The data in your application has massive value and the need for data-driven insights is increasingly important to your customers. How do you keep your product relevant to your end-user and at the same time maintain competitive advantage? Charles Caldwell, VP of Product Management at LogiAnalytics discusses the key considerations on the impact a robust analytics layer can have on user engagement and on what you need to do to build analytics for the future.

1. Logi Analytics Confidential & Proprietary
Click to edit Master title style
Delivering Product Value Means
Delivering Insights
Charles Caldwell
VP Product Mgt Logi Analytics

2. Logi Analytics Confidential & Proprietary
Software is
eating has
eaten the
world

3. Logi Analytics Confidential & Proprietary
Applications Focus on Specific Problems
In Context Structure the Decision Close to the Action
3

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Analytics essential & apps not meeting expectations
Essential
Remote Work
Meet Expectations
• 82% consider analytics to be significant
• 72% users personally access analytics
• 57% report increase in importance since COVID-19
• 64% expect analytics usage to increase
• 99% of users spend time looking for information
• <50% report user friendly, timely analytics

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5
Not (Just) Reporting
Insight Exploration Recommendation

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Insight
Provide Information at the point of action
Increases velocity of the decision
Question to ask:
What insight will help me know that I’m taking the right
action?
Typical Implementation:
Visualization inline within the application
Requirement Complexity:
Typically, Low

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Exploration
Open ended ”browsing” of information to discover “unknown
unknowns”. Great for exposing new options or
opportunities.
Question to ask:
Are there unexpected relationships in the data?
What data should the user look at?
Typical Implementation:
Self-Service data exploration, ranging from “guided” flows to
“here is some data, go crazy”.
Requirements Complexity:
Typically, High.

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Recommendation
Tell me what I should do next.
Best practice, past practice, or peer groups.
Question to ask:
Where do we know enough to recommend an action with
confidence?
Where do we have domain expertise the end-user does not?
Typical Implementation:
Visual listings, Benchmarking, alerting and notifications.
Requirement Complexity:
Low to Complex

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The Build Journey
Basic
Requirements
Proof of
Concept
Exploration
Recommendations
Scalability
Time
Complexity

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The Build Journey
Basic Requirements
Proof of Concept
Minimum Viable Product
• Basic charts or graphs
• Static visualizations, no interactivity
• Built with UI Components
• Limited in functionality
• Static Analytics Dashboard
• Inline Insights within Existing UX

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The Build Journey
User Adoption
Advanced Analytics
Scalability
• New feature and enhancements
• Ad-hoc requests
• Integration with workflows
• Security, customization, extensibility & APIs
• Cloud deployment with elastic scale
• Leveraging existing technology stack

13. Logi Analytics Confidential & Proprietary 14
Not (Just) Reporting
Insight Exploration Recommendation

14. Logi Analytics Confidential & Proprietary
Click to edit Master title style
Delivering Product Value Means
Delivering Insights