Astute business owners and managers have one thing in common: they invest a lot in understanding various aspects of their businesses, and what better way to do that than to utilise data? When sourced and processed properly, data can tell you more about your business than you would expect, hence empowering you to make decisions that lead to improvement.
However, you will need to leverage high-quality data for your business to reap maximum success. In that case, then, here are a couple of tips to improve data quality for your business:
1. Embrace Data Automation
Manual data entries, updates, and other related activities pose serious challenges to the overall quality of the data you use for your business. That’s because manual methods come with a range of issues, from human errors to slow output. Given that high-quality data should be accurate, consistent, relevant, complete, and timely, manual input and handling will present serious limitations.
Luckily, there isn’t a shortage of the tools and solutions you can use to bring data automation to your business. Such software will help you manage, validate, profile, harmonise, and standardise data. Take for instance, a top-rated bulk phone number validator from a reliable provider like Trestle. This one ensures that all the phone numbers in your contact lists are accurate and tied to valid users.
2. Have a Clear Data Auditing Process
If you’re invested in improving the overall data quality in your business, the one thing you never want to underestimate is the power of data audits. Implementing a powerful data auditing strategy allows you to keep assessing various data types against your preferred quality standards. That way, you know what issues to look out for and the best solutions to prevent such problems from reoccurring.
Besides, data audits go beyond just ensuring that all the data in your organisation remains within the set requirements. They also give your team a tool to measure performance as far as improvement goes. That way, they know if certain solutions are working or not, and whether to look for better solutions or keep improving the ones they have.
3. Reduce or Eliminate Data Silos
You don’t want to store and manage data separately in varying systems. This not only makes it more challenging to harmonise changes and enable updates, but it can also bring additional issues like duplication. That’s why it’s a good idea to eliminate siloed data from your organisation.
This will go a long way towards facilitating better data governance and consistency while eliminating redundancy. In addition, the data will be accessible to everyone who needs it, and at the required moment, hence increasing reliability within the organisation. You can always implement solutions that ensure controlled access to data so no one is accessing what they shouldn’t.
4. Establish a Data Quality Improvement Culture
This solution will likely yield the best results since everyone in your company will be well aware of the need to maintain data quality. There will be a couple of things you have to do such as creating data quality policies and guidelines that promote tactics for enhancing data quality.
When you nurture a culture of commitment towards high-quality data, your employees will have a reason to put in the work to promote data accuracy, completeness, and consistency. Such collective commitment not only makes it easier for everyone in your team to take ownership of data integrity, but employees can also give their opinions on improving data quality more freely.
Promote Data Quality With Trestle
There’s a lot you can do, even beyond these 4 tips, to enhance data quality in your organisation, depending on your individual goals. It all starts with assessing your current data quality and then implementing the right solutions based on the objectives you have.
Trestle is a solutions provider that offers data verification, validation, and enrichment tools to ensure you’ve got all the features you need to improve data quality in your company. Visit trestleiq.com today to learn more and get started on the journey to data excellence.