How to avoid a data management headache post M&A

Earlier this year, Invesco's chief executive, Martin Flanagan, was quoted as saying that "a third of the asset management industry could disappear over the next five years". It follows Invesco's acquisition of OppenheimerFunds in support of Flanagan's comment that "Scale matters more than ever". The recent trend towards market consolidation shows no signs of abating as the pressures of mounting fees, rising costs and demanding regulations take their toll on profit margins. Firms are seeking to get bigger, diversify into new asset classes, generate savings and offset pressure on fees through mergers and acquisitions (M&As). Technology consolidation has an important role to play in generating these savings and firms need to quickly bring their disparate systems together to maximize the opportunity. What role does data management play in supporting these initiatives and capitalizing on the opportunities that follow a merger or acquisition?

For firms that are seeking to scale quickly or move into new markets, M&A is often the obvious answer. As two firms seek to come together, the importance of robust data management is, however, often underestimated. A range of challenging data issues come to the fore following a merger which include data fragmentation and the consolidation of engrained silos. Two firms built up over many years have different yet overlapping data sets pertaining to clients and their investments. Obtaining a consolidated view of these data sets is critical to identifying opportunities post-merger and supporting growth.

The data management challenge is magnified when firms do not have formal data management solutions in place for managing their securities, parties and product data. This is more common than you may think among fast-growing asset managers where technology may not have kept pace with business growth. In these instances, legacy systems may struggle to manage the new data sets and asset classes that often come into a business following a merger. As firms scale up and start trading in new securities, stronger reporting rules will also apply which in turn require more robust data lineage capabilities.

These are important factors to consider following a merger or an acquisition, but they can also seem like a distraction. Asset managers want to focus on their core business - retaining clients, winning new ones, launching new funds or moving into new asset classes or geographies - rather than data management.

This five-step plan for helping fast-growing asset managers integrate disparate data sets post-merger or acquisition will help smooth the way and provide a solid platform for future growth:

1. Introduce a robust data governance operating model

The starting point for any data management strategy needs to be data governance. But, what does this mean in practice? Firms need to establish a foundation for data governance which includes a defined organizational chart with assigned roles and responsibilities, executive sponsorship for the initiative and a charter that outlines the scope of the data governance capability. A robust model should also include communication and training plans developed by role so that everyone across the new, combined organization is aware of their responsibilities.

2. Eliminate data siloes

There's no denying that data siloes are bad for business. They can slow the pace of a newly formed firm, they threaten data accuracy and they make it harder to spot opportunities for growth. Firms need to develop a comprehensive plan for how to get rid of data siloes and inform all employees. An important step is the simplification and consolidation of the firm's technology infrastructure. A single instance of an enterprise data management solution can validate, enrich and reconcile data from across multiple sources helping to eliminate internal and external data silos.

3. Ensure data lineage and transparency

The high number of systems and data transformations following a merger or acquisition can make it challenging for firms to provide regulators with the required level of transparency on their data integration and aggregation processes used for reporting. This is particularly important as firms look to comply with regulations such as BCBS 239, GDPR and Solvency II. Data lineage provides insights into which business and technical transformation logic has been applied to the firm's data and by whom. It also delivers a better control process that reduces errors and provides confidence in the figures reported to internal management and supervisory bodies.

4.Identify and reduce non-core spend

Cost savings are typically top of mind following a merger or acquisition. Identifying opportunities to control the costs of managing and maintaining increasing volumes of data will be well-received. Firms should look to obtain an accurate view of their data consumption across the firm and optimize its usage by removing duplicate or unused data sources and subscriptions. Moving to a cloud-based or managed service data management solution can also help firms to reduce fixed costs and the expenditure associated with hardware, applications and management.

5. Standardise data definitions

Finally, firms should consider developing a data dictionary. This is a set of information describing what type of data is collected within a database, its format, structure and how the data is used. In many respects, a data dictionary can be thought of as the rules by which all the data within a firm's system needs to abide by. The data dictionary is also the bedrock for data lineage, which will support a firm's regulatory reporting requirements. A good data dictionary can improve the reliability and dependability of data and reduce redundancy ensuring that firms have a solid platform for growth post-merger or acquisition.

Following these five steps will remove the potential for a data management headache post-merger or acquisition and will set firms on the right footing for growth; after all, that's what M&A is all about.