Self-Service Analytics

Achieving self-service BI requires much more than simply publishing a comprehensive set of reports and dashboards. To implement a set of effective self-service tools, a comprehensive solution must be architected the enables the following:

A key prerequisite to enabling self-service is providing a single pane of glass through which business users can access all relevant reporting and analytics irrespective of the BI tool or other analytics tool that was used to create the data visualization.

Metric Insights provides a unified BI Portal  that enables the integration of data analytics tools from across the enterprise behind a single uniform access layer.

In order to deploy a true self-service BI environment, end users must have access to the insights gained through machine learning through the same access layer as they use for traditional Business Intelligence solutions. This allows effective decision making by business users who can easily employ the most relevant analytic solution independent of BI tools.

Metric Insights provides the ability to integrate R Shiny and Python applications together with traditional BI tools and custom ad hoc solutions to deliver true self-service Business Intelligence.

Perhaps the most critical functionality that is required to achieve self-service analytics is the ability to certify content in a manner that is consistent across a variety of tools and technologies. With certified content, a business user knows that an analyst is vouching that the correct data sources were used in building the analytics and that the data presented in the visualization is of high data quality. The real-time confidence instilled by the presence of certification provides users with the necessary confidence to make critical decisions using the data irrespective of whether the data is located in a BI Tool, an R Shiny Application, or in traditional tools like Excel. Metric Insights provides the ability to certify content using workflow automation in which team members review and certify content before it is published for broad consumption in the portal. This approach ensures that a visualization that uses new data is reconciled to other certified content and generates trust in published analytics.

Mastering the language of data requires not only access to the correct metrics but the contextual understanding needed to analyze data and draw critical conclusions from the meaningful information. Every organization captures metadata that is critical to effective data management. This can include flagging of PII data, identification of data stewardship for analytics, or key lineage information. This metadata is often collected in separate metadata management tools like Collibra or Alation or resides in custom-built applications or large excel spreadsheets. Metric Insights can import this critical metadata from any source and makes this Metadata available to business users thereby making their search for answers an informed data literate journey.

A major barrier to effective self-service is the fact that the BI tool security model often restricts analytics to a small audience. A user may fail to uncover a relevant visualization simply because she was not previously granted access to that solution. Self-service can only occur if most business analytics are discoverable to users. When performing a critical analysis, Business users must have the ability to search and uncover content that they may not yet have access to and then to initiate a request for this content.

Metric Insights provides the ability to make analytics content discoverable without compromising the enforcement of security around sensitive data. Users without access are shown metadata for discoverable content and a blurry image and they can request access to the visualization. The access requested is then routed to the appropriate analyst for approval.

Self-service analytics can only be achieved in the context of a well-designed data management strategy in which thoughtful data governance is applied to data access across all enterprise data assets. Business users must understand the lineage of data to know the rules that were used in SQL to compute key metrics and to understand key lineage and data sensitivity information. For example, knowing that the data contains personal data that is sourced from the corporate big data environment may inform how the user engages and distributes the data.

Metric Insights enables effective data governance by enabling the tagging of analytics with extended metadata the enables business users to understand the sensitivity, lineage, and classification of the data displayed in any visualization.