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.

For related information about this topic, see Choosing the Right Data Model.

The data model is used to:

  • Connect effectively and securely to multiple data sources
  • Handle advanced permission management, including data security rules and sharing management
  • Enhance the original data in several ways - including custom tables, custom columns, custom import queries, custom code, and calendar manipulation (for example, 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 designer business requirements can be optimized and enhanced through data model optimizations.

To determine the best data strategy it is important to consider:

  • The OEM customer data management user cases and requirements, (described below).

  • OEM architecture and how it relates to data security. See Sisense Multitenancy and OEM Architecture for more information.

OEM Customer Data Management

OEM customers need to analyze their customer's data to determine the type of data model they should use.

The common use cases are:

One model for all customers, where the data is separated logically:

  • The data is mixed in the source, and in the data model.

  • Data security rules are applied to restrict the data shown to each customer

  • The OEM builds the data model and dashboards. The data model is usually large and complex.

  • The customers are only viewers.

  • It can be an ElastiCube or Live model, depending on the data volume.

  • Regulations may prohibit use of this model.

One model per customer, where the data is separated physically by the OEM:

  • The OEM separates the data by customer in the source via the schema

  • Each customer has their own data model with the same schema.

  • The OEM builds the data model and dashboards. Dashboard maintenance is easy for the OEM as changes are made once and applied to all customer data models.

  • The OEM customers are dashboards designers and viewers.

  • It can be an ElastiCube or Live model, depending on the data volume.

One model per customer, where the data is stored by the customer:

  • The data source belongs to the OEM's customer

  • Each customer has its own data model with the same schema

  • The OEM builds the data model and dashboards.

  • The OEM customers are dashboards designers and viewers.

  • Live models are used.

  • Strictest regulations may require this model.