The past 12 months have been busy ones for data management and technology provider Arcesium.
Winning the award for Most Innovative North American Data Management Provider in A-Team Group’s Data Management Insight Awards USA 2023 and opening a new office in Portugal were among the highlights of a year that also saw the New York-headquartered company expand its sustainability data offering via a partnership with ESG Book.
It also authored a probing paper, published by A-Team group, on how asset managers are harnessing modern data strategies to meet new economic challenges.
Now Arcesium is preparing for a centre-stage role in next month’s A-Team Group Data Management Summit New York City. Data Management Insight caught up with Arcesium’s institutional asset management segment head Mahesh Narayan, to explain the company’s thinking behind the latest developments in data management.Data Management Insight: The theme of your keynote address at the DMS New York City will be “Leveraging Modern Financial Data Platforms for Digital Transformation”. Can you tell us what a modern financial data platform looks like?
Mahesh Narayan: Modern data platforms enable large-scale data integration, advanced analytics workflows and visualisation and reporting to help unlock the value of data within asset managers.
Since they are purpose built for the investment management industry, they come with out-of-the-box financial data models and connectivity to a host of financial markets data vendors, service providers, and various external, and legacy systems.
They harmonise disparate information for an accurate and centralised single source of data. It is important that they offer easy-to-use self-service data analysis and visualisation tools to build dashboards and generate reporting.
On the technical front, these platforms are cloud native, built API first, leverage industry standard open-source technologies and are built to scale effectively, enabling companies to scale out and scale up.
DMI: What investment management processes are being optimised using modern data platforms?
MN: There are almost no limits to the use cases that modern financial data platforms can be used to automate and optimise.
The most common uses cases among asset managers cut across various post-trade and data management activities. These include data aggregation across internal platforms; performance and risk analytics; fund reporting and NAV oversight. They also encompass ESG portfolio analytics; data product creation for downstream systems; historical data management; private markets data management and public and private investment data integration.
DMI: How are institutions achieving this?
MN: Identifying high-value use cases that can be operationalised on these platforms is a critical first step. Implementation is usually a three-, six-, and nine-month iterative cycle of loading data sets, building connectivity, extending data models, building out enhanced datasets and then automating workflows on top.
The speed of implementation of these platforms lends itself to asset managers achieving live use cases in a relatively short period of time.
DMI: How is AI in its various forms being utilised in pursuit of this?
MN: We are in relatively early stages of how AI is being used today in these platforms. Traditional AI methods are already being used to improve data quality, outlier detection, automate alerts, connect datasets and other processes.
Generative AI (GenAI) is expected to have a huge impact in the coming years. We can imagine GenAI automating data pipeline creation and new dataset creation. We should expect data querying and ad-hoc reporting to work off text commands.
GenAI should simplify new data-set analysis and connectivity with current data sets. We can also expect GenAI to materially improve data quality management and tracking and usage reporting.
DMI: Are organisations taking sufficient consideration of use-case needs and costs before implementing AI in their data estate?
MN: We see organisation operating across the maturity curve. Many are getting their teams up to speed in this area while others are in early stages of implementing AI capabilities in some form.
It is good to see that a lot of the decisions being made are use case and ROI driven. Costs remain a challenge across technology and talent/engineering. Financial data platforms have a key role to play to help ease the challenges here.
- Mahesh Narayan will be delivering a keynote presentation at A-Team Group’s Data Management Summit New York City on 26 September. Click here to find out more or sign up for attendance below.
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