Data: Avoiding Excess Baggage

We’re now on Part 3 in our New Year’s Resolutions for those with nagging doubts about the quality of their data, turning fears into practical steps that can make a genuine difference to business results.

As the title suggests, this week’s edition is all about losing weight, and like all weight it doesn’t just amass overnight. It takes weeks and months of habit-forming activity to build up the worrying spare tyre, and yes, we’re still talking about data!

Where data is concerned, losing weight equates to reducing manual work, so let’s call this resolution sweating out the manual tasks using improved quality monitoring and task automation to make the bank lighter, faster and more flexible.

The classic causes of manual workarounds for banks include the imposition of new regulations, the transferring of teams or departments, and IT hardware or platform upgrades. Just as in the non-data world, we might start with good intentions for a clean slate, but life gets in the way: timeframes have a tendency to become rushed and problematic, and in the end some bad habits are brought over and the same old problems recur.

Data gets siloed, systems become obsolete or de-scoped, formats aren’t standardised and the clunky, uncomfortable weight starts to gather in dusty corners.

To avoid this problem, it’s sensible to measure, measure and measure again. Having a solution that can be plugged into data sources internally and externally, and validate them against consistent standards, will give a sense-check before data is formatted for a regulation or lifted and shifted onto the new system; similarly, for regulatory reporting, measuring to the actual regulation can provide insights into specifics of where data is failing and allow it to be returned for correction.

All of this needs to be automated to avoid manual work encroaching and old habits flaring up. If the solution can be scheduled to run when a file is uploaded or downloaded, or constantly monitor for changes in data to be re-run, then the bank’s specialists can operate on and learn from the outputs rather than spend hours manually formatting the inputs, and everyone goes home much happier having made a difference to the bank.

"...everyone goes home much happier having made a difference to the bank."

Similarly, if data is tagged with owners and users of the data (something critical in a GDPR data flow mapping exercise, for example), when checks on the underlying data quality throw up inconsistencies or errors it’s worthwhile having a way of returning the data in breach to the correct owner for remediation. This has the double benefit of revealing key areas where problematic data is occurring, helping target points of failure more efficiently.

"...the key is choosing where to start."

Removing manual workarounds is fine at a conceptual level, but just as with any of these resolutions, the key is choosing where to start. Beginning by proving the concept with a subset of data records is a thoroughly sensible approach that can deliver fast results; just as in the gym, where it’s easier to build momentum if results can be demonstrated quickly, it’ll be much easier to win the case for data quality with interactive dashboards showing the improvement in quality over time.

Doing so will give the bank the immediate ability to calculate the reduction in errors, the corresponding reduction in manual work and the bottom-line benefits in reduced cost to the business.

Then of course, the longer-term health benefits of spare-tyre removal are precisely that: long-term, and a bank starting now on losing excess baggage is sure to deliver both immediate benefits as well as longer-term improvements in regulatory compliance and customer experience.

So, assuming your own personal resolution wasn’t “read fewer blogs by Matt Flenley”, I’ll see you sometime next week for the final part: a data and finance take on resolving to eat more healthily (the reduced-guilt version, of course!).