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3 tips for maintaining data quality

When a foundation of quality data underpins your business initiatives it has the potential to positively transform your organization. However, a lack of trustworthy data can prevent your organization from maximizing the value of this asset. That’s why it’s essential to ensure that you have strong operational data quality at the foundation of your business. With the right processes and technology in place, you can achieve a trusted data source. If you are struggling to achieve the high-quality data you need, don’t fret! Here are our top 3 tips for maintaining data quality:

Secure C-level buy-in and departmental support

Whether you are building a data quality program from scratch, or working to improve the current processes that are in place, securing the right stakeholders is a must. What’s more, securing C-level support is important, but it’s no longer enough! Data is an essential resource across your entire organization, which means your data quality program should take an enterprise-wide approach. Securing key stakeholders and support across departments within your organization can be just as important that acquiring C-level support. At a departmental level, there will be more insight into specific areas where quality data will truly add value and make an impact. Once you have achieved departmental support, it can ease the process of securing C-level buy-in as you have the support of key stakeholders across the business and have concrete examples to support how quality data can add value.

Dedicate a data ownership team

Once you have achieved buy-in across your organization and among your C-suite, it’s important to dedicate a specific team of people to lead your organization’s data quality strategy. One significant challenge that organizations face when trying to leverage a successful data quality program is a lack of clear authority over the data. Without data ownership in place, your program runs the risk of becoming disorganized or siloed and therefore loses some of its value. Historically, it has been assumed that IT should take sole responsibility for the data practices of an organization. In fact, according to our 2018 global data management benchmark report, 51 percent of organizations today employ their IT department to manage their data. By investing in a role like the Chief Data Officer, and then hiring data stakeholders like data stewards, your organization can build a data quality program that’s focused on the business needs of your organization. Therefore, it brings data quality to the forefront of the minds of business users and enables a sustainable data quality program.

Invest in the right, relevant technology

Make sure your data quality program is set up for success. Establishing key stakeholders and dedicating a clear data ownership team are a few important steps your organization should take, but a crucial step to maintaining data quality overtime is investing in the right technology. According to our global data management benchmark report, human error is a top factor contributing to data inaccuracies (49%). However, human error can be tough to combat, as data is collected through multiple sources and accessed by multiple people throughout the course of its lifecycle. Not to mention the increasing volumes and democratization of data access makes the possibility for human error even more prevalent. By leveraging a trustworthy tool to identify data inaccuracies and inconsistencies before they enter your database, you can be confident that you have trusted data to fuel your larger business initiatives. 

Let’s face it; achieving a trusted data source is no small task. It requires the collaboration of multiple business stakeholders across your organization to develop a strategy that can be sustained in the future. Reliable operational data quality is something every organization needs at its foundation in order to be successful and remain competitive. These three tips can help you to define the right strategy and take your business to the next level.

Are you struggling to achieve support within your organization for your data quality program? Linking data errors to negative business outcomes will help to justify investment in data quality tools.

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