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Once the decision to replace an investment performance system has been made, most firms kick off the effort by mobilizing a formidable complement of resources, all charged with a single objective: selecting the vendor(s) who will make it so. Sponsors, steering committees, stakeholders, managers, experts and analysts are convened. RFPs listing thousands of criteria, elaborate scoring schemes, long lists and short lists are constructed. Initial and targeted demos, proof-of-concept, final selection, management approvals and contract negotiations are conducted. Boxes are ticked. This elaborate pageant commands vast amounts of both internal resources and calendar time — once the winner has finally been announced, a festive celebration is held, and backs are patted all around.
Not long after, someone inevitably realizes that the sources of data required to feed the new beast haven’t yet been rigorously identified or vetted. A scramble to piece together extracts from multiple feeds and databases — some of these, perhaps, declared internally to be a “gold copy source” — ensues. Tragically, the full truth won’t be discovered until much later, usually when the new system begins to produce output for user acceptance testing. And it’s always very bad news: your input data is not performance-ready.
Even when the new performance system replaces a legacy one, this scenario crops up. “The old system was fed by the same data — and it worked, didn’t it?” The problem, usually, is that the old system didn’t work well enough — if it had, you wouldn’t be replacing it. If your upgrade is contemplating any of the following, it’s likely that new data sources and requirements will need to be specified, identified and validated:
- New asset classes, derivatives, time zones, or benchmarks
- More complex client, custodial, and portfolio structures
- Augmented and more flexible security classification structures
- Corrections, extensions or enhancements to legacy performance calculations
- New attribution methodologies
Compounding the shock, firms frequently operate under the illusion that the vendor they’ve selected — the winner of their pageant — will be fixing these problems for them. They won’t be. Not only is it not the vendor’s responsibility to correct input data flaws, it’s an impossibility — that’s your data. If security identifiers are mis-aligned, or market values for merger transactions are missing, only you can locate and remedy the fault — at a cost that, this late in the project, is certain to demolish budgets and timelines.
Fortunately, a cure for this common blind spot is available: the Performance Data Readiness Assessment (PDRA). A detailed inventory of all data sources: their availability, frequency, relationships, attributes, and quality metrics necessary to satisfy performance demands. Followed up with a thorough and critical evaluation of the current data state, identifying gaps relative to requirements and recommending remedial options.
Furthermore, the good news is that a PDRA can easily be conducted — in fact, should be conducted — in parallel with the performance vendor selection process. Early discovery of input readiness gaps prevents unwanted data “surprises”, which — if not uncovered until much later — can cause grievous collateral damage to the project. Consequently, remediation can also begin earlier, and further improve the chances of success.
HOW MERADIA CAN HELP
By aggregating lessons learned from dozens of performance engagements with the industry’s top managers, Meradia has developed a detailed PDRA methodology, comprising a comprehensive set of input data sources, attributes and fitness tests, together with recommended options and strategies for remediation of identified problems. With this tried-and-true tool, your performance transformation can hit the ground running after the selection pageant, and be confident of a successful, on-time and on-budget implementation.
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