The Pillars of Data Quality Management: A Guide to Mastering Them All

According to Gartner, poor data quality issues can generate an additional $15 million in annual costs on average. In fact, it is not only about financial losses, but also affects other levels, such as less reliable analysis, poor governance and risk of non-compliance, loss of brand value, slowing down corporate growth, etc.

For all these reasons, having quality data has become a fundamental value for companies that want to continue to innovate and stand out from the competition. We analyze its principles, best practices, and the keys to avoid falling into poor-quality data.

What is Data Quality

Data quality refers to the degree of accuracy, consistency, completeness, reliability, and relevance of data collected, stored, and used in an organization or in a specific context.

High-quality data is essential for making informed decisions, performing accurate analyses, and developing effective strategies. It is also necessary to properly function other technologies, such as artificial intelligence or IoT solutions.

Maintaining their high quality is a crucial factor for companies to obtain valuable and correct information, make the best decisions, and achieve their objectives. In fact, data quality has a direct influence on operational efficiency, as it gives departments the accurate information they need for day-to-day tasks, such as inventory management and order processing. It also affects customer satisfaction and new business opportunities by enabling more effective marketing and sales strategies based on accurate customer segmentation and targeting.

Data Quality Dimensions

Data quality dimensions are critical aspects used to assess the health and usability of each organization’s data. They provide a framework for effectively identifying and correcting quality problems.

The most important dimensions are:

• Completeness: refers to whether a data set contains all the necessary records, as a complete data set allows for more comprehensive analysis and decision-making.
• Accuracy: refers to the degree to which the data accurately represents real-world values or events. To ensure accuracy, it is necessary to identify and correct errors in the data set, such as incorrect entries or misrepresentations. To improve it, data validation rules can be implemented to help prevent inaccurate information from being entered into the system.
• Consistency: represents whether the same information stored and used in multiple instances matches. It ensures that analyses correctly capture and leverage the value of the data. It is difficult to assess requires planned testing across multiple data sets, and is often associated with the accuracy of the data.
• Timeliness and topicality: these ensure that data is current and relevant when used for purposes such as analysis or decision-making. Outdated information can lead to incorrect conclusions, so it is essential to keep data sets up to date.
• Singularity: refers to the absence of duplicate entries in a data set. Duplicate entries can distort the analysis by overrepresenting specific data points or trends. The primary action taken to improve the uniqueness of a dataset is to identify and remove duplicates.
• Granularity and relevance: these two ensure that the level of detail in the dataset is fit for purpose. Too much granularity can lead to unnecessary complexity, while too little can render the data useless for specific analyses. Striking a balance between these two aspects ensures that you get relevant and actionable information from the data.

Data Quality and Governance

Both data quality and data governance are two indispensable factors for companies wishing to become a data-driven enterprise. They may be independent practices, but they are highly related.

In summary, you cannot have data quality without good governance. In fact, organizations need proper data governance before even considering an enterprise-scale data quality tool.

Data governance affects security, privacy, accuracy, compliance, roles and responsibilities, management, integration, and so on. It is used for different tasks such as increasing transparency around data, standardizing systems, policies, and procedures, solving problems, and ensuring regulatory and organizational compliance.

All these tasks are necessary to improve and monitor data quality, as good governance allows creators and users to work on the same platform, which enables better communication and shared understanding of data quality.

Although the data may need a massive overhaul to improve its quality, this experience can be leveraged to adjust data governance policies and procedures to incorporate new data. Thus, using this overlapping perspective is the most useful when designing joint strategies on data governance and data quality.

To achieve successful incorporation of both practices, data teams must ask themselves questions (Where to start? Which data to focus on? Which data may be out of scope? Which has the greatest business impact?) from two different angles:

• Critical data elements: identify what is critical to the business, either through a regulatory report, a KPI, etc.
• Value of data: calculate the lifetime of poor-quality data or the risk associated with it, focusing first on those areas with the highest risk.

In both cases, once organizations identify and prioritize areas of concern, they can use data governance to create a collaborative framework for managing and defining policies, business rules and assets to provide the necessary level of data quality control.

Once it is clear how data flows through the organization and what the standards are, it is easier to ask the data quality team to translate these standards into data quality rules and enforce them on the data in those systems.

Data Quality Monitoring

To maintain and improve data quality, it is necessary to incorporate techniques and best practices into daily data management routines.

The most effective techniques include:

• Data profiling: review existing data to detect anomalies, patterns, or inconsistencies.
• Standardization: applying uniform formats across all data sets.
• Cleaning: correcting or removing inaccurate, incomplete, or irrelevant data records.
• Data enrichment: enhancing data from internal and external sources for greater context and value.

Regarding best practices:

• Periodic data quality assessments to proactively detect and address problems.
• Clear business rules that guide data inputs and avoid common data errors.
• Hire experts as data analysts who can use advanced analytics tools.
• Zero-defect data approach to achieve data quality that borders on perfection.

Data Quality Management

Establishing data quality standards is essential to ensure consistency and accountability in your organization’s data. Some of the principles of data quality management are as follows:

1. Focus on business needs: The primary focus of data quality is to meet the requirements of the data quality dimensions according to business needs.
2. Leadership: It is important that leaders from all departments align on a common set of strategies, policies, processes, and resources.
3. Stakeholder engagement: Data quality is everyone’s responsibility and to achieve this, all employees must work within a framework where they can raise issues that cause poor data quality and have clear ways to address and prevent them.
4. Process-based approach: A comprehensive and successful data quality and management program must take into account all business and technical processes that acquire, produce, maintain, transform, or disseminate data. Understanding how they interact with each other and what results they produce will be key to optimizing the data ecosystem.
5. Continuous improvement: data management should be understood as a program that needs to be continually re-evaluated and adapted to keep up with internal and external conditions.
6. Data-driven decision-making: Decision-making can be challenging, but with useful data, facts, evidence, and reliable analysis, more objective decisions can be made.
7. Relationship management: Data quality management not only encompasses internal stakeholders but also extends to data management tool providers, suppliers, and consumers.

These data quality management principles can be applied in many different ways. As such, how each organization implements them will depend on the specific nature and challenges they face. What is common to all is that they will find many benefits in establishing a management program based on these principles.

Data Quality Framework

A data quality framework provides a structured approach to managing and improving data quality across all business operations. It ensures that data is accurate, complete, and reliable.

To create a data quality framework, you will need to consider aspects such as:

• Define roles and responsibilities
• Establish data quality rules
• Periodic evaluations

This framework must be adaptable to changing business needs while remaining robust to the challenges posed by new types of data and emerging technologies.

Implementing a comprehensive data quality framework ensures a reliable foundation for your information systems, fostering confidence in your data and the decisions derived from it. That’s why at Plain Concepts we offer you a Data Adoption Framework to become a data-driven enterprise.

We help you discover how to get value from your data, control and analyze all your data sources, and use data to make smart decisions and accelerate your business:

• Data analytics and strategy assessment: we evaluate data technology for architecture synthesis and implementation planning.
• Modern analytics and data warehouse assessment: we provide you with a clear view of the modern data warehousing model through understanding best practices on how to prepare data for analysis.
• Exploratory data analysis assessment: we look at the data before making assumptions so you get a better understanding of the available data sets.
• Digital Twin Accelerator and Smart Factory: we create a framework to deliver integrated digital twin manufacturing and supply chain solutions in the cloud.

We will formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analysis services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can condition your decisions and boost your business.

Extract the full potential of your data now!