IHS Markit: Where to focus first in data management

This article is sponsored by IHS Markit

Where are investors focusing their attention when it comes to data analytics and use cases?

Jocelyn Lewis: Investors, continually in search of greater portfolio transparency, require solid operational infrastructure to enable their teams to analyse market trends and KPIs relative to their portfolios, benchmark their performance and use that analysis as a competitive advantage. Such analysis drives capital deployment strategies and encourages the adoption of technology to organise data for consumption and reporting across teams and various investment strategies.

A lot of data received by investors is still in PDFs or Excel formats. The data is therefore difficult to cleanse, compare, normalise and analyse. With the increasing volume of data, investors are keen to implement strategies for the systematic collection and digitisation of their data to gain transparency, facilitate analytics and drive informed decisions supported by advanced analytics, reporting and understanding.

What about managers? How can they improve processes and workflows for better data analytics?

JL: Managers are focused on data management, especially as they process more information from their portfolio companies and curate reports for various constituents, including their internal management teams, investors and leverage providers. They are focused on improving their workflows to ensure their data, including loan balances, market values, financial reporting and performance metrics derived from them, are received in a timely fashion, processed consistently and stored in a central location to achieve a single source of truth.

For example, they want to see LTM EBITDA as of 31 March consistently across their portfolio management dashboards and in their reports. Additionally, managers require integrated systems to reduce duplicate data entry and maintain consistency, specifically as it relates to loan balances and associated performance metrics for their time-sensitive quarterly reporting.

By maintaining a single source of truth, managers query their digital data to expeditiously generate reporting on clean data and save time by not retracing their steps back to the source documentation for each investor request.

Also, the most technologically advanced managers are embracing artificial intelligence and machine learning (AIML) within their data management process because they see substantial benefits in reducing manual processes and the associated bottlenecks. Facilitating information sharing across teams and deriving analytics informs better decisions to achieve higher returns.

What should firms prioritise when it comes to ensuring robust data processes are in place?

Elina Gokh: One of the first things firms should focus on is centralising the data flow inputs to establish the golden source of truth; retiring bespoke systems, manual cleansing techniques and multiple touchpoints. That means bringing structure to the data through technology, because the more sources the data comes from, the harder it is for firms to operationalise it and scale it without strong digitisation tools. Centralisation also allows you to spotlight missing data points more easily.

JL: Firms today are focused on creating scalable processes for growth and developing efficiencies to lower their operating costs for managing data ingestion systematically. By embracing the outsourcing of important, time-sensitive but time-consuming administrative functions, their teams have time to spend on other value-additive, high-priority initiatives.

EG: Of course, not all outsourcing options are equal, but when firms can leverage a provider who builds the technology, firms benefit from a model where the biggest incentive is to provide the greatest efficiency.

How can data be better organised for LP transparency?

JL: For LP transparency, using technology is essential, as reporting volume and granularity as well as reporting frequency have increased. The questions being asked from LPs range from details on operations, processes and controls to the issues of valuations, diversity and ESG. That increased reporting cadence requires digital data organisation to expedite the process, because managers who want the competitive advantage of quick reporting can’t afford to get disrupted or bogged down by investor requests.

The organisation of data also allows for expedited reporting in the face of a lack of standardisation among due diligence queries from LPs, who have comprehensive questionnaires with unique questions and formats, creating an intensely manual process primed for digitisation.

How can fund managers extract maximum benefit from machine learning and AI going forward?

JL: There is huge potential here and managers really need to stress to their teams that AIML supplements rather than replaces their existing capabilities. It is about helping people perform their functions using machine learning, teaching the machines, for example, that if you’re getting financial statements, this is the information that needs to be extracted and is going to be relevant for analysis and reporting. The same is true for relevant investment terms, where you can teach a machine where in a loan agreement they are going to find the interest rate and amortisation terms, for example, and to read that data through many documents, time and again. AIML facilitates more efficient teams, saves time and produces reliable data.

EG: It goes beyond data management. Teams are leveraging AI as an enhanced way to communicate. Portfolio managers, traders and credit analysts can leverage AI technology to more effectively collaborate on investments they are considering or to track correspondence between teams with regards to a particular credit. That leads to faster and more accurate decisions, approvals and timely execution.

How might the next generation of data and analytics tools further benefit managers and investors?

JL: What’s coming down the pipe is more digitisation tools to improve operational enhancements and to leverage the same digital data for alpha generation. Managers are hiring the best talent and want the best tools in place to support their investment decisions.

ESG reporting is an area that is ripe for digitisation. Portfolio companies are reporting more ESG metrics and investors are asking questions about such metrics more frequently and in greater detail. Given the frequency and depth of ESG reporting, digital tools for organising, analysing and reporting on this information are in demand.

EG: Event-driven notifications are a growing focus with managers and investors: instead of sifting through volumes of data, they can identify a single, critical and actionable activity. That might mean getting an alert the minute a company gets a rating downgrade, for example, or a borrower makes a funding request. This event-driven collaboration flows into optimisation engines that can be built to help firms make the most of their investments and make timely decisions.

Managers are fatigued by trying to make a single system, proprietary or vendor-owned, fit all parameters, strategies, asset classes and lifecycle hurdles. Instead, there is a renewed focus to bring together the best-in-class tools across the front-to-back life cycle with the expectation that the data and insights need to flow effortlessly through the systems, extracting maximum benefit. The interoperability of systems, vendors and workflows is no longer an unfathomable futuristic ideal, but a demand that must be met by all industry participants.

Jocelyn Lewis is executive director, private debt strategy, financial services, and Elina Gokh is managing director and global head of credit solutions at IHS Markit