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
Jeremy Hurwitz, Principal and Founder, InvestTech Systems Consulting
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