Qlik (NASDAQ: QLIK) is a leader in visual analytics. Its platform-based approach is designed to meet customers’ growing needs from reporting and self-service data discovery, to centrally deployed guided analytics and embedded analytics. The company says approximately 36,000 customers worldwide rely on Qlik solutions to gain meaning out of information from varied sources, exploring the hidden connections within their data that lead to insights that ignite good ideas. According to the company, what sets Qlik apart is the associative model that empowers everyone in an organization to see the whole story that lives within their data, simply.
Qlik Sense Cloud Services Training Support in Doha Qatar
Data is a critical resource for every organization. We depend on data every day to keep records, produce reports, deliver information, monitor performance, make decisions, and much more. The data resource is on par with financial and human resources as a core component of doing business, yet data management practices are often quite casual. Data governance brings the same level of discipline to data management as is typical when managing financial and human resources.
Building a data governance program is a complex process that focuses people, processes, policies, rules, and regulations to achieve specific goals for a managed data resource. Successful and effective data governance depends on clear goals and well executed activities that match governance practices to your organization’s needs, capabilities, and culture. A continuously evolving program is necessary to keep pace with trends such as cloud services, big data, and agile development. This course provides fundamental understanding of data governance concepts and techniques that is essential to start a new governance program or evolve an existing program.
You Will Learn
- Definitions and dimensions of data governance
- Key considerations and challenges in building a data governance program
- The practices, roles, skills, and disciplines essential to data governance
- The qualities that make good data stewards and stewardship organizations
- The processes of developing, executing, and sustaining data governance
- Activities, issues, and options when building a data governance program
- How maturity models are applied for data governance
- The importance of adapting data governance for trends such as big data, cloud services, and agile development methods
Data quality and data governance professionals; BI/DW managers, architects, designers, and developers; data stewards, data architects, and data administrators; anyone with a role in data governance or data quality management
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Best practices for adopting secure, governed self-service analytics
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