The document discusses challenges facing asset management firms including increased competition, new products and regulations, and more demanding investors. It argues that to address these challenges, firms need a holistic and agile operational environment with a unified information ecosystem and effective data management strategies. This includes applying best practices like data provenance, integration, and analytics to achieve a cohesive and trusted data environment across the organization.

1. ASSET MANAGEMENT OPERATIONS
CHALLENGES AND THE NEED FOR AN AGILE OPERATIONAL
ENVIRONMENT
VAHID ATEFI-AGHAYAN
PRIVATE CONSULTANT
VAHID@INTELLIGENTDATASTRATEGIES.COM

2. CHALLENGES
• Increased Competition
• New products, including increasing passive products
• New distribution channels, including Robo-Advisors
• Complex and shifting regulatory environment
• Increasingly more educated investors, requiring more transparency
• Increased demand for improved margins
• Complexity in growing data sets and how to harvest them

3. COMPETITION
• In the recent years passively managed products has proven to be
performing as good or better than the actively managed products at a
lower cost.
• New automated advisory channels also known as Robo-Advisors
have been an attractive option to the Investment Advisors to the Gen-
Xers.
• Increased competition and access to lower cost passive product, has
created a market force to drive down the margins.

4. REGULATORY COMPLIANCE
• Asset Management firms are faced with a substantial cost burden to
stay compliant. Many firms see regulatory requirements as a top
challenge. This is even more substantial for firms that are operating in
an international markets.
• Many firms are looking into a holistic approach across functional
organizational structure, processes, and systems and data to meet
these challenges.
• Success of a holistic enterprise is deeply dependent on a unified
information ecosystem, and effective data management strategies.
These strategies include data quality, integration, timely delivery,
analytics, reporting and knowledge sharing.

5. GLOBAL STRATEGY
• Many middle to large size firms are looking into the global
strategy to extend their market share. The global market
along with new opportunities impose new challenges as
well. Some of these challenges include:
• More complicated regulatory requirements for operating
associated with the new markets
• Discrepancies in operational processes, definitions of terms &
concepts, in addition of duplications in systems and data sets,
poses a substantial challenge and cost to the global firms.

6. INVESTORS
• Investors’ needs & Requirements
• Due to the nature of intermediary business inherently there is a cloak of mystery
over the changing investors’ needs.
• Investors are increasingly becoming more educated and requiring
more transparency and control over the management of their assets.
• There has been a rebirth & growth in the Managed (SMA/UMA) products in the
recent years
• Due to the investors having a bigger say in the objectives & investment plans
than the traditional pooled vehicles, naturally there is a higher level of
management and reporting cost for the asset management firms.

7. UNIFIED SYSTEMS AND DATA ECOSYSTEM
•What are the benefits of unified, holistic data
ecosystem?
• Better understanding and visibility into the data assets
across the organization
• Operational efficiencies (from back, middle, and from
office operations)
• Reduce risks (compliance and exposures)
• State of Trust in the data we act on.

8. HOW TO ACHIEVE A COHESIVE DATA ENVIRONMENT
• Applying the best practices data management principles,
practices and policies, including:
• Data Provenance & Linage: What are the origins of data, our assumptions and
criteria from the data sources, and what we did with the data to consume it.
• Data Profiles: Understanding the types and classifications of the data
• Data & Business Terms Catalogs: Having a common vocabulary and understanding
of our key data elements across organizations
• Metadata & Sematic Data Models: Data that describes the content in which the data
is being used and how the business sees the data objects.
• Master Data Management: having a centralized source for all valid views of our
reference data, their classifications and hierarchies.
• Data Integration, Blending and Augmentation
• Analytics and Self-service Data Discovery
• Knowledge sharing