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Is Data Management Supporting or Hindering your Firm’s Growth?

Author: Jeremy Hurwitz

11 | 17 | 2016

Data Management Video.jpgOriginally published in the SimCorp Journal of Applied IT in Investment Management on November 17, 2016

By: Jeremy Hurwitz, Principal & Founder and Brian Lollar, Senior Consultant, InvestTech Systems Consulting

Enterprise Data Management (EDM) is more than mastering data

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. 

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.


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.

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Posted in: Data Management | Chief Data Officer | Data Governance | SimCorp