Telesperience Data Integration[1]

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Using data integration to drive down costs and

increase profits in high transaction industries

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Sponsor’s message

In 2006 Talend revolutionized the world of data integration when it released the first version of

Talend Open Studio. After four years of intense research and development, and with solid financial

backing from leading investment firms, Talend is now the recognized market leader in open source

data integration.

But Talend is not just the leading open source data integration vendor – we’re also thought leaders,

working at the cutting edge of next-generation data integration solutions. Our portfolio encompasses

data integration (operational data integration and ETL for Business Intelligence), data quality and

master data management (MDM) technology. And unlike the quickly consolidating traditional

vendors offering proprietary, closed solutions, Talend offers a completely new vision of data

integration. Not only do we shatter the traditional proprietary model by supplying open, innovative

and powerful software solutions with the flexibility to meet the needs of all organizations, but we’re

also making data integration solutions affordable for organizations of all sizes and for all integration

needs.

Telesperience research shows that modern businesses are facing rising demand for data integration.Coping with this demand means that manual integration is no longer a viable option. Yet specialist

data integration tools are often too expensive and simply don’t offer the range of functions or the

flexibility required by today’s data integration specialists. If you’re one of those companies

wondering how you’ll deliver the data integration projects your business is demanding on time and

to budget then maybe you should be looking at Talend’s solutions.

To find out more about what we can do for you visit www.talend.com, where you’ll find a wide

range of information about our solution, a library of thought leadership and data integration

resources and, of course, open source data integration, data quality and MDM software which you

can download today.

Yves de Montcheuil

VP Marketing, Talend

January 2010



Telesperience Research 2010

1 Summary

Data integration is often seen as merely a technical discipline; but this ignores the vital role it plays in

helping enterprises achieve their business goals.

It is essential that business managers, as well as technical staff, understand how better data

integration can help deliver against key commercial goals, such as helping the organisation become

more efficient, agile, innovative and customer centric. Business managers should think of data

integration as ‘information integration’, which supports better business decisions, makes the business

more operationally efficient, and helps deliver an improved customer experience, service innovation

& differentiation. Well-integrated data also helps organisations identify new markets and new

business opportunities, and improves their ability to cross-sell and upsell to existing customers.

This paper outlines why data integration is an important weapon in an enterprise’s competitive

arsenal. It explains how integrating data can help companies minimise their operational costs and

maximise their opportunities. It also presents the findings of primary research Telesperience has

conducted in the telecoms, banking, computing/IT and government sectors. Key findings include:

the main drivers for DI initiatives are improving the customer experience (64%) and reducing

costs (55%)

a pattern of high and increasing demand for DI. Twenty-seven per cent of companies said their

key goal in 2010-11 was simply to cope with increasing demand for DI from the business

46% of companies said their current DI tools do not fully support what they want to do

72% per cent of companies told us their DI projects often over-run on time, budget or both

poor DI leads to higher direct costs. Fifty-five per cent of companies we spoke to said it led to

higher manpower costs and 37% said they aimed to reduce the manpower required to support DI

projects in the next 24 months DI is a strategy for lowering costs and improving business performance. Poor DI leads to huge

and often poorly-understood indirect costs. Forty-six per cent of companies told us that poor DI is

preventing them from taking advantage of new commercial opportunities

DI costs are high and 73% of companies said their costs were either too high and could be

reduced, or poorly understood and often more than anticipated. DI costs are driven up by

common project management challenges such as poorly-defined business requirements.

Manpower costs are also high and firms have identified increased automation as a desirable way

of reducing overall costs. This suggests increased use of DI solutions in the next two years

Telesperience Issues Paper: Using data integration

to drive down costs and increase profits in high

transaction industries



2

2 Definitions and key concepts

Data integration (DI) is not just a technical challenge, but also a key enabler of many of today’s

strategic business goals, such as reducing costs, improving the customer experience, innovating,

managing the business more effectively and complying with regulation or legislation. DI also helps businesses identify new opportunities and increase wallet share from existing customers. It is

therefore vital business managers understand why it is necessary, exactly what it is & what it

encompasses, and the business benefits it delivers.

Why is data integration needed?

DI is necessary because of the way information systems have been architected and grown over the

last 20-30 years - a situation exacerbated by rapid organisational expansion, tactical investment

strategies and M&A activity. Typically, this has resulted in enterprise data being fragmented across

multiple IT solutions and their corresponding data silos. At the same time, enterprises now want to

use more data, and a more diverse range of data, for both operational and strategic purposes. This is

driving the need for DI – particularly in larger organisations.

What is data integration?

Defining DI may seem obvious, and practitioners may have an intuitive understanding of what it

involves, but in fact it is far harder to define than it might first appear. This is because the term

implies different things depending on the context and upon those using it. In essence, DI involves

combining different sets of data to provide a unified view of all the relevant data. It is a concept that

is implemented through a combination of methodologies and technologies, and it encompasses

database migration/upgrading, application migration/consolidation, operational DI and integration to

support data analytics. Operational DI also facilitates initiatives such as customer data integration

(CDI) – aimed at improving the customer experience and the profitable operation of the company.Analytical DI is used to support business-level initiatives such as business intelligence.

What does data integration encompass?

There are a number of approaches to delivering DI and these can be used individually or in

combination, as appropriate:

data consolidation involves collecting data from multiple data sources and consolidating it in a

persistent data store. This may involve, for example, migrating data from multiple existing

datasets and consolidating these into a single persistent dataset as part of an application

migration or consolidation initiative. A number of technologies can be used to assist with data

consolidation, including extract-transform-load (ETL) tools and so-called 3G data migration tools

data federation provides a unified view of an organisation’s data through a single interface,enabling disparate data sets to appear as a single homogeneous data set to the user. This is also

known as enterprise information integration or EII

data propagation involves replicating data from different sources in different locations and

encompasses enterprise data replication (EDR), database log scrapers and change data capture

(CDC) tools. Enterprise application integration (EAI) technology supports the integration of

application systems, enabling them to exchange data using standard interfaces

data access uses search capabilities to increase the accessibility of data. This is also known as

enterprise information access or EIA.

A key concept in DI is ‘legacy data’, which can be structured or unstructured, and includes paper

documents, database records (sometimes stored on mainframes), spreadsheets and word processordocuments. Other technologies that are frequently employed in DI initiatives include discovery and




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profiling tools, metadata and master data management software, reporting tools and data quality

tools. Few tools can operate in multiple DI modes or have the functional scope to cover a wide range

of tasks. Typically, organisations still employ a combination of tools to perform complex DI tasks, and

hand-coding is still very common. Specialist tools are, however, being transformed into suites and DI

solutions – often as a result of M&A activity.

Firms have recently begun moving from batch-oriented DI to realtime or online DI. Realtime DI is not

ubiquitously appropriate and can impose a significant cost and performance overhead. Some

applications and decision-making processes will only ever need to operate in batch and so realtime DI

is unnecessary; others will benefit significantly and deliver higher commercial value through access to

the most up-to-date data. Change Data Capture tools, for example, ensure that target datasets are

updated as changes are made to data sources, so realtime queries are based on the most current and

accurate data. EAI technology is often used for realtime operational business transaction processing.

3 How does poor data integration drive up costs?

Poor DI increases the costs for businesses in a number of ways (see Figure 1). These costs are either: direct costs – for example, due to higher hardware and software licences, and the requirement for

greater manual effort

indirect costs – for example, opportunity costs, costs arising from poorer or slower business

decision-making, or as a consequence of negative impacts on customers (resulting in higher rates

of customer churn and the costs incurred from dealing with complaints).

Telesperience research conducted amongst large enterprises in the telecoms, banking, computing/IT

and government sectors found that the most commonly cited costs of poor DI were operational

inefficiency (73% of respondents), opportunity costs (55%), and higher labour costs due to the

requirement for manual intervention (55%). The fact these costs are recognised as significant is not

related to their size. Costs arising from higher cost operations and the use of extra manpower may be

significant and quantifiable; however, although harder to quantify, the cost of lost opportunities may

dwarf these figures. It is important that indirect costs are taken into account when considering the

cost:benefit analysis of a DI exercise, and the ROI of a DI tool or project.

Figure 1 Scenario 1: poor data integration increases costs & leaks value from the organisation

Costs up

In the red: the

inefficient enterprise

High hardware costs

High software costs

High operational costs

High opportunity costs

High customer care costs

Slow & inaccurate service

delivery

Inability to adapt or innovate

Slow time-to-market

Too much manpower required

Operational

inefficiency

Poor customer

experience

High churn rates

Reactive customer care

Poor/ineffective marketing

Slow time to resolve

SLA breaches

Sub-optimal

commercial

experience

Slow and sub-optimal

decision making

Poor investment decisions

Risk of non-compliance

Poor competitive positioning

Key data integration drivers Most common costs of poor data integration

Costs up

In the red: the

inefficient enterprise

High hardware costs

High software costs

High operational costs

High opportunity costs

High customer care costs

Slow & inaccurate service

delivery

Inability to adapt or innovate

Slow time-to-market

Too much manpower required

Operational

inefficiency

Poor customer

experience

High churn rates

Reactive customer care

Poor/ineffective marketing

Slow time to resolve

SLA breaches

Sub-optimal

commercial

experience

Slow and sub-optimal

decision making

Poor investment decisions

Risk of non-compliance

Poor competitive positioning

Key data integration drivers Most common costs of poor data integration Source: Telesperience 2010



Driving down costs using better data integration

4

4 Current data integration drivers

Sixty-four per cent of organisations told us that the main driver in their organisation for DI was an

initiative to improve the customer experience. Cost reduction (55%) and compliance (46%) were the

next most commonly-cited drivers (see Figure 3). Going forward, organisations said that in the next

24 months decreasing human effort (37%) and coping with increased demand for DI from the

business (27%) were their main goals.

These findings present a pattern of ongoing – even overwhelming – demand for DI. Thirty-seven per

cent of organisations told us they recognise the need to remove manual intervention from the process

and a further 18% told us that speeding up project timescales was important to them. The

consequence is likely to be increased use of technology to speed up, automate and make DI projects

cheaper, as well as to help organisations cope with the sheer demand for DI from the business. The

telecoms, computing/IT and government sectors were particularly concerned about coping with

increasing demand for DI; banking was the only industry that cited ‘reducing risk’ as a key goal –

although we believe all organisations should be seeking to reduce risk.

Figure 3 Drivers of data integration projects

5% 10% 15% 20% 25% 30% 35% 40%

Cope with increasing

demand for DI from

the business

Decrease time taken

for data integration

projects

Decrease cost

Decrease risk

Decrease amount of

human effort

10% 20% 30% 40% 50% 60% 70% 80%

Cost reduction

Modernisation

Improving the

customer

experience

Compliance

Other

Current DI drivers 2008-9 Future DI goals 2010-11

5% 10% 15% 20% 25% 30% 35% 40%


demand for DI from

the business

Decrease time taken


projects

Decrease cost

Decrease risk

Decrease amount of

human effort

5% 10% 15% 20% 25% 30% 35% 40%


demand for DI from

the business

Decrease time taken


projects

Decrease cost

Decrease risk

Decrease amount of

human effort

10% 20% 30% 40% 50% 60% 70% 80%

Cost reduction

Modernisation

Improving the

customer

experience

Compliance

Other

10% 20% 30% 40% 50% 60% 70% 80%

Cost reduction

Modernisation

Improving the

customer

experience

Compliance

Other

Current DI drivers 2008-9 Future DI g oals 2010-11

Source: Telesperience 2010

5 Using data integration to deliver commercial success

Integrating business data creates a wide range of benefits for an organisation (as shown in Figure 3),

helping to deliver commercial success and competitive differentiation. The potential of DI will only

be realised, however, if businesses expertly manage the DI process. The huge scale of potential

savings and commercial benefits is such that business managers should be championing DI projects,

helping set their goals and monitoring their delivery. It is vital that DI receives the support, buy-in

and involvement of the business if it is to be successful. Fifty-five per cent of companies we spoke to

said poorly-defined business requirements were raising their organisation’s DI costs. If the business’s

needs and objectives are not clearly captured at the onset, and monitored throughout, then the project

is highly unlikely to deliver against the organisation’s business goals. This is a common cause of

projects failing to achieve their objectives or running over on cost or time.

In this section we look at three main areas that businesses can focus on to deliver the commercial

benefits we have outlined and to reduce costs:

reducing the cost of the DI project itself

reducing the direct costs of poor data integration

reducing the indirect costs of poor data integration.




5

Figure 3 Scenario 2: well-integrated data is a vital component of commercial success – value isretained by the business

In the pink:commercially

successfulenterprise

Hardware costs optimised

Software costs minimised

Operational costs minimised

New opportunities identified and

acted upon

Lower customer support costs

Fast and efficient service delivery

Supports adaption and

innovativion

Fast time-to-market

High level of automation

Optimal

customer

experience

Churn rate lowered

Proactive customer care

Personalised marketing

Fast time to resolve

Improved performance

against SLAs

Optimal

commercial

experience

Fast and effective decisionmaking

Better investment decisions

Improved compliance

Better competitive positioning

Key data integration drivers Addresses most common costs of poor data integration

Cost

optimisation

Operational

efficiency

In the pink:commercially

successfulenterprise

Hardware costs optimised

Software costs minimised

Operational costs minimised

New opportunities identified and

acted upon

Lower customer support costs

Fast and efficient service delivery

Supports adaption and

innovativion

Fast time-to-market

High level of automation

Optimal

customer

experience

Churn rate lowered

Proactive customer care

Personalised marketing

Fast time to resolve

Improved performance

against SLAs

Optimal

commercial

experience

Fast and effective decisionmaking

Better investment decisions

Improved compliance

Better competitive positioning

Key data integration drivers Addresses most common costs of poor data integration

Cost

optimisation

Operational

efficiency


Reducing the cost of the DI project

More than 55% of companies we spoke to said their DI costs were too high and could be reduced, and

a further 18% said the costs were poorly understood or often more than anticipated. We asked

companies what was contributing to higher DI costs (see Figure 4). Most of the factors cited are well

understood; most are avoidable; all can be mitigated. Yet these same factors continue to cause

problems repeatedly. The best performing companies have taken steps to manage these issues. They

understand the need to invest in good project management, see DI as a valued skillset, and recognise

that the business should be intimately involved in DI projects. Companies that undertake only a

relatively small number of DI projects are understandably particularly vulnerable to over-runs,

because they lack the experience to avoid well-understood pitfalls. However, even experienced

companies continue to make the same mistakes and run into the same problems. This is exemplified

by the fact that only 28% of companies told us they were confident they could deliver projects to time

and budget, while 72% admitted that they often over-run either on time or budget (or both).

Figure 4 Factors driving up data integration costs

0% 10% 20% 30% 40% 50% 60%

Manual effort

Cost of tools/hardware

Tools do not fully support what is needed

Poor project mgt

Lack of expertise

Lack of support/conflict w ith vendorsSIs consultants etc

Internal politics or conflict

Delays/lengthy project cycles

Repeating or multiplying effort, lack of r e-use

Over-estimation of data quality

Business requirements not well defined

0% 10% 20% 30% 40% 50% 60%

Manual effort



Poor project mgt

Lack of expertise







0% 10% 20% 30% 40% 50% 60%

Manual effort



Poor project mgt

Lack of expertise











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Optimisation checklist A: delivering more efficient DI projects

A1. Capture business requirements accurately at the start of the project

A common cause of project delays and failures. The business needs to be represented in the project team because

technologists cannot be expected to understand business priorities. Business priorities need to be monitored

throughout the project – particularly if it is expected to be lengthy – to ensure that the project continues to deliver

against these needs as they change and evolve.

Achievable: ✔✔✔✔✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: faster project times, more business-oriented results, greater buy-in from business users

A2. Don’t overestimate the data quality

A common cause of project delays and unplanned costs. Don’t assume that data quality is good or that you necessarily

know where the data you need is. Put time and money into the budget to deal with data quality. It’s no good integrating

data if that data is poor quality, so addressing data quality is an important element in the success of your data integration

initiative. However, don’t get too carried away with data quality – it is not practical or cost-effective to have 100%

accuracy.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: better end results, more accurate project planning

A3. Plan an achievable project – don’t overscope

Data integration projects need clear scoping and teams need to be mindful of scope-creep. Sometimes delivering

against business goals can mean changing the priorities of the project; but scope-creep will cause delays and delays

cost money because business priorities are likely to shift the longer the project takes to complete. The best performing

companies with regard to DI usually have the fastest and most targetted projects.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: faster project times, faster time to benefits, buy-in from business users

A4. Sort out the politics

Our research demonstrates that internal politics and conflict are a major source of costly over-runs. Do not underestimate

the potential for conflict and ensure that you have buy-in from all stakeholders. Put effort into communication,

requirements capture and achieving buy-in. Monitor the situation throughout the lifetime of the project. Do not allow the

project team to become isolated from the business.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: better results, boosts uptake/buy-in, less risk of over-runs

A5. Review your use of DI technology and pay attention to software licensing and other DI tool costs

Many organisations will have redundant or duplicated DI tools and may still be paying out for software licences,maintenance and so on. For each type of DI select a tool and standardise on it if possible, removing redundant tools.

Consider using a multi-mode solution that can be re-used for other projects, and consider whether open source

technology is a viable alternative. Around two-thirds of companies already employ at least some

open source DI technology1 and its use is increasing. By reviewing your use of DI tools it is possible to save considerable

sums while also benefitting from state-of-the-art technology that will help you achieve better results.

Achievable: ✔✔✔✔✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: access to state-of-the-art technology reduces requirement for manual coding/intervention

1 Usage Landscape: Enterprise Open Source Data Integration, Talend

Reducing the direct costs of poor data integrationPoor DI raises operational and capital costs directly by inflating hardware costs, software costs and

manpower costs. With the increasing emphasis on cost-optimisation, and the current pressure on

capital and operating budgets, this is wasted money that could be redeployed for innovation or

cashed in to fund lower pricing or higher dividends. Moreover, DI projects themselves can result in

inflated costs to the enterprise due to licensing terms and the requirement for high levels of manual

effort.

Hardware costs

Twenty-seven per cent of organisations told us that poor DI was increasing their hardware costs.

Hardware costs can be inflated due to:




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storing duplicated data – some data duplication may be necessary for operational or performance

purposes, but reviewing legacy infrastructure often reveals datasets that are duplicated for no

good reason. This increases storage costs and also leads to operational inefficiency

aging hardware infrastructure – legacy hardware is usually more costly to maintain than the

modern equivalent, while also having lower performance. The trigger for some data migration

projects is to reduce the cost of legacy hardware platforms. In our survey, 18% of companies saidDI issues were preventing them from modernising their infrastructure, and this was driving up

their costs

use of high-end hardware – companies such as Google have changed the rationale of hardware

strategies. Google famously uses commodity hardware, and does not even invest in the best

commodity hardware, but rather that which provides the best ‘cost per query’. This raises a fresh

challenge for other firms to match this cost:performance profile which, in turn, means they will

require a DI solution that can support this type of infrastructure refresh

under-utilisation of hardware – in the past there was a tendency to deploy hardware for each

application, regardless of whether there was existing capacity elsewhere within the organisation.

This leads to a higher than necessary cost profile, which can be significantly reduced by

consolidating data and applications onto a smaller number of hardware platforms.

Adding on new data centers, which can cost up to USD100 million each, is not a viable option when

server utilisation is low (industry estimates suggest server utilisation is currently only around 15% on

average, and that around 10% of servers are unused although they are still consuming power,

cooling, space and maintenance resources). It may not be possible or desirable to build new data

centers due to space restrictions or the need to comply with green targets such as reduced power

consumption. Strategies such as migrating and consolidating data on a modern, lower-cost platform,

employing virtualisation and cloud computing are all being used to lower the cost of hardware for

organisations. DI technology is an enabler of all of these initiatives.

Case study: potential for saving money on hardwareThe scope of savings on hardware is entirely dependent on the legacy environment, but can be considerable.

An example from Sun Microsystems exemplifies this potential. Sun calculates that upgrading from a legacy

system to Sun-optimized Oracle CRM on Sun Fire T5440 servers delivers hardware savings of USD1-5 million within

five years, ROI in the range of 700-1900%, and power and floor space reductions of up to 90%.

These results are based on tested and verified benchmark results published at

http://www.oracle.com/apps_benchmark/html/white-papers-siebel.html

Software costs

Most organisations are paying too much for their software. A review often reveals:

software that is no longer being used or is underused, but is still being supported and

maintained. Typically, 75% of the lifetime cost of software derives from maintenance costs, which

means managing it and understanding lifetime TCO is of vital importance

licensing costs that have risen exponentially due to the pricing model being used. Many

organisations still only consider the initial purchasing price rather than TCO and are shocked

when they realise how much software is costing now their business has grown

licensing costs that are inflated because the IT department bought more than it needed (eg to get

volume discounts or because the organisation has subsequently contracted in size)

inflated costs for licensed software where lower-cost viable alternatives exist. Many IT users buy

into a brand. Where such software is delivering significant added value then there may be a

business case for paying more; but sometimes the software is delivering no more added value

than a lower cost or open source alternative

departmental purchasing strategies that fail to leverage volume economies

‘bloatware’ costs – where IT departments buy far more functions than they need or are used. This

also adds to hardware, training and maintenance costs.




8

After reviewing software spend, the logical next step is a rationalisation programme to reduce the

cost of software to the organisation, and this is where DI has a key enabling role to play in helping

companies migrate critical data to new applications or to support application consolidation.

However, the cost of a data migration project can also be inflated by software licensing. This is

because organisations may select a tool with a poor price:performance ratio, unfavourable licence

terms and so on. Tools are often bought on a case-by-case basis, rather than organisations properly

assessing and investing in a tool that is reusable, presents a good price:performance proposition and

will have a favourable TCO in the long term. In fact, 27% of organisations we talked to said that the

cost of DI tools significantly impacted on their project costs.

Case study: potential for saving money on softwareSpecialist companies exist to help organisations understand their usage of software and hardware as a precursor

to a data migration or consolidation initiative. For example, IT discovery vendor Tideway, recently acquired by BMC

Software, says that one of its customers, a major European power utility, used the insight it provided to reduce Oracle

licence renewal exposure from GBP3.2m to GBP850,000 - simply by getting a better understanding of the licence

position. By capturing an accurate view of their hardware assets and software licences, firms are able to

initiate end-of-life programmes and consolidation initiatives, delivering additional cost saving opportunities.

Manpower costs

Many companies are faced with higher than necessary operational costs (OPEX) because the legacy

software/platforms they use require considerable manual intervention and the skills needed to

maintain them are scarce and expensive. This also raises the risk profile for an organisation. The

requirement for excessive manual intervention can result in a further hidden cost: most companies

have limited resources, so valuable skilled staff are often tied up just keeping legacy IT running rather

than being used to drive innovation or add value. This slows the innovation and renewal cycle, and

means organisations are not maximising the value of their skilled staff.

Case study: using opensource technology to save money on DI projects and softwareAccording to Talend, a vendor of opensource data integration tools, opensource technology can help organisations

save money by automating tasks that previously required manual scripting. A recent survey by Talend found that many

(55%) large enterprises are still invoking manual scripting to keep information flowing across the organisation. Talend

says use of opensource technology is on the rise, with 31% of companies reporting they combine commercial applications,

opensource solutions and database utilities to meet their data integration needs.

Telesperience research suggests that 18% of respondents are currently using opensource technology within their

business, 9% are actively investigating it and 55% are interested in using it or have not yet ruled out the possibility of using

it. But how does opensource technology save organisations money?

• Predictable pricing – eg Talend bases its pricing on numbers of users not CPUs or data volume

• Use of scalable but low-cost hardware

• Low R&D costs – since many innovations/functions are developed by the community there is a re-use of competencies

• Low start-up costs, making it much cheaper for one-off projects

Talend says that its tool has a much wider range of competencies than many rivals, because of the constant, activedevelopment and innovation by community members.

See: Usage Landscape: Enterprise Open Source Data Integration, Talend

In terms of the migration process itself, 55% of companies told us their data migration projects

involved excessive manual effort, and this was a major contributor to DI costs. Rising demand for DI

from the business, combined with limited budgets, means that throwing more people at the migration

is increasingly unviable. The reason for so much manual effort was revealed by the fact that 46% of

companies we spoke to said that their data migration tools do not fully support what they need to do,

and this results in more manual intervention than is desirable and ultimately higher costs. This often

occurs because of the use of specialist DI tools rather than firms employing flexible DI solutions that

can operate in more than one DI mode.




9

Optimisation checklist B: cutting direct costs from poor data integration

B1. Pay attention to licensing costs

Data integration software costs can stack up quickly. Pay attention not just to upfront costs but lifetime TCO when

selecting a data integration tool. Typical tools range from USD200,000 to USD500,000 for licensing plus another

USD50,000 to USD100,000 for maintenance. Consolidating and standardising tool use and investigating morecost-effective options (such as opensource technology) can quickly save large sums.

Application consolidation can lead to huge savings in licensing costs, and this is enabled by DI technology. You are

advised to cut out ‘bloatware’ due to over-specification of functions. Look for applications that enable you to add on

modules or functions when you need them. Pay attention to licensing terms – are you buying more licences than you

actually need? Will costs rise out of control if your business grows?

Achievable: ✔✔✔✔✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: easier/cheaper training and management

B2. Pay attention to storage costs

Gaining cost efficiencies from modern, higher performance hardware is only possible if you can switch off legacy

hardware. DI over-runs result in paying out for overlapping licensing, leasing, maintenance and so on, which can

quickly rise to millions of dollars of unnecessary extra cost. By investing in a reliable, low-risk DI solution you will be

able to deliver migrations on time or even before time. This will save you far more than it costs, because the faster you

can switch off legacy, the faster you can get to the benefits of the new infrastructure.

Achievable: ✔✔✔✔✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: less manual intervention, less downtime

B3. Automate as much as possible

Often costs rise with data integration because companies choose the wrong tool for the job. This means they end up

having to write manual scripts and manage far more of the process manually. They may even decide not to use a tool

and integrate manually. This drives up costs, makes over-runs more likely and is far less efficient. It is really important

to understand the type of integration you are trying to achieve and select the right solution for the job. Third-generation

data integration tools are more flexible, more configurable and more functional than previous generations and suitable

for more complex data integration scenarios.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: faster time-to-benefits, minimise use of scarce and expensive skilled resources

B4. Do you need all the data?

Costs rise because organisations store duplicate data, data they no longer need and so on. There is a cost associated

with keeping, integrating and migrating data records, and it is essential that you understand what that cost is and

use this to help decide which data you need to keep. Taking the approach of moving it and then deciding what to prune

results in higher migration costs, extended timescales and the temptation to never consolidate/prune the data. Prune back

what you don’t need before you migrate or integrate.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: compliance, faster DI projects, higher data quality

B5. How flexible is your DI tool?

Ask yourself: could you save money by being able to re-use the tool for further projects? Can the tool operate in more

than one DI mode – allowing you to get rid of other specialist tools and save on licensing/maintenance? Is it flexible

enough to accommodate changes in business objectives part-way through the project?

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $Other benefits: faster project times, greater chance of successful project

Reducing the indirect costs of poor data integration

The indirect costs of poor DI may be recognised in the best-performing organisations, but even then

are often not fully understood or captured. Indirect costs may be so large that it makes DI a ‘no

brainer’ for the organisation as a whole, but because responsibility and consequences are fragmented

between departments the importance of DI to the business may not be recognised. For example,

inflated customer support costs and a poor customer experience may be bad for the company, but if

this is caused by the poor performance of one department (such as poor fault management - itself due

to not having access to the right data at the right time), then the affected department (e.g. customer

support) may not be able to influence change. Fixing the problem might also not be a high priority for




10

the department causing it. This is why the business consequences of poor DI need to be considered at

an enterprise level and action prioritised according to the commercial impact.

Failure of business-level analytical initiatives, such as business intelligence (BI), is often not due to the

software itself, but instead is rooted in poor DI or data quality. These projects promise great

improvements in business performance, but there is little point in spending large sums on analysing

data if the data is poor quality, incomplete or inaccessible.

The most obvious indirect cost of poor DI (46% of organisations said this was a big issue for them) is

that it prevents the business from taking advantage of new commercial opportunities and increasing

their market share. This results from supporting systems not being in place to support new service

rollout; or because vital customer data is not available for analysis to inform better decision-making,

new product design, promotions or pricing strategy.

Case study: minimising opportunity costs and opening up valuable new revenue streamsWhen BT decided to migrate its FeatureNet customers - large corporates using BT VPN services and VAS - from a

custom-built legacy solution for billing, tariff management and dial plan management to Convergys's Geneva solution

it knew the migration would be complex and potentially risky if not managed well. The legacy solution supported some

of its largest and most valuable accounts, a significant number of complex, multi-layered tariffs, as well as multi-tiered

and multi-layered discount schemes. Using modern DI technology de-risked the project and enabled it to be completed

in around six months. This meant BT could launch innovative new services to its FeatureNet customers 12 months ahead

of schedule, delivering significant new revenue streams which would have been lost or delayed if the migration had failed

or had been late.

Poor DI also drives up churn - primarily because it leads to a poor and inconsistent customer

experience. Twenty-seven per cent of companies told us that poor DI resulted in increased customer

care costs, and 9% said it was leading to higher churn rates. In our view, the recognition of these

effects is likely to be underestimated.

Optimisation checklist C: cutting indirect costs from poor data integration

C1. Understand the total effect of sub-optimal DI

It is essential to take a business-level view of how poor DI is raising costs for your business. You need to understand

not just the direct costs to the data owner, but how this is impacting on customer care, churn rates, market positioning and

your ability to rollout new services, prices or promotions. Also understand how poor DI will lead to the failure of expensive

and desirable business-level initiatives such as BI projects. Be aware, however, that whatever figure you come up with is

likely to be underestimated, since understanding some of these costs is difficult. But capturing the impacts and estimating

the cost is important to building the business case for improved DI. Monitoring the ROI post DI project is achievable – in

the form of eg reduced churn rates, lower customer care costs and new revenues from services not previously supported.

Achievable: ✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: better corporate governance, improved commercial performance

C2. Don’t underestimate risk and compliance issuesDepending on your industry and locality, a range of legislation and regulation will apply to your data and corporate

governance. One of the biggest costs for some organisations is brand damage due to poor publicity. Poor data integration

may mean you cannot comply or cannot prove compliance. It may result in embarrassing press stories, which in turn can

have a negative impact on stock prices. If you operate in an industry where brand reputation is important, then DI should

be high on your list of action points due to its potential to cause embarrassment and loss if not handled well.

Achievable: ✔✔✔✔✔✔✔✔✔✔✔✔ Cost savings: $ $ $Other benefits: maintain brand values and stock price, avoid regulatory or legal action




11

Conclusion

Data integration (DI) plays a key enabling role in many important operational and strategic

initiatives. The costs of poor DI and the business benefits of delivering good DI are such that it should

be high on business managers’ agendas. DI cannot be seen as merely a technical task, because without

the intimate involvement of the business in DI projects they will not deliver to their potential.

The demand for DI will continue to rise in large enterprises and this is why selecting the right

solutions is essential – it is no longer viable to manage with a combination of spot tools and manual

integration. ‘Throwing bodies’ at the problem is also not an option. Companies should consider the

full range of options available to them and investigate 3G DI tools that offer a much wider range of

functions, are more flexible and can operate in more than one DI mode.

Companies that are able to deliver DI projects quickly, reliably and at low cost will out-perform their rivals,

because they will be able to exploit new technologies and insights to gain competitive advantage and reduce their

costs.

Acknowledgements

The authors would like to thank all those companies and individuals who helped with our DI

research and generously gave their time and expertise. In particular, we would like to thank Talend,

who provided sponsorship to fund this paper, Sun Microsystems, BT and BMC Software.

About TelesperienceTelesperience is a UK-based telecoms analyst firm focused on how technology impacts both the

commercial and customer experience. It is wholly-owned by Babworth Ltd, a provider of research,

publications and writing services to the global Internet, Communications and IT markets.

The scope and focus of Telesperience is as follows:

• the commercial telesperience – to analyse how key IT technologies impact on telecoms service

providers’ businesses

• the customer telesperience - analysing how key IT technologies impact on the end customer

experience.

Telesperience was founded in 2008 by an experienced team of telecoms IT analysts who wanted to

provide a more convergent view of the telecoms market, focusing on business and customer issues.

We consider where the problems lie with legacy technology, and how companies can transition to

provide a more positive telesperience for their customers and a more profitable business for

themselves.

Telesperience’s open source research programme relies on the goodwill of companies who fund

research in order to make it free at the point of delivery. We endeavour to ensure that our research

remains objective and independent – the steps we take to do this are outlined on our website, but the

most significant is using experienced and respected analysts who have a track record within our

industry. Report sponsors are always acknowledged, so readers are aware who is funding the

research programme.

Find out more about Telesperience at www.telesperience.com and www.microsperience.com.

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