The Lean Thinking Guide to Operational Excellence

Lean thinking principles have been integral to Toyota’s rise from near-bankruptcy in the 1940s to the world’s biggest auto manufacturer.

While lean thinking has been closely associated with the auto industry, five key lessons can be equally applied to financial institutions’ mission-critical data management environments – bringing with them powerful internal productivity and operational benefits that can translate into a sustainable competitive advantage.

1. Continuous improvement

Continuous improvement (known as kaizen in Japanese) is central to lean thinking. It is based on the Plan → Do → Check → Act cycle for making changes, monitoring and adjusting popularised by business theorist and engineer W. Edwards Deming. The emphasis is on improving the system through ongoing modest iterations that together create significant results. This systematic pursuit of excellence helps enhance quality, eliminate waste and increase efficiency.

The same principle applies in data management.

All wealth managers have (often sizable) data quality problems – exacerbated by mushrooming data volumes. Fixing everything can feel like a Sisyphean task. Large-scale data projects that aim to get on top of data once and for all get overly complicated and take too long. Teams suffer frustration and overwhelm.

The better alternative: start chipping away at issues and maintain momentum. Identify a priority set of data problems. Fix those. Then move on to the next.

Breaking down the effort into bite-sized, doable chunks in this way makes data remediation more manageable, and the effort more sustainable.

2. Empower staff closest to the task

The success of a lean methodology, notes the Kaizen Institute, depends on empowering all employees to “identify problems, propose solutions, and actively participate in implementing improvements.”

Rather than a top-down approach, effective data management needs buy-in from the frontline operations teams that are handling and remediating data on a day-to-day basis.

Resolving data is not a once-and-done project. It’s a BAU process requiring daily checking. So you need to keep staff motivated.

‘Gamifying’ the data cleanup is one way to make data correction tasks more engaging. Team members get a daily dopamine release from the tasks completed, with the motivation it breeds to check off more items and keep moving forward.

Enabling staff to see where and why an issue emerged and how to fix it also helps guard against repeating the same mistakes. Fostering incremental improvements that become embedded in the process compounds to much greater accuracy and efficiency over time.

3. Flag problems early

Lean principles focus on surfacing data issues early and visually so they can be addressed before too much damage is caused.

Hidden problems create hidden risks that multiply over time. Poor data spreads like an infection through systems and processes. It corrupts everything from investment decisions and client communications to regulatory compliance and business strategy.

Issues become increasingly difficult to trace to their source, and more difficult and expensive to rectify. The direct and indirect costs are often underappreciated though, leaving firms unaware of the real and accumulating impact on their client relationships and profitability.

With a lean thinking early warning approach, teams can collaborate on robust root fixes rather than scattergun reactive repairs that allow the same issues to occur time and again.

4. Build quality in

The best way to stop data problems becoming embedded and spreading is to build quality in at every stage of the process.

That’s not how much of the wealth management industry operates.

“Traditional data quality follows a deceptive pattern: Wait until data reaches its final destination—warehouse, lake, reporting system—then check correctness,” notes Sandesh Gawande, CEO of data reliability platform iceDQ.

These end-of-process audit checks mean that by the time errors are discovered they’ve already cascaded across accounts, valuations, suitability assessments and regulatory submissions.

Building quality in by checking data at source and across systems prevents errors from spreading downstream. And it removes the constant data error firefighting that eats up teams’ focus and resources, and magnifies operational costs.

5. Eliminate waste and rework

Eliminating waste (muda) – and the unnecessary consumption of resources that results – is one of the main principles of the Toyota Production System’s Just-In-Time operating approach.

In the wealth management industry, operational waste remains endemic. Widespread data integrity problems force firms to:

Stripping out inefficient remediation processes and optimising data quality instead frees staff to focus on more productive, revenue-generating activities, while increasing institutions’ operational capacity and scalability.

The pursuit of excellence

By working to boost productivity and remove waste and cost from firms’ processes, lean thinking principles can help wealth managers reduce business overheads and optimise profitability.

They can also enhance firms’ regulatory compliance and the value they deliver to clients, further promoting their competitiveness – the topic for our next blog.