How to Achieve High-Quality Data for Accurate Analytics

In today’s era, data plays a vital role in helping organizations shape business strategies and make informed decisions. While the vast amount of data collected holds great potential, its true value depends on maintaining high-quality data. Inaccurate or inconsistent data can result in flawed insights, wasted resources, missed opportunities, and even reputational damage. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually.

In this blog post, we will explore what data quality means, its key characteristics, common issues, and best practices to help you manage and maintain accurate, reliable data

What is Quality Data and Why Does It Matters? 

Data quality evaluates how well a dataset adheres to established standards for accuracy, consistency, reliability, completeness, and timeliness. 

Understanding the importance of data quality is critical for organizations aiming to succeed in a data-driven world. Here’s why investing in high-quality data matters

• Facilitates informed decision-making, enabling better strategic choices and outcomes.
• Promotes operational efficiency by streamlining processes and reducing the need for corrections.
• Supports compliance with regulations and improves the report accuracy, maintaining transparency and trustworthiness.
• Reduces errors and rework, resulting in cost savings.
• Better customer needs to enable personalized services and improved customer satisfaction
• Uncover hidden insights to fuel product innovation and gain competitive advantages

Data Quality Characteristics 

Data quality characteristics are key criteria used to evaluate and measure the overall integrity of a dataset. These characteristics are typically assessed through specific data quality attributes

The six most common data quality attributes that organizations monitor include:

Accuracy

The degree to how well data represents real-world events or an agreed-upon source. Given that multiple sources can report on the same metric, it’s crucial to designate a primary data source and use other sources to verify its accuracy. For instance, tools can compare trends across different data sources to ensure they are aligned, thereby reinforcing confidence in the data’s accuracy.

Completeness 

Refers to the extent to which amounts of data are present and usable. A high percentage of missing values can result in biased or misleading analysis if the data does not accurately represent typical samples

Consistency 

Compare data uniformity as it is shared across different applications and networks, as well as when sourced from multiple locations. Verifying data trends and behaviors across different sources helps organizations trust the actionable insights derived from their analyses.This principle also applies to relationships between data elements; for instance, the number of employees in a department should not surpass the total number of employees in the company 

Timeliness

Assesses the volume of duplicate data within a dataset, ensuring that no redundant or overlapping information exists across all datasets. Analysts use data cleansing and deduplication techniques to improve a low uniqueness score. For instance, when examining customer data, each customer should have a unique customer ID

Validity 

Determines how well data conforms to the required format based on business rules. Formatting generally encompasses metadata, such as acceptable data types, ranges, patterns, and other specifications

Uniqueness

Evaluates the volume of duplicate data within a dataset, ensuring that no redundant or overlapping information exists across all datasets. Each record should appear only once. Analysts use data cleansing and deduplication techniques to improve a low uniqueness score. For instance, when examining customer data, each customer should have a unique customer ID

Additional dimensions to track

Reliability reflects how consistently and dependably data performs over time, ensuring that it remains accurate and reliable throughout its lifespan.

Relevance gauges how well data aligns with business needs, which can be particularly challenging with emerging datasets.

Precision examines the level of detail and granularity in data, confirming that it accurately represents the required specificity.

Understandability measures how easily the data can be understood by users, preventing confusion and misinterpretation.

Accessibility assesses how readily available and easily accessible the data is for authorized users, ensuring it is available when needed.

Common Issues That Undermine High-Quality Data

Poor data quality can lead to inadequate decision-making, higher costs, compliance risks, and reduced efficiency. To address these issues, it’s essential to understand common data quality problems, their causes, and effective solutions for mitigation. Let’s explore these aspects to enhance data quality management and optimize performance

How to Improve Data Quality? 

To facilitate accurate, reliable, and useful data from the start and minimize data quality problems, companies are adopting the Data Quality Management (DQM) process. 

Here’s a typical DQM process:

1. Define Data Quality Standards and Governance 

The first step is to define clear data quality criteria and standards based on business and regulatory requirements. This includes defining what constitutes “good” data and setting benchmarks for accuracy, completeness, consistency, and timeliness. 

Establishing comprehensive data governance rules and guidelines is equally crucial. These should cover aspects like data collection, ownership, storage, usage, and sharing within the organization, ensuring that consistent data management practices are maintained.

2. Data Profiling

Once data is collected from various sources, data profiling is the next step in data quality management. This process involves exploring and analyzing the structure, content, data types, and relationships between datasets, as well as assessing data quality issues such as duplication, incorrect formats, and missing values. The insights gained from data profiling are crucial for guiding the data cleansing process. 

3. Data Cleaning

This step consists of identifying and correcting errors within a dataset. Key tasks include: 

• Handling missing values by imputing values, removing records or flagging them for further analysis
• Correct typos, incorrect formatting, and inconsistencies 
• Standardizing data across different sources and formats
• Remove duplicate records 

4. Data Validation

Create and enforce validation rules and checks to verify that data adheres to predefined format, rules and standards. These rules should encompass various criteria such as data type, format, range, and consistency to maintain data integrity.

Some companies have started implementing automated checks and validation during data entry or migration. By performing consistent checks across large datasets, these systems improve productivity and reduce the potential for human error in data quality management 

5. Data Monitoring & Auditing 

Continuous monitoring of data quality metrics is crucial for maintaining high standards of data integrity. This process involves regularly tracking various indicators of data quality, such as accuracy, completeness, consistency, and timeliness. By consistently monitoring these metrics, organizations can ensure that their data remains reliable and effective over time.

It’s also advised to perform regular audits to thoroughly assess compliance with established data quality standards. These audits involve reviewing data management practices, evaluating adherence to data quality criteria, and identifying areas where improvements are needed. 

6. Continuous Improvement

Based on monitoring results and assessments of data quality initiatives on business outcomes, you should continuously identify and implement improvements to data processes and systems. This may involve refining data quality strategies, enhancing management practices, and leveraging new technologies.

Additionally, it’s important to provide employees with regular training on data quality best practices and tools to ensure they are equipped to maintain high standards.

Summing up

Data quality management is crucial for any team aiming to enhance data analytics, address common issues, and implement robust control standards. 

To achieve this, a comprehensive set of tools and technologies is needed to assess, improve, and maintain data quality. Our experts, with over 20 years of experience, are well-equipped to guide you in selecting the right tools and developing effective data quality strategies. We have a proven track record of helping companies across industries complete data projects—from collection and cleansing to analytics and reporting—with precision and expertise