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Is data management supporting or hindering your firm’s growth?

Enterprise Data Management (EDM) is more than mastering data
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Jeremy Hurwitz, Principal and Founder, InvestTech Systems Consulting, explains why data management is so important when looking for growth.

Read this article and learn about:

  • What an EDM strategy means to scaling and sustaining growth
  • Successfully implementing data governance and data quality initiatives
  • The difference between data governance and data quality
  • Why you can’t avoid repeat quality issues if you don’t measure them
  • Why the CDO’s impact should be felt across the business
 Hurwitz 
Jeremy Hurwitz, Principal and Founder, InvestTech Systems Consulting
 Lollar 
Brian Lollar, Senior Consultant, InvestTech Systems Consulting

Today’s asset management firms are being challenged to cope with tightening margins, increased data costs, heightened regulatory scrutiny, and pressure to support a wider variety of products and investment types while at the same time, outperforming the markets. A central component to all of these challenges is a firm’s data.

A recent industry study1 found that data quality and data management are frequently seen as inhibitors to growth and obstacles to solving operational bottlenecks. InvestTech Systems Consulting’s2 current engagements support these findings and this article presents our viewpoints on Enterprise Data Management (EDM) and how EDM supports a company’s growth strategies and operational efficiencies.

What is the need for EDM?

In the current asset management landscape, data is moving at warp speed. Many firms are introducing complex portfolio strategies and fund structures, which have increased risk exposure, sensitivities, and the demand for advanced risk scenarios. Firms are also introducing data complexities from near real-time IBOR platforms, complex analytics requirements, and the desire for advanced business intelligence (BI) capabilities. A consequence of these advancing business drivers has been a broader recognition that data issues are obstacles to supporting growth strategies and operational efficiency.

What are these impacts to operations and growth? Some examples of data management issues frequently affecting our clients are:

  • Failed trades and process failures arising from “poor” data, resulting in costs such as trade corrections, needing to re-state NAVs, or backdate accounting events.
  • Delays in meeting reporting deadlines to clients because of time needed to cleanse “poor” quality data, resulting in increased reputational risk exposure.
  • Challenges meeting regulatory requirements due to missing or “poor” data.
  • Problems meeting the data requirements and delivery deadlines of the front office because of data, timing, and incomplete total AUM, resulting in portfolio management limitations.
  • Difficulties launching new investment products, on-boarding new clients, or growing through acquisition due to manual processes enacted to combat “poor” data, resulting in limited growth opportunities. 
A consequence of these advancing business drivers has been a broader recognition that data issues are obstacles to supporting growth strategies and operational efficiency. Jeremy Hurwitz and Brian Lollar, InvestTech Systems Consulting

Implementing an EDM strategy that encompasses data governance and data quality is not easy. According to their 2015 Data Management Benchmarking Survey3, the EDM Council found that more than 80% of surveyed firms had established an EDM program (Chart 1), yet only slightly more than 40% had established data governance, and fewer than 20% had implemented measurable data quality efforts.

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Chart 1: Key Areas of Data Management: Industry Composite View, 2015 Data Management Industry Benchmark Report, EDM Council, Inc., 2015.

Why such a significant difference? We have found five common challenges, which will likely resonate with many readers:

  1. No centralized data governance structure due to sponsorship, budget, and priority constraints; The C-level does not see the ROI or value proposition for dedicating resources to data governance initiatives.
  2. No formalized nor centralized view of data quality; data quality is often redundantly performed across multiple business lines and functions but lacks an enterprise view to measure improvements and to resolve problems at the source.
  3. No target operating model to manage project or strategic driven data events, business as usual (BAU), or other data events such as mergers, acquisitions, or onboarding new sub-advisors and clients.
  4. Lack of “true” business ownership (“it’s an IT problem to fix”) or accountability (“the data is fine in my system/process”). Firms need to establish clear data ownership and firm-wide data collaboration.
  5. Incomplete understanding of the connection and difference between data governance and data quality.

What is data governance?

Data governance is the set of policies, procedures, and standards by which data quality is executed. It involves establishing transparency, trust, accountability, and availability of your firm’s most valuable asset: investments data. Enterprise data governance comes into play when business units or managers find that they cannot – or should not – make independent data related decisions without understanding the broader impact across the firm. To accomplish Enterprise data governance, firms must bring together cross-functional teams to make interdependent/collaborative decisions toward providing high quality data services to stakeholders.

Hurwitz-interview-journal

 

What is data quality?

Data quality is the enforcement of data governance through a methodology to measure key data quality metrics and KPIs. It is the feedback mechanism to show the effectiveness of data governance policies and standards. Most organizations already have data quality functions performed in various silos, especially with investments data and reconciliation. The functions being performed here address data issues that are specific to that system and/or process, but frequently do not consider the larger picture. To truly support the enterprise, data quality should be viewed as more than data cleansing and scrubbing. Data quality initiatives should focus on addressing the data quality issues at the source vs. after the fact, and should be measured against key business metrics and value factors.

What is technology’s role in EDM?

Technology tools are critical to ensure the success of any data quality effort and should be defined and integrated within the firm. That being said, technology tools alone cannot achieve data governance, nor resolve data quality issues. An effective EDM strategy is one that results in an EDM operating model, which puts all the pieces together around the technology (people, process, IT, data). Establishing a right-sized and effective EDM strategy, which includes disciplined data governance and data quality models, is key to navigating the complexities of EDM technology and exploding data demands. 

Much has been written about the importance of a business vs. IT driven data management and data governance program. We strongly advocate this as a requirement for a successful and sustainable EDM programJeremy Hurwitz and Brian Lollar, InvestTech Systems Consulting

An effective EDM program will establish a framework to centrally monitor and track data through the Data Governance Office, usually headed by a business reporting Chief Data Officer (CDO). There also needs to be business-centric data documentation and collaboration tools to expose the data lifecycle. Through this central monitoring and reporting of the data management efforts, there is less of a need to break down the silos. This monitoring and tracking need not be heavy-handed. We have found that a lightweight data quality function, with a well-defined process and supporting data management tools, usually provides the business with enough end-to-end data transparency across business functions and IT systems to surface areas for process improvement and issues requiring data governance. CDOs should report back to their focused working groups, data council, and executive sponsors with measurable results of these efforts, as both a tool for awareness as well as encouragement for more business ownership and ultimately more effective governance of data.

 

Conclusion

A successful and effective EDM strategy is increasingly becoming a core requirement for asset management firms, driven by ever-increasing data challenges and complexities in the market today. In order to maximize EDM’s impact on growth strategies and operations, an effective EDM strategy should have the appropriate ownership, roles, technology, and an understanding that data management is more than just ensuring the data in a system or report is correct. It is also understanding the data that is a critical asset to your firm, and your organization’s data acquisition process, lineage, usage, security, and timeliness must be both agile and well governed. An effective EDM strategy should take these different data management capabilities into account and ensure that data management is being performed across the enterprise, including critical shared and unique data sets across research, portfolio management, middle and back-offices, client services, account management, risk management, and sales/distribution data services.

EDM can provide a firm with the methodology and tools to better understand its data (what it is, where it comes from, what it means, where it’s going, etc.) so that it can ensure the data is correct for the business need and minimize costs associated with the data life-cycle (data acquisition through processing and consumption). It is also through this understanding that firms are able to put processes in place to assist with strategic growth and operational improvements. 

About the authors

Jeremy Hurwitz is InvestTech Systems Consulting's Principal and Founder. He has over 25 years of experience in the investment systems technology field working with global institutional investment organizations. Jeremy has developed extensive knowledge and expertise in investment theory and process, operational efficiencies as applied to Enterprise Data Management and technology architectures disciplines.

Brian Lollar is a Senior Consultant for InvestTech Systems Consulting with over 18 years of experience in the Investment Management Technology industry specializing in Investment Operations, Investment Accounting, and Enterprise Data Management.

About the company

InvestTech provides expert investment management systems and outsource solutions consulting specializing in data and operations, portfolio management, analysis, trading, compliance, accounting, performance, attribution, risk, business intelligence and reporting.

References:

1/en/insights/insights-resources/infographs/inhibitors-to-growth

2 http://www.investtechsystems.com/

32015 Data Management Industry Benchmark Report, EDM Council, Inc., 2015

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