Ensure data quality in Databricks with BiG EVAL integration for reliable insights and streamlined processes.
[ ... ]Data Quality Management
Use the best strategies and tools that make you a data quality expert.
Two things are needed to improve your information quality in a sustainable manner so your company profits most from trusted data. You need a strategy that suits your and your companies needs best, as well as an efficient toolsupport. Following you find articles and information about strategies and tools you can use and implement immediately.
Software-Solution
BiG EVAL Data Quality Management
The automation solution BiG EVAL DQM supports you in all tasks regarding data quality management. It makes you capable of automatically apply ongoing quality checks onto your enterprise data, it provides you a quality metric, and supports your quality problem solving processes.
Are your databases piling up with incorrect customer addresses, duplicates, and contradictory values? These are signs of poor data quality. Don’t ignore the signs! Studies show that incorrect data often leads to unpleasant consequences.
BIG EVAL can help by finding data errors, monitoring data systems, and keeping data quality high.
The easiest way to start your data quality project successfully.
Discover now test cases and data validation rules that can directly be applied to your enterprise data.
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Expert Articles
Here you find the best articles published by our experts, that support you in leveraging your information quality.
Data Quality as a Competitive Advantage: Beyond Compliance
Discover how data quality enhances market differentiation, builds customer trust, and drives innovation for strategic decision-making.
[ ... ]How to Get Budget and Executive Buy-In to Improve Data Quality
Stop focusing on technical data quality measures. Learn how to build a compelling business case that unlocks budgets for improvement actions.
[ ... ]The Yin and Yang of Data Quality Metrics: Balancing Accuracy and Completeness
Discover the art of balancing accuracy and completeness in data quality. Boost insights with cutting-edge strategies and automation.
[ ... ]Exploring the Impact of Data Reliability on Decision-Making Processes
Discover how the reliability of data can shape and influence the decisions we make in life.
[ ... ]Business Agility with BiG EVAL’s Integration Capabilities for Data Quality
This article explores how BiG EVAL’s integration capabilities can help modern organizations achieve greater business agility by streamlining data validation and testing workflows, automating subsequent actions, and improving collaboration and communication between teams.
[ ... ]Why Automatic Data Cleaning Is Like Putting Out a Fire With Gasoline
Learn why correcting and cleaning data at its source is better than using automatic tools to make your data trustworthy and reliable.
[ ... ]How Your Business Will Benefit from Moving Your Data Warehouse to the Cloud
What are the advantages of migrating a data warehouse to the cloud? Learn how automated data validation tools ensure the quality of your migration.
[ ... ]Is a SQL Minus Query helpful for efficient data validation?
Learn about SQL Minus Queries in data validation. Are they efficient? Are they reliable? Do they make sense in your specific scenario?
[ ... ]Strategizing How to Improve Data Quality in Business
Strategizing How to Improve Data Quality in Business Technical measures when strategizing how to improve data quality in business can seem very attractive, yet their end result may be undesirable. The world has advanced in every possible way. As a result, businesses have evolved as well. Henceforth, their owners and workers have too taken a step [ … ]
Does Your AI/ML Model Learn From Unchecked Information? A Vital Yet Intriguing Question!
Do Your AI/ML Models Learn From Unchecked Information? A Vital Yet Intriguing Question!What happens if you don’t give data quality the emphasis it deserves when employing it in artificial intelligence learning algorithms? Data quality is of outmost importance in the world of AI/ML. This is because the reliance of an AI model on data it gets [ … ]
Good Housekeeping: Using Data Validation Tools for Data Cleanup
Good Housekeeping: Using Data Validation Tools for Data CleanupWhether you are working with individual files and databases or huge data warehouses that handle information from multiple sources, cleaning up your data supported by automated data validation tools is paramount. What Is Data Cleansing?Data cleansing, also known as data cleaning, data cleanup, or data scrubbing, is [ … ]
Data Catalog: What It Is and Why You Need One
Data Catalog: What It Is and Why You Need One According to a report by IDC, the global datasphere is predicted to grow from 33 zettabytes in 2018 to 175 Zettabytes in 2025. What this means is that companies now have access to enormous data spanning multiple departments and sources. However, companies still face two main challenges [ … ]
How to build an operational data quality process
How to build an operational data quality processIn a recent article, we outlined ‘How to create a DataOps process for Data Quality and Data Testing’.Our goal with that article was to share an architectural framework and process for DataOps within a typical data analytics and warehousing environment where data quality and testing were critical.In this article [ … ]
Build or Buy a Data Validation Testing Solution? Make the Right Decision
Should you buy a data quality automation solution or build your own? This article discusses the pros and cons and shows what has to be considered.
[ ... ]How to Create a DataOps Process for Data Testing with Data Quality Software
Take a look at DataOps and see how to operationalize Data Quality and Data Testing in a complex Data Warehouse environment.
[ ... ]Data Validation: What, How, Why?
Data Validation: What, How, Why?Have you ever wanted a way to ensure the data you collect is accurate, qualitative, and healthy? If you have, you’re not alone. Fortunately, data validation testing already provides a solution to the problem. Studies show that many enterprises base their decisions on outdated, uncorroborated data, which has negatively impacted their [ … ]
