Simon Doherty, head of
Teradata’s Financial Industry Centre of Expertise in
EMEA. argues that post-crisis, banks need new ways to draw
value from their data

Over the past two or
three years, nobody in business – and especially in banking – could
say that life – or the markets – are wholly predictable.

But bankers still rely
heavily on past patterns to help anticipate future events and
behaviour.

Most of the analytical
techniques used to create business intelligence in the financial
services industry involve looking back at what has happened and
modelling the future using this information. 

Data mining, for
example, is a highly valuable activity, but it is based on what has
gone before. Post-financial crisis, the world is a different
place from the past and outcomes are not so certain.

This does not detract
from the value of data; rather, it indicates the need to be
 smarter about the way data is used.  It is, perhaps, the
one asset which is worth more post-crisis than before.  
During a particularly tense week in the early stages of the crisis,
one bank reported that data queries went up by 600% as senior
executives, shareholders and politicians alike all asked pertinent
questions.

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However banks and other
financial institutions with only operational IT systems or where
data is kept only in department-specific silos are at a
disadvantage. Operational systems have to be very good at doing
what they do, such as running current accounts. 

Different technological
approach

They are honed to answer
the questions the bank knows will be asked.  But, when it
comes to the business as a whole, nobody knows what the future
questions might be and this requires a different technological
approach. 

If someone is given a
pack of cards and asked, for example, to find the three of hearts,
it would take them some time to look through the pack to find
it.   An operational system knows that it is going to be
asked for the three of hearts and so it has it on top of the pack,
ready. 

However, what happens
if, at random another card is requested, such as the ace of
spades?  An operational system is not designed to respond to
random questions like this; however a data warehouse is – and it
adopts a specific approach to do so. A  data warehouse
does the equivalent of giving 52 people each one card so that
someone comes up with the requested card immediately.

This is how massive
parallel processing in an enterprise data warehouse works.
Analytical tools are not primed for any standard interrogations,
because there aren’t any.  Instead it is configured to respond
as quickly as possible to the unexpected.

This approach enables
repositories of broadly-based, pan-organisational, integrated data
that can help address complex issues such as risk, regulatory
compliance, financial management – and even to help shape future
product or service development.

The real art is in using
this rich mix of data to help foresee events as they are about to
happen.  For example, data taken from a website may be used to
alert a call centre that when a customer contacts them, they will
be interested in, say, mortgages or insurance, as these were the
products over which their computer mouse was hovering.  If
this “event data” can be combined with information about the
profitability of this particular customer and their propensity to
buy, then the information is worth even more.

The same is true of
assessing credit risk.  Banks can pre-empt a problem by
combining up to the minute event data such as spending profile,
number of trips to an ATM, and visits to an online account with
analytical data from different sources such as credit rating, total
amount of credit accumulated.

The conjunction of
profitability with risk data is also particularly important
post-credit crunch.  Using the right integration of
information, banks can enhance their management of capital,
ensuring the best return and sandbagging against unexpected losses
in a volatile environment.

Increasing importance of data
warehousing

But one of the main
reasons why data warehousing is more important now than ever, is
because it is an investment in future change. Once a data warehouse
is in place, there is no limit to the amount of event management,
calculations, analysis, reporting or decision making it
supports.  Consequently, what can appear to be a major
challenge for one bank, is a small issue that can be solved almost
instantaneously for another with the help of a data
warehouse.

For example, one
European bank had to implement software to deal with an important
regulatory requirement. It did not have an enterprise data
warehouse and ran out of time to implement one. 

It had to resort to an
individual solution instead, even though this involved coding tens
of thousands of lines of data.  A few years later, further
regulations came into force and this prompted another serious
upheaval for the lender. 

By contrast, another
similar bank – one which implemented a data warehouse at the
beginning of the process – hardly noticed the more recent
regulations as compliance had become a straightforward
issue.

There is little doubt
that the industry is witnessing an era of consolidation – not only
between different banks and other institutions – but also between
departments and functions. 

The amalgamation and
integration of data within the data warehouse is a bi-product of
this.  For example, the global regulatory standard Basel II,
and the emerging Basel III, often involves the financial department
doing the reporting, but the risk experts providing the data that
supports the calculations needed.

Doing these calculations
within the data warehouse where all information is rapidly
accessible makes perfect sense.  It stores not only the
results but also the input data and the calculations themselves to
give complete transparency surrounding the concluding figures and
the steps taken to reach them.

Data is an expanding
commodity.  The volume available has risen significantly with
developments such as the increasing use of internet banking
applications and looks set to continue to grow with the soaring
interest in social media and networking.  But banks and other
businesses need to make this data work harder for them.

There used to be a
misconception that enterprise data warehousing was, in itself, a
risky business.  But with the experience, knowledge and power
that has been developed over the past years, vendors such as
Teradata have eradicated any uncertainties, if, indeed, they really
did exist in the first place.

The risk for banks in
the future is not realising what is possible; and, more
specifically, in being unable to reach an answer before rival
lenders.   The winners will be those who prepare a route
to those answers; even though they have no idea yet what the
questions might be.

Simon Doherty is a
career banker and qualified accountant.  While working for
NatWest for 18 years he was responsible for supporting data
warehouses across a number of business areas.  He is an
acknowledged expert in risk and financial management and has worked
with many European financial institutions in these
areas.