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At Data Front our approach to reporting data is one of agnosticism. We provide the tools to manage your data understanding that a one size fits all approach is a mistake. We understand that the provenance of your data is unique and that it can be sourced from a variety of places including your own reporting systems and third-party data providers (including Data Front). However, we stand firm on the belief that your data should be consolidated in a cloud native application that can be accessed through API.
Data centralization presents a major efficiency gain in the reporting process because it reduces redundant work and ensures a consistent message. We encounter redundancies across financial reporting due to processes that do not connect to a centralized database. Many if not most asset managers pull data repeatedly for different reporting initiatives such as web profiles, sales decks, regulatory documents and other marketing materials. It is not uncommon to see different teams source the same data for the same reporting period from different places—which brings us to the next problem—data provenance.
If your data provenance is not consistent, your reports will not be telling a consistent story either. We encounter frustration across sales teams that rely on delivering a consistent message to advisors and investors only to be undermined by data variation across reports. For example, the risk report in a sales deck may say one thing and the website may be saying another.
Data Front has developed LiveDoc MDM, a Master Data application designed to consolidate reporting data in one place. It offers the ability to restate data for any reporting period and track data over multiple reporting periods. In other words, it is bi-temporal which is important for maintaining the quality and provenance of your data because it allows users to see how data changed over time and restate data for any given reporting period. Consider that historical NAVs are occasionally restated and that will affect performance calculations too, for example.
LiveDoc MDM also allows users to manage the qualitative data that rarely has a permanent home. Fund manager information; commentaries; investment objectives; benchmark descriptions. It’s all data, even if it changes infrequently. It often resides in spreadsheets, and there are often multiple versions of the same information. Consider a fund manager change and how this information is disseminated across the various fund reports. It is often manual and decentralized leading to out-of-date materials and inconsistencies across reports.
Qualitative data extends to legal disclosures because if they are treated as data, compliance is baked into the reporting process. LiveDoc MDM allows users to curate legal disclosures and provide metadata - information about how the disclosures should be applied on the various reports. Data Front has also developed logic in its reporting application to apply disclosures on all reports, so they are applied consistently.
The ability to call this data through API is crucial to having this data flow to your reports and maintaining consistent messaging. The challenge is that your reports are often produced using different vendors, applications, and technologies. A one size fits all data model will not work. Data formatting needs to be customized for the various reports, and a flexible API that can model the data to meet these different requirements is essential to connecting the data to your reports.
A GraphQL API is an excellent tool for creating custom queries for financial reporting where multiple parties have a stake in consuming that data. GraphQL is a query language for your API that allows users to understand the data in scope and pull only the data they need. In contrast to a Rest API it is more efficient and customizable.
At Data Front we provide the tools to centralize, manage and query your data when you want and in the format you need resulting in the elimination of redundant processes, faster time to market, and greater oversite and error reduction across the reporting process.