Data Analytics as a Service in Asset Management: Moving from assumption to fact-based modeling
As new technology impacts when, where and how large financial institutions conduct business, many asset management firms are shifting their mentality from “I want to do digital” to “Show me how digital helps me become more competitive.” Data sits at the forefront of these conversations with both structured and unstructured datasets expanding exponentially. In this article, David Donovan examines the shift toward fact-based data models within the asset management industry as firms look to gain an edge on their competitors and respond to mounting margin pressure.
Around the world and across industries, firms in some form or another are implementing or further building out their digital capabilities within their organizations. In fact, worldwide spending on digital transformation technologies is expected to surpass $2.1 trillion in 2019, according to International Data Corporation (IDC), the global market research, analysis and advisory firm.1 As such, the asset management industry is now focused on how components within digital, such as data analytics, cloud or mobile, can create real solutions for customers and optimize the workplace.
Data analytics, for one, is at the cusp of moving beyond marketing and customer segmentation to the heart of what research analysts and portfolio managers do: transition from assumption-based modeling to fact-based behavioral data capture. Ultimately, this transformation will give asset managers an advantage over stock market performance predictions at a cost that is palatable and at a time when margin pressure is intensifying.
Growth in social media, mobile, Internet of Things (IoT) and advances in technology to capture structure and unstructured data in the “micro-moments” of customer interaction has created a vast trove of data often termed “big data.” The powerful combination of this data with the right algorithm correlation models and team of experienced industry professionals can give asset management firms power to create fact-based models. With platforms available today supported by teams of data scientists and industry experts, data analytics as a service is rapidly emerging in the asset management industry.
Moving to a fact-based prediction model
Traditionally, asset management firms have relied on smart people looking at company financials, market data and assumption models to forecast growth of stock and other asset classes. These models depend on historical knowledge, financial engineering and public data to build predictions. The assumptions are then vetted, validated and fine-tuned by analysts tracking companies.
But what if there was a better way to leverage multiple streams of data, both structured and unstructured, in large datasets across social media alongside behavioral insights? What if there was a way to then build a correlation directly with stock performance combined with industry expertise, data analytics and behavior signals from consumers and markets to create underlying assumptions for analyst models? The result could potentially revolutionize research analyst models for buy-side firms and large financial institutions with true predictive modeling based on fact signals instead of assumptions. By connecting real consumer behavior with disparate internal and external data sources, firms can create a method for making these signals both visible and actionable.
Asset managers are already using incredibly sophisticated analytics for clients, sales channels, advisors and complex regulatory requirements. For example, J.P. Morgan Asset Management recently won an award for its analytics platform, Sparta, which includes real-time calculation of performance, contribution and attribution in addition to on-the-fly grouping and advanced ex-post risk analytics.2
With advances in artificial intelligence (AI) and correlation models supported by data scientists, platforms exist today that provide tools for the next generation of investment management and research capabilities. IDC predicts that the big data and business analytics market will grow from $130 billion to $203 billion by 2020.3 The banking industry is expected to be a big driver of this increase in spending.
Data analytics as a service emerges
Asset management companies, hedge funds and proprietary traders are investing to find innovative ways to use big data for investment research. The best of these investments will more than likely be closely guarded secrets. However, as an example of the lateral thinking being applied to data, Goldman Sachs is using satellite images of retail car parks as an indicator of retail sales both at an aggregate level and for individual companies.4 This data forms part of the investment model while human judgment is still key to the investment decision. But by providing tools and clear signals to investment research teams, allowing them to deliver correlations beyond financial data, firms can establish an advantage over competition.
With the current market dynamics, building a team of scarce talent, specifically highly skilled data scientists and deep industry experts who can filter signals from a massive amount of data, is almost prohibitively expensive. Such is the case with the technology infrastructure to process and manage massive amounts of data across a wide variety of data collection points.
Even as the cost of data storage and processing has declined considerably, each firm still requires a costly level of customization. If organizations choose to select freeware or open source information, the cost to build, maintain and truly customize remains an expensive proposition.
Still, cloud computing has helped increase the speed and reduce the cost at which we can process structured and unstructured data. Multi-tenant platforms, which are customized to a firm’s specific needs for data collection and augmented by a team of expert data scientists and analytical people, can allow companies to cost-effectively leverage data analytics as a service.
Data analytics is at the cusp of moving beyond marketing and customer segmentation to the heart of what research analysts and portfolio managers do: move from assumption-based modeling to fact-based behavioral data capture.
A competitive edge
A number of data analytics platforms that combine the expertise of data scientists, data collection and industry experts while delivering a fact-based model of stock market and behavioral insights are emerging. These platforms offer a solution to asset management firms at a reasonable price point and a limited capital investment to get started. Combining these platforms with in-house expertise offers a viable data analytics as a service model to investment firms of all shapes and sizes.
Data used to be expensive, proprietary and the “secret sauce” for investment management firms. As information becomes more ubiquitous, data analytics and data science can give asset managers
an edge by turning raw data, assumptions and customer behaviors into fact-based, actionable knowledge.
1 International Data Corporation (IDC), “IDC Forecasts Worldwide Spending on Digital Transformation Technologies Will Surpass $2 Trillion in 2019,” https://www.idc.com/getdoc.jsp?containerId=prUS40978116
2 Waters Technology, “AFTAs 2016: Best Analytics Initiative: Buy Side—J.P. Morgan Asset Management (Sparta), http://www.waterstechnology.com/waters/analysis/2480253/aftas-2016-best-analytics-initiative-buy-side-jpmorgan-asset-management
3 Finextra, “Advanced Logic Analytics to bring big data analytics to financial services market,” https://www.finextra.com/pressarticle/67731/advanced-logic-analytics-to-bring-big-data-analytics-to-financial-services-markett
4 Finextra, “Advanced Logic Analytics to bring big data analytics to financial services market,” https://www.finextra.com/pressarticle/67731/advanced-logic-analytics-to-bring-big-data-analytics-to-financial-services-market
David Donovan serves as a Senior Vice President and Global Portfolio Lead, as well as part of the Sapient Global Markets’ leadership team. He has been involved in a number of key strategic initiatives, such as helping banks meet industrialization goals, build better operating platforms and leverage globalization to reduce cost and grow revenues.