A data transformation is underway in the investment management sector
Leading managers believe that master data management (MDM) capabilities — including shared ontology, semantics, governance and stewardship processes — enable cross-team data sharing, allowing them to outperform competitors that have failed to invest. Of all the data domains, client Assets under Management (AUM), flow and revenue data is arguably the most valuable dataset within an enterprise. It has an incredibly high ROI and should be a top priority for every manager.
However, establishing a view of the economic performance – AUM, flows and revenue – by product for each client requires a mature data management capability. This capability will include a flexible data management platform that resolves the idiosyncrasies of AUM & Flow data; data literacy in the C-Suite to fund the necessary investments to build and maintain a platform and a data quality mentality in the staff that run the operating model.
Harnessing solutions for enterprise data
With analytics powered by client AUM & Flow and revenue data, an executive can articulate a clear case for resource allocation, cost control and investment decisions from KPIs, including:
- The economic performance and future potential of clients (by client segment, geographical location, business unit, channel and region)
- The performance of products and strategies (by client and distributor, client segment and region)
- The performance of sales and marketing teams with measures like annualised revenue on accurate views of net new money
- The logic for an acquisition or merger, including assessments of the post-integration performance of the combined and legacy businesses
The importance of enterprise-wide datasets
Paradoxically, the extreme value and demand for AUM & Flow data across all the areas of a business tend to block investment in a single, strategic store of data with cutting-edge MDM capabilities, doing so to govern and exploit data in a secure, controlled and agile manner.
However, many asset managers end up with the reverse effect. For many, the management and use of data becomes an ungoverned ‘free for all’ characterised by:
- The existence of multiple stores of similar data across the firm
- Tactical, manual patches of the data required to address chronic data quality issues in the AUM & Flow dataset
- Expensive and complex data processing that is partially duplicated across functions, creating confusion where clarity was sought
- Complex reconciliation processes to align functional views of the truth
- Distrust in the accuracy and reliability of all data, resulting in reversion to spreadsheets and other tactical measures
The impact of this ‘free-for-all’ approach results in an unsustainable and unavoidable cost of £100 million ($137 million) per year, incurred by managers governing the data in a non-strategic way. What’s more, this lack of governance also sees many decisions becoming ‘missed’ at a functional and enterprise level, as data labelled as ‘directionally accurate’ is too high-level or inaccurate to support the decision.
So, what can businesses do to avoid this happening?
Five ways to master AUM & Flow and revenue data
- Quantify the lifetime enterprise cost of bad data
This should include the hard costs of duplicative data management processes and expensive reconciliation work, as well as the cost of blocked capabilities of strategic agility and data-driven management processes.
- Iteratively evidence the business value
Focus on high-value use cases across the enterprise in Strategy, Finance, Sales, Marketing, Service, Risk & Compliance and Product Management.
Examples include monitoring and reporting the performance of the clients and sales teams post-acquisition or merger in both the legacy businesses and the newly combined entity.
- Define a comprehensive data integration strategy from day one
If duplicated functional silos are to be decommissioned and replaced by a single strategic source of Client Master AUM, Flow & Revenue data for the enterprise, there must be the means to transport the right data, to the right data consumers, at the right time. Otherwise, tactical sources will spring up to fill the gap. This tall order is frequently unresourced and is a common reason why enterprise initiatives fail.
- Build resiliency into the solution architecture
Business-level Service Level Agreements (SLAs) in line with ‘data as a service’ principles should be supported by a secure, agile change capability. Having this structure in place is necessary to accommodate the rapidly escalating numbers of data sources, data volumes, poor data quality, as well as fast-moving analytical and data science requirements.
- Cultivate senior sponsorship and business engagement
It is imperative to persuade functional leaders that the enterprise solution will solve their issues and not become another corporate pyramid in the sand. This requires political capital and an ability to engage with technologists and data engineers to establish a roadmap that will require multi-year investments and concurrently deliver progressively better data every quarter.
Many investment managers have senior executives with the organisational ‘mana’ to sponsor complex data projects. However, they are often not natives in digital or data and inadvertently miss critical decisions, simplify requirements or make compromises that fatally undermine the solution’s effectiveness.
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