5 Tips for Good Data Quality

18 Jan, 2022 •

quality level meter

Many businesses have added data-driven insights supported by speedy and accurate analytics to the top of their new year’s resolutions. In order to achieve their data goals, companies must forgo their outdated practices and adopt new programs covering data generation, quality, management, utilization, maintenance, and governance. No matter how powerful a tool or algorithm claims to be, it will not be effective without high-quality data.   

This sentiment is best expressed through the programmer, Daniel Keys Moran,  

“You can have data without information, but you can’t have information without data”  

Daniel Keys Moran

Surprisingly, data quality issues permeate everyday life more often than one would think. For instance, repeated spam calls, unwanted marketing emails, and canceled restaurant reservations result from poor data quality.  

But, what exactly is data quality?  

Data quality is data that accurately represents the entity it describes. It is also considered to be high quality if it effectively satisfies the requirements of a given criterion. Data quality is highly dependent on the use-case of the data asset, but there are five dimensions that we can use to assess sound quality data.  

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Let’s review each of these individually.  

  1. Accuracy: It refers to the correctness of inherent data, i.e., how close is the data to the phenomenon it seeks to describe  
  2. Completeness: This is the characteristic of good data to represent the entirety of the situation.  
  3. Consistency: The ability of data to describe similar situations in a similar manner  
  4. Timeliness: It lies within the timebound expectation for describing data  
  5. Relevance: It is specific to the event being described and applicable to business requirements  

Let’s take a real-life example of how data quality issues can cause severe problems for businesses: Take Mary, a data analyst at a large medical devices company. The medical devices company wants to ascertain customer sentiment and feedback of their product to come out with a new product feature. Mary is working with product review data collected by a third-party marketing agency. She finds major issues with the consistency and completeness of the data. She cannot be certain of the accuracy either. She raises these issues with her manager, but she does not have an alternative data source, nor does tools to help her with her analysis. After completing her analysis, she presents her findings to senior company management. A decision to come out with an unexpected feature is made. The company now believes it is making data-driven decisions and diverts significant resources to the development of a new feature. Data quality issues tend to cascade into larger problems that could have been easily prevented.  

How do businesses take the proper steps to ensure data quality in their organizations?  

1. Enable good governance  

The first step is to enable good governance to control the generation/purchase of data assets. Understanding the business requirements and establishing stringent checks to ensure that the data profile is consistent with the organization’s needs can be a big step in alleviating poor data quality concerns. Defining and managing metrics to keep track of progress is essential. Simplicity and transparency are also best practices that are implemented.  

2. Ensure end-to-end visibility  

The next step is to ensure end-to-end visibility of all data assets in an organization. Allowing for visibility into the entire organization’s data can help avoid duplicity and guarantee completeness. Moreover, clear definitions among owners and users make communication and access control smooth and easy. Visibility is not just at a high level. Understanding the content of each individual asset through a business glossary can help track relevance and consistency.  

3. Data Quality Management program  

Establishing a centralized data quality management program and dedicated team can help identify bottlenecks, spearhead quality assurance, establish best practices and standard operating procedures. This can also help with organization-wide data literacy and efficient change management. 

 4. Usage of data quality tools  

Adopting data quality tools, automation software, and artificial intelligence can greatly enhance the productivity of the existing data assets in the organization by accelerating discovery and revealing hidden insights. AI/ML is used to automate insights and drive data intelligence.  

5. Data lineage program  

The fifth step an organization can take is establishing a data lineage program. Phasing out outdated data that is costly to maintain can be an opportunity to improve on existing data assets and build new data quality. Understanding the lifecycle of data assets is key to data quality improvement.  

Let’s now take a look at DvSum, a powerful automated augmented catalog that can help your organization make strides towards their data quality goals 

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