Taking out the “Garbage” – Data scoring, not so boring William Bailey, Head of Product, InvestCloud Getting the Numbers Right A problem as old as the software industry itself, “garbage-in, garbage-out” began back when data entry was still done by feeding punch cards to the computer. This refers to the software’s dependency on the quality of the data it is given to process. Even though finance is more forgiving than the engineering calculations required for a moonshot, it is still an industry that separates winners from losers based on a fraction of a single percent - You have to get the numbers right. Based on InvestCloud’s experience of 600+ client implementations, data quality is found to be the most common reason for an unsuccessful or delayed implementation. While we make a big deal about InvestCloud’s platform being data-source agnostic, it is equally true that we are dataquality zealots (uncompromising in pursuit of the truth – think The X-Files). Due to the significance of the issue, InvestCloud has developed a comprehensive hundred point data scoring process to identify potential gaps in advance. This process has allowed us to ensure, before we begin a project, the “garbage” has been taken out. As all clients will understand the data issue is driven by poor enterprise systems and challenged operations that often result in epidemic use of Excel – always a sign of poor systems and data issues. In this formal scoring process, we evaluate 10 separate elements of data quality, following some of the more critical elements. Data Source Integrity and Maturity The foundational elements of scoring data quality are the integrity and maturity of the data source. Source integrity has to do with the level of control and consistency with which the data is managed. Points are deducted for home-grown or locally managed Excel processes, the highest points added are for the fully consolidated views available from an established portfolio accounting system. Data source maturity refers to the degree to which a firm’s data definitions and structures are normalized to industry standards. To assess a firm’s compliance with these standards, InvestCloud has developed a Common Interface Compliance (CIC) standard that comprehensively covers the broadest set of security and transaction types available. From broad fixed income and multi-currency coverage, to futures and derivatives, InvestCloud’s CIC is the gold standard for data-source maturity. Grading for source maturity deducts points for idiosyncratic feeds managed locally with Excel. The point adds are given for source data that is CIC, with even higher points if the data is managed using InvestCloud to host the source data on our true-cloud Interactive Data Repository (IDR). Reconcilable Differences Another element we address in scoring data quality are the client’s reconciliation process and pricing sources. Although firms generally understand that effective reconciliation is a guard against fraudulent activity and human error, it is also important in providing firms with a clearer awareness of investment spending limits. As the industry is concerned over improved risk controls and have more demand for transparency into the reconciliation process, this area is more important than ever. In terms of how we score this area, there are deductions for data that
is not reconciled daily and during the process when pricing is being manually entered. Neutral weighting is given for firms that rely on custodial pricing and reconciliation. Bonus points are given for reconciliation as firms that supplement their custodial services with independent services which addresses corporate actions, will be awarded the highest points for their fully out-sourced administration. Likewise, points are added for pricing based on supplementing custodial pricing one or more 3rd party pricing services. Know your PLACE There is a range of other areas that we address concerning the richness, consistency, and the likely accuracy of a firm’s positions, holdings, and transactions, both current and historical. We also examine the methods and reliability of a firm’s performance calculations. The primary theme running through all of the scoring elements is the degree to which a firm has a “PLACE” (Primary Location of Access, Control, and Entry). We find that the presence of gaps in a firm’s data is primarily attributable to what we have termed “MVOTT” (Multiple Versions Of The Truth). There is an inherent risk of failure when a firm is combining multiple disparate data sources for similar data-types. These are just a few elements of our 100-point data quality scoring. The point is that our clients and prospective clients can rest assured that we take every measure possible to ensure you have a great experience. Once you become a client, you will have under-go the therapy and necessary data analysis to achieve a score that enables InvestCloud to take the appropriate approach to improve the client situation. William Bailey (Head of Product) leads InvestCloud’s product vision and development. He is a selfproclaimed “reforming nerd” and the resident expert on buy-side trading, performance, and reconciliation amongst other topics. Will’s experience ranges from engineering features for radar jammers and forward looking infra-red (FLIR) cameras at Northrop Grumman to leading teams and for build, deployment, and rollout of Order Management Systems (OMS), Portfolio Accounting Systems (PAS), and other mission critical applications for large financial institutions at Accenture and The Conifer Group. Will holds a BS in Computer Engineering from Northwestern University.