Defining Data Quality Standards For Your SMB

Did you know that poor data quality costs organizations an average of $12.9 million annually? 

While decision-making is at risk without data, poor-quality data can result in more serious consequences, like reputational damage, financial losses, compliance risks, and reduced efficiency. Therefore, maintaining a data quality standard for your business is crucial to survive in a hypercompetitive globalized market. 

But what exactly is a data quality standard, and who creates it?

Let’s find out in the blog below!

Introduction to Data Quality Standards

Data quality standards are guidelines to ensure data accuracy, consistency, and reliability.  

They serve as a roadmap to achieving and maintaining data integrity across a company. Following these guidelines results in improved operations, enhanced customer experience, cost-effectiveness, and more. 

Here’s why your company needs data quality standards:

Why Your Company Needs Data Quality Standards

Companies draw data from 400 different sources on average, including customer feedback, sales reports, market analysis, internal data, etc. However, if the data is inaccurate and inconsistent, it can hinder the company’s success. Data quality standards ensure flawless information flow across the organization. 

The standards also provide specific steps to improve privacy and protect sensitive information, such as user personal information, by outlining steps to enhance privacy and protect sensitive information.

Data quality standards guide toward better data organization by ensuring structured, formatted, and standardized data. This offers all stakeholders a single source of truth, introducing compatibility and trustworthiness in the workflow. 

Examples of common data quality standards

Some of the most commonly used data quality standards include:

• ISO 8000: ISO 8000 is a globally accepted international standard recognized for its emphasis on data and information quality, including general aspects of data quality, data governance, data quality management, data quality assessment, master data quality, and industrial data quality.
• Completeness: This refers to data without missing values and all necessary information present within a dataset.
• Validity: It assures that the collected or procured is accurate and reliable.
• Relevancy: It is the meaningful data that can be used to make informed decisions and provide value to the business.
• Timeliness: This refers to the availability and readiness of data. This also involves ensuring data is updated in a timely manner and maintains its relevance over time.

Each organization has distinct needs depending on its data handling workflows. Distinct needs call for personalized standards based on operational dynamics. However, the primary objective of employing data quality standards remains the same, i.e., maintaining high-quality data to make informed decisions and achieve business goals.

Core Dimensions of Data Quality

The core dimensions of data quality were presented by Diane Storm and Richard Wang in 1996 and have been accepted since then. These dimensions prepare data for extracting valuable insights and allow shaping business operations tailored to provide maximum value to end-users. These dimensions include:

Validity: Adhering to Specified Formats and Ranges

Data validity ensures that data is appropriate based on predefined business rules. This means that the data should conform to established formats and ranges and follow the rules set by specific industry standards or a company’s regulatory framework. 

For example, accepting a specific field as numeric data, rejecting invalid postal codes, or accepting ages within the range of 30-60 only. These rules depend upon business requirements or industry standards, and adhering to them ensures data validity. 

Accuracy: Ensuring Precise Data

As the name suggests, accuracy refers to correct data representing real-world values. In cases with an acceptable error margin, the data values must be within the acceptable margin. Inaccurate records lead to poor decision-making, ultimately resulting in financial and reputational loss.  

Accuracy is measured through primary research or compared against third-party references. For example, a European citizen fills in a survey form with the average income in Euros when the expected value is USD. 

Completeness: No Missing Values

Completeness ensures that a database contains no missing values. Complete records contain all the required information without emphasizing accuracy or validity. Incomplete datasets result in inadequate analysis and distort actual findings.

For example, a user submits an online order with an incomplete address. You could use this data and run an email campaign asking your clients to update their shipping address.

Consistency: Harmonized Across Systems

Consistency in data refers to the information stored in more than one place, providing the same format and value. Consistency ensures uniformity in decisions across the organization and reliable and easy data integration.

For example, an employee database is updated upon appraisals, but one record has inconsistent salary values in two separate tables. This introduces confusion and could lead to serious consequences, such as employee conflict.

Timeliness: Up-to-date Information

Timeliness ensures data is stored and updated regularly in a database. Timely information reflects current scenarios, ensuring reliable decisions. Outdated data results in misguided business operations, which can be reduced by maintaining data quality standards. 

For example, using off-season user data to plan holiday marketing campaigns will result in poor campaigns and financial loss.

Relevancy: Importance to Decision Making

Relevant data benefits an organization and reflects its needs. It is measured by tracking data that is used in business decision-making. Irrelevant data results in aimless analysis and impactless decisions.

For example, patient hair color data is irrelevant when hospitals decide whether a patient should be discharged from the hospital.

Uniqueness: Eliminating Duplicates

Data uniqueness refers to eliminating duplicate data to avoid resource wastage. Duplicate data might also result in unreliable information due to multiple instances of one record. Ineffective targeting and inaccurate decisions are also consequences of duplicate data.

For example, multiple instances of customer data might result in inaccurate demand estimates, which can waste resources and cause missed connections.

Implementing Data Quality Standards

Implementing data quality standards takes more than just documenting rules for the entire organization. Effective implementations start with identifying data needs and goals, as well as measuring operations. The process involves the following steps:

Establishing Clear Metrics and Goals

Clear goals guide toward establishing data quality rules. The goals depend upon business dynamics, industry, and expectations. The two important aspects of establishing clear metrics and goals are:

• Clarity: A clear definition of  ‘high-quality data’ for your business sets the foundation for an efficient implementation. While the definition is likely to be composed of core data quality dimensions, it varies according to specific use-cases within your organization.
• Measurability: To measure your success, each element of data quality should be quantifiable as quantifiable metrics allow for comparing results with expectations.

Example Goals and Metrics

Goal: Improve Data Accuracy

• Metric: Data Error Rate
• Measure: Percentage of records containing errors.
• Target: Reduce data error rate to less than 1% within six months.

Goal: Enhance Data Completeness

• Metric: Data Completeness Percentage
• Measure: Proportion of data fields that are fully populated.
• Target: Achieve 95% completeness across all critical data fields within the next quarter.

Goal: Increase Data Consistency

• Metric: Data Consistency Score
• Measure: Number of inconsistencies identified during data audits.
• Target: Decrease inconsistencies by 50% over the next year.

Goal: Ensure Data Timeliness

• Metric: Data Timeliness Rate
• Measure: Percentage of data entries updated within the required timeframe.
• Target: Ensure 98% of data entries are updated within 24 hours of the relevant event.

Goal: Improve Data Validity

• Metric: Data Validity Score
• Measure: Number of records failing validation checks.
• Target: Reduce invalid records to less than 2% within the next six months.

Tools and Technologies for Enhancing Data Quality

Various state-of-the-art tools and technologies are available today to improve data quality. These tools are divided into two categories: Data Observation Platforms and Data Quality Monitoring Tools.

Let’s have a look at how these tools assist in improving and maintaining data quality standards:

Data Observability Platforms

Data observability platforms monitor your data and alert you about the health of your data and data systems. Spotting anomalies in data provides insights into how data behaves, changes, or deviates from norms or expectations.

Monitoring freshness, quality, volume and completeness, schema, and lineage is the core functionality of data observability platforms. Integrating them into your workflows allows:

1. Enhanced transparency: Data observability tools offer insights into a business environment, including operations, transactions, and processes. Moreover, these insights enhance transparency across the organization and help meet necessary data quality standards to avoid compliance issues.
2. Dynamic error detection: Real-time monitoring and alerts enable on-time error detection, resulting in prompt issue resolution, avoiding severe disruptions, and cost savings.  
3. Root-cause analysis: Data observability platforms offer a root-level diagnosis by pinpointing bottlenecks and performance issues. A root-level diagnosis guides business to the cause and solution of the problem.

Thus, data observability platforms simplify the complex data management process by ensuring timely prevention and resolution of issues and boosting productivity.

Examples of data observability tools: 

1. Monte Carlo: montecarlodata.com – Monte Carlo provides end-to-end monitoring of data pipelines, automated anomaly detection, and data lineage tracking.
2. Datafold: datafold.com – Datafold focuses on data quality and observability, offering features like data diffing, data lineage, and anomaly detection.
3. Bigeye: bigeye.com – Bigeye offers comprehensive monitoring and anomaly detection for data pipelines to ensure data reliability.
4. Databand: databand.ai – Databand provides observability for data engineering workflows, offering monitoring, alerting, and logging features.
5. Anomalo: anomalo.com – Anomalo offers automated data quality monitoring and anomaly detection for data warehouses and data lakes.
6. Lightup: lightup.ai – Lightup provides data observability and anomaly detection for modern data stacks, ensuring end-to-end visibility into data pipelines.
7. Soda: soda.io – Soda provides tools for data monitoring, testing, and observability, focusing on ensuring data quality and reliability.
8. Accio: accio.com – Accio focuses on ensuring data quality and integrity across data pipelines with automated anomaly detection and root cause analysis.
9. Metaplane: metaplane.dev – Metaplane offers data observability for data engineering teams, providing tools for monitoring data quality, lineage, and pipeline performance.
10. Great Expectations: greatexpectations.io – Known primarily as a data validation tool, Great Expectations also provides features for data observability and monitoring through automated testing and validation.

Data Quality Monitoring Tools 

There are tools aimed at specific tasks, like deduplication, to remove duplicate records from data, and tools that monitor all dimensions of data quality.

Some of the benefits of using data quality monitoring tools are:

1. Real-time updates: Real-time alerts about deviation of data from quality standards guide teams toward instant correction and save time.
2. Variety processing: Data quality tools are multi-functional and can process different data types, formats, and sources without losing efficiency or accuracy.
3. Comprehensive audits: Last but not least, they facilitate regular audits to evaluate and improve your current data quality standards.

Investing in the right tools to maintain high-quality data standards opens the way for sustainable success. However, no tool is a silver bullet solution to poor data quality, and picking the right tools depends on business requirements and data quality objectives.

Examples of Data Quality Monitoring tools

1. ClicData: clicdata.com – ClicData is a cloud-based platform that combines data management and analytics tools in which you can cleanse, standardize, combine and enhance your datasets. 
2. Talend Data Quality: talend.com – Talend Data Quality offers a comprehensive set of tools to profile, clean, and mask data, ensuring accuracy and consistency across data sources.
3. Informatica Data Quality: informatica.com – Informatica provides powerful data quality tools that help organizations cleanse, standardize, and enrich their data.
4. Ataccama ONE: ataccama.com – Ataccama ONE combines data quality, governance, and master data management into a single platform, offering automated data profiling and cleansing.
5. Trifacta: trifacta.com – Trifacta provides data wrangling solutions that help users prepare, cleanse, and transform raw data into structured formats suitable for analysis.
6. DataFlux: sas.com – DataFlux, part of SAS, offers data quality tools that help organizations improve data accuracy and consistency through data profiling, cleansing, and matching.
7. DQ Global: dqglobal.com – DQ Global specializes in data quality management solutions that help organizations cleanse, match, and deduplicate their data.
8. Talend Trust Assure: talend.com – Talend Trust Assure is another tool from Talend that focuses on ensuring data integrity and quality through comprehensive monitoring and validation.
9. Precisely Trillium: precisely.com – Precisely Trillium provides robust data quality solutions that include data profiling, cleansing, enrichment, and monitoring.
10. IBM InfoSphere QualityStage: ibm.com  – IBM InfoSphere QualityStage helps organizations manage data quality through data profiling, cleansing, and matching.

Benefits of Adhering to High-Quality Standards

If implemented correctly, data quality standards can streamline business operations and enhance decision-making, leading to customer trust. Some of the benefits of adhering to high-quality standards include:

Cost Reduction and Efficiency Gains

Using high-quality data results in cost reduction and improved efficiency in business processes. Poor-quality data requires data analysts to validate their analytics to ensure good decision-making. However, when data is examined manually, there is always a chance of oversight, which results in poor decision-making. This can negatively impact customer experience, cause a trust deficit, and damage business reputation. 

For example, 50% of data scientists dedicate 30% of their time or more to data preparation, and 47% believe maintaining data quality is the most difficult task. 

Clear data quality standards allow businesses to stay on track and make long-term gains. Reducing manual data correction saves time and allows you to focus on profitable activities.

Impact on Decision-Making Process

Every business needs to make decisions to achieve goals and success. However, informed decision-making requires accurate and reliable data. High-quality data standards ensure that data is accurate and error-free, generating analyses that reflect the ground truth of internal and external business matters.

For example, due to the use of an outdated data format, Public Health England (PHE)  failed to report nearly 16,000 coronavirus cases.

Reliable data leads to accurate business intelligence, which guides well-informed strategic decisions. 

Fostering Customer Trust through Reliable Data

Every business, big or small, has to process customers’ data to understand their purchasing habits and preferences. This allows for building personalized customer experiences. However, incomplete, inconsistent, or misleading customer information results in inaccurate assumptions about customers, risking customer loyalty.

For example, Unity Technologies mistakenly ingested bad customer data into its system, resulting in inaccurate predictions. As a result, the company faced a decline in investors’ trust and a loss of approximately $110 million.

Accurate anticipation of customer needs and habits is the foundation for delivering customer satisfaction. Reliable data yields accurate customer analytics, which is a crucial factor for succeeding in a highly competitive market.

Cost savings, informed decision-making, and enhanced customer trust are the keys to successful business. Committing to high-quality data standards allows businesses to unlock these benefits and reach their goals.

Promoting a Culture of Data Quality Within Organizations

Promoting a culture that values data quality is no less important than implementing robust technical controls. It means nurturing an environment where every team member appreciates the critical role of high-quality data in driving business outcomes. Two key elements to promote this culture are effective training and awareness programs and establishing a dedicated data quality team.

Training and Awareness Programs

Comprehensive training programs highlighting the importance of data quality standards create an aligned and collaborative environment. These programs also promote awareness about the importance of high-quality data collection, management, and use.

Training and awareness programs that begin with introductory sessions provide employees with a foundational knowledge of crucial aspects of data quality. Hands-on workshops showcasing real-world applications within the scope of your business enhance employee understanding.

Employee training and awareness is an ongoing process that requires regular refresher courses to keep staff members updated on new developments, policies, and systems.

Emphasizing ‘data quality best practices’ throughout the training programs significantly enhances everyone’s knowledge and understanding of maintaining high levels of data reliability. Instead of just the IT department adhering to high-quality data needs, highlighting its importance for every department is crucial to ensure goal orientation. 

Wrapping Up: Train Your Team To Improve Your SMB Data Quality Standards

Training and awareness programs are just the beginning. The key to success is creating a strong, devoted team committed to an organization’s adherence to its data quality standards.

This dedicated alignment guarantees:

1. Accountability: A designated team of experts ensures that all parts of the company stick carefully to their allotted roles in maintaining optimal information accuracy.
2. Expertise: The designated team consists of professionals with extensive knowledge of maintaining standards and providing prompt correction to deviations from standards. 
3. Adapting Standards over time: The team stays updated about evolving trends in the data world and uses that knowledge to regularly reassess your firm’s existing standards.

An environment focused on implementing and maintaining data quality results in consistent, valuable, and reliable outcomes.

Implementation of data quality standards is not limited to technological changes but requires human effort to achieve significant success. Continuous education and dedicated resources aimed at building a robust culture ensure data integrity across the organization.