Choosing a Data Strategy for Embedded Self-Service

Data management is the process of ingesting, storing, organizing, and maintaining the data created and collected by an organization. The data model used:

  • Connects effectively and securely to multiple data sources
  • Handles advanced permission management, including data security rules and sharing management
  • Enhances the original data in several ways - including custom tables, custom columns, custom import queries, custom code, and calendar manipulation (e.g. fiscal year) - to prepare the data for analytics

The Sisense platform enables the customer to create a semantic layer that is integrated into their automatic and manual data pipelines. OEM customers create and manage the data model as part of their data architecture. The data model is fully integrated into the customer's data sources to support advanced analytics in scale and support multiple use cases. OEM customers build and maintain their data model using dimensional modeling knowledge. The model has a direct effect on the dashboard designers' analytical capabilities and the overall dashboard performance. OEM customers invest a lot of effort to achieve an efficient data model to better serve their analytics. Many dashboard designers' business requirements can be optimized and enhanced through data model optimizations. Aspects that must be considered to determine the best data strategy: