Data matters to Financial Services firms. It is their stock-in-trade, a strategic asset that without an accurate and timely data set they cannot operate effectively, they cannot price risk fully and their capital allocation calls are unlikely to be optimal. Data is the ultimate collateral of these firms. For many, it requires a transformational change in their systems, technology and processes How then do you embed strategic data into your enterprise architecture? Read 2 minute guide

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2 minute guide
Data and the enterprise
mission: putting data at
the core

2. building a best-in-class enterprise architecture is
not easy
»» Financial Services firms require a rock solid ability to execute, agility to respond to
opportunity and the ability to scale (sometimes quickly) if they are to successfully
deliver and compete.
»» Decades of never ending corporate activity and the IT spaghetti that has followed,
as well as the pressures placed on ever more demanding regulatory compliance, all
add to the challenges to operate effectively and efficiently.
»» At one level, the focus falls very quickly on building and operating specialised
systems to execute specific processes and support particular functions within the
(often disparate) enterprise architecture.
»» It is hard to fault the logic of “getting things done” but the potential consequence
of taking such a tactical view means that Financial Services organisations run the
risk of viewing data merely as the by-product of their system interaction not the
key outcome, asset or business driver, and therefore run the risk of losing strategic
momentum.
why does data matter?
»» First and foremost, data is an asset. It lies at the centre of everything that financial
services firms do. It is effectively their stock-in-trade that has to be managed right
across their respective value chains in each and every market segment. It is perhaps
the most important collateral they own.
»» No matter the skill level of a fund manager, their effectiveness will be hindered
without accurate and complete data – not only will they find it difficult to
understand the current state of the market and its future trends, they will also
struggle to understand their true exposures at any point in time. How do they
trade, where do they allocate capital, how can risk be fully understood or managed
without having the right data to hand?
»» Data needs to be timely, consistent and of good quality to make optimal decisions
and alleviate operational and regulatory risk.
»» Yet, even if these arguments about data’s strategic importance are not fully
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3. pg
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convincing, it should not be forgotten that data remains an expensive asset both in
terms of its acquisition cost as well as the reality that too often firms spend valuable
time and resource working to fix their data problems.
»» In reality, the Financial Services sector is characterised by firms that have grown
through widespread acquisitions, meaning that they are often managing data across
different platforms, each of which has its own data model and database structure.
»» In addition, many firms have adopted a “best of breed” approach to their systems
architecture, joining together the best front office system with the best fund
accounting system with the best risk system and so on. All of these systems will
share data, and without a defined data strategy the disadvantages of change can
often outweigh the benefits.
»» If data really is the Financial Services sector’s stock-in-trade, then effective data
management is key. It assists with cost and risk reduction as well as operational and
compliance management plus it can also achieve revenue generation and increased
return on investment.
»» Better quality data means improved time to market for new funds and products,
Ability to deal with new regulations
e.g.SolvencyII,MiFIDII,DoddFrankandthelike
Reduces financial pressures
e.g. Reducing overall operating and technology
spend over time, increased ROI
Improved compliance and risk management
e.g. Any compliance rule can only be accurate if the
data is complete and up to date
Creates a stronger base for other future
strategic architecture initiatives
e.g. IBOR or other strategic change programmes.
Extends global/regional capability and
integration
e.g. Allows for greater standardisation of operating
models, systems and data architecture.
Consistency of data improves user and
client experience
e.g. Performance numbers, risk measures and client
reporting
Reduces latency
e.g. Providing accurate positions and true exposure
as close to real time as possible.
Increases transparency and data lineage
e.g. Who changed what, why and when.
Checklist: Benefits of strategic data management

4. reduced service failures, fewer lost opportunities, e.g. missed corporate actions,
and the ability to increase the velocity of credit extension and trade activity.
why should data be elevated to the strategic level?
»» Quite simply, data is an asset not just a cost. Consequently, firms should consider
working at getting more from their data whilst at the same time dealing with the
pressures of reducing the cost of managing it.
»» Depending on the scale of the firm involved this will be particularly pertinent
when a firm has multiple locations uses perhaps multiple order management
systems, investment accounting systems or has more than one outsource provider.
»» Being able to achieve an holistic picture of global holdings and risk exposures
become of paramount importance.
what are the consequences of getting data wrong?
»» If the assets (or if it can be likened to stock-in-trade) are not managed correctly,
then to varying degrees it increases their exposure to risk of error or regulation
breach, which could in turn limit their operational effectiveness, financial
performance or even put their on-going continuity at risk.
»» It should not be forgotten that there is a real danger getting the data wrong can
have significant reputational risks.
first steps towards an effective data management
strategy – building the case for change
»» Nothing can happen, nor should happen, until there is widespread acceptance and
buy-in throughout the organisation that data should occupy a position of primacy
in the enterprise architecture.
»» An initial data requirements document should be prepared that maps out the
current state of data within the organization and sets out the key aspirations that
an organization would look to attain.
»» This should be followed by the assembly of a detailed business case that is
convincing and clear about the objectives, expected benefits and costs that any
data transformation programme would require.
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5. Better quality data
Leading too ... Better investment decisions, which
alleviates the risk of making wrong investment
choices and ultimately saves money for any firm.
Standardising models and data architecture
Leading too ... Better placed to deliver new business
initiatives both locally and globally.
Better-managed operational data
Leading too ... “On the fly” fixes to be made where
data needs to be fed into systems rapidly.
Faster (and more accurate) data
Leading too ... Better quality start-of-day positions
and consequently the ability to trade with confidence
earlier in the day.
More accurate classification of data
Leading too ... Reductions in the time spent on data
maintenance by people who should be concentrating
on portfolio management and earning fees.
Performance and risk numbers requiring
fewer manual re-checking
Leading too ... Faster period end reporting.
Client reports being produced faster with
less re-runs and manual checks
Leading too ...Reductionsinmonth-endbottlenecks
and an enhanced end-client experience.
Increased accuracy
Leading too ... Better regulatory compliance by
avoiding breaches, late filings or trading errors thereby
avoiding fines or sanctions.
Reduced manual intervention - saving
direct people costs
Leading too ... IT being able to run leaner support
teams.
Reduction in extraordinary staffing costs
Leading too ... cost reduction, for example, not
having to re-run a data feed at 3am due to a system
failure that incurs additional costs.
ROI FACTORS
pg
4/7.

6. »» Normally, the level of change required to successfully implement an enterprise
data management strategy is not a marginal effort. It can require wholesale,
sometimes transformational, changes – unless the organization can start with the
proverbial clean sheet of paper (which is often unlikely).
»» Highest-level active executive sponsorship and support is essential because
strategic data management often requires extensive budget and investment.
This follows because of the required changes to systems, processes and policies
that will require potentially specialist resources for the build whilst maintaining
business as usual.
»» Unequivocally, everything needs to be “in-play” in terms of possible review and
replacement including systems, processes and people. There is often a major
culture change required whatever happens as typically firms will store the same or
very similar data in multiple systems and look to rationalize their entire process.
»» Strong communications and change management initiatives will be needed.
»» It should be remembered that with data management initiatives many of the ROI
factors will not be so tangible or easily measured so finding those that are will
greatly help the business case.
after the business case has been accepted, what
next?
»» This really depends upon each individual firm’s circumstances and requirements
but there are some key elements that should be considered. In summary, firms will
need:
• A data governance programme.
• A tool or some tools to improve data quality – commonly called an
operational data management application or firms could consider
outsourcing this piece.
• Somewhere to store the now high quality data – a data hub.
• A tool or tools to query and publish data from the data hub – this may
include MIS and client reporting.
pg
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7. »» It may be possible and in fact desirable to implement these areas piecemeal in this
kind of order. However, one of the main complications that often arises is that
business as usual needs to continue. Therefore, there may be a need to bring in an
operational data management tool working on existing data hub or hubs first in
parallel and then switch to a new hub later (assuming a complete replacement is
deemed necessary).
A DATA GOVERNANCE INITIATIVE
»» Data governance requires the establishment of hierarchies, boundaries and
ownership, pushing for data quality and accuracy, creating an enterprise vision of
data, securing investment and buy-in from senior management.
»» The right balance within and between these elements needs to be achieved:
• Policies
• Procedures
• Culture/behavioural changes
• Continuous improvement
• Strategic versus tactical horizons
AN OPERATIONAL DATA SOLUTION
»» An operational data management layer aims to provide enriched, accurate and
consistent data to data users allowing them to distribute formatted and fit for
purpose data to downstream consumers in a timely and efficient manner.
A DATA WAREHOUSE OR DATA STORE STRATEGY
»» Some central hub needs to be created where data is cleaned, stored and shared
between all enterprise systems. This is often called a data warehouse and it is
where data (and its strictures and structures) are defined in a data dictionary or
structured data model.
MANAGEMENT INFORMATION SYSTEMS (MIS)
pg
6/7.

8. »» Data from different systems and locations should be consolidated into summarised
management reports.
REPORTING / BI TOOLS
»» Various tools are used to provide consolidated querying and filtering of data into
for example client reporting packages.
pg
7/7.