Asset management sales and marketing efforts are fueled by data. The faster firms get clean, accurate product performance data in the hands of consultants, advisors and wholesalers, the better their chances of closing deals and increasing inflows. Effective governance for investment data is the foundation for this effort.
A growing number of firms are using marketing content automation solutions to quickly create and update pitchbooks, collateral materials and website product pages. Yet, many are still using inefficient, ad hoc processes for collecting, storing and distributing the source data that populates these communications.
These processes, coupled with a lack of formal governance around data collection and usage, increase product information’s time-to-market. Also, they raise the likelihood of errors that could result in reputational damage and regulatory sanctions.
A variety of internal and external data sources collect product-related information. Then, it is stored in a combination of databases and spreadsheets. It isn’t uncommon to find one or more associates spending significant time manually cutting-and-pasting data from different sources into separate spreadsheets for every share class of a particular mutual fund. The manual and repetitive nature of this task leads to human errors. If not caught, these errors lead to serious consequences.
Even if data is compiled efficiently, risks remain if there’s no central process for managing its distribution.
In many firms, product spreadsheets are emailed to various users or stored on many different departmental servers. If an error is found, the original spreadsheet will need to be updated. Unfortunately, locating and overwriting these outdated files can be a logistical nightmare. The issue is compounded if the data has already been automatically or manually moved into the content.
How can organizations reduce these data-related risks and inefficiencies? By creating a data governance policy and investing in enterprise data management solutions that can automate the collection, validation and distribution of product data.
The Benefits of Effective Investment Data Governance
Effective data governance policies establish best practices for collecting, compiling, formatting, validating, updating and distributing data. When people comply with them and use the right tools, they significantly improve efficiency, eliminate duplicate efforts, and minimize errors.
A data governance policy shouldn’t be created in a vacuum. It needs to be the product of a collaborative effort where information technology, investment management, sales and marketing, and compliance stakeholders work together to fully understand the full spectrum of data that is being collected and how it is being used.
Creating a Data Inventory
Many firms tend to dig into an investment data governance or data warehousing project by first evaluating available technologies. However, the true first step is documentation. Every approach will require inventory and mapping efforts. By making this effort first, firms will discover new things about their business, data and technical needs. Documentation will significantly improve the ability to manage data quality and reliability.
Every data point currently being collected needs to be identified and inventoried in detail. This includes information communicated to the public as well as non-public data points that define products fundamentally. These include the countries or regions where products are available for investment or the channels (institutional, broker/dealer, RIA firms, etc.) through which they may be sold.
“Each data point should be fully annotated to document where it comes from, the person (or role) responsible for collecting it, how often it is updated, and how and where it’s being used,” says Robert Juergens, CTO, Synthesis Technology.
“It’s critical to establish deadlines for data delivery and reflect those in the inventory. Think of these deadlines as policies that are documented and enforced. This creates accountability, which is imperative for data governance,” says Juergens.
A data inventory should be stored in a document or spreadsheet, and reviewed by stakeholders thoroughly. This review will identify which data points a firm needs to continue to collect and use and which may be retired. It can also help uncover situations where duplicate data collection is occurring, creating a consolidation opportunity.
The final inventory document should be available to those who collect and use this data. In turn, greater communication and information-sharing will be encouraged. It’s also a good practice to review data inventory at least once a year. This ensures accuracy and alignment with business objectives.
Using Investment Data Management Solutions to Empower Data Governance
Data inventories can go a long way toward improving data governance. However, they can’t eliminate inefficiency when data collection and distribution is performed manually.
One solution is to invest in a centralized enterprise data management system to automate data collection and usage.
“Data should sit in the middle of an Asset Management firm and feed all areas of the business, connecting investments, operations and distribution functions in a central ecosystem. An enterprise data management layer can ensure that that the relevant data is accessible to everyone who needs it,” says Brett Nielsen, a Director with Alpha FMC, a consulting firm serving asset managers and wealth management firms.
This, of course, has been a mantra of IT departments for decades. Yet, many asset management firms are well behind the curve of moving to data management systems. One reason for this may be the selection of home-grown or generic technologies. Unfortunately, these require a tremendous amount of development or adaptation before they can be tuned for effective use.
Enterprise Data Management For Asset Managers
There are many data management options available for many asset managers. Though, an enterprise data management system provides the most flexible, trustworthy and cost-effective solution.
An EDM automates the process of collecting, validating, storing distributing the four main categories of product data: composition, characteristics, exposures and performance. A good EDM system fully integrates with a firm’s internal and external data sources. Also, it integrates with applications that use this data for portfolio analytics, client reporting, or marketing content production.
On the collection side, a well-implemented data hub will automatically acquire and validate data points for all products. This includes their underlying vehicles from a firm’s portfolio management systems and external research sources. Then, it stores this information in product databases.
On the user side, different groups in the organization tap into the data hub to access the information they need. Investment teams run formulas on data sets for portfolio analysis. Marketing teams export accurate and validated product-specific data in spreadsheet formats for sales and marketing deliverables. Firms that use marketing content automation instantly import data into pitchbooks, factsheets, fund brochures, websites and other on-the-fly materials. Finally, wholesalers fulfill specialized requests for data from their clients and prospects.
“When it’s set up right, a data hub pushes mastered data to the downstream systems that use it. Not only is that more efficient, but operationally this provides greater control, governance, and validation of investment data within the organization. By keeping as much of the logic in a central location, you know that once a change is made, it will be reflected across the enterprise, decreasing duplicative administration in downstream systems, and the associated risk of inaccurate or outdated information.” says Nielsen.
Speed of Data Drives Inflows
It’s critical to provide accurate, updated product performance data as soon as possible. In this pressure-cooker environment, firms can’t afford to let error-prone data collection and usage processes dull their competitive edge. Ultimately, the quality and availability of data drive costs. So, the economics of investing in processes and tools that improve speed and quality can be extremely compelling.