In an uncertain environment, where organizations and investment teams are under pressure to find new sources of alpha, having a strong data and analytics foundation can help them unlock value and create a competitive advantage that extends well beyond the near-term turbulence. Organizations that embraced data aggregation, quality controls and structured data a decade ago are motivating the larger industry to reimagine its path forward. Many investment managers today are now prioritizing fundamental elements of data management to create a foundation that improves the speed and ease at which they can access data as well as increase the value of insights available through analytics.
Here are four key data and analytics considerations that investment manager organizations should keep in mind to help drive investment performance, optimize operations and capital-allocation decisions and realize efficiencies:
While headlines and hype focus on the potential of unstructured and alternative data, artificial intelligence and machine learning capabilities, and the power of the cloud – the reality is that most investment management organizations are still grappling with many of the most basic data-quality needs. Investment managers once considered adoption of datacentric foundations and associated technologies as means to manage middle- and back-office needs. While many talked about data as an asset, those who embraced a true data-centric operating model across the larger business enterprise remained quite rare. Few institutions focused on using such capabilities as a distinct competitive advantage.
Today, however, the data and analytics capabilities that previously yielded an operational edge are becoming table stakes to compete in a market in which analytics are counted on to drive investment performance, optimize operations and capital-allocation, and realize efficiencies in a hyper-competitive and unforgiving market. There is an awareness that a robust data management foundation is needed before organizations can “turn on” more value-added emerging technology use cases. The reimagination of data management capabilities will not only help organizations better store and manage data, but also enable them to adapt to trends such as predictive - rather than descriptive - analytics.
One of the most striking changes compared to previous eras is that organizations today acknowledge that no one solution can serve all their data needs. The fact that so many are again focused on fundamental data capabilities - revisiting and reimagining data initiatives from previous eras - speaks to lessons learned from earlier system implementations. Anecdotally, many of our clients pursuing transformation initiatives today will cite “vendor lock-in” as one of the prevailing risks they want to avoid. Also, for many software and service providers, offering open and modular architecture that can easily integrate with third party products is becoming a norm. At BNY Mellon, this has been a key underpinning of our software, content and services offerings.
As part of the more comprehensive transformation taking place across investment management, most firms recognize a pronounced need to improve literacy around data and analytics. Even basic skillsets such as understanding data vernacular can improve the organization’s overall data IQ. This can be a daunting task for large organizations given the variety of skills and domain expertise of the workforce. There are three basic steps each organization can take:
To help foster a culture of data democratization, some organizations created a chief data officer position within their C-suite or operate a data and analytics center of excellence. Efficacy of such groups are often limited by several structural barriers. Among the most common obstacles of ensuring that everyone has access to data is that legacy systems simply weren’t built for the exponential growth in the volume, variety, or velocity of data. They also weren’t built for the growth in the sources of data and certainly aren’t equipped with the flexibility needed to easily complement structured data sets with unstructured data. But even if CIOs could have predicted the expansiveness of today’s sprawling data ecosystem ten years ago, the technologies and distribution needed to accommodate their foresight didn’t exist.
Many investment managers are now focused on educating their broader organization around adopting new data tools and how to generate insights through data. Firms that have strong interest in making data science accessible for business users well know this goal will demand a combination of a data-centric platform, next-generation cloud based technology and employee upskilling. The market is exploding in terms of available data science and visualization tools – so the key is to find products that are interoperable with existing ecosystems, scalable (in terms of functionality, supported use cases) over time without locking up too much capital, and can be used by business users without formal “data science” training.
These are the same steps that we leveraged inside BNY Mellon and which can help provide a blueprint for organizations as they continue through their own digital journeys. The learnings are based on our 20-year track record in data solutions and which led to the formation of Data and Analytics Solutions two years ago. As part of our natural evolution and a continuation of our data strategy, our focus in the coming years is on shifting from data management to building more robust analytics capabilities. This will be accelerated through our open-architecture approach and by proactively forming strategic alliances with key players who share our vision to create better client experiences. At the end of the day, the need for investment management organizations to create a mature data management and analytics foundation has never been greater. Those that don’t act will risk being left behind, while proactive action can drive a distinctive competitive advantage.
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