In this article, Backstop Solutions Group's Robert Goldbaum, who is vice president of product and market strategy, outlines ways that institutional investors can boost their process for operational due diligence.

Nearly every asset allocator in today’s industry employs an internal operational due diligence team or contracts with an outside firm providing ODD as a service. Asset allocators are performing more in-depth due diligence than ever before, but many are still struggling to streamline their due diligence practices, and effectively organize and process the vast amount of data that is collected from ODD requests.

Without a standardized, repeatable and data-driven process for both gathering information requests from fund managers and analyzing the results, allocators run the risk of having an unwieldy repository of documents and notes. Third-party research management and data management systems can help aid in this process by providing the functionality needed for aligning contact, document, activity and data management with the research process.

Following are four tips on how to best maximize third-party applications for an effective operational due diligence process:

Foster efficiency

It is crucial for investors to consider the impact of how to perform their due diligence. One frequently cited complaint from managers is that investors tend to duplicate efforts, asking for the same information many times.  Although sometimes this is done purposefully to catch inconsistent responses, many of these duplicative documentation or hard-data requests often occur unintentionally when there are inefficient processes or data management practices in place. Since the ODD teams typically are used only after an initial screening by the investment team, this phase of the review process is usually a major source of redundancy.

By using systematic processes and technology, firms can prevent such duplicate efforts and create efficiencies by fostering communication between team members. The reporting these systems enable should help ensure adequate data coverage at any stage of the review by spotting gaps or inconsistencies in the responses and generating follow-up requests.

Reduce subjectivity

The ultimate goal for allocators is to create a repeatable, predictable process that reduces emotional decision-making. Implementing a quantitative approach to looking at qualitative data can help frame the expression of such subjective analysis, and can also aid in aligning all members of an investment team and avoiding misinterpretations.

To facilitate this approach, a grading or ranking scale could be set at the onset of the ODD process. Using an established set of numbers as a grade will clearly define where an opinion falls into a relative scale. These grades can be layered on top of other collected information and can be used to create a rules-based scoring matrix.

For example, when reviewing how a manager uses service providers, such as a fund administrator, the level of services contracted could each have their own pre-defined grade scale. A manager that uses a third-party provider for only monthly NAVs may score lower in that category than those getting the daily NAV services and more frequent reconciliations. In separate service provider reviews, each of the services offered could also have competency grades at the administrator level, resulting in an objective, multi-factor scoring of the manager’s operations.

“Break the paragraph paradigm – turn thoughts into data”

Having a system in place is a good starting point, but systems are only as good as the information they contain. They can only add value and time savings through analysis, reporting and comparisons if the information is in a format that reduces the need for human processing. For example, a written paragraph may contain valuable information and insights, but by its very nature, a paragraph requires someone to read the words and decipher their contextual meaning in order to understand the data it is attempting to express.

Rather than providing a list of questions that require short text answers, using a system that can create customized forms with check-the-box fields and multiple choice lists can help standardize and speed the ODD process. This type of structure works to “break the paragraph paradigm” by turning thoughts into data. Data can be easily viewed and understood by the viewer, reviewed in a time series, and reported on in a graphical format. In addition, this approach facilitates uploading to – or direct integration with – other systems.

Create and manage big data

The IT-related definitions of “Big Data” usually refer to massive amounts of transactional, social media and other structured and unstructured data gathered and analyzed by large organizations. However, many companies – regardless of size, industry and volume of available data – are finding relevancy in related concepts and practices.

These concepts can also be applied to the ODD process, encouraging managers to collect operational due diligence information in the form of structured data wherever possible. As the data fills out, patterns and correlations may arise to show that certain aspects of operational rigor and preparedness not only reduce the chance of loss, but also may drive the likelihood of positive performance.

The rise of algorithmic trading and other rules-based processes are examples that allocators could use to model parts of their process. Manager allocation may not always get distilled down to a formula, but having some of these concepts embedded in their decision support systems allow for business continuity, internal and external transparency, and addresses the common complaint from CIOs that there is simply too much information out there for any human or team to handle alone.

Putting the proper processes in place to guide and capture ODD efforts – such as collaborative systems that foster the creation and use of actual data – can ultimately lead to better investment decisions, greater peace of mind and more efficient use of everyone’s time.