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BI Trends

Platform for Self-Service Analytics

By Alvin Wong | June 27, 2014
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The digital age has revolutionized the concept of self-service in all aspects of our lives. As consumers, we fully embrace the do-it-yourself mentality when it comes to making purchases online, searching online forums for advice, and self-checkout at the supermarket. In much the same way, business intelligence users now demand that information flows freely in the workplace enabling them to make informed decisions without asking IT to fulfill every request for information.

The Continuum of Self-Service

Successfully delivering self-service analytics is much more than just implementing a data discovery tool and letting users run free with their data. What users want out of self-service depends on how they prefer to work with data and how inquisitive they are. As a result, self-service BI dashboard capabilities span a continuum: on one end, information consumers interact with pre-formatted displays you create for them, in order to track established metrics and answer expected questions; business analysts, on the other end, interact directly with the data you give them access to, in order to discover new insights on their own and answer questions as they arise.

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  • Interactive Reports and Dashboards – pre-formatted sets of tables and charts with pre-configured filters and drill downs; users can choose visualizations and lay them out on the dashboard.
  • Data Analysis and Dashboard Personalization – the ability to slice and dice a given set of data, and make new calculations; users create new tables and charts to be added their dashboard.
  • Querying and Analysis – from a pre-configured metadata layer that helps users understand the data, users pick the data set of interest for analysis; users then create new tables and charts to be incorporated into reports and dashboards.
  • Report and Dashboard Authoring – users create reports and dashboards that are published within the application and laid out for export or printing.
  • Visual Data Discovery – visualizations are automatically recommended based on the data users select for analysis, freeing users to explore their data to discover insights; data is automatically profiled eliminating the need to configure a metadata layer.

Users will be compelled to move between different types of capabilities to understand and explore the data. For example, users might start from a pre-formatted report or dashboard, drill into a deeper analysis of the underlying data, create a new visualization highlighting a new performance metric, and then publish that new insight back into a dashboard for the benefit of all users. With Logi Analytics, you deliver a complete range of self-service capabilities and engage users in the entire lifecycle of consuming information, analyzing data, and sharing insights.

Delivering Self-Service While Maintaining Control

Empowering users with greater freedom to use data on their own does not mean losing control of the data. Regardless of how self-service capabilities are delivered, whether it is in the users’ preferred applications or via mobile, you should maintain centralized control in the following ways.

  • Secure Data Access – Grant individuals access only to the data they need. Integration with existing authentication systems makes it easier for users to access the self-service capabilities.
  • Auditing – Maintain records of who has accessed what information. Not only does this ensure proper data security, it also acts as a way of monitoring usage and identifying areas for improving self-service capabilities.
  • Scalability – Make self-service available to all those who need and want it. If successful, user adoption will spread across the organization, and the time users spend engaged in self-service activities may increase beyond expectations.

Self-service analytics is a must-have for businesses that strive to run high-performance, data-driven organizations. Logi Analytics empowers business users with intuitive self-service capabilities and IT with the platform to rapidly deliver self-service in a secure and scalable way.

 

About the Author

Alvin Wong has an extensive background in solution architecture and implementation of SaaS and business intelligence applications. Alvin earned his MS in Engineering Management from Stanford University and BS in Electrical Engineering from Cornell University.

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