Building an Effective Data Management Strategy

August 2013

Experian Data Quality

Manage your dataManage your decision makingBuilding an Effective Data Management Strategy

Table of contents

03 Introduction

04 Data: Yesterday, Today and Tomorrow

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

Experian QAS3 - Building an Effective Data Management Strategy

IntroductionIn 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 CurryManaging Director

Experian QAS (UK&I)

Jason StamperEditor

Computer Business Review

Johny MorrisCo-founder

iergo

Janani DumbletonSenior Consultant

Experian QAS

Tristan TaylorProduct & Marketing Director

Experian QAS

Malcolm WhitehouseIT Executive

Help for Heroes


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 five-fold to 20155.

DataYesterday, 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.

“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.

1 http://www.information-age.com/industry/uk-industry/285916/when-it-all-goes-wrong2 http://www.computerweekly.com/news/2240083453/Cadbury-Schweppes-to-cut-project-risk-by-reusing-code3 Data Migration – 2011, A White Paper by Bloor Research, Author : Philip Howard, Publish date : December 20114 Experian Global Research. Author: Dynamic Markets, Publish date: December 2012.5 http://www.gartner.com/newsroom/id/1898914


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.

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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Improve

Software Tools


Four pitfalls in data migrationYesterday

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.


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 issueData 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 issuesFailing 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 whatVery 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.


Real world solutions to the data quality challengeToday

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

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.

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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Engage


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.

saves £40,000 a year;

frees around 277 working days a year

is a safeguard against fraud; and

improves customer service.

The charity estimates that this data analysis and enhancement:

Improve

A major services company was faced by the challenge ofworsening 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.

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.


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.

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.

With Experian, the company:

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.

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.

Working with Experian, this organisation:

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.


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

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.

record that is shared and leveraged across the company in near real time.

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.

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.

Working with Experian, P&O now has:

P&O Ferries’ IT Project Manager Rani Tarumarajan says, “We have better visibility into individual customers and can deliver increasingly targeted messaging.”


The changing world of data and its impact on governanceTomorrow

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’.

60%

40%

20%

0%

What is the primary driver for those clearly defined policies around data?


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.


Overcome the barriers to your data governance strategyTomorrow

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. ScopeBefore 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?


II. PrioritiseNow 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. CollaborationThe 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. AutomationAnd 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.


The importance of a data governance strategyMalcolm 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.

As a CIO at organisations in the public, private and third sectors, I’ve come to understand the absolute necessity of a data strategy. In the modern world, every business of whatever size needs to collect, control, understand and leverage its data if it’s to thrive — and indeed, survive.

So, let’s go back to basics…what exactly is a data strategy. From a business perspective, it’s about defining the value in data held and used across an organisation together with the underlying rules for its use and management — managing data and resulting information as a strategic asset.

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.

Without widespread ownership and buy-in, a data strategy simply becomes a document that sits on a shelf — unloved and neglected. To be effective, it needs to be couched in terms that demonstrate real world value. Remember: this is a STRATEGY, not a PROCESS.

So let me take you through the challenges and solutions that I’ve encountered in large and small organisations — the former in a monolithic government department and the latter for a small but rapidly expanding charity. It will hopefully help you to develop a strategy that works for your business.


Data strategy — in a large organisationIn 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.


Data strategy — in a small organisationThinking 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 cross-organisational collaboration, so data governance isn’t just dumped on IT.


About Experian QASExperian 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.

0800 197 7920 [email protected] www.qas.co.uk

To find out more: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.

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Building an Effective Data Management Strategy