Data Refinery & Distribution

Qualis® is a new class of software—Data Refinery & Distribution Platform—that overcomes warehouse/mart/lake deficiencies to harmonize data firm-wide. Qualis delivers a forward looking data architecture—as recommended in standards such as BCBS-239—that supports apps to solve problems not only of today but well into the future.


Integrity Deficit ⇒ Ambiguity and non-compliance

Asset Management firms are organized in silo'ed business units, each needing firm-wide data to operate. Since each silo invariably applies its own set of business rules—massages the data—to make it usable, lineage is irreparably lost. Loss of lineage leads to ambiguity and is unacceptable for regulatory compliance.

Specifically, when data is distributed first and then massaged in silos—often in Excel— lineage is lost (1, above ). Informal sharing of massaged data between units further voids lineage (2). Furthermore, conflicting terminology between silos leads to additional ambiguity (3).


Refine first, then Distribute ⇒ Retain indisputable, business-friendly lineage

Qualis uses algorithms to first refine—normalize, apply waterfall hierarchies, map namespaces, validate, and cross-check—data to establish firm-wide TRUTH. From that truth, Qualis generates use-specific views—while retaining end-to-end lineage—that are then distributed when needed for research, trading, compliance, and risk aggregation.


Popular Apps

Qualis provides a platform to build client-specific Apps to solve complex business problems in unique ways. Here are some of the more popular Apps.

1. "NIXEL"

    Overreliance on manual, Excel processes is a wide-spread concern. The "London Whale" debacle is a good example, "...volatility appeared lower than it should have because of issues with this model that can, in part, be traced back to how Excel was used." Qualis can extract institutional knowledge buried in critical spreadsheets into a structured environment with proper controls, and yet, continue to distribute Excel-friendly views without diminishing worker productivity.

2. CROSS-INVESTMENT RISK

    When funds invest in other funds via other funds, cross-investments occur. Without a systematic way to continuously assess cross-investment exposures across the firm, the collapse of any one fund could rapidly create a vicious circle of declining performance. Qualis can algorithmically unravel cross-investments with indisputable lineage so that firm-wide risk can be managed effectively, and in time.

3. HISTORICAL DATA MIGRATION

    Companies migrate from one system to another for a variety of reasons—M&A, consolidations, obsolescence etc. While vendors may assist with software migration, historical data from retiring systems is often discarded (due to complexity), severely limiting look-back horizon. Qualis can serve as the data-hub, sourcing historical data from older systems and feeding the newer system continuously. This robust, automated, repeatable, and scalable solution delivers accurate history, shielding the user from ugliness of conversion. Qualis does not limit depth of history.

4. FIRM-WIDE, MULTI-ASSET AUM

    Master/feeder fund structures allow firms to offer lifestyle/allocation products with a handful of core investment strategies. With deeply nested investments routinely crossing regional, asset-class, market, and currency boundaries, it is a daunting task to avoid double-counting of AUM for regulatory reporting. Qualis can algorithmically analyze shareholder and position information to calculate AUM 24/7 along each nested path with indisputable lineage, eliminating the need for repeated manual manipulations during reporting cycle.

5. FIRM-WIDE DATA INTEGRITY ARCHITECTURE

    While warehouses, data marts, EDMs and data lakes have simplified data management, they have created data silos with little ability to ensure that assumptions are consistent across the firm. For example, are cash, accruals, and derivatives modeled consistently across the organization? Qualis implements rigid accounting system like algorithmic check on firm-wide data to ensure that every "credit" has an equal and opposite "debt", no matter the silo. Discrepancies, if any, are highlighted with full lineage so that analysts can rectify (or explain) the variance at the source. Without this level of strict integrity enforcement across the firm, downstream systems—operating in silos and using incomplete/inaccurate data—risk reaching detrimental conclusions resulting in poor investment decisions.