Data Quality: Is it a Process or a Technology?

Data quality is critical to the success of businesses in a variety of industries. It serves as the cornerstone for successful decision-making, performance analysis, and strategy planning. However, the nature of data quality is frequently contested, with some viewing it as a process and others as a technology. By examining both views, I seek to give a comprehensive overview of what data quality really is. I will examine the interaction of data quality procedures and technology, emphasising their distinct responsibilities and importance in ensuring high-quality data.

Data has become an indispensable asset for organisations, enabling informed decision-making, enhancing operational efficiency, and gaining a competitive edge. However, the value of data heavily relies on its quality. Poor data quality can lead to flawed insights, erroneous conclusions, and ultimately, misguided actions. The question of whether data quality is a process or a technology is of significant importance, as it sheds light on the fundamental aspects of managing and maintaining high-quality data.

What is Data Quality?

Data quality is a measure of how well a dataset is suited to its unique purpose. It assesses the state of data based on variables such as correctness, completeness, consistency, dependability, and timeliness. Data quality is important because it serves as the foundation for trustworthy business choices, whereas data integrity raises the bar to achieve superior business outcomes.

Here are some crucial data quality points:

Measuring data quality: Measuring data quality levels can assist organisations in identifying data mistakes and determining if the data in their IT systems is appropriate to serve its intended purpose. Data quality has six major dimensions: correctness, completeness, consistency, validity, uniqueness, and timeliness.

Importance of data accuracy: Data accuracy is an important feature of high-quality data. The data utilised must be correct in order to avoid transaction processing issues in operational systems and incorrect results in analytics applications. In order to guarantee that business leaders, data analysts, and other end users are dealing with reliable data, inaccuracies must be found, documented, and corrected.

Data quality issues: Duplicated data, incomplete data, inconsistent data, erroneous data, poorly specified data, poorly organised data, and poor data security are examples of data quality difficulties.

Data quality rules: Measuring data quality dimensions helps identify the opportunities to improve data quality. Data quality rules ensure that data represents the real-world entity accurately, completely, and consistently. The automated rules help identify data errors quickly and provide a constant update on the state of data health.

Consequences: Poor quality of data can lead to inaccurate analysis, erroneous conclusions, and can be overall expensive by wasting human effort and time. Therefore, data quality is essential for businesses to make informed decisions and achieve better outcomes.

Data Quality as a Process

Data quality as a process comprises the actions and practices involved in ensuring the Validity, Consistency, Integrity, Completeness, Accuracy and Timeliness of data. This viewpoint highlights the human aspect, emphasising the significance of data governance, data stewardship, and data management practices. Data profiling, cleaning, validation, and integration are critical components of the data quality process. These actions strive to discover, remedy, and avoid data quality concerns at various phases of the data lifecycle.

Here are some of the benefits of good data quality:

• Improved decision-making: Data-driven decisions are more likely to be accurate and effective if the data that is used is of high quality.
• Increased efficiency: Excellent data quality can help to reduce errors and improve the efficiency of business processes.
• Reduced costs: Errors and inconsistencies in data can lead to costly mistakes. Good data quality can help to reduce these costs.
• Improved compliance: Organisations that are subject to regulations, such as financial institutions, need to ensure that their data is accurate and complete. Good data quality can help them to comply with these regulations.
• Enhanced customer experience: Competent data quality can help organisations to provide better customer service by providing them with accurate and up-to-date information.

Here are some of the challenges of achieving good data quality:

• The sheer volume of data: Organisations are collecting and storing more data than ever before. This can make it difficult to keep track of all of the data and to ensure that it is of high quality.
• The complexity of data: Data can be complex and difficult to understand. This can make it difficult to identify and correct errors.
• The lack of resources: Many organisations do not have the resources to invest in data quality initiatives.
• The lack of awareness: Many organisations do not understand the importance of data quality and therefore does not make it a priority.

Data Quality as a Technology

Data quality as a technology focuses on the tools, techniques, and technologies used to assess, monitor, and enhance data quality. It recognises the role of automated processes, data quality software, and advanced algorithms in detecting and resolving data quality issues. Data profiling tools, data cleansing software, and data quality dashboards are examples of technologies that facilitate data quality improvement. Furthermore, data quality technologies enable real-time data monitoring, data lineage tracking, and the implementation of data quality rules. There is a plethora of technologies available which can help you with data quality.

Here are a few data quality tools available on the market:

For more information, visit https://www.gartner.com/reviews/market/data-quality-solutions

The Interplay between Data Quality Processes and Technologies

While data quality can be viewed from two distinct perspectives, it is important to note that the process and technology aspects are not mutually exclusive. Rather, they are interdependent and complementary. Effective data quality management requires a symbiotic relationship between process and technology. Data quality processes provide the framework for identifying and resolving data quality issues, while data quality technologies offer the tools and automation necessary to scale and streamline these processes. One cannot exist without the other.

However, where organisations fail in implementing a successful data quality project, is the focus in which those initiatives are applied too.

For instance, a few questions I’ve been asked with my answers (paraphrased, not actual responses):

Although these questions are very valid (responses have been dramatized), these type of questions are not helpful when starting your data quality journey.

Instead, a more pragmatic approach is to ask the following questions:

• What is the goal of the data quality transformation project?
• How does poor data quality impact our business processes and decisions?
• What are the specific data quality issues we currently face?
• What data sources are involved, and what is their overall quality?
• What data quality metrics and standards should be established?
• What are the data quality requirements of our stakeholders and customers?
• How can we measure and quantify the impact of data quality improvements?
• What are the root causes of data quality issues in our organisation?
• What data governance practices and frameworks are currently in place?
• What are the data quality roles and responsibilities within our organisation?
• What are the costs and risks associated with poor data quality?
• What technology infrastructure and tools are needed for data quality management?
• What data quality processes and workflows should be established?
• How can we ensure ongoing monitoring and maintenance of data quality?
• What are the legal and regulatory requirements related to data quality?

Achieving a Comprehensive Data Quality Eco-System

The following key considerations should be taken into account:

I believe that to achieve comprehensive data quality, organisations must adopt a balanced approach that integrates people, process and technology. People to drive data quality, Process to govern and Technology as the enabler.

In conclusion, data quality is a multidimensional term that includes both process and technology. While data quality procedures are concerned with organisational and human elements, data quality technologies give the tools required for automation, scalability, and efficiency. To achieve complete data quality, organisations should use an integrated strategy that utilises both process and technology. This indicates that data quality initiatives must include responsibilities related to People, Process, and Technology, rather than focusing just on Technology implementation as the primary answer to project success. Organisations may achieve a competitive edge in today's data-driven world by realising the full value of their data.