Achieving Operational Excellence:
The Growing Role of Big Data

 As seen in the 2016 Mutual Fund Service Guide

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Using data science in the back office to improve processes and business performance.

Mike Maltby, Product Manager


Operational excellence has always been and continues to be the strategic goal and nirvana for asset managers and service providers. Operational procedures that have developed from constant human interaction and paper-based processing continue to haunt organizations that strive for operational efficiency. This has opened the door for technology providers to promote their evolving technologies and control environments powered by straight-through processing.

Yet, it is no secret throughout the mutual fund industry that true operational excellence continues to remain elusive. Mutual fund companies and third-party administrators continue their quest for operational excellence. The quest is fueled by the enticement of significant upside potential including:

For mutual fund companies, operational excellence enables the quickest delivery of the most accurate, highest-quality data to the front office.

For third-party administrators, operational excellence means reduced costs, better service value to clients and an ongoing competitive advantage.

Transforming Operations from Reaction to Action

A third generation of back-office operations, empowered by the emerging data science revolution, is on the horizon — some might say it has arrived. Currently, fund firms and administrators possess enormous amounts of operational data that, with proper analysis, is actionable to drive improved internal processes, accuracy and overall operational performance. This “big data difference” can drive a firm’s operational decision-making to evolve from a basis of historical analysis to data-driven prediction.

Fund firms not yet leveraging big data analytics are working with an outdated operational paradigm. In these firms, the tracking and reporting of key performance indictors (KPIs), although enabled by technology, retains a significant manual component. These KPIs include, but are not limited to:

  • Straight-through process rates
  • Number of accountants per portfolio
  • Number of manual processes
  • Service level agreement reviews
  • Trade volumes
  • Reference data accuracy rates

By and large, firms assess key performance indicators in a historical context — reviewing what has already happened — and apply their learnings in an attempt to improve performance in the future. Consider the time cost of analysis, reporting and recommending, and then implementing action plans. It is clear that there are significant efficiencies still to be gained.

Sweating the Small Stuff with a Big Data

A big data analytics platform combs through the detail of operations to detect patterns that can empower firms to nimbly and rapidly evolve their KPIs into predictive analytics. Viewed broadly, the advantages of predictive analytics fall into three categories:

  1. Anticipating and planning for future operational resource needs
  2. Maintaining/retaining operational efficiency gains as needs and demands change
  3. Predicting operational “pain points” and deploying resources to them strategically, not in a reactive, “fire drill” manner by putting focus to the anomalies and automating the routine processes

As a consequence, organizations can plan for and execute real business changes in service of strategic goals.

Firms can realize significant business value by utilizing predictive analytics driven by big data. Operational improvements may include:

  • Improved service levels by trending SLA process steps and providing insight into operational activities
  • Reduced processing times by providing operations usage patterns
  • Reduced structural costs by identifying opportunities where automation and straight-through processing can be leveraged in place of manual operations
  • Reduced risk and increased transparency by leveraging a real-time position “health meter” to view critical intraday operational metrics
  • More insight of resiliency and operational risk to the fund board

With increased efficiency comes increased agility. By maximizing the use of predictive analytics, firms can positively position themselves for rapid response to market opportunities.

Technology that can provide consistent and reliable data is a critical component for toppling the remaining barriers to operational excellence. Once executives have the tools — they must harness the potential and maximize the capabilities.

The challenge for fund companies and third-party administrators — and it is assuredly a challenge they can meet — lies in finding the mission-critical data points, harnessing that data and applying the findings to their operations. Moving to this predictive model, firms can not only bring relief to operational pain points, but they can also drive significant, organization-wide change over the long term.

The views expressed within this article are those of the authors only and not those of BNY Mellon or any of its subsidiaries or affiliates.

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