Self-Service BI
What it means, why it matters, and best practices. This guide provides definitions and practical advice to help you understand and practice modern self-service business intelligence.
Self-Service BI Guide
What is Self-Service BI?
Self-service BI empowers business users to explore data and generate insights as well as to create dashboards or reports, without relying on IT, data scientists or analysts. The premise of self-service business intelligence is to give all employees access to insights that will help them make better decisions, regardless of analytics skills.
Why Self-Service Business Intelligence Matters
As the pace of business accelerates, business users need faster, easier access to data that will help them keep up with, and stay ahead of the changing landscape.
With traditional business intelligence, users who wanted data insights had to submit a request to IT, wait for their turn in the queue, and go back and forth answering clarifying questions — all before they could get answers. Because the process took so long, many times the insights came too late.
Plus, many people don’t use data when making business decisions because they feel like their skills are lacking. Relying solely on intuition results in errors and suboptimal decision making that businesses cannot afford in this day and age.
If organizations want to build a truly data-driven culture, they need to do a better job of streamlining access to data and making easy-to-use analytics applications readily available. Increasing employee data literacy through training and optimized business processes is just as critical.
Benefits of Self-Service BI
- Faster Insights: When business users can access data from any source at any time, without relying on IT, the insights flow faster, and decisions can be made without delays. Freed up from defining and communicating business requirements to other teams, users can more efficiently gain insights, make faster decisions, and stay one step ahead of the competition.
- Deeper Discovery: Business users shouldn’t be restricted to linear, query-based analysis. Instead, they should have the freedom to explore relevant data any which way they like. An Associative Engine lets users uncover blind spots in data that would otherwise go unnoticed with query-based tools. Analytics are recalculated after each click while highlighting associated and unrelated values so users always have context to guide their analysis and discover unexpected insights.
- Increased Data Literacy: Data literacy is the ability to read, work with, analyze and argue with data. One-third of employees believe that data literacy will help them be more productive, while 22 percent believe it can help them reduce stress.1 Self-service BI tools can help organizations develop a data literate culture by giving all employees the opportunity to learn the language of analytics, easily visualize and explore data and advance their analytical skills.
- Less IT Burden: With traditional business intelligence solutions, IT teams spend their time clarifying business requirements and preparing reports that are surely outdated by the time they’re delivered to data consumers. A self-service solution gives users the tools to build dashboards and reports to conduct analysis on their own. That means IT has more time for high-value work such as tending to data governance, ensuring data is clean and consistent, and prioritizing mission-critical application development.
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Examples of self-service BI
- Freeform exploration and discovery: The most effective data analytics technologies facilitate free-form exploration and discovery with intuitive capabilities like natural language search, conversational interaction, and interactive selections. This empowers users of all skill levels to ask any question and quickly zero in on relevant information to get an answer or instantly shift in a new direction when they discover something that sparks their curiosity.
- AI-powered insight suggestions: Self-service BI tools leverage AI and machine learning to automatically surface insights, helping users get value from their data faster, increasing data literacy. By auto-generating and prioritizing contextually relevant charts and visualizations, users can quickly identify and explore emerging trends and outliers to make important new discoveries.
- Embedded analytics in daily workflows: Employees are far more productive when they can access relevant data and discover insights, right where they work, everyday, no matter which application they’re using. The best self-service BI technologies support embedding relevant charts, tables and dashboards into applications such as CRM, ERP and other operational systems, so everyone has immediate access to analytical insights whenever they need it.
Self-Service BI Best Practices
- Governed access: BI tools that enable self-service let IT govern data quality while freeing business users to easily access and analyze that data. Governed libraries of data, measures, and dimensions let everyone work from a single source of the truth.
- Data integration: An organization’s data typically sits in multiple systems. But an analytics platform with a native integration layer can bring it all together, allowing IT to quickly integrate new data sources quickly, eliminating backlogs.
- Collaboration: Business users should have access to pre-built, governed data models, visualizations, and analytical worksheets that allow them to easily share data. Users should also be able to add their own content to apps for use by others.
- On-demand apps, dashboards and reports: An effective self-service analytics solution allows users to quickly create and share analytics apps, dashboards and reports. It also makes it easy to integrate data analytics assets into other apps and websites.
- Anytime, anywhere access: Self-service analytics solutions should support touch-enabled, responsive and full featured mobile interaction offering a seamless user experience across devices so a path to discovery and insight is always available.