15 May 2019

Incorporating data governance into everyday processes

By Nicholas Nolan

Data integrity is paramount. If there is a glitch in operations that slows down a process or an error in a statement that goes to a client, chances are that faulty data is the culprit.

This raises two questions for investment management firms: How can investment managers make sure data generated from portfolio information is accurate, conforms to agreed definitions, and is available to other applications without batch processing? And are there portfolio accounting tools that can help in the data management process?

To answer the first question, let’s break down the flow of data into three specific areas:

  1. Inputs – All the information flowing into a portfolio accounting system
  2. Governance – The rules and validation process we apply to that data
  3. Outputs – Where information from the accounting system has to go:  reconciliation, risk management, performance measurement, perhaps a data warehouse, and any other systems where the data is needed.

 

When we talk about inputs, it’s important to understand what type of data is flowing into the portfolio account system, when, and how – the process for getting data into the system. Inputs include allocated trades sent from an order management system (OMS), corporate actions, security pricing and FX rates from external market data providers, client reference and account information from service providers, and security master data for all listed and derivative securities held in a portfolio.

Data governance refers to the overall management of the availability, usability, integrity and security of data used in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures.

Data governance starts with knowing what we need to check for, whether our process for checking data is effective, and whether we are receiving quality data in the first place, for example:

  • Were all trades loaded properly into the accounting system?
  • Is every security in a portfolio set up accurately per the security master?
  • Do we have prices for all securities?
  • Do any trades have an impact on NAV over a certain tolerance?

 

If we have data issues or inefficiencies, we need to understand the root causes. These questions are easier to answer if a firm has automation in place to execute these data import and transformation processes, so that only exceptions need to be followed up and resolved.

With good data governance, firms can be more confident in their portfolio accounting systems’ outputs – for instance, delivering start-of-day positions to the OMS, feeding position, transaction and cash files to the reconciliation system, or running performance. Once a good reconciled data set has been validated, firms should be able to run reports dynamically in a timely fashion.

When we look at the flow of data this way, it becomes apparent that data governance is really pivotal in ensuring accuracy in downstream processes from the portfolio accounting system. The primary benefits of having “perfect data and perfect processes” are many: on-time period closing and reporting, increased efficiency with fewer manual processes, and reduced operational risk. Firms can satisfy client expectations for transparency and their information and processes can be easily audited.

In answer to our second question above, there are indeed tools that can assist in ensuring higher data quality through automation. Learn about improving data governance with Advent Lumis™ for the Geneva® portfolio accounting platform.