How do you become a better data quality professional? I get asked this question a lot and it’s often by those who are looking to learn new skills in technology, tactics and methodologies for data quality.

I found that the biggest leaps in my data quality career came from thinking outside the “data quality bubble” and looking at situations through a completely different lens. Just as photographers use a different lens to change the perspective of an image, data quality professionals must also observe their projects and challenges from completely different angles.

A good example is data duplication, a common problem that plagues all organisations. The causes of duplicate data are varied, but we often mistakenly focus on the technical solutions initially and then explore longer-term causes at a later date.

The problem is that we need to look at the situation from multiple angles:

• Management need to demonstrate that their call centre workers are highly efficient because they have been given strict profitability goals to achieve.
• Call centre workers are incentivised to reach their bonuses by closing calls as rapidly as possible.
• Customers hate to give too much personal data for fear of excessive marketing.
• The marketing and sales team have been instructed to accelerate social media engagement, so they request more personal data than many customers feel comfortable with.
• The data matching algorithm can’t cope with creating golden records from more than one system.
• The customer search facility is slow and has weak functionality, meaning many names can’t be found quickly - so new records have to be created.
• Customer complaints about billing and other data errors account for 35% of all call centre inquiries.

Of course data quality technology can help in many ways:

• The search capability could be improved so that call centre staff operate faster and are less likely to make mistakes.
• The data matching algorithm can be improved so golden records are “blended” from multiple sources.
• Historic data can be cleansed so that billing errors are less likely.

Data quality technology skills and implementations can clearly play a role, but by merely looking through the lens of a data quality technician you miss the bigger picture. You need to understand the entire environment to really understand the many causes and where best to deploy (if anywhere) your data quality technical skills.

In summary, to become a better data quality professional you need to understand and address the behaviours that are perpetuating the issues observed. Only then can you create a long-lasting solution and a more fulfilling career.