold Bullion prices on the Shanghai Gold Exchange (SGE) while also tracking the performance of a private equity fund in Luxembourg and an active exchange-traded fund (ETF) in Australia. Granular information from different asset classes in an array of regions across the world present incongruous, disparate datasets that must be pooled and centralized for cohesive analysis.
Best-in-class companies are 30% more likely to have a dedicated data management solution.
Source: Aberdeen Strategy & Research
Institutional investors ultimately want a unified container to access fund data, instrument reference data, pricing data, internal data, company financials, earnings estimates and geospatial data. Integrating and managing this data in the cloud streamlines the information in a way that’s interoperable and allows for a seamless workflow that can be implemented into an overall strategy for decision-making for front, middle, and back office.
Legacy data management systems are in desperate need of a refresh. Immediate, frictionless access to actionable data that’s uniform across the business – without the need for manual reconciliations – allows institutional investors to run their portfolios in a way that accelerates decision-making while lowering operational costs by outsourcing data operations when considering data related to both public and private assets. They can then use this organized data for pipeline management, along with analytical and operational workloads.
In fact, best-in-class companies are 30% more likely to have a dedicated data management solution to ensure that valuable data is identified, categorized and optimally employed, per Aberdeen Strategy & Research.5
Ingest, normalize, synthesize
In order to access and utilize consistent data that looks and feels the same, from different providers and both internal and third-party sources, institutional investors can use Fusion Containerized Data from J.P. Morgan, which ingests, normalizes and harmonizes data from multiple sources and types into one all-encompassing vessel. This creates consistent data across multiple securities services providers and saves your organization from time-consuming data wrangling.
This end-to-end solution provides investors with consistent and enriched data across business services, leveraging a new common semantic layer to model and standardize data across multiple providers, sources, types and structures. Fusion ingests data and uses algorithmic association and common identifiers to normalize it at scale and reduce errors caused by incorrect identifiers. It processes and links data, making it interoperable and ready for AI and ML applications with well-structured data containers in clean, optimized rows and columns, with the same identifiers, consistent across all sources to provide a single panoramic view of your portfolio and accounts. Investors can access dataset source files in consistent containers anytime, using cloud-native channels, including API, Jupyter Notebook, Databricks and Snowflake. Fusion has collaborated with Snowflake to demonstrate a concrete use-case for Containerized Data and how it can speed up the implementation of AI given that the data arrives in a clean state. For Snowflake users eager to leverage the Snowflake Cortex product, a suite of AI features that use LLMs to analyze data and build generative AI applications, it is necessary to utilize clean data. Containerized Data by Fusion at J.P. Morgan provides institutional investors with the consistent data that can fuel these future-looking capabilities.
Complete your end-to-end workflows with containerized data
Your data looks and feels the same, ready to be used across your operating model. Fusion ingests, normalizes and harmonizes data from multiple sources and types, including from J.P. Morgan Securities Services and additional providers.
Gain a complete overview of each element across your portfolio and accounts, integrated into a single panoramic view. With all your investment data normalized and linked, you can see your Custody,
Middle Office, Fund Accounting data and more, including public and private assets, in one place. Save time and resources cleaning data and reduce operational risk, with datasets delivered in a standardized format.
View, filter and drill down into your Securities Services data using an intuitive exploration tool. Easily jump between different data domains, portfolios and accounts. The linked data model enables interoperability and viewing of your harmonized data.
Easily access and integrate your normalized securities services data into your tech stack using modern cloud- native channels, including API, Jupyter Notebook, Snowflake, Databricks, and more. Benefit from all your containerized data, consumed and integrated wherever you are, in any data environment.
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