As data infrastructures and operating models continue to mature, firms are realizing the true value of data in new and increasingly powerful ways. Exciting data science initiatives are taking root across the asset management landscape. Innovation labs are no longer a luxury for the deep pocketed few. Advanced capabilities and scale are now afforded to all who are willing to invest the time and effort needed to reap the associated rewards.
Just what exactly is “data science?”
At a foundational level, the term refers to how data is sourced, ingested, modeled, interrogated, and delivered for data consumers. Although we have been gathering, storing, and using data for decades, the way we do so has fundamentally changed in recent years, opening a wave of new possibilities. Access to cloud platform services, new sources and types of data, advanced technologies for rapidly moving data at scale, user friendly machine learning and AI tools, and data delivery capabilities from basic BI to sophisticated knowledge graphing are all becoming more readily available at attractive price points. The guest list to this party extends beyond the early adopter crowd and we now see a growing early majority joining the fun!
What value does data science offer to the investment community?
The ultimate value in pursuing data science activities is to generate insights and efficiencies. The use cases for data science within the investment community are only as limited as our imagination and capacity to focus talent accordingly. All aspects of running a 21st century asset management firm are ripe for review through a data science lens. Right now, asset managers of varying sizes across the globe are using data science techniques to reveal actionable investment insights, increase market share through data driven marketing efforts, help clients make better investment choices, and to address compliance and regulatory demands.
What are some considerations in thinking about data science strategy?
There are many challenges that need to be addressed in formulating a data science strategy and executing related efforts effectively. Lessons continue to be learned with each step in the journey. Disparate data, legacy systems, outdated data models, ungoverned data, skills gaps, and lack of innovative thinking are among the primary barriers firms must address. Successful and pragmatic data strategy endeavors can help drive out technology and operating structures that need attention as firms position themselves for forward looking successes.
How does an investment manager incorporate data science into operational strategy?
Strategic assessments are an important tool in helping firms determine where they want to go and how to get there from where they are today. With data science becoming an increasingly key component of the overall operating model, detailed attention to current state technology and operating platform gaps and constructing a balanced roadmap to the future state are essential. There are many options for internal, external, and hybrid offerings to help firms make incremental progress leading to their desired strategic target model. Selecting the right partner in facilitating this process is an excellent first step!
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