In his thought-provoking article, “Data Quality and Miracle Exceptions,” Jim Harris eloquently captures the intricate nature of data management challenges

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Two points about data quality

Two fundamental points emerge when discussing data quality: its ongoing and sustainable nature and the absence of a magical solution, whether it’s a novel methodology, advanced technology, or innovative process.

As Depeche Mode’s single from the 80s suggests, finding the right balance between methodology, process, and technology is essential for successful data governance and data quality initiatives.

Succumbing to the sales-pitch

Drawing from our extensive experience, we’ve observed that businesses often succumb to the influence of persuasive sales pitches. Consultants-driven salespeople tend to champion their methodologies, hoping to sell numerous resources, while others mistakenly believe that the mere implementation of a tool can resolve all data-related issues.

However, a sensible approach necessitates a fusion of sound data quality methodology, valuable experience, and appropriate technology, fostering a sustainable and ongoing capability for data quality within organizations.

Case Study

Allow us to share an insightful client case study that exemplifies the importance of striking the right balance.

We were brought into the process at the tail end of a rigorous evaluation, where the client had already allocated a substantial budget for a large team of SQL programmers to analyze and clean their data. However, we demonstrated that with our tested methodology and a robust data quality platform, we could achieve the objective effectively using just two consultants, all at a fraction of the alternative’s cost.

Understanding the scope

Furthermore, it’s crucial to emphasize that when embarking on a data cleaning project, the initial requirement of “cleaning our data” often lacks a comprehensive understanding. Through our experience, we’ve learned to manage expectations and guide clients through an initial data quality audit, leveraging our expertise to align their understanding with reality. It’s important to note that profiling the data and connecting the results to the actual business needs are vital steps, as they allow for accurate scoping and cost estimation.

Data quality is an ongoing program

As Jim astutely points out, data management is an ongoing endeavour; it does not possess an end date. In my previous post, “Data Quality is Not a Project,” I emphasized this fact.

It becomes critical to manage expectations, as any approach lacking consideration for sustainability (which includes cultural shifts, refined processes, and automation) ultimately leads to wasteful expenditure.

Even tactical projects, such as data migrations, provide an excellent opportunity to define the need for ongoing data excellence. Moreover, the data cleansing process employed during such projects can be leveraged and reused in the new system, amplifying its value.

Data quality improvement: Ready to boost data quality? Learn from the experts at Master Data how to navigate the intricate landscape of data quality affairs.

Any sensible approach must combine methodology and experience with appropriate technology in order to enable a sustainable, ongoing data quality capability within the organisation.

How data quality builds data integrity: Understand how data quality lays the foundation for robust data integrity. We delve into the correlation between data quality and integrity, shedding light on crucial strategies.