For financial markets firms, efficiency is becoming as important a differentiator as speed and scale. As a result, firms are delving deeper into predictive analytics to realize faster time to value and improve operational performance and decision outcomes.
Technologies that speed pattern recognition in ever-growing data sets – including big data – provide deeper insights into trends and behaviors that can translate into millions of dollars in opportunities and costs. Firms are increasing investments in predictive analytics amid an explosion in data sources and instruments to exploit them and a torrent of new regulations that are squeezing revenues, margins and profitability.
Predictive analytics is an iterative process that begins with an understanding of the question the user wants to answer. By exploring the relationships among different variables using correlation analysis, users can build sophisticated mathematical models that can cut through the complexity of modern computing systems to uncover previously hidden patterns, identify classifications and make associations.
Success with predictive analytics relies on choosing the right data set, assuring the quality of the data, and validating models and algorithms used to analyze the data. But financial firms have been challenged by data management issues caused by incompatible systems and myriad data silos. As they now consolidate data centers and focus more on efficiency, a unified approach to data management is becoming a priority. Firms recognize the substantial opportunity to achieve a better return on data assets (RDA).
Information sets have become too vast to understand all of the interactions and dependencies necessary to manage risk and return effectively. Advances in underlying technologies now allows for real-time analysis on massive sets of both structured and unstructured data. The ability to integrate historical data with newer sources of information to predict market trends and rapidly implement strategies is becoming the primary differentiator.;