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IT integration - the key to successful M&As

See also: Royal Sun Alliance case study

As the growth in Merger and Acquisition (M&A) activity continues unabated, more and more insurance companies will need to untangle the complex challenges of IT integration. Some are better prepared than others, says Katherine Hammer of ETI.

It’s not just the insurance industry that's experiencing an unprecedented level of M&As. Strategy Base from Clarus Research reports that, in the 2nd quarter of 1998, global merger and acquisition activity for transactions greater than $500 million totalled $631 billion -- as compared to a total of $810 billion for the whole of 1997, which itself was also a record year.

In the US alone, Houlihan Lokey's Mergerstat reported 9,192 M&A transactions and announced deals across all industries in 1999 -- totalling a staggering $1.418 trillion. This is almost triple the recorded $495bn in deals in 1996.

As has been witnessed in recent years with the creation of industry leaders such as Royal & SunAlliance and CGU, the benefits of M&As can be huge. However, there are many challenges that must first be addressed before synergy or economy of scale are realised. In one example from the banking world, LloydsTSB has so far been unable to combine the Lloyds and TSB Back Office systems - with the result that customers of each bank are unable to access the same banking services and promised cost savings have not yet materialised.

A possible explanation for this kind of predicament can perhaps be found in the low priority given to integrating IT systems when organisations merge or acquire. In fact, a typical M&A Top Ten 'To Do' list cites the first three priorities as being: Financial & legal incorporation, HR & physical plant congregation and Product & service consolidation. The issue of IT integration usually comes a poor tenth - demonstrating that M&A game plans fail to recognise that there can be no product and service consolidation without IT integration, and that often the effort required to integrate disparate IT architectures and a myriad of legacy systems can be huge.

The Difference Between Success and Failure

With large proportions of company revenues being spent on IT, it's no surprise that one of the economies of scale most organisations hope to achieve after a merger is a greater efficiency in their data processing. On the surface, this would seem simple enough, as most companies have some applications, such as general ledger, payroll, and so on, in common.

Yet, it is precisely this area of application and data consolidation that can prove to be the difference between success and failure for M&A projects. Even seemingly similar applications can prove extremely challenging to successfully integrate.

Forrester Research estimates that building, maintaining and supporting application integration accounts for 30 per cent of the average corporate IT budget even without absorbing the costs to perform the data consolidation required after a merger or acquisition.

To maximise the potential return on investment of mergers and acquisitions, the necessary integration between the two companies must be achieved quickly -- minimising the disruption to customers.

Economy of Scale or Diversification?

There are generally two drivers for M&A: Economy of Scale and Diversification -- each of which brings its own set of IT challenges.

Where two companies offer similar services to similar customers, the key driver for M&A is often the desire to achieve greater economies of scale – that is, to sell to more people, more efficiently. One of the key problems here is getting a consolidated view of the customer.


For example, Mr Jones might have both a life insurance and motor insurance policy with the same insurer, each with a different policy number. Because the customer does not renew or alter their life insurance policy on a regular basis, the insurer's records show a previous address for the customer - which is different to the motor insurance policy, which was only acquired four months ago.

Under the company's existing database structure, there is no easy way to identify that it is the same Mr Jones that has both the motor and life insurance policies with the company.

Now consider that Mr Jones might also have a household contents policy with the second of the merging organisations and it is easy to see how difficult it can be to get a consistent 'corporate' view of the business and its customers.

Mr Jones, seeing that all three of his policies are now with the same insurer, believes he should be able to enquire about each of them in a single call - but unless the insurer has successfully tackled the issue of data integration, this may well not be the case.

The Challenge of Diversification

Where the two organisations offer different, if not entirely unrelated, services to a wider range of customers, the anticipated benefit of the M&A is more likely to be revenue diversification -- i.e. selling a wider range of services to more customers. In this scenario, the two companies can hope to leverage one another's penetration in certain markets while also enjoying the benefits of a consolidated infrastructure.

As in the example above, a major challenge remains the integration of different IT systems. Here also, a major issue can be the fact that, unlike companies which sell the same services to similar customers, the business applications of the two organisations are likely to be completely different -- not just in the way data is stored and accessed but in the very information recorded. Often the systems will hold very different data in different ways.

A common element to either of these scenarios is the customer. After all, without the customer, you simply don't have a business. Through all this data and systems integration it is imperative to maintain good customer relationships -- which means that customer data must be accurate and available.

Issues in Data Consolidation
When a company undergoes a merger or acquisition, it can take one of three possible IT approaches:

Allow the IT organisations to continue independently for a period of time, using a data warehouse to create a corporate view of the enterprise.
Migrate to one of the organisation's applications.
Migrate to a best-of-breed configuration of applications (for example, one company's life insurance system and another's motor insurance system).

Options 1 and 2 above are more likely to be adopted by companies seeking economies of scale, while option 3 is more practical for companies looking for diversification. Regardless of which of the above methods is chosen, inevitably there will be an amount of data and application integration to be performed. In every case, efficient data integration is key to success, whether this be in cost-effectively loading and refreshing a data warehouse, migrating one organisation’s data to the applications of another, or a combination of migrating data and creating new interfaces between applications.

Bridging the Technology Gap

When confronted with two different applications that suddenly need to work together, organisations are faced with the need to implement bridging interfaces. Traditionally, the most popular method of creating interfaces between disparate IT systems has been for teams of software developers to write them manually.

With increasingly large and complex migrations involving multiple data sources however, hand-coding can be a risky, lengthy and potentially expensive option. It also tends to be an inflexible approach when it comes to accommodating rapid change, and does not allow for the extensive re-use of code. Forrester Research estimated that over $100 billion was spent by companies handcoding interfaces in 1999.

Cost is not the only issue either. According to research by the Standish Group, around 30 per cent of data migration projects fail - making integration a highly risky business. So, finding an effective, reliable solution is an absolute must.

There is, however, an alternative to manual coding. Data migration tools can dramatically cut the man hours involved with creating bespoke interfaces and can reduce the likelihood of human error. Most data migration tools rely on ‘metadata’ to help bridge disparate applications.

What is Metadata?

The ability to merge with, or acquire, another company and integrate their data with your own -- delivering almost instant competitive advantage -- is often no more than a pipe-dream to many IT managers. But for those IT managers with the foresight to adopt an effective metadata strategy, this can represent a challenge they are happy to deliver against swiftly and efficiently.

A major problem faced by many companies looking to consolidate data after a merger or acquisition arises because few organisations really understand the state of the data in their operational systems.

Essentially, metadata can be described as 'data about data'. It is a way of understanding an organisation's data that not only helps guarantee the integrity of data but also ensures that an organisation can quickly determine what must be modified when changes in business process require changes to the systems that handle business-critical data.

If an organisation uses products that produce descriptions of what users have done, this information (metadata) can help an organisation build a history, or audit trail, of its data by tracking changes made over a period of time. In terms of your data history, it tells you where you've come from, where you've been, when you got there, what you did on the way and what would happen if something changed.

Without metadata, the audit trail of changes to data is either easily lost or incomplete - meaning that, when the time comes to integrate - a huge amount of man hours must be spent 're-learning' how the data elements are stored, formatted, used, and related to other data elements.

Metadata comes into its own when companies need to integrate disparate applications - as is often the case with mergers and acquisitions. Armed with the right information about an organisation's data, changes to applications and the migration of data to new applications can be achieved much faster and with far less pain than would otherwise be the case.

Building a Metadata Strategy

Regardless of whether an organisation is actively involved in current M&A activities, building a metadata strategy is crucial to future success. IT systems are constantly evolving and the added complication of e-commerce is escalating the need data integration management.

Pursuing a metadata strategy involves building a system of record – a metadata repository - which defines data elements, their attributes, and their interrelationships so that the knowledge acquired in implementing one project can be reused in others.

If an organisation required that the products they use automatically capture and exchange a metadata audit trail, the task of building this system of record would be greatly simplified.

There are two types of software products in particular that are essential to a successful data integration project. A metadata management tool can help automate impact analysis, identifying and highlighting the parts of an application that are affected by changing requirements; a data integration tool can generate the new integration code necessary to create the required interfaces to load and bridge applications.

In a recent example, Royal & SunAlliance used a suite of products for data integration, ETI•EXTRACT(, to migrate 1.3 million insurance policies from IMS and Adabas to a new Oracle-based processing system.

The tools enabled the insurer to gather information about both the source and destination environments - helping to generate the interfaces necessary to move the data successfully. Royal & SunAlliance estimates that, by using an automated tool over manual hand-coding, they saved almost 30 man-years of interface programming.

By building a metadata strategy and collecting metadata, companies can steal a march on competitors because they can then integrate data and applications for future mergers and acquisitions much faster, cheaper and more effectively.



Dr Katherine Hammer is co-founder, president & CEO of enterprise data integration management company ETI. She also currently serves as co-chair of the Meta Data Coalition, which she co-founded in 1995.


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