Big data helps asset management industry across value chain
BY Staff | March 22, 2019
Across the entire asset management value chain, data is helping traditional managers improve, a new study by McKinsey & Company found.
Data can improve investment performance by helping eliminate bias in decision making, discovering new sources of alpha through alternative data sets and enhancing the research process, the study said.
But big data isn’t just helping with traditional investing- it’s helping across the value chain.
Asset managers are using data and advanced analytics to reinvigorate or redesign their current distribution models, the study said. Industry players are building deep wells of client data to help them better analyze characteristics, so asset managers can better target the right clients, with the most effective means, in the most timely way. While clients have traditionally fallen into types, making that segmentation more granular is helping asset managers take a more effective and targeted approach, the research found.
“Our work with asset managers has shown that this type of behavioral-based segmentation of clients and subsequent adaptation of sales efforts can free up 15 percent or more of existing salesforce capacity and increase sales from priority client relationships by up to 30 percent,” the study said.
Harnessing data also has the potential to improve productivity in the middle and back offices by increasing the automation of certain time-consuming processes and improving risk management, it noted.
But human talent is still required.
“One of the greatest challenges asset managers face is in recruiting and retaining distinctive data and analytics talent,” the research said. “Those who get it right recognize that business-as-usual analytics resources are typically not sufficient, and that attracting and retaining distinctive talent typically requires a vibrant community and a strong talent plan (for example, career paths and robust professional development).”
In addition to human talent, other markers of successful big data adoption include prioritizing efforts based on business value, recognizing how analytics can benefit from cross-functional team involvement and keeping the end user in mind, McKinsey added.
“Leading firms are applying these tools and insights to improve distribution effectiveness, investment performance, and productivity in the middle and back offices. While some firms are using analytics to enhance productivity of existing practices, others are taking advantage of these new capabilities to ask more fundamental questions about their operating models,” the paper said.