威尼斯国际官方登录’s client, an independently-owned investment advisory firm with over $50 billion AUM, needed to modernize their ability to manage market data, especially pricing data. The firm’s existing data architecture consisted of Excel-based processes to manage, download, review and maintain important market data assets underlying daily positions and portfolio valuations. With continuing growth, the firm recognized that this process was limiting scalability and operational efficiency, and potentially exposing the firm to unnecessary risk.
To recommend future state options, 威尼斯国际官方登录 needed to understand the current state of the market data along with the firm’s IT infrastructure and future technology direction. 威尼斯国际官方登录 examined all processes, data and technology that played a role in daily asset pricing. Ten distinct manual daily processes leveraged a variety of sources including Bloomberg and IDC. All data gathering, associated validation and distribution were reliant on user-defined, Excel/VBA spreadsheets. To fully understand some of the legacy files, we needed to understand and deconstruct underlying VBA code. Beyond data consumption, storage and usage patterns were also examined to create a complete picture of daily workflows.
Within the eight weeks allocated for the engagement, 威尼斯国际官方登录 delivered future state recommendations that included functional specifications (context data flow diagrams, state transition diagrams and conceptual data models) as well as future state process flows and roles. 威尼斯国际官方登录 guided the client through an optionality exercise to examine the pros and cons of different approaches alongside estimated costs. Each of the potential solutions was scored for its ability to meet the operational efficiency and scale demands, potential to reduce or eliminate risks, gaps and weaknesses, as well as the flexibility to be extended for future data needs. After gaining agreement on the appropriate approach from the stake holders, 威尼斯国际官方登录 detailed a transition plan that included sunsetting legacy processes while minimizing impact to business-as-usual processes.