What are the key components of a self-service analytics solution?

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:

  • All relevant data visualizations and analytics must be available through a self-service portal that must be well organized and search-enabled.
  • Analytics from across the enterprise, whether they be dashboards using BI Platforms like Tableau or Qlik or analytics solutions built by data scientists using R and Python, must provide business users with a uniform user experience that supports effective data discovery.
  • Certified content must be distinguished from other data analytics so business users know which data analysis can be trusted because it is based on high quality and consistent data sets.
  • Discoverability must be fully enabled and integrated with access request workflows.
  • The self-service analytics solution must be integrated with the enterprise data governance strategy.
Deploying a self-service Portal

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.

Uniform user experience across solutions

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.

Uniform Content Certification

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.

Business Users can limit searches to only retrieve certified content and can see certification details when accessing information in the portal.

Discoverability & Access Request

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.


Data Governance

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.

To learn more about how Metric Insights can help you enable self-service analytics in your enterprise, drill down further into this topic by watching one of our webinars.

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