4 Best Practices to Ensure Data Quality for Large Organizations

Over the years, data-driven decision-making has become a norm for businesses, regardless of size and scale. According to a recent article published in Forbes, more than 90% of companies want to prioritize data initiatives in 2023 to keep pace with the trend. However, handling huge volumes of information can be tricky for businesses, specifically large organizations. 
As a business owner, you may struggle with data quality concerns, considering that data downtime has nearly doubled year on year in 2023. According to a survey, the time to resolution for quality issues has surged by 166%. 
Poor quality can translate into costly errors, inaccurate insights, and compromised decision-making processes. Moreover, these issues cost American businesses a staggering $3.1 trillion a year.
Fortunately, large organizations can address this concern by implementing a few best practices to maximize the potential of their data assets. In this article, we will share the ones you can rely on to ensure data quality regardless of its volume.

Define Quality Parameters

The meaning of data quality may differ for various organizations. Some may prioritize criteria like accuracy and consistency when measuring quality. To others, it may be about reliability and completeness. Either way, high-quality data allows businesses to make accurate projections and forecasts, anticipate operational challenges, and develop success strategies.
However, relying on numbers that have not been cleaned and vetted is risky for decision-makers. You must be cautious to ensure that the reports and analyses generated from this information are correct.  
The following parameters can help you determine the quality of your data:

• Completeness ensures that crucial information is not missing 
• Accuracy is about verifying the validity of information
• Uniqueness prevents inconsistencies due to duplicate and repetitive values
• Up-to-date entries refer to real-time data for creating relevant reports

According to Dataloop.ai, using a technology solution is the best way to analyze vast quantities of unstructured data originating from diverse sources. Most large organizations collect information from different sources and struggle to structure and manage it. Look for a platform that cleans, curates, versions, and routes your data to ensure its quality.  

Establish Data Governance Guidelines

Following data stewardship laws and regulations is an important aspect of maintaining its quality. If you fail to comply with the regulations, you may face fines, penalties, and more severe consequences. A robust data governance framework is a must for a large organization with various teams utilizing organizational and customer information.
Ideally, the governance framework should define policies, standards, and procedures for managing data quality across the organization. For example, you must set clear roles and responsibilities regarding data collection, access, management, and quality assurance. Also, ensure compliance with relevant regulations such as GDPR and CCPA.

Train Your Team

According to a Forrester study conducted in 2022, 82% of decision-makers expect employees in every department to have basic data literacy. Also, nearly 70% of employees will have to heavily use data by 2025, as per projections. Undoubtedly, training your team should be a priority to minimize data quality concerns for your large organization.
As your business continues to source and collect information from different locations, your team should know how to handle it. The last thing you want them to do is stack data instead of keeping it clean, relevant, and up-to-date. Set up a training program to educate employees regarding the cost of poor-quality data, quality improvement initiatives, and real-world application of these initiatives. 

Implement Quality Assurance Processes

A robust quality assurance process is the key to the accuracy, consistency, and integrity of data throughout its lifecycle. Start with data profiling and assessment to understand its structure, characteristics, and value to the organization. Also, use manual inspection techniques and automated profiling tools to identify anomalies, inconsistencies, and inaccuracies.
After cleaning up data in the initial stage, consider establishing ongoing validation checks, cleansing routines, and enrichment processes. A set-and-forget approach does not work when you expect flawless quality from high data volumes. You can invest in automation tools to streamline the process for your organization.
Regular audits and reviews should also be a part of the quality assurance process as they help you identify areas for improvement and address them. Engage internal or external auditors for reliable recommendations and independent assessments.
In conclusion, large organizations must have a proper quality strategy in place to maximize the value of data-driven decision-making. Without focusing on quality, they may lose their way and make wrong decisions based on inaccurate information. These best practices can help them create a data-driven culture that sets them apart in the competitive market.