Philip Orlando, CFA
Senior Director, Consulting
Philip (Phil) Orlando is a Consultant at Cutter Associates and brings over eleven years of consulting, organizational strategy, client service, operations, accounting, and IT experience to Cutter clients. Phil has been involved with multiple Front, Middle, and Back Office strategy engagements covering accounting systems, portfolio management and trading, pre and post-trade compliance, risk management, and data management and governance. Phil also has extensive experience with client and sales management (e.g. client reporting, CRM).
Prior to joining Cutter Associates, Phil was an Organizational Strategy Business Analyst at State Street Bank where he worked on several large-scale projects including the implementation of an enterprise workflow tool and a new inquiry management system. He also guided a program purposed to transition complex cash, reconciliation, and trade tasks from the business units to centers of excellence (COEs).
Previously, Phil was an ETF Client Operations Account Manager at State Street Bank. He launched new ETFs & on-boarded new clients, managed relationships with several ETF sponsors, and crafted specifications for IT resources to continuously improve the accuracy, reliability, and efficiency of ETF processes.
Phil holds a B.S. in Finance from Stonehill College and is also a CFA charterholder.
Product reference data may sound boring, but it's extremely valuable to your firm. Here are six warning signs that the processes, technology, and data supporting your product reference data function may fall short.
When accurate and readily available, product reference data clarifies why a certain product may be the right fit for an existing client or a new prospect. Firms can use product reference data to differentiate products from the competition and conveys its DNA to the marketplace. To ensure timely, accurate, and consistent messaging, it’s paramount that your firm strategically manages its product reference data.
Here are six warning signs that the processes, technology, and data supporting your firm’s product reference data function may fall short:
1. Your firm lacks an all-encompassing list of investment offerings
This is when your company website doesn’t adequately describe everything you have for sale. Where can a prospect find the investment strategies offered through institutional separate accounts? Where do you list mutually exclusive pooled products and the full spectrum of flavors (aka investment strategies) they’re offered in? Perhaps your firm also lacks an enterprise-wide consensus on the definition of a product, solution, and/or investment strategy, which could partially explain not having a grasp on the above.
2. Your people ask silly product questions
If your firm doesn’t define product reference data terminology and schemas, it leads to organizational inefficiency, with your staff sounding ignorant about your offerings. Firms need to practice cross-functional collaboration to establish a common nomenclature, especially when more than a handful of systems and downstream teams use these data elements. Solidified business terms and definitions that are insufficiently documented or communicated also can lead to chaos. You want to avoid a situation where your people don’t know which data element is used for which purpose.
3. Sales, client service, and product teams suffer from inconsistent, inaccurate, and incomplete insights
When the interpretation of your firm’s product reference data varies across departments, it leads to inconsistent messaging and confusion. Sales and client service teams also may think they have limited exposure to and use for product reference data. But what they’re missing is that defining and modeling product reference data attributes is foundational before solving for dynamic insights, such as AUM broken down by investment strategy. Inflexible reference data models that aren’t tailored to a firm’s business can significantly inhibit querying and accessing the data. Filtering data that’s grouped based on poorly designed relationships can lead to inaccurate AUM results (e.g., double counting).
4. Lack of role clarity
It’s often unclear within organizations who’s responsible for the following roles related to a particular product or set of products:
- Determining values of data attributes
- For instance, who is responsible for determining if the product uses leverage? Or, if it’s a ’40 Act fund, who provides the CUSIP?
- Population of data values or updates to data values
- Changes to data attributes (e.g., new, modifications, deletes)
- Your firm may need to introduce new ESG-related attributes at some point. Also, times arise when the acceptable values of a data attribute no longer support the business’ needs.
- Fixing data issues
Another reason for the lack of clarity could be that your organization does not formally govern product reference data. Product reference data, because it’s internally created, unique to each firm, and mostly static, with wide-ranging consumption needs, has different governance requirements versus other data domains.
5. Product reference data is hard to find or consumed through silos
Product data consumers who lack the institutional knowledge to understand how to access product reference data can present a significant burden to the firm. Key-person risk, where a firm relies on one person for everything product data-related, is not an ideal solution.
Firms that go without a single comprehensive product master often resort to manually scraping data from spreadsheets, emails, publications, and websites to access product reference data. In addition to its inherent inefficiency, this approach makes a firm vulnerable to errors and risks.
In a slightly better case scenario, firms rely on numerous data repositories for product reference data, where consumption differs by region, investment vehicle, or use case (e.g., marketing publications versus sales versus regulatory reporting).
In our December 2022 CutterCast, Managing Product Data, half the firms identified data dispersed across multiple repositories as a top challenge. From a data management and stewardship perspective this could spell disaster, especially if your firm does not define authorized sources. A situation where products have been gradually added across varying domiciles over time might serve as an excuse to stand up and maintain multiple data stores. But this is a tactical solution ─ not a permanent one!
6. It takes a long time to onboard investment vehicles and strategies
Technology deficiencies or data management gaps can lead to longer-than-necessary turnaround times for a new investment vehicle or investment strategy. This outcome could stem from the lack of a strategic and cohesive technology strategy (e.g., master system, Business Process Management), flawed data processes/procedures, or some combination of the two.
If you haven’t defined the product onboarding workflow to the point where it encapsulates all identified tasks and impacted parties, this could also be the culprit. Cutter will cover standardization of the product lifecycle, which includes onboarding, in an upcoming article.
These are all signs that a firm lacks robust processes and technology for managing product reference data. To learn how to correct these problem areas, stay tuned for additional articles and blog posts. If your firm is struggling with its product reference data, Cutter can help.
Read our whitepaper, Implementing a Product Master? Here Are 8 Best Practices to Consider Before Getting Started, to learn more.