Data Quality And Data Governance: Where To Begin?
Melissa AU Team | |
There’s no denying the importance of data in today’s world. When used the right way, data can give you a competitive edge over everyone else. According to the McKinsey Global Institute, data-driven organizations are 23 times more likely to outperform competitors in terms of new customer acquisition and have a 19 times higher likelihood of achieving above-average profitability.
However, becoming data-driven is easier said than done. The amount of data organizations collect is immense and sifting through it to extract high-quality data is a challenge.
To be able to make data-driven decisions, organizations must have access to reliable, trustworthy data. This is where data quality and data governance enter. The two terms refer to separate practices but they are fundamentally related.
What Are Data Quality And Data Governance
Data quality talks about data attributes that must be met for it to qualify as good quality data. The most important data quality attributes include accuracy, validity, completeness, consistency and uniqueness.
Data governance refers to the practices and strategies used to standardize and automate data management. It provides a framework for how the data is to be collected, stored, accessed and used. Additionally, it includes defining roles and responsibilities to streamline the process.
Here’s an example to help distinguish between the two.
A database has two records for the same individual. One record mentions the customer’s name as Mr Anil Singh and the other records it as Mr A. Singh. This is an indicator of data quality that must be addressed. To meet high data quality standards you must have a single record for each customer.
So, which record do you keep?
This is where data governance comes in. Data governance rules will help you decide which record is to be kept and which is to be removed. Some organizations preserve the oldest records while others preserve the latest records. It is important for all parties dealing with the data to follow the same rules in order to avoid confusion.
What’s The Connection Between Data Quality And Data Governance?
You cannot improve data quality without good data governance rules. At the same time, if you haven’t defined data quality standards, you won’t achieve much by implementing data governance rules. Data governance impacts data management as well as keeps data secure, private and compliant with regulations. It brings data creators and users to a common platform so that they can be on the same page. To be most effective, data quality standards and governance procedures must be strategized together.
Together, they also help ensure compliance. For example, in healthcare, patient information must be protected and kept private. If there’s an error in data entry, a patient’s records may be mistakenly shared with someone else. To avoid such situations, you can set data quality standards such as always entering a patient’s full name and then design data governance strategies to implement this.
Incorporating Data Quality And Data Governance Standards
Ideally, a data governance structure needs to incorporate data quality dimensions and processes. This helps ensure that the data is reliable and can be used to make meaningful decisions. Ultimately, this should also be aligned to the organization’s long-term plans.
Here’s what you need to do.
Define Data Quality Criteria
The first step is to determine which aspects of data need to be focused on and how to prioritize them. You need to identify which elements are critical to your business and the risk associated with poor data quality. You will need to focus on the elements with the highest risk.
Once the area of focus has been defined, you can use data governance to create rules and a framework for addressing them. Once data standards have been set and you understand how data flows through the system, you can work on creating appropriate governance rules.
Bring IT And Business Users Together
It is essential to have IT and the business users on the same page and collaborating to define and improve data quality and data usage. Data owners need to be able to define the processes and systems involved and business users need to be open about the standards they require data to meet. Both parties need to agree on requirements, rules and policies.
For example, business users may realize that they require customer addresses to include Zip codes. The IT team can then incorporate address auto-complete options to ensure that addresses are always added to your database complete with their Zip codes.
Use Data Governance Capabilities To Enable Data Quality
Once you have determined where to focus data quality efforts, you can run the processes to identify data errors. Seeing the processes work as errors are identified and corrected is confidence-boosting.
A good data governance foundation and strong policies help improve data quality and in turn, increase the trust an organization has in its database. Data governance provides the required context and understanding to ensure access to high-quality data.
Involve Data Owners Into The Data Quality System
The individuals or teams responsible for implementing data governance policies must be included in the data quality system as well. They should be empowered to have a direct impact on the quality of information at various stages to streamline workflows and reduce the risk of having erroneous information enter the system. All the parties involved should also be able to participate in data quality improvement initiatives.
Improving Data Quality And Data Governance With Melissa
Once data governance rules have been established, you will need to find the right tools to help with improving data quality. Melissa has over 30 years of experience in helping companies around the world design solutions that improve their data quality.
The range of data quality tools available help with ensuring data accuracy, updating contacts, completing records and dealing with duplicates. Melissa helps companies verify names, addresses, phone numbers, email addresses and so on. The improved data quality can quickly show results in terms of a better understanding of customer requirements, an increased lifetime value of customer records and higher ROI on marketing campaigns.