Data infrastructure optimization software
Data integration and quality software
Data availability and security software
Cloud solutions

For An Effective Data Quality Strategy, Context Is Everything

When it comes to data quality, one size does not fit all. To build an effective data quality strategy, you need to take context into account.

The reason why is simple enough: If data quality is defined as the ability of a given data set to meet an intended purpose, then the question of whether or not you have quality data hinges on what you intend to do with the data.

This isn’t to say that certain overarching features are important parts of data quality in almost all cases. Data quality usually depends on having data that is as free as possible of inconsistencies, redundancies, inaccuracies and missing information.

Still, the extent to which you can tolerate problems like these without compromising data quality depends in large part on what you want to do with your data. And that’s why data quality is all about context.

To illustrate the point, here are some examples of how the exact requirements for achieving data quality can vary from one context to another.

For an Effective Data Quality Strategy, Context is Everything - banner1

Mail-Based Marketing Campaigns

Imagine your company wants to execute a marketing campaign through the mail. The goal is to send marketing letters to everyone who has purchased a product in the past.

In this case, maintaining an accurate database of names and addresses for previous customers is important.

Still, the risk of having some inaccurate or redundant data in the data set that will be used to drive the mail campaign is relatively low. It’s not a big deal if a few marketing letters are returned to sender.

And it’s not the end of the world if you accidentally mail the material twice to the same household, which could happen if you have redundant entries for the same individual in your database. (Maybe you have dual entries for a woman under both her maiden and married names, for example.)

In this scenario, you can maintain data quality and achieve your goal while accepting a relatively high amount of errors and other problems within your database.

Age- and Gender-Specific Marketing Campaign

Let’s vary the above scenario a bit and say that instead of targeting all previous customers with the mail-based marketing campaign, your company wants to send marketing materials to customers of a specific age and gender.

data quality strategy, marketing

In this case, potential customers could be a bit offended — and the company lampooned on social media — if they receive marketing material that was intended for people of a different age or the opposite gender. Even just a handful of mistakes could be problematic.

That’s why, to achieve this type of marketing campaign, you need a high degree of accuracy in your data set. If you are not confident that your data is accurate when recording ages and genders, you are probably better off not carrying out the campaign in the first place.

By the way, this isn’t a hypothetical scenario. A company that manufactures razors made just this kind of mistake in a recent marketing campaign.

Email Marketing Campaign

As one more example, let’s vary our original scenario a bit again. This time, instead of launching a mail-based marketing campaign, you want to carry out the campaign via email.

The biggest difference here, beyond the obvious fact that your data set needs to include email addresses rather than mailing addresses, is that people are likely to be more bothered if you accidentally send multiple emails to the same person due to redundancies within your data set.

Consumers hate being bombarded with emails — especially the same emails from the same company. If you don’t check your data set for redundant entries, you are likelier to annoy your prospects than convert them through your email campaign.

Conclusion

Again, in any context, it’s important to have a data quality strategy in place, and to be prepared to find and address the types of data problems that can undercut data quality.

However, you can achieve the best balance between effort and results by tailoring your data quality strategy to the goals that you seek to meet. Context is everything.

Check out our eBook, Supercharge Your CRM with Built-In Data Quality, to see how Syncsort’s Data Quality software can ensure that your organization has clean and real-time data.