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How to deliver high quality data

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As many of you are aware by now, data quality is something I am passionate about. After all, without ensuring the quality of its data, no company can make the right level of decision-making to improve operations, enhance the customer experience, and drive business growth.

Recently, I came across an interesting industry article that examines what companies can do to consistently deliver high quality data. The piece, 7 Steps for Consistently Delivering High-Quality Data, provides a sound and very beneficial approach for companies to follow. The steps also sparked my thinking on a few other details that can be considered, based on my experience, and so I wanted to share my views on each of these.  

1. Get the whole company involved

While whole company involvement is absolutely crucial, in my experience, this it is also one of the most problematic steps. I have seen clients grapple with the dilemma of how to establish a sound data governance function across a complex business structure and then still empower it to make crucial decisions that can be implemented and enforced by the data stewards across the various business lines. This is where ‘data champions’ (as the author suggests) become vital to help drive this complex, often politically charged piece of work inside the company.

2. Determine which data is necessary

This makes perfect sense. In a complex business environment, companies simply cannot tackle all their data at the same time. Prioritisation is essential. What it comes down to is having an honest look at the business benefit versus the cost of bad data.

3. Make an honest assessment of your database

While the author refers to database in the singular, I believe this is something that must be considered across ALL databases. It means companies must perform data profiling across every database in the organisation. Let’s be honest, very few companies exist with only one database in place. Most have the same data duplicated in some form across multiple environments, all with various degrees of consistence and accuracy.

6. Develop a data security model

Within the broader approach of ensuring data quality, I would recommend that this step (listed as 6) is moved higher up in the process. Understanding the security, especially around the privacy requirements in a very regulated world (think GDPR and POPIA), is just as important as conducting data profiling. A business must draw up a future state as well as a remediation plan to reach that future state.

4. Create a data backup plan

Of course, this is a crucial step, though it is not a singular process. Data remediation is often a drawn-out process that must dovetail with development and test releases going into production. And for each of these steps, a data backup is required to roll-back in the event of a problem.

5. Clean up your data

In reality, I believe that there are two parts to this step. Firstly, a company must clean up the mess that already exists – in other words, data remediation. However, it also must make the required changes to applications, interfaces, user behaviour, and the like to ensure those bad data practices do not continue to propagate more bad data into the environment. This is a never-ending process and cannot simply be ticked off the list.

7. Monitor and maintain data

Yes, this is an ongoing process especially as the big priority items get addressed. Companies must keep moving down the list of priorities.

While data quality can absolutely be achieved, the process around it never really stops. Companies that follow such steps and place data quality at the centre of their data journeys will reap rewards well into the future.  

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