As the investment environment has changed, drivers of data management have evolved accordingly. In the pre-2008 environment, data management within asset management firms comprised centralized operational teams, focusing on:
- Data timeliness for key business processes
- Data quality and accuracy
- Cost management – both visible and hidden costs.
While these drivers remain relevant today, feedback from the RIMES outreach program reveals new strategic drivers that increase the data management burden.
All firms face additional data management challenges that include:
- Improving transparency and control to support data governance and evidence regulatory compliance
- Enabling and facilitating strategic business change
- Shortening time to market by increasing business agility and responsiveness.
As data management becomes more strategic, it has moved up the corporate agenda and is receiving close attention at executive level. But, firms need to understand the potential economic benefits of any course of action before committing to a significant investment.
In our experience, RIMES clients report both quantifiable and qualitative benefits. These include:
- Improved productivity and operational efficiency
- IT resource savings with improved data feed and delivery maintenance
- Improved ability to scale without additional headcount
- Faster time to market
- Reduction in third-party legacy vendor fees.
- Improved data quality and accuracy
- Increased agility and responsiveness
- Better risk management and risk mitigation
- Access to expertise.
A recent study, commissioned by RIMES and conducted by Forrester confirms these benefits but goes further to establish the potential return on investment (ROI) that an individual firm could achieve on a managed data service.
The study adopted Forrester’s proven methodology to asses the Total Economic ImpactTM (TEI) of a managed service. It offers a robust framework and recognizes that all benefits have a positive impact on the business even though qualitative benefits may be hard to enumerate in the short term. Quantitative benefits will be discussed in more detail in a subsequent paper.
Flexibility is also central to the TEI framework. Forrester defines flexibility as an investment in additional capacity or capability that can be turned into a business benefit for some future additional investment. In effect this provides an organization with the right or ability to engage in future initiatives but not the obligation to do so.
For example, a managed service equips a firm to implement new projects, such as launch new funds or support a more complex investment strategy with little or no incremental capital expenditure. A revenue expenditure model means that costs can be aligned with business success.
One organization noted that as it rolled out new projects in the future, it would continue to see the faster time-to-market benefits and project cost savings from RIMES. Organizations interviewed were also confident in the ability of RIMES to partner with them on future product offerings and additional services.
Flexibility may also generate additional benefits, for example facilitating business change, winning new mandates, and simplifying regulatory compliance.
The exact value of flexibility will vary according to each organization but in most cases RIMES clients report it as significant.
Risk – the Forrester TEI framework also accommodates the inclusion of risk metrics that include ‘implementation risk’ and ‘impact risk.’ The greater the uncertainty the wider the potential range of outcomes for cost and benefit estimates so the model remains firmly rooted in practical reality.
The Forrester TEI framework has been designed to calculate the potential benefits of managed data services for any asset management firm. We are encouraged by its power and flexibility and would like to help you measure the potential benefits for your own organization.
The RIMES Managed Data Service (RIMES MDS)
RIMES MDS provides our clients with the means to address their key buy-side data management challenges. It can improve service levels, ensure quality data for disparate business functions consuming data, manage the TCO (Total Cost of Ownership) of the full data management workflow and provide the business intelligence required to implement effective data governance processes and procedures.