Introduction

Losing weight is a challenging endeavour, often pursued with a specific end date in mind. Similarly, data quality improvement projects can be daunting, with targets set to clean up data within a fixed timeframe.

Unfortunately, both endeavours often face setbacks due to the difficulty of the tasks involved.

In weight loss, we struggle with cutting out “fattening foods” like cakes, alcohol, and fats, leading to feelings of deprivation or hunger.

Similarly, data quality projects can become mundane, taking time away from addressing immediate operational issues, without any immediate rewards.

The business case for data quality must look at both incremental benefits, and the sustainability of the program.

The Banting Diet Analogy

Just as many of us “cheat” on our diets by sneaking forbidden snacks, data quality projects can encounter similar challenges.

We set a target date to “clean up our data” – revisit our processes, do root cause analysis, and assign issues to various operational staff to fix.

It’s hard!

The remedial work is boring, it cuts into the time available to deal with immediate operational issues, and it has no immediate reward.

Not surprisingly, some identified data quality issues may slip through the cracks or get ignored, leading to less effective results. Root cause analysis gets dropped, or the fixes necessary are postponed or ignored

Even when data is cleaned up, it can quickly decline in quality once attention shifts away from it, just like how weight often returns after losing it due to reverting to bad eating habits.

Embracing a Sustainable Data Quality Lifestyle

For long-term success, weight loss experts recommend a lifestyle change, like the popular Banting diet, which focuses on a satisfying, balanced diet that leaves individuals feeling full and prevents overeating.

Similarly, sustainable data quality requires a fundamental shift in how we approach it. Rather than treating data quality improvement as a one-time effort with fixed deadlines, it should become a seamless and integrated part of every data-related interaction.

The Data Quality Banting Approach

To achieve ongoing data quality, we need to adopt the “Data Quality Banting Approach.” This approach emphasizes the following principles:

1. Integrate Data Quality into Every Interaction: Make data quality an inherent part of everyday data operations, without adding extra burdens to operational staff.
2. Utilize Enterprise Data Quality Tools: Invest in robust data quality tools that streamline the process and ensure consistent results.
3. Establish Data Quality Processes: Implement well-defined data quality processes across the organization, promoting a culture of data excellence.
4. Track Meaningful Data Quality KPIs: Monitor key performance indicators that measure data quality progress and identify areas for improvement.

By embracing the Data Quality Banting Approach, your organization can shift from unsustainable, one-off data quality fixes to an ongoing and thriving data quality ecosystem.

Data quality isn’t just a buzzword; it’s a vital component that can make or break success.

But have you ever wondered: Can data be a liability? Understanding this question is crucial for navigating the modern landscape

Conclusion

Just like achieving sustainable weight loss, sustaining high data quality requires a lifestyle change. By prioritizing data quality at every level of your enterprise and utilizing the right tools and processes, you can ensure ongoing success in managing your valuable information assets. Embrace the Data Quality Banting Approach and unlock the true value of your data!

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How can banks compete with mobile payments? By using the data they have about their customers to create compelling value.