Commentary: 3 must-haves for data in institutional asset management

Asset and wealth management firms have conventionally not been at the forefront of technological innovation. This probably reflects the conservatism of the sector. Some would also say that innovation is, perhaps, at odds with the cautious and reliable image that an asset manager wants to portray to customers.

But this situation is not tenable. It is well documented that active asset managers are finding it more and more difficult to attract funds. The movement of money to passive funds and the squeezing of margins is predicted to continue.

Morgan Stanley recently estimated that there would be a further 36% drop in revenue from active asset management by 2023.

To create new, more personalized products to go after broader segments of the market, to more intelligently manage real estate, to offer a more digital experience and, ultimately, to generate more alpha, firms must get smarter about their use of technology and, in particular, data.

Fortunately, this challenge is recognized within the industry. I was recently at the 2019 InvestOps investment management technology conference in Tampa, Fla. Time and time again, data was cited as providing the foundation for making existing operational processes more efficient, for creating new types of products and distribution channels and providing the underpinning use of technologies such as robotic process automation and artificial intelligence.

All financial services firms, including those in institutional asset management, are increasingly using data, next-generation database technologies, machine learning and artificial intelligence to better manage risk, spot opportunities and deliver more personalized products to customers, whether that's a high-net-worth individual or a pension fund manager.

Here are three core requirements necessary within any data infrastructure in order to deliver what institutional asset managers need:

Asset managers need a 360-degree view of all data across legacy systems as a significant first step in their digital transformation journey. These data hubs need to be able to ingest data as is, whether that be structured data from trades or unstructured data locked in legal documents or spreadsheets or internet of things data from retail investments.

Each piece of data is important to the overall picture of performance, risk and opportunity. All of it needs to be at the fingertips of the institutional asset manager, and the analytics and applications that support it. Data hubs scale beyond the limitations of existing systems, ingest data from all sources and cleanse that data.

Regulation in financial services now cuts across both the buy and sell side. New research from JWG indicates that, of the 374 legislative initiatives it expects to be targeted at financial services firms by 2021, more than 90% are applicable to both buy- and sell-side firms. Asset managers, pension funds and hedge funds have traditionally delegated much of regulatory compliance to the sell side. That has made the buy side much less well equipped in terms of resources and know-how to deal with the oncoming deluge of regulations. Having good data governance — meaning you can trace where the data came from, how and if it was changed and by whom and when — is critical to meeting regulatory requirements. Data governance that follows and tracks data is critical. As such, institutional asset managers need technologies that put data lineage and provenance at the heart of any data initiative. When asset managers know the hows and whats of data it means that data can be trusted. And trust is critical to enable any firm to derive actionable insights from data, or to share it with others. The role of artificial intelligence in financial services, and its ability to move the needle to higher performance, will be directly related to the quality and trustworthiness of the data feeding the AI machine.

Having a view over all data is good but it only becomes truly useful when that data can be accessed safely by different groups both within and outside a firm. Indeed, a major issue with the data lake concept has been data security and managing access. With the right technology this issue can be solved. With flexible and granular access controls, with encryption by default, the right subset of data will be accessible only by those people and automated processes that have the right privileges. Responsibility for managing access can be taken away from applications and put close to the data.

This approach gives an organization more confidence, leading to more sharing, leading to more value being derived from the data.

In the competition to attract investors, to manage operations more effectively and to stay compliant, asset managers must have the exploitation of data as a core part of their strategy. And to be effective and agile at doing that requires the right technology foundations. Never has technology, and making the right choices about what technology to use, been more important in asset management.

Giles Nelson is chief technology officer, financial services, at MarkLogic Corp., San Carlos, Calif. This content represents the views of the author. It was submitted and edited under Pensions & Investments guidelines, but is not a product of P&I's editorial team.