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Earlier this year, Invesco's chief executive, Martin Flanagan,
was quoted as saying that
"a third of the asset management industry could disappear over the
next five years". It follows Invesco's acquisition of
OppenheimerFunds in support of
Flanagan's comment that "Scale matters more than ever". The
recent trend towards market consolidation shows no signs of abating
as the pressures of mounting fees, rising costs and demanding
regulations take their toll on profit margins. Firms are seeking to
get bigger, diversify into new asset classes, generate savings and
offset pressure on fees through mergers and acquisitions
(M&As). Technology consolidation has an important role to play
in generating these savings and firms need to quickly bring their
disparate systems together to maximize the opportunity. What role
does data management play in supporting these initiatives and
capitalizing on the opportunities that follow a merger or
acquisition?
For firms that are seeking to scale quickly or move into new
markets, M&A is often the obvious answer. As two firms seek to
come together, the importance of robust data management is,
however, often underestimated. A range of challenging data issues
come to the fore following a merger which include data
fragmentation and the consolidation of engrained silos. Two firms
built up over many years have different yet overlapping data sets
pertaining to clients and their investments. Obtaining a
consolidated view of these data sets is critical to identifying
opportunities post-merger and supporting growth.
The data management challenge is magnified when firms do not
have formal data management solutions in place for managing their
securities, parties and product data. This is more common than you
may think among fast-growing asset managers where technology may
not have kept pace with business growth. In these instances, legacy
systems may struggle to manage the new data sets and asset classes
that often come into a business following a merger. As firms scale
up and start trading in new securities, stronger reporting rules
will also apply which in turn require more robust data lineage
capabilities.
These are important factors to consider following a merger or an
acquisition, but they can also seem like a distraction. Asset
managers want to focus on their core business - retaining clients,
winning new ones, launching new funds or moving into new asset
classes or geographies - rather than data management.
This five-step plan for helping fast-growing asset managers
integrate disparate data sets post-merger or acquisition will help
smooth the way and provide a solid platform for future growth:
1. Introduce a robust data governance operating
model
The starting point for any data management strategy needs to be
data governance. But, what does this mean in practice? Firms need
to establish a foundation for data governance which includes a
defined organizational chart with assigned roles and
responsibilities, executive sponsorship for the initiative and a
charter that outlines the scope of the data governance capability.
A robust model should also include communication and training plans
developed by role so that everyone across the new, combined
organization is aware of their responsibilities.
2. Eliminate data siloes
There's no denying that data siloes are bad for business. They
can slow the pace of a newly formed firm, they threaten data
accuracy and they make it harder to spot opportunities for growth.
Firms need to develop a comprehensive plan for how to get rid of
data siloes and inform all employees. An important step is the
simplification and consolidation of the firm's technology
infrastructure. A single instance of an enterprise data management
solution can validate, enrich and reconcile data from across
multiple sources helping to eliminate internal and external data
silos.
3. Ensure data lineage and transparency
The high number of systems and data transformations following a
merger or acquisition can make it challenging for firms to provide
regulators with the required level of transparency on their data
integration and aggregation processes used for reporting. This is
particularly important as firms look to comply with regulations
such as BCBS 239, GDPR and Solvency II. Data lineage provides
insights into which business and technical transformation logic has
been applied to the firm's data and by whom. It also delivers a
better control process that reduces errors and provides confidence
in the figures reported to internal management and supervisory
bodies.
4.Identify and reduce non-core
spend
Cost savings are typically top of mind following a merger or
acquisition. Identifying opportunities to control the costs of
managing and maintaining increasing volumes of data will be
well-received. Firms should look to obtain an accurate view of
their data consumption across the firm and optimize its usage by
removing duplicate or unused data sources and subscriptions. Moving
to a cloud-based or managed service data management solution can
also help firms to reduce fixed costs and the expenditure
associated with hardware, applications and management.
5. Standardise data definitions
Finally, firms should consider developing a data dictionary.
This is a set of information describing what type of data is
collected within a database, its format, structure and how the data
is used. In many respects, a data dictionary can be thought of as
the rules by which all the data within a firm's system needs to
abide by. The data dictionary is also the bedrock for data lineage,
which will support a firm's regulatory reporting requirements. A
good data dictionary can improve the reliability and dependability
of data and reduce redundancy ensuring that firms have a solid
platform for growth post-merger or acquisition.
Following these five steps will remove the potential for a data
management headache post-merger or acquisition and will set firms
on the right footing for growth; after all, that's what M&A is
all about.
Posted 15 July 2019 by Devendra Bhudia, Solutions Director, Enterprise Data Management, IHS Markit
IHS Markit provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
