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METADATA DRIVEN OLIVIER APPLICATIONS AND POWERFUL DATA INTEGRATION Olivier Express or A Olivier Technical Brief Document October 2012 qlikview.com Introduction This technical brief highlights a subset
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How to fill out metadata driven qlikview applications

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To fill out metadata driven QlikView applications, you can follow these steps:

01
Begin by understanding the purpose of the metadata driven QlikView application. This involves identifying the specific business requirements and objectives that the application aims to address.
02
Gather the necessary metadata. This includes collecting information about the data sources, such as databases, spreadsheets, or APIs, that will be used to populate the application. Additionally, collect details about the data fields, their data types, and any relationships or hierarchies between them.
03
Define the structure of the application. This involves organizing the data fields into dimensions and measures, which will form the basis for analysis and visualization in QlikView. Create hierarchies and associations between the dimensions to provide users with a richer understanding of the data.
04
Populate the metadata into the QlikView application. Enter the collected metadata information into the appropriate fields and properties within QlikView, ensuring that it accurately reflects the intended structure and relationships.
05
Validate and test the metadata driven QlikView application. Thoroughly review the data mappings and associations to ensure they are correct. Test the application with sample data to verify that the expected insights and visualizations are being generated accurately.
06
Continuously maintain and update the metadata driven QlikView application. As the data sources or business requirements change, make the necessary updates to the metadata, dimensions, and associations to ensure the application remains up-to-date and useful.
Regarding who needs metadata driven QlikView applications, they can benefit a wide range of individuals and organizations.
01
Business Analysts: Metadata driven QlikView applications provide analysts with a powerful tool for exploring, analyzing, and presenting data in a structured and intuitive manner. They can leverage the application to gain valuable insights, identify trends, and make data-driven decisions.
02
Executives and Managers: Executives and managers can utilize metadata driven QlikView applications to monitor key performance indicators, track business metrics, and gain a holistic view of their organization's performance. The applications can help them identify areas that require attention, understand the impact of strategic decisions, and drive data-informed strategies.
03
Data Stewards and IT Professionals: Data stewards and IT professionals can benefit from metadata driven QlikView applications as they facilitate data governance, data quality management, and data lineage tracking. These applications provide a centralized platform for managing and governing data assets, ensuring data consistency, and establishing data lineage for compliance and auditing purposes.
In conclusion, filling out metadata driven QlikView applications requires a systematic approach, including understanding the purpose, gathering metadata, defining the structure, populating the data, and ensuring continuous maintenance. Such applications can be valuable for business analysts, executives, managers, as well as data stewards and IT professionals.
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Metadata driven QlikView applications are applications where the data and structure are determined by metadata, allowing for dynamic reporting and analysis.
Organizations or individuals who use QlikView for data analysis and reporting may be required to file metadata driven applications.
To fill out metadata driven QlikView applications, users need to input the required metadata information into the application's interface.
The purpose of metadata driven QlikView applications is to provide a flexible and dynamic solution for data analysis and reporting.
The information reported on metadata driven QlikView applications typically includes data sources, data models, and metadata definitions.
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