Today, we find ourselves in a much different marketplace than we saw a decade ago. There has been a shift in focus to deploy enterprise-level database architectures as a means to introduce efficiencies, realize cost savings, and satisfy regulatory reporting requirements. Investment strategies that cross multiple asset classes are becoming the rule rather than the exception. The creation of smart datasets to fulfill the promise of “big data” analytics is becoming increasingly important as organizations look to optimize their content resources to gain long-term value.
This evolution requires the deployment and execution of an effective data governance strategy and framework that includes extended identification schemes to support and connect business entities, funds, people, and securities on an enterprise-wide basis.
More and more asset managers are looking outside traditional datasets in search of alpha-generating signals. To get an edge on the market and competitors, looking beyond structured data into the world of unstructured data is no longer an option, but a must for systematic and quantitative investors.
Data Governance and Mapping
Data silos introduce redundancies and inconsistencies that limit productivity and increase cost. As more information becomes available in the market, implementing a company-wide smart data solution allows for increased efficiency and scalability while providing flexibility.
Financial clients globally are challenged to aggregate operational, financial, and third-party content at an enterprise level. Both entity-level and security-level resolution and integration are becoming increasingly strategic from a data governance and data management perspective. The ability to connect disparate datasets and support the connectivity of these datasets can yield significant benefits including lower cost of ownership, increased productivity, and long-term strategic value creation via enterprise innovation.