Investment management firms are generating and consuming terabytes of data. But delivering on business intelligence for investment management is often challenging, because the volume of data is increasing and it is often kept in numerous data stores. These stores can be on-premise or in the cloud, and they exist because firms may have been looking to segment sensitive information, or it was originally implemented for a single use, but has since proliferated across an organization. These multiple stores introduce additional complexity for firms, particularly when they are looking to apply business intelligence or visualization tools, such as Power BI, Tableau and others, to get more insights out of that same data. This can be resolved by feeding it through an enterprise data management (EDM) or data warehouse system and then putting the business intelligence tools on top of that.
Data pipelines optimized for speed and performance
Whether the data is stored in the cloud or on-premise there must be properly configured data pipelines, optimized for both speed and performance. Pre-requisites for getting good results from a cloud-based visualization tool include being able to extract relevant data and avoid replicating data incorrectly into a new, third location. Business intelligence for investment management can be cost effective, but many system landscapes in which visualization tools operate require users to collect information from multiple sources and put it into a third location to then feed the tool with that data. This adds to issues around control and reconciliation, not to mention the security issues of having the data in another location that needs to control permissioned access.
The first step for deploying business intelligence for visualization is understanding your firm’s target operating model and how data is going to be consumed by the different teams and systems across the organization. The target operating model covers all business functions, including risk management, investment management, trading and execution, settlement and reconciliation, pre- and post-trade compliance monitoring, management of positions and exposures, and investment research. Teams and individuals include middle office data stewards, front office portfolio managers, investment managers, the CDO, risk managers and traders. Firms must understand how a data warehouse containing key business intelligence can pull data from different locations, or replace those fragmented locations, to serve all these types of users, with data slicing, dicing, drill-through and aggregation for visualization.
An EDM solution can help ease the integration process of a BI tool within an organization. In addition to centralizing and normalizing data across an investment firm, EDM solutions allow for ease of access to this data through a set of APIs, which allows teams to quickly and easily publish this data to their preferred BI tool.
Business intelligence for investment management – the driver
Increased business costs squeeze margins for data acquisition and acquiring clients, causing firms to have to change their operating models. This change includes eliminating silos, consolidating disparate data sources to a single platform, and integrating business intelligence for investment management tools to tie all the pieces of the puzzle together.
A resilient operating model that supports BI visualization should be underpinned by an enterprise technology stack with an integrated set of tools for risk workflows, research, portfolio management and trading, middle and back-office functions, corporate actions, trade settlements, cash movement and more. It should also be able to serve different personas and new areas for the marketing and distribution of investment products.
Before the proliferation of these BI tools, there were separate tools across an organization that would serve either a single persona or a limited set of personas. BI solutions break these silos by collecting and standardizing information which can then be utilized by all personas across an organization. As an example, this model provides an information workflow that can filter unrealized gains and losses over pre-set thresholds, aggregate unrealized losses by name, and identify which portfolios have corporate actions to be addressed. That is the kind of BI visualization model that will lower business and operational costs using transparent processes that let firms’ users self-serve to get the best out of the best available data.