Traditional analytics leaves up to 70% of your business data behind. The next generation of analytics must have an “all data accepted” motto.

1. Webinar: Problems with Traditional Analytics
Dark Data

2. Agenda
➔ Introduction
◆ Problems w/Traditional Analytics
● Webinar Series
➔ The Dark Data Problem
➔ Qrvey Solution
➔ Analytics and Dark Data

3. Problems with Traditional Analytics
Dark Data Performance User Adoption

4. ● Unusable
○ >70% of Data not used for Analytics
○ Unstructured and Semi-Structured
○ 3rd Party Applications, Social, Etc.
● Time Wasted
○ 80% spent on Data Preparation
○ Complex Processes
○ Heavy Skill sets
● Incomplete Analysis
○ Unable to get the complete picture
○ Incorrect metrics, predictions, results
Dark Data
Problems with Traditional Analytics

5. Qrvey Solution

6. Machine Learning
Modern ArchitectureAll Data Analyzed
Self Service
Fully Embeddable
All-in-One Platform
Moving Analytics Beyond Visualizations
Self Service
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7. Qrvey Cloud Native Architecture
“If we could start over, we would choose this architecture”
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8. 11

9. Analytics and Dark Data

10. ● Traditional Analytics tools connect to
structured (flat) data:
○ Flat Files (CSV)
○ Databases or similar
○ JDBC, ODBC connections
○ Non-hierarchical tables
Dark Data
What is Dark Data?

11. ● Images
● Video
● Audio
● Text files
● Documents/PDFs
● Hierarchical Data
● Emails
● Social Media
● Data Feeds/APIs
● Messages
● User Forms
● Surveys
Dark Data
What is Dark Data?

12. Data Summary / Profiler - Understand all Data Formats
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13. Advanced Analytics - Image and Text Analysis
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14. Advanced Analytics - Image and Text Analysis
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15. Self-Service Flows (Data & Metric Triggers, Alerts, Actions)
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16. ● Collect Data from Users
● Analyze and Profile
● Automate Actions
● Visualize Dashboards
Dark Data
Use-Case: Automate User Data

17. Problems with Traditional Analytics
Dark Data Performance User Adoption

18. Webinar: Problems with Traditional Analytics
Dark Data