• All
  • Cloud
    • Solutions
    • Virtualization
  • Data
    • Analytics
    • Big Data
    • Customer Data Platform
  • Digital
    • Digital Marketing
    • Social Media Marketing
  • Finance
    • Risk & Compliance
  • Human Resources
    • HR Solutions
    • Talent Management
  • IT Infra
    • App Management Solutions
    • Best Practices
    • Datacenter Solutions
    • Infra Solutions
    • Networking
    • Storage
    • Unified Communication
  • Mobility
  • Sales & Marketing
    • Customer Relationship Management
    • Sales Enablement
  • Security
  • Tech
    • Artificial Intelligence
    • Augmented Reality
    • Blockchain
    • Chatbots
    • Internet of Things
    • Machine Learning
    • Virtual Reality
  • All
  • Cloud
    • Solutions
    • Virtualization
  • Data
    • Analytics
    • Big Data
    • Customer Data Platform
  • Digital
    • Digital Marketing
    • Social Media Marketing
  • Finance
    • Cost Management
    • Risk & Compliance
  • Human Resources
    • HR Solutions
    • Talent Management
  • IT Infra
    • App Management Solutions
    • Best Practices
    • Datacenter Solutions
    • Infra Solutions
    • Networking
    • Storage
    • Unified Communication
  • Mobility
  • Sales & Marketing
    • Customer Relationship Management
    • Sales Enablement
  • Security
  • Tech
    • Artificial Intelligence
    • Augmented Reality
    • Blockchain
    • Chatbots
    • Internet of Things
    • Machine Learning
    • Virtual Reality
BI And Data Warehouse Testing: Identifying Data Integrity Issues At Every DWH Phase

BI And Data Warehouse Testing: Identifying Data Integrity Issues At Every DWH Phase

Tricentis
Published by: Research Desk Released: Jan 19, 2021

Data integrity can be compromised at all DWH/BI phases: when data is created, integrated, moved, or transformed. However, testing of data warehouses is usually deferred until late in the cycle. If testing is shortchanged (e.g., due to schedule overruns or limited resource availability), there’s a high risk that critical data integrity issues may slip through the verification efforts. Even if thorough testing is performed, it’s difficult and costly to address any data integrity issues exposed by this late-cycle testing.

Download this paper to explore strategies and best practices for catching data integrity issues as early as possible—to reduce the resources required to address them and ensure that data integrity issues don’t compromise the accuracy of the data that business leaders rely on. You’ll learn ways to identify quality issues in:

  • Data warehouse design
  • Data sources
  • Each ETL phase
  • Staging and loads to the data warehouse
  • BI reports

Welcome Dear

Thank you for your interest and your registration with Teradata. Please confirm your e-mail address to complete your registration by clicking here

Yes, confirm my

By confirming this, you give Teradata your consent to send you information on our data analytics products and services or invitations to events and webinars by e-mail from time to time. You can revoke this consent at any time by clicking on the unsubscribe link at the bottom of each of our e-mails. We assure you that we treat your contact details with the utmost care. Detailed information on how we store and use your personal data or how you can exercise your rights regarding your personal data can be found in the global Teradata Privacy Policy.