Data Quality Tools

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Data Quality Tools

Benefits
Analyst Research
Resources
Definition
FAQ

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Cost of Poor Data Management

Poor, unbridled data quality management is causing enterprises to miss out on new opportunities, delay their problem-solving abilities, and inaccurately forecast their next steps.

91%

of organizations cite performance issues related to data quality

https://www.aidataanalytics.network/data-governance/articles/data-quality-crisis-new-survey-reveals-77-of-organizations-have-quality-issues

$15M

Average financial loss per year

https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement

33%

Of analysts spend 40%+ of their time vetting data, before it’s useable

https://www.forrester.com/report/Build-Trusted-Data-With-Data-Quality/RES83344

Benefits

BMC Data Quality Tools Ensure Your Data Is

Error Free Consistent Complete AI Ready Available Compliant Automated

Error Free

Make better decisions with the most accurate data on hand. Data is cleaned and processed to improve data integrity and reliability.

See how

Consistent

Mitigate the business risks of poor, inconsistent data. Data is organized into a consistent format to help you leverage every point possible.

See how

Complete

Don’t waste time analyzing skewed information. Our big data quality tools consolidate all the necessary and contextual data you need to choose your next steps with confidence.

See how

AI Ready

Protect your investment in AI solutions. Fuel these tools with high quality, clean data for accurate insights and optimal AI performance.

See how

Available

Support optimal decision-making, operational efficiency, and customer satisfaction. Our data quality management tools give you timely access to data that matters.

See how

Compliant

Protect your data, your bottom line, and your reputation. Our data quality software is equipped with dashboard-type analytics for a quick view of your compliance status in real-time.

See how

Automated

Eliminate time-consuming, error-prone tasks. Our data quality platform empowers your analysts to do what they do best: analyzing clean, relevant data that drives your business forward.

See how

Benefits

BMC Data Quality Tools Ensure Your Data Is

Error Free

Make better decisions with the most accurate data on hand. Data is cleaned and processed to improve data integrity and reliability.

See how

Consistent

Mitigate the business risks of poor, inconsistent data. Data is organized into a consistent format to help you leverage every point possible.

See how

Complete

Don’t waste time analyzing skewed information. Our big data quality tools consolidate all the necessary and contextual data you need to choose your next steps with confidence.

See how

AI Ready

Protect your investment in AI solutions. Fuel these tools with high quality, clean data for accurate insights and optimal AI performance.

See how

Available

Support optimal decision-making, operational efficiency, and customer satisfaction. Our data quality management tools give you timely access to data that matters.

See how

Compliant

Protect your data, your bottom line, and your reputation. Our data quality software is equipped with dashboard-type analytics for a quick view of your compliance status in real-time.

See how

Automated

Eliminate time-consuming, error-prone tasks. Our data quality platform empowers your analysts to do what they do best: analyzing clean, relevant data that drives your business forward.

See how

ANALYST RESEARCH

Forrester: BMC named a leader in enterprise service management (Q4, 2023)

Read the report

Resources

Explore More Data Quality Tools and Techniques

Article/Blog

Intro to Data Management

Follow our step-by-step and best practices guide to data quality management (DQM). Learn how to leverage insights to improve customer experience, innovation, top-down decisions, and more.

Learn more

Definition

What are data quality tools?

Data quality management tools improve your ability to trust and fully leverage data for mission-critical decisions. They ensure access to compliant, complete, and accurate information. Data quality services employ cleansing and validation techniques, fill data gaps, achieve completeness, standardize formats, and facilitate timely data updates.

Learn more

FAQ

What are the five measures of data quality?

The five key measures to assess data quality are accuracy, completeness, consistency, uniqueness, and validity.

1. Accuracy: The correctness of data, ensuring it aligns with real-world values.

2. Completeness: How much of the necessary data is present.

3. Consistency: Data uniformity across disparate sources.

4. Uniqueness: Each data point is distinct, avoiding duplicates.

5. Validity: If the data adheres to predefined rules or formats.

Data profiling is a valuable initial step, identifying completeness and accuracy issues.

A more comprehensive approach involves using a Data Quality Assessment Framework (DQAF). DQAF examines data against specific quality standards.

Organizations can further refine their data quality assessment through custom data quality metrics, such as data-to-error ratio, empty values, percentage of dark data, or time-to-value ratio.

What are the four categories of data quality?

The most commonly recognized four dimensions of data quality are:

1. Accuracy: The data correctly reflects the real-world values it represents.

2. Completeness: All necessary data is present.

3. Consistency: Data may be pulled from different sources, yet it is processed into a consistent format for analysis.

4. Timeliness: Data is available when needed and is up-to-date.

What are the six C's of data quality?

The six C's provide a framework for assessing and improving your data. The six C’s of data quality are:

1. Current: Data is up-to-date and relevant.

2. Complete: All necessary data is present.

3. Clean: Data is free of errors, inconsistencies, and duplicates.

4. Consistent: Data is uniform across sources and systems.

5. Credible: Data is reliable and trustworthy.

6. Compliant: Data adheres to regulatory standards.