By Julia Valentine, MBA
Imagine a world where perfect market research information is available to everyone at the same time. In this scenario, the concepts of investment and business competition would be radically changed—not just within the medical and healthcare sectors but far beyond. If perfect information were available to everyone, it would be difficult if not impossible for any one entity to identify an underserved market or underpriced asset. When companies and investors develop the expertise to spot mispricing and other indicators and anomalies, they often start with identifying a way to collect vital data that provides an informational advantage over their competition.
The most obvious way in which main street companies and investment funds gain an informational advantage is through developing a proprietary way to collect data. Alternative data, or data that supplements traditional financial data available through annual financial reports or exchanges, provides a valuable window into market or consumer behavior. Satellite data can track shipments and parking lots. Credit card data can illuminate who’s spending what and where.
Most companies buy data feeds from over 400 alternative data providers. So prolific is this emerging sector, a recent report reveals the global alternative data market size is anticipated to reach $143.31 billion by 2030 driven, in large party, by the escalating number of data providers coupled with ever-evolving procurement and extraction methods. While North America currently dominates the space (67%) and is forecasted to do so through 2030, alt data-driven market research is also burgeoning globally.
However procured and analyzed, one thing is certain: companies slow to use alt data expose themselves to extreme opportunity loss at best and operational peril at worst–as addressed in the topics list bulleted below.
Proprietary ways to acquire and combine alternative data for added insight are essential. While using available sources of alternative data certainly add insight, truly proprietary data can be collected through the use of multi-modal AI. Multimodal AI is a new AI paradigm in which various data types (image, text, speech, numerical data) are combined with multiple intelligence processing algorithms to achieve higher performances.
Companies using multi-modal AI amplify their competitive advantage due to faster data collection and improved ability to programmatically turn long form content (visual, audio, text) into relevant pieces of information resulting in significant time efficiencies and faster decision making. Multimodal AI often outperforms single modal AI in many real-world problems. Proprietary data can be collected from social media, web and other sources that are not available to the competition.
When proprietary data is collected, timely analysis makes a difference. In a rapidly changing world, it’s essential to operate with timely and accurate data. Research & Development departments need to react to the current customer needs. Investment firms react to rapidly changing market conditions. Every market player needs to consider how to utilize alternative data in the best possible way.
Agile companies pivot quickly when they are able to obtain proprietary data and analyze it quickly. Wholesalers and retailers can change their inventory every few weeks because they can analyze their sales data, trends and other data points in a timely way and create the merchandise that answers identified up-to-the-minute consumer behaviors. Gaining insights from alternative data is only valuable when they can be parlayed into market action in a way that is cleverly conceived and masterfully executed.
Editor’s Note: Julia Valentine, MBA, a solution-focused FinTech Advisor to boards and management teams, is Managing Partner at AlphaMille—a full-service company providing technology advisory and consulting services worldwide relating to software development and implementation, data analytics and data management, model valuation, and more.
