Data quality is a ‘doing’, not a ‘having’.
Data Quality

Written by: Phil Husbands

My core interests and specialist expertise are in enterprise data strategy, data capability development and data-driven organisational change. My work focuses on building the operational and people-focused capabilities, which are essential for ensuring that data enables businesses to work more effectively, make better decisions and achieve their goals.
Data quality is a ‘doing’, not a ‘having’.

You can’t afford to give your data a free ride. Keeping your data safe and flowing costs time, money and effort. So your data must earn its keep. This is a key principle in positioning data as a high-value asset.

A great way to follow this principle and target data value, is to think about data as having ‘jobs’ to do. For example, jobs like ‘give us actionable insight’ or ‘feed this algorithm’ or ‘create revenue as a data product’.

BUT – whilst it’s important to pinpoint the jobs your data need to do, it’s equally important to make sure your data are up to those jobs.

Are your data fit and strong enough to do the jobs they need to do?

It’s no good setting goals for your data, without taking steps to ensure your data can deliver on those goals. This defines the link between data value and data quality.

Think about data quality in the context of data ‘jobs’…

You don’t want your data to do its jobs only once. You want your data to keep on doing its jobs every day, week or month – so that your data continually deliver value.

Correspondingly, that means you need to continually manage the quality of your data, to make sure that it’s always up to those daily, weekly or monthly jobs it needs to do.

Data quality isn’t something you can do once and then sit back and relax. Data quality isn’t a project. Data quality isn’t an initiative. Data quality isn’t something that’s achieved. Data quality isn’t something that a business has – it’s something that a business does.

Data quality is a ‘doing’, not a ‘having’.

Sure, that might seem a bit philosophical. But this kind of language is really important for highlighting and embedding the difference in people’s minds, and ultimately helping to create the right data culture.

Until AI completely takes over, data quality depends on how people behave with data. Make sure the people in your business don’t perceive data quality as a box for someone else to tick. Help them to see data quality as something they need to be aware of or involved in, everyday. Then it will be far easier for your data to do its jobs well, and deliver higher value to your business.

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