Andrew Joss, head of industry consulting EMEA, Informatica

Andrew Joss

There’s a lot of jostling at the top of the tech leaderboard as we move into 2017. Mergers and acquisitions (M&As) are a good way for companies to climb that board. They help businesses rapidly expand their skill set, customer base and revenue.

But in the digital era, how does that work in practice? When a merger or acquisition takes place, in large part it’s the information exchange that matters. But how do you merge two companies whose essential data resides in different on-premise and cloud systems?

Why focus on data?

The standard M&A due diligence process is designed to evaluate the ‘fit’ of prospective businesses, but it should also evaluate the fit of their data. Good data exchange is essential to drive value from the new synergies between the two companies.

Companies must be able to use their shared data as quickly as possible to drive financial value – for example, by boosting up-sell activities. It’s important to consider what insights the combined data will provide. Most importantly, the data needs to fit together for one goal.

Some big names have already used a strong data-merging strategy during their M&As. For example, when BNY Mellon was founded, the data of its two constituent companies (The Bank of New York and the Mellon Financial Corporation) was rapidly integrated. This helped the bank to quickly reap the benefits of the merger. It reduced the cost of data integration for its Asset Servicing project by 50 per cent and combined its client base with minimal customer interruption.[1]

Data-merging difficulties

However, despite the potential rewards, creating value from two companies’ data is easier said than done. There are a lot of potential pitfalls. For example, how similar are the forms of data storage? Will it be possible to integrate the two sets quickly, or will it require a major re-coding? Format, volume, location and movement are all important considerations.

Additionally, data may be captured and managed differently in different organisations. Standards, processes and procedures of data governance can vary widely, and the quality and relevance of the information may be far from optimal. The core technologies used to process company data can also provide an obstacle – some companies have cloud models in place; others operate large, organic legacy systems. Traditionally it has taken a lot of planning and work to integrate data during the M&A process.

Building a new system

So how do you make it work in practice? Take customer data as an obvious starting point. The company is likely to utilise the larger pool of customer data for up-sell, cross-sell, retention and win-back purposes.

Unfortunately, many companies don’t have a single holistic view of that data, so it’s difficult to integrate. What’s more, M&As automatically create a split system that has to be unified. So even if both parties do have a holistic view of customer data there is still the challenge of creating a new version for the post-M&A organisation.

The time and resources it takes to do this could slow down value delivery – which is a problem given rapid value is one of the main purposes of M&As. The activities that follow (marketing campaigns, for example) will also be heavily impacted by how well it’s achieved – poor data leads to poor decisions.

Make the most of what you’ve got

To tackle M&A data-related issues, organisations must first identify the potential future value associated with merging data sets – what are they trying to achieve? Then, with that in mind, identify the right data types to be merged (for example, potential up-sell contacts), assess their compatibility with each other, and plan how to integrate them in the new system.

Once the planning is complete, companies should proceed to match and merge these ‘key data entities’. This should be done with the highest possible level of accuracy. When merging customer data, for example, companies should determine if they have separate records relating to the same customer and then use them to create a ‘golden view’ of that customer using the consolidated information. With the system up and running, it’s then essential to run regular quality assessments and remediate any data inconsistencies.

Data: leading the way for M&A

This is a very simplified set of steps. The idea is to get institutions thinking about the potential value of the data post-M&A, and what they will need to achieve that value. This same approach can also act as a basis for reducing the risk of data leaking out during the merge through unwatched channels (old websites, for example) and to help decide which apps can be retired.

Businesses considering their next M&A venture must focus on data from the earliest stages of planning. If you can’t reliably integrate with your acquisition or partner’s data stores, you risk losing much of the potential value of a merger. Information is the most valuable business asset – company leadership must put data front and centre in M&As.