Data Management Strategies Designed for Success

Are current data management tools keeping pace with the growth of data they are designed to manage? If not, how should they change to manage today’s data sets?
Rob Brachowski, Eagle Investment Systems: With the growth of data types, data volumes, and frequency comes both opportunities and burdens of Big Data, where firms mine high volumes of data to reveal trends and gain insights for business growth. With growth and transparency demands, innovative tools offer deeper views for business decision-making by slicing and dicing information to uncover trends inherent in the data. This is a significant differentiator and creates opportunities for leveraging business intelligence (BI). By providing more flexible client-data analysis, BI tools used in conjunction with a data-management solutions help firms gain new perspectives on their information. It also enables deeper data analysis and visualization of larger data sets or historical trends. Also, with the growth of derivatives, tools need to offer deeper exposure analysis. The best solutions extend beyond reference data to represent all data across the investment management landscape, allowing for integration of investment data in a central warehouse.

From a technology/architecture perspective, how are traditional data-management tools ineffective at storing and managing very large data sets? How are the latest data management tools adept at managing increased data volumes, velocity and variety? 
Brachowski: The need to manage data in a real-time manner has also seen the maturation of secure message-based technologies such as JMS and web-services, which, when coupled with the adoption of cloud, adds to the challenge firms face in terms of managing integration across vendors and providers across multiple networks and sometimes time-zones.  The solution must offer robust integration capabilities that leverage industry standards such as XML to meet the real-time data needs of the business. To effectively support the current challenges, data-management solutions have matured to only managing data exceptions, reducing manual reviews of data already deemed “good”. The latest tools also capture large data sets that support the validation and enrichment needed for various consumers and to support data exploration. These solutions isolate data that is fixed both logically and physically, and provide both the users and other systems with access to only the correct data required.

Do effective data-management initiatives start with developing clear data governance policies, or are they primarily technology related?   
Brachowski: The most effective initiatives combine the best of people, process and technology, starting with understanding the business requirements, which include clear data-governance policies. However, to be successful, the technology component must be prominent from the beginning.  The business considerations and technology initiatives go hand-in-hand. If greater focus is on one over the other, the initiative will have a lower chance of success. To map out the technology accurately, one must understand the requirements of data governance and procedures. In the project-planning process, data governance leads to the business requirements, which leads to technical requirements. You can’t accurately do one without fully assessing the other.

When designing and implementing a company-wide data-management strategy, what are the common pitfalls that buy-side firms need to consider?
Brachowski: Managing data as a strategic asset needs to be part of the business culture and must be deployed on a platform designed to support the investment management industry and not a collection of point-to-point solutions. A pitfall, commonly made in the 1990s and persisting today, is placing the accounting system at the center of the investment process, when those systems are not designed to provide the storage, transformation, consolidation and transparency required by middle-office systems and regulators. Another pitfall is not extending the value of a data-management solution beyond security master files and out across the data management throughout the investment business process to fuel BI and reporting including, regulatory, risk, and compliance.

What’s next in the evolution of data management?
Brachowski: We see demand to realize the next level of value from the data with BI, analytics and mobility. Vendors are looking to integrate the best solutions in these areas, rather than build them. With cost and efficiency pressures increasing the use of hosting and outsourcing, the market has begun to understand the needs and benefits of a solid data-management strategy deployed on an investment-data management platform. Finding a service provider that understands the investment management business and its data management complexities is key to overcoming long-standing enterprise data management issues and realizing the potential value of an outsourcing strategy.

Rob Brachowski is reference data product manager at Eagle Investment Systems.  Visit www.eagleinvsys.com for more information.