What’s Old is What’s New: Data is the Past, Present and Future of Investment Management
A recent survey sponsored by Northern Trust indicated that 98% of managers are seeking to incorporate data science to optimize their investment performance. The fact is that this has been a primary focus and the buy-side has been analyzing data from the beginning because data analysis is, in large part, how investment decisions are made. Data analysis, now called Data Science, is an evolving field that will be powered by emerging technologies and software tools that have the potential to make this multidisciplinary endeavor more efficient and productive than it has ever been.
It’s All About the Data
Buy-side firms have been awash in a sea of data since the very beginning. This data comes from many different sources, including market data providers, research providers, portfolio management and trading systems (OMS/PMS), third-party and proprietary databases, marketing/CRM, stand-alone reporting systems, spreadsheets, and much more. The central problem for the buy-side is that this data is contained in many different silos. According to NT, 66% of respondents leverage 5 to 8 sources of investment data, which makes analyzing a firm’s complete data set very difficult. In order to perform effective data analysis, a unified data set is an essential requirement.
Garbage In, Garbage Out
Over the years, different solutions from third-party providers have developed to address the need for more effective data aggregation, including data warehouses and stand-alone reporting systems. The challenge with these solutions is that they have been difficult, time consuming and expensive to implement. The end result has often been systems that have become largely legacy-based in themselves as emerging technologies like BI (Business Intelligence Reporting) and AI have become disruptors. Effective BI Reporting and AI have the potential to put powerful tools directly in the hands of the investment managers themselves, giving them the ability to more effectively aggregate and store data to conduct effective analysis, the end goal of data science.
The Future of Data Science: The Melding of BI and AI Tools
BI Reporting offers a better solution that can help solve the challenge of more effective data aggregation and analysis, while AI tools can help with scrutinizing the data itself (i.e., attempting to solve the garbage in/garbage out problem) as well as assisting with the analysis of the data using better algorithms that can be directed to drive quality outcomes. At INDATA, we have made significant investments in both of these areas within our Architect AI Data Analytics solution, which has been purpose-built to address these needs and offers an effective out-of-the-box solution for investment managers looking to optimize performance, increase transparency and improve the bottom line.