The Cleaner

April 22, 2014
in Lean

Applying Lean to data quality drives value

Over the past year, we have been busy applying Lean principles to yet another domain: data quality. While few would question the value of timely and accurate data, it’s equally important to consider the cost of inaccurate data. Here are three ways Lean can be applied to data quality and why it matters.

1. Makes data better and better.
Virtually every interaction leaves a trail of data. Regardless of the channel, information is exchanged; what matters most is the value generated for consumer in exchange for this data. Data that is inaccurate, inaccessible, or inconsistent doesn’t generate value, for the consumer or the organization.

A recent study by the Data Warehousing Institute cites the following facts:

  • 50% of companies have no plan for managing data
  • 50% of companies overestimate the quality of their data
  • Up to 12% of an organization’s revenue may be at risk due to poor data quality

First West has recently developed a comprehensive data governance strategy, introduced a data issue management process, established data quality targets, and established clear accountability for data quality at all levels of the organization.

This work was sparked, in large part, by staff applying Lean thinking to data. Over time, barriers to data accuracy, consistency, and accessibility are being removed, enabling the creation of even greater value for the consumer.

2. Lean means going beyond cleaning up ‘bad’ data to dealing with the root cause.
Poor data quality leads to errors, processing delays, duplication of effort, and wasted time and talent. But most importantly, it leads to a poor  experience. With up to 75% of data quality problems arising from manual data entry errors by employees, getting it right the first time is critical. The Lean practice of “poka-yoke,” or error-proofing, works toward this and can involve anything from business process changes to system changes, all supported by tools that help reveal errors and root causes so they can be corrected, rather than remain hidden and continue unchecked.

Applying poka-yoke to banking systems and other applications can have a significant impact on data quality, For example, making simple changes to the user interfaces by removing text-entry fields and introducing pick-lists with preset values can dramatically shrink the margin of error. We recently applied this thinking to occupation and industry codes, making it easier and faster for staff to enter this important information correctly the first time.

3. Lean enables flow.
The Lean concept of flow can easily be applied to how information flows. Information flow lies at the core of what many companies really deliver today. Flow attempts to ensure smooth, sustained motion in a process, from start to end—today’s consumers expect nothing less. Maintaining flow also drives data consistency and quality, which are both important to a positive member experience.

Consider this:according to a recent Gartner survey, the average person moves 11 times in their lifetime. Here’s how that implication often plays out in a typical credit union scenario:

A member informs a teller in the branch that she has moved to a new address. The member naturally expects the address change to be instantly applied to any account or portfolio she has with the credit union—banking, wealth, insurance, you name it. While there are a few regulatory reasons why this can’t happen in a snap, expecting the member to contact each line of business would represent an impedance to flow—and create waste, another thing Lean aims to eliminate.

Developing a smooth, largely automated internal process to securely facilitate the flow of these types of updates across lines of business accelerates flow and reduces duplication of effort—and the corresponding potential for error. We are in the process of building this solution here at First West.

Lean tools and philosophies can be brought to bear on any facet of business operations.

Are you applying Lean in the area of data governance or in other disciplines across your organization? I’d love to hear your questions, tips and experiences.

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