How to observe the impact of modernisation through a data quality lens

At some point, your business or IT leaders will decide – enough is enough; we can't live with the performance, functionality or cost of the current application landscape.

Perhaps your financial services employer wants to offer mobile services, but building modern apps via the old mainframe architecture is impractical and a replacement is needed. Or maybe your existing silo-based architecture has become too costly to maintain and a lower cost alternative is required.

Whatever your modernisation needs, I can guarantee that one of the business priorities will be to understand how functionality can be improved. Cue new target systems and development partners in endless presentations of their "forward-looking" creations.

Along with speed and cost improvements, you soon become dazzled by the one-touch automation functionality and big-data-ready architectures offered by the target vendor.

In one project, I witnessed an organisation spend £10,000,000+ on a new target system that seemed to have every conceivable function their future business desired. The only problem was, they forgot to consider:

Improving the feature set of existing systems is a mainstay of legacy modernization, but it is the question of "feature uplift" that causes so many problems (particularly during migration and go-live). That's because people forget that function and data are intricately linked.

Data, in its simplest form, is an inanimate array of bits and bytes. It stays like this until an application feature comes along and does something useful with it.

Likewise, those latest and greatest features offered by the target vendor will only function if they can use the right data, with the right qualities – such as validity, accuracy, completeness, consistency and integrity.