In June 2013, Experian hosted a Data Management Summit in London, with over 100 delegates from the public, private and third sectors. Speakers from Experian and across the data industry explored the challenges of developing and implementing data quality strategies - and how to overcome them. Read on for more information.

1. Experian
Data Quality
Manage your data
Manage your decision making
Building an Effective Data Management Strategy
August 2013

2. Table of contents
03
Introduction
04 Yesterday, Today and Tomorrow
Data:
06
Four pitfalls in data migration - Yesterday
08
Real world solutions to the data quality challenge - Today
12 The changing world of data and its impact on governance - Tomorrow
14
Overcome the barriers to your data governance strategy - Tomorrow
16 The importance of a data governance strategy
19
About Experian QAS

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Introduction
In June 2013, Experian hosted a Data
Management Summit in London, with over
100 delegates from the public, private and
third sectors. Speakers from Experian
and across the data industry explored the
challenges of developing and implementing
data quality strategies - and how to
overcome them.
About Authors
Joel Curry
Managing Director
Experian QAS (UK&I)
Johny Morris
Co-founder
iergo
Tristan Taylor
Product & Marketing Director
Experian QAS
Jason Stamper
Editor
Computer Business Review
Janani Dumbleton
Senior Consultant
Experian QAS
Malcolm Whitehouse
IT Executive
Help for Heroes

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Data
Yesterday, today and tomorrow
Joel Curry is Managing Director of Experian QAS in the
UK and Ireland, leading data quality initiatives in blue
chip organisations in Europe and the US for the past 20
years.
One of the most striking
aspects of data management
in the UK today is its diversity.
Some organisations are just
starting their data journey,
beginning to capture and
harness information to gain a
business advantage. Others
are nearing their destination,
building a single customer view
and cross-channel marketing
capabilities. Others still are at
various points in between.
This brings challenges. It makes it
impossible to talk about the state of
data usage today, simply because
‘today’ for one organisation might be
‘yesterday’ for another. But whilst the
current state of adoption might not be
linear, the data environment is.
Looking back, we can see that even very
large organisations have struggled to
manage their data effectively. In 2005,
furniture retailer MFI1 took a £17 million
hit following a botched IT project and
consequent data failures. In 2006, food
giant Cadbury Schweppes2 saw £12
million disappear into a data black
hole caused by problems in a SAP
implementation.
Fast forward to today and
findings from a host of studies
suggest that these problems
are far from historical: Bloor
Research found that 38 per
cent3 of data migration projects
over-run or are abandoned
altogether; an Experian and
Dynamic Markets4 study shows
that 94 per cent of organisations
suffer from common data
errors; and analyst Gartner
believes that spending on data
governance needs to grow fivefold to 20155.
“While the impact varies
from industry to industry,
it essentially boils down to
several common business
pain points: operational
inefficiencies; multiple versions
of the ‘business truth’; wasted
IT and marketing budgets;
reduced accuracy and
confidence in decision-making;
increased customer churn; and
increased risk of falling foul
of tightened regulatory and
internal compliance.”
Ovum: Quality Business Starts
with Quality Data, Whitepaper,
August 2013.
http://www.information-age.com/industry/uk-industry/285916/when-it-all-goes-wrong
http://www.computerweekly.com/news/2240083453/Cadbury-Schweppes-to-cut-project-risk-by-reusing-code
3
Data Migration – 2011, A White Paper by Bloor Research, Author : Philip Howard, Publish date : December 2011
4
Experian Global Research. Author: Dynamic Markets, Publish date: December 2012.
5
http://www.gartner.com/newsroom/id/1898914
1
2

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And peering into the future, we
can see that challenges aren’t
going to go away. It’s estimated
that global business data
virtually doubles every year. As
companies try cope with this
massive growth in volume and
complexity, regulatory regimes
are growing in strength and
imposing increasingly severe
penalties on those that fail: the
Basel III Framework demands
that Boards manage data risks;
and the UK’s FSA imposed
more than £300 million in fines
in 2012.
Co
Ineffective data management conjures
a perfect storm of operational
inefficiency, squandered resources,
poor planning, lost sales and regulatory
risk. Sobering stuff.
It’s clear that effective data
management has moved from soft
aspiration to hard-boiled business
necessity. If companies are to be
efficient, customer-centric and legally
compliant, they need to be able to be
proactive players rather than reactive
fire-fighters: migrating yesterday’s
data to effective software resources,
ensuring today’s data is of the highest
quality and then harnessing it to
business objectives for future success.
Organisations need to
create a virtuous circle of
analysing (profiling), improving
(enrichment) and controlling
(monitoring/reporting) their
data - and Experian’s Data
Management Summit aims to
help you achieve this.
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Four pitfalls in data migration
Yesterday
Johny Morris is co-founder of specialist data migration
company iergo and author of the book ‘Practical
Data Migration’. His insight comes from 25 years in
the industry and 15 years spent taking companies —
including the BBC, Barclays and BT — through the
data migration process.
I’ve helped many organisations
to make sense of and derive
benefit from their data. I’ve
seen hundreds of challenges,
but it’s telling that all of them
fall under four overarching
themes. These common pitfalls
transcend company, size, sector
and geography.
This is curious given the fact that the
theory is so straightforward. Any data
migration follows a standard process:
you take the source applications;
apply extract/transform/load (ETL)
rules; load the tool; and deliver to the
target system. Simple, isn’t it? The
answer is, of course, “no” because
this model assumes data in the source
applications is in pristine condition, but
in my experience this is rarely, if ever,
the case.
Most legacy systems are
the data equivalent of
Frankenstein’s monster. Over
the years, the original database
has been hacked about, split
up, put back together, merged
with other systems and been
controlled by a variety of rules
and systems. Addresses, for
example, are all over the place
with postcodes and telephone
numbers crammed into the
wrong fields, and no two entries
ever alike. What this means is
that, when you extract data into
the ETL tool and try to load it
into the target system it never,
ever fits.

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Getting data into a fit state for migration is where the unwary drop
into the big four pitfalls:
Failure to understand the scale of the legacy problem
Organisations NOT surprised by the poor state of their data when
they embark on a migration are few and far between. It’s often deep
into the project by the time that anyone realises that data quality is
going to derail the whole thing.
Overstating the ability of IT to deal with the issue
Data quality isn’t solely an IT remit — it’s collected and used by
the whole business so it’s an organisation-wide responsibility.
Expecting IT to fix everything isn’t going to work.
Poor prioritisation and management of issues
Failing to understand where problems are likely to arise before
starting a migration — and compounding this by having no systems
to tackle them — is the perfect recipe for a hashed project.
Misunderstanding who has signed up for what
Very sensibly, many organisations bring in external expertise during
data migrations. What’s not so sensible is, with the lack of insight
resulting from the first three pitfalls, organisations don’t know
what it is that they’re asking these third parties to provide. This
often results in costly, time-consuming and relationship-shredding
standoffs.
All of this leads to something I call ‘responsibility gap’. IT attempts to deliver the
migration but needs input from other parts of the business. IT asks the relevant
departments to provide answers, but without the necessary skills, these departments
pass back incomplete solutions. IT then tries to move forward but hits the same
problems and hands them off to the departments. It’s an ever decreasing circle that
can go on for years — believe me, I’ve seen it.
So how do you solve it?
Profile: the first task is to whip your data into shape: rubbish
in, rubbish out applies here. No matter how fantastic your target
system, if the data you load is flawed, your shiny and expensive
new solution will be too. Use profiling software to cleanse and
standardise the data you hold. It will be well worth the investment.
Relationships: at the same time, start building internal
relationships between IT and other departments. This allows the
identification of likely issues before the migration and ensures joint
solutions when unexpected problems crop up during the process.
Collaboration: software tools may be created to solve a single
problem, but a migration spans a plethora of issues. It’s imperative,
therefore, that the software you invest in works with other solutions
you are using. Making sure this happens will allow you to prioritise
and solve problems.
Procurement: the insight that the first three solutions provide will
help to inform the organisation of what it needs from its suppliers.
Being able to plan for foreseeable challenges and mitigate the
unforeseeable means that now, when something needs fixing,
everyone knows who is responsible.

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Real world solutions to the data
quality challenge
Today
Tristan Taylor is Product and Marketing Director at
Experian QAS, where he has seen the full range of data
quality challenges — and the common solutions that
can overcome them. Tristan’s focus is on developing
data quality products that help organisations turn data
into insights that drive measurable benefits
Co
In modern data management,
the key to success lies in the
ability to be pre-emptive and
proactive. Waiting for problems
to hit your organisation before
tackling them is only ever going
to go one way: badly. Taking
control now will save time,
money and reputation.
This paper has already touched
upon the internal trinity of good data
management: ANALYSE; IMPROVE
AND CONTROL.
I’m going to add a fourth, external
element — the business outcome
— which is an improved ability to
ENGAGE.
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Whilst it’s always valuable to understand the theory of this
process, the proof, as they say, is in the pudding. So I will take you
through four Experian QAS case studies that explore these four
dimensions of data management and show how a focus on data
accuracy brings real-world benefits.

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Analyse
Since 2004, a UK based charity has distributed around
£6 billion to good causes in the UK. One of its aims is to
ensure that this money is distributed fairly across the country,
and to where it will drive the strongest social benefit, which is no
easy task.
To do this, the organisation
obviously needs to understand
its audiences by analysing
factors such as geography and
demographics. Dealing with
over 40,000 applications for
funding every year, it needs to
communicate with applicants
effectively. Contact data must
be as accurate and up to date
as possible.
The charity estimates
that this data analysis and
enhancement:
saves £40,000 a year;
frees around 277 working days a year
is a safeguard against fraud; and
improves customer service.
Working with Experian, this charity has cleansed its databases, with a bulk
validation of around 300,000 contacts to remove duplications and other
inaccuracies. Experian has also applied additional data sources so that information
was enriched, allowing audience segmentation and enhanced understanding. By
understanding the geographic distribution of funds and the deprivation index in
various localities, the charity is best able to meet its objectives around effective
distribution of funds to applicants.
Improve
A major services company was faced by the challenge of
worsening repayment rates and default, making a big dent
in its bottom line. The company identified that poor data quality
was a significant contributing factor.
Experian worked closely
with the company to identify
precisely what the problems
were and to implement
solutions. Experian convened a
working group that numbered
representatives from across the
organisation. The existing state
of data quality was mapped,
a data strategy measurement
framework was created and
a data quality scorecard
introduced — all aimed at
fixing the immediate problem
and embedding a long-term
solution.

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One startling finding was
that customers’ mobile
numbers weren’t collected
systematically, nor validated
on collection. It meant that the
Collections Department often
didn’t know how to contact
customers who had fallen
behind.
With Experian, the company:
established a clear connection
between poor data quality and
higher default rates — proving the
value of data quality investment;
identified problem areas and
implemented targeted solutions
that were aligned with business
objectives;
helped to make data quality a
business-wide concern that was
clearly quantified;
introduced a wide range of
strategic systems and processes
for a permanent fix.
Control
In 2009, a public sector organisation found
inconsistencies within its consumer database
Following the recommendations
of an independent review, this
organisation worked with
Experian to validate its data
control processes. The project
involved close working with
staff to map and understand
how data was used across the
organisation, conducting a
Governance Review and Data
Process Audit.
Working with Experian, this
organisation:
was able to prove the efficacy of its
data systems and processes to key
audiences, with trusted third party
validation;
received a comprehensive report
into its data governance; and
was left with a clear roadmap for
future improvements to the control
of its data processes.
Engage
P&O Ferries collects huge amounts of cross-channel
customer sales data through, for example, call centres, at
ports and online. The company understood that it was not
harnessing the real value of this information fully — with data sat
in silos and not integrated to drive the business.

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Working with Experian QAS,
the company embarked on
creating the infrastructure
needed to deliver a Single
Customer View. The first step
was to embed data quality
systems and processes
that ensured the accuracy
of data and the ability to
harness this information to
business objectives across the
company.
Experian and P&O Ferries introduced
point of capture software that
corrects addressing errors in real
time and encourages the collection
of additional addressing information;
database management solutions so
that data held is constantly checked
and updated; and solutions that
identify and link customers across
channels into a single customer
record that is shared and leveraged
across the company in near real time.
Working with Experian,
P&O now has:
a single view of its customer across
multiple transactions and channels,
never more than 24 hours old.
far more effective engagement, with
marketing approaches to individual
customers based on behaviours
exhibited in the past 24 hours;
driven efficiencies and cut costs
across the company.
With a SCV in place, P&O Ferries now
accesses data within 24 hours of capture,
ensuring that business decisions are
informed in near real-time.
P&O Ferries’ IT Project Manager
Rani Tarumarajan says,
“We have better visibility into individual customers and
can deliver increasingly targeted messaging.”
Whilst each example is unique, I think that these studies also
demonstrate the common themes of proactive data management:
the need to get a true picture of current organisational data
quality; the need to assign a monetary value to data quality; the
need for executive ownership; and the creation of a culture where
data is both respected and appreciated as a business driver.

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The changing world of data and its
impact on governance
Tomorrow
Jason Stamper is Editor of Computer Business Review,
with industry expertise that is widely recognised and
in constant demand. Jason has been voted one of the
most influential people in Enterprise Management,
ranked as one of the top journalists to follow on Twitter
and is a sought after speaker at industry events.
Whilst the old saying has it that
there are “lies, damn lies and
statistics”, the latter can give
us a meaningful glimpse into
today’s data environment. And
by understanding where we are
now, we can see where we’ll
be tomorrow. In a Computer
Business Review survey into
UK data governance in October
2012, the headline appears to be
that there’s good news and bad.
So, let’s dive into the numbers. In just
one year, senior management buy-in
to data governance has grown by 4 per
cent. Today, 84 per cent of organisations
report that their top teams understand
the impact of poor data — up from 80
per cent in 2011.
Why bother?
So far so good — but whilst the
number of organisations taking data
seriously is growing, 16 per cent of UK
businesses still don’t see the damage
that poor data can wreak. This is not
so encouraging, and the reasons
why companies have embraced data
governance should be a sobering
lesson to those that haven’t.
Our survey showed that the
run-away winner as the primary
driver for data governance is
‘legislation/compliance’ — well
over half of those questioned.
And the second most cited
driver is the closely related ‘risk
management/mitigation’.
What is the primary driver for those clearly defined policies around data?
60%
40%
20%
0%

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This is hardly surprising given the powers now available to the
Information Commissioner’s Office (ICO). Today, breaches of data
regulation can lead to fines of up to £500,000 and, in some cases,
imprisonment.
But it’s also good to see that a growing
number of organisations recognise the
carrot as well as the stick in the data
equation: ‘revenue optimisation’ and
‘operational efficiencies’ claim third
and fourth spots.
Why not?
With such compelling reasons —
both proactive and reactive — for
good data governance, why have
one in six UK companies not yet
had their data light bulb moment?
Perhaps unsurprisingly, the three Cs
of complexity, complacency and cost
dominate thinking (or lack of it) in these
organisations.
Who’s leading?
Data understanding has come a very
long way in a relatively short time, but
even in those companies that ‘get it’,
there is some evidence that they don’t
get all of it. Most worryingly, close to
a fifth of UK companies fail to assign
responsibility for organisational data or
don’t know who is responsible.
Slightly better news: over 70 per
cent of UK organisations do assign
responsibility to a part of the business.
But, many respondents to our survey
(over 40 per cent) still see IT as data’s
designated driver. Of course IT is going
to be a major player, but in a world
where every business is data driven,
data is everybody’s business.
Encouragingly, well over a quarter
of respondents understand this fact,
stating that each part of business is
responsible for its data, with just under
10 per cent going the extra mile and
investing in dedicated data champions.
Where are we going?
And I’ve left the most interesting
finding till last: as we’ve discovered,
84 per cent of companies understand
the impact of bad data, but over a third
have yet to invest in the technology that
will embed accuracy and management
control. That’s a curious disconnect
between understanding and action.
Companies that have invested in data
governance technology report a mixed
picture in terms of returns. About a
third are benefitting from the positives,
but a fifth report indifferent or little
value.
So what conclusions can we
draw from these good news/
bad news results? For me, it’s
clear that the data argument
is largely won, but the battle
to translate understanding
into business value is still
raging today. Tomorrow’s data
environment will be one where
organisations implement total,
rather than partial, solutions
— where technology, skills and
processes combine to deliver
real business advantage.

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Overcome the barriers to your data
governance strategy
Tomorrow
Janani Dumbleton is Senior Consultant for Data
Governance and Strategy at Experian QAS. Her
expertise comes from the sharp end of data
management, delivering complex projects in areas as
diverse as business process improvement, enterprise
architecture and multi-channel customer relationship
management.
As the Computer Business
Review survey shows, the
biggest barriers to creating
a data governance strategy
are complexity, complacency
and cost. These three Cs are
interlinked and self-sustaining.
When an organisation looks
at how complex their data
environment is, it assumes that
costs are going to be huge and
returns on investment limited
— so why change?
The nightmare scenario is an
organisation that holds vast amounts of
data in silos. This data can encompass:
transactional data such as quotes,
orders and sales; master data, which
can contain employee, customer,
location and product information;
reference data with categories and
segments; and analytical data that
holds information on organisational
metrics and KPIs.
If an organisation is made up of
separate divisions — let’s say a retailer
that also operates financial and legal
services — the complexity of data is
multiplied and enough to scare all but
the most hardened data evangelist.
The dream, then, is to move all the
data components out of these silos
and contain it in a single, coherent and
easily exploitable ‘big data’ resource.
Nice idea, but how do we turn
the dream into reality? We
need to introduce some order
to the chaos — we need a
system. And we’ve come up
with a four pronged approach
to skewer your data to business
objectives.
I. Scope
Before you can sort out a data governance problem, you have to
admit you’ve got one. And then you’ve got to get to grips with what,
precisely, that problem is.
To help you, take the time to understand the types of data
you hold and collect. Is it:
Party Data - customer, patient, citizen, vendors and/or social
profiles?
Product Data - categories, SKU, pricing, hierarchies,
availability?
Financial Data - accounts, transactions, debt, risk?
Location Data - gazetteer, grid locations, sites, locations?

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II. Prioritise
Now you can accurately assess the data resource you hold, identify
who within the organisations uses it and how they use it (what’s
it for?). Ask yourself the question, how important is data for each
unit, and what is the impact of this usage on other divisions?
In retail, for example, customer contacts such as email/ postal
addresses and mobile/ landline numbers might be critical
for marketing and sales. In finance, contacts might adhere
to separate rules — an email address might be the channel
for marketing messages but landlines only ever used for vital
account information. The legal division might have different
rules again, where all contact channels are limited to agreed
service communication, with no sales or marketing applications
whatsoever.
III. Collaboration
The next objective is to build a common platform for all of this data
that allows each division to access the data they need, when they
need it and in the ways they need to.
Essentially, it’s creating a common taxonomy so that each separate
part of the organisation is speaking the same data language. If
Retail calls a landline number a ‘home number’, Finance calls the
same thing a ‘landline contact, and Legal calls it a ‘main telephone
number’ data is never going to be consistent, nor drive all parts of
the business.
Now, instead of three inconsistent databases that can’t talk to
each other, you’ll have one that serves the needs of each part of the
business equally. By embedding this consistency, you are issuing
data with the passport it needs to escape from its silos.
IV Automation
.
And finally, it’s always worth remembering Alexander Pope’s
maxim that “to err is human.” No matter how well trained,
dedicated and managed your staff, they will make mistakes that will
undermine your data governance strategy.
By automating as many steps in the data governance process as
possible, the more you mitigate risks associated with inaccurate or
inappropriately handled data.
Essentially, these four steps will take you to a place where people,
processes and technology are perfectly aligned to deliver an
effective data governance strategy — and your business can
begin to exploit the benefits of Big Data.

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The importance of a data
governance strategy
Malcolm Whitehouse is an IT executive with more than
30 years’ experience of harnessing the value of data.
He has developed expertise working at senior levels in
government and the private sector, and now works for
the Help for Heroes military charity.
Without widespread ownership and
As a CIO at organisations
buy-in, a data strategy simply becomes
in the public, private and
a document that sits on a shelf —
third sectors, I’ve come to
unloved and neglected. To be effective,
it needs to be couched in terms
understand the absolute
that demonstrate real world value.
necessity of a data strategy.
Remember: this is a STRATEGY, not a
In the modern world, every
PROCESS.
business of whatever size needs
So let me take you through
to collect, control, understand
the challenges and solutions
and leverage its data if it’s to
that I’ve encountered in large
thrive — and indeed, survive.
and small organisations —
So, let’s go back to basics…what
the former in a monolithic
exactly is a data strategy. From a
government department
business perspective, it’s about
defining the value in data held and used and the latter for a small but
across an organisation together with
rapidly expanding charity. It will
the underlying rules for its use and
hopefully help you to develop
management — managing data and
a strategy that works for your
resulting information as a strategic
asset.
business.
Taking this down a level, a data strategy
will encompass a huge amount of
individual components, including
Master Data Management, storage
and archiving, data sharing policies,
information lifecycle management, data
cleansing, data enrichment and data
standards.
It’s at this point that it can all get very
geeky, with highly complex models
that only mean anything to information
nerds like me — and sometimes even I
struggle. It’s also at this point that many
organisations lose their audience — the
whole thing seems far too complex,
abstract and divorced from day-to-day
realities for it to have any relevance to
the rest of the business.

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Data strategy — in a large organisation
In this particular large government organisation, data management
systems had grown up over time, evolving and morphing as new
technologies, people, policies and practices came on stream. It
meant that legacy systems were confused and degraded through
years of misuse and mis-codification. Data sat in silos, blocking
the ability to share it. Individual ‘renegades’ created their own,
personal systems, processes and workarounds that further
fractured the system.
Frankly, it was a mess and many organisations in a similar situation
simply ignore the problem because it’s too much to think about.
When you’re faced with a job that big you need to break it down
into manageable chunks. Our first step was to understand what
was going on — identifying how data was actually managed at the
start of the project and how it should work in the future. Now we
knew our start and end points, we could begin to map the journey.
We identified the gaps and inefficiencies in the existing system
and then understood how to remedy them. Planning at the front end
was a key success factor, allowing us to prioritise the activities
that delivered the most benefit. With this information in place, we
could then quantify and build the people, skills, processes and
technologies needed to turn data from a burden into an asset.
In terms of practical actions, effective training
and, wherever possible, automation standardised
data entry and ensured its accuracy and integrity.
Using metadata broke down data silos and started
information flowing across the department. And
finally, clear, consistent and coordinated data policies
routed out the data renegades.

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Data strategy — in a small organisation
Thinking that a data strategy in smaller organisation will be easier
is a mistake — at a charity such as Help for Heroes the issues are
different but just as challenging.
Whilst a large organisation can allocate personnel to focus on a
data strategy, in a small one data management is nobody’s day
job. Communication was ad-hoc rather than structured, which led
to organisational blind spots — data sat in isolated spreadsheets
with no ability to share or analyse. Budgets were always small and
under pressure and what technology there was had been acquired
to deal with tactical, not strategic, requirements. Processes too
grew up out of immediate operational need, so they varied from
department to department.
‘Catching the wave’ in a small organisation is critical if data isn’t to
become a big mess and at Help for Heroes we worked to minimise
data growing pains.
When resources are limited, prioritisation is crucial —
organisations need to focus on the data that brings them deeper
customer insight. In a charity, profiling and segmentation, for
example, help to grow the volunteer and donor base, whilst also
moving these supporters up the engagement ladder so that they
deliver maximum value.
As with a large organisation, we are scoping out how
data is handled today and how we want it be managed
in the future to start the mapping process. Another
key task is to assign a value on the data we hold so
that we know how to use it most effectively. And, as
before, we are identifying the people, skills, processes
and technologies we need to implement an effective
data strategy.
To summarise the key takeaways, every organisation NEEDS
a data governance strategy, which must be proportional
to requirements. Executive buy-in and drive is critical, as
is communication across the organisation so that people
understand the value of data — prioritising quick wins will help
to demonstrate this. Better communication enables crossorganisational collaboration, so data governance isn’t just
dumped on IT.

19. Experian QAS
19 - Building an Effective Data Management Strategy
About Experian QAS
Experian QAS is a division of Experian Plc that provides
a unique combination of software, data and services to
solve a variety of data management challenges. We enable
companies to analyse, improve and control their data
thereby enabling them to make better business decisions,
reduce their risk and drive down costs through operational
efficiencies.
We provide a comprehensive toolkit for data quality projects
combining our market leading software with a vast scope of
reference data assets and services. Our mission is to put our
customers in a position to make the right decisions from accurate
and reliable data. The size and scope of data management
projects varies considerably but the common factor in all ventures
is unlocking operational efficiency and improving customer
engagement.
We see the potential of data. Whether it’s in enabling ambulances to
be sent to the exact location of an emergency or ensuing financial
organisations are compliant against their industry legislation – data
accuracy makes all the difference to service provision.
If you would like to find out
more about how Experian
can help you to create a data
strategy that drives your
business, contact us today.
To find out more:
0800 197 7920
info@qas.com
www.qas.co.uk
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