5 ways to tackle your data problems

• All wealth managers have problems with the quality of their data.
• Firms’ data will need to be more accurate, complete, accessible and timely if they are to thrive in an increasingly AI-powered world.

If better data quality is fundamental to wealth managers’ current and future success, the next question is what to do about it?

Here firms have five options.

1) Ignore it
That’s the ideal approach if you want to pile more work on your ops team, add costs, antagonise clients with shoddy service, and heighten the risk of regulatory censure and fines.

2) Rely on your existing vendor systems
Trust they have the necessary data validation capabilities built in and are taking care of this for you.

Spoiler alert: they don’t. Systems may provide field validation with look-up tables to select from, as well as checks that, for example, email addresses and National Insurance numbers are in the right format – but the verifications won’t be anywhere near comprehensive enough to catch all your bad data issues.

3) Carry out manual four-eyes checks for data errors
One firm we spoke to even conducted six-eyes checks before sending out client reports. Four- and six-eyes checks are a massive waste of resource, tying up staff whose time could be used on more profitable tasks. Plus, as with any human endeavour, such checks still miss problems and make mistakes.

4) Build a data remediation capability in-house
Given today’s data volumes and complexity, process automation has become essential. With a proprietary build, you can tailor it to your data architecture, business rules and unique quality issues. The building process forces you to understand and codify your data quality requirements, knowledge that becomes embedded in the organisation. You can quickly modify rules and add new ones as business needs change without waiting for vendor updates.

But an effective data quality infrastructure requires deep domain knowledge and financial commitment. The upfront investment to design, build and test the system, and maintenance costs to keep up with new data sources and tackle emerging quality issues are substantial. Integrating with disparate data sources and systems to get the data is complex, as is resolving data quality problems.

Scalable performance is essential. And it is not a time-limited project, but a business-as-usual process, requiring daily checks. This all diverts resources from the core activities that differentiate your business and generate revenue. A proprietary system also creates a key person dependency, as firms won’t have many people with the requisite business and system knowledge to create and support an in-house build.

Emailing in-house extracts written in Microsoft Power BI and Excel spreadsheets around a wealth management organisation is common. People often don’t read them though, so flagged problems sit in inboxes and still get missed.

5) Buy a data cleansing solution from a specialist provider
No prizes for guessing which option we recommend. But there are good reasons.

A battle-tested commercial tool will incorporate best practices honed from the real-world experiences of multiple wealth management organisations. By building from scratch you’ll likely waste resources on reinventing the wheel, making preventable mistakes and overlooking vulnerabilities that a mature solution has already solved.

Strength in numbers

At DCI, we have a community of users, enabling everyone to piggyback off each other. Firms pay for the system but not the rules, which we build for free, quickly, for as many people as want them. So if company A says it needs 10 rules to check certain items, as soon as those are published we offer them to anyone else that could benefit. We even have company helpdesks feed ideas for rules back to us for problems they see arising repeatedly.

These pooled fixes create solutions for issues users may not have even considered or realised they had. For instance, we provided a collection of fee checks recently for a client. When they turned them on, they discovered multiple fees hadn’t been charged to clients for several years.

The rules in turn contain explanations for what they are addressing and how to remedy the problem. Each work item has a full audit of every step from when it was found to when and how it was resolved. Those details become a form of training for staff, helping them understand the correct processes they should follow and checks that need to be made along the way.

Beyond the extensive library of rules and workflow-managed resolution tools available within the DCI system, we have developed user-friendly dashboards and reports that identify where problems lie, data resolution statuses and if a particular rule is being breached consistently. This gives management ready visibility into the state of their operations, where training issues lurk and what they can do better.

We have also incorporated gamification elements designed to engage staff and make them want to find and fix problems. After all, the most effective solution in the world is useless if staff don’t use it.

By removing “sand from the engine” with a proven data system, you can create more capacity within your operations to grow your business and profit from emerging opportunities.