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Big Data LDN 2018: NAVIGATING COMPLEX DATA MODELS WITH RELATIONAL SEARCH
- 1. Navigating Complex Data Models with Relational Search
- 2. Shaun Leach Senior Systems Engineer ThoughtSpot
- 3. Team Technology Customers Search + Enterprise Search & AI-Driven Analytics Platform Mid-to-large Enterprise Palo Alto, CA Headquarters $306M Funding Magic Quadrant Visionary RELATIONAL SEARCH AUTOMATED INSIGHTS ThoughtSpot Overview
- 4. Agenda 1. Elements of Data Complexity 2. Demo 3. Q&A
- 5. Elements of Data Complexity
- 6. What’s in a data model?
- 7. Common Data Models for Analytics Star Schema Snowflake Schema Galaxy Schema
- 8. What makes data complex?
- 9. How do you simplify complex data? Summary Tables Materialized Views OLAP Cubes Database Indexes Columnar Data Storage
- 10. 4.8 To build one report DAYS 1.2 Humans at Work BILLION 1.5 Data Experts MILLION The need for data experts has created a massive bottleneck
- 11. It’s impossible for experts to anticipate all your questions Products Plans Transaction Date Income Brackets LocationX = Billions of Combinations X X X LG G5 iPhone 6 Samsung Galaxy 10 GB 6 GB 4 GB Yesterday Last Quarter Last week $150 - 300K $75- 100K $100 - 150K New York Houston Boston Unlimited Last Month $300K+ Los Angeles iPhone X
- 12. What would it take for any business user to answer net new questions about data on their own?
- 13. Gartner: Search & AI will change the way you ask questions about data By 2020, 50% of analytical queries will be generated via search, natural language processing or voice, or will be automatically generated. “ Augmented Analytics is the Future of Data and Analytics July 2017
- 14. Scales to Billions of Rows Analyze all your data from multiple data sources with sub-second performance Easy Cross-Source Analysis Easily connect to multiple data sources and analyze across using a shared metadata model Lightning Fast Performance Data automatically indexed in-memory so you can drill anywhere without cubes, hierarchies, or drill paths Company-Wide Governance Granular column and row level access control to a single, shared data model for every group and user Search to Analyze Data Anyone can use search to build reports and dashboards in secondssales product last month california 2014: The World’s first Relational Search Engine
- 15. INSIGHTS AT THE SPEED OF THOUGHT Top jersey age group gender Gross margin top 10 jerseys Top Sales by Store State Top 5 stores west region Sales Product California Last Month Answer the “next question” in seconds
- 16. Any Data Source Embed AnywhereSearch & AI Driven Experience ThoughtSpot: Next Generation Analytics Platform Embedded Relational Search Embedded Charts Embedded Charts Embedded Dashboards
- 18. Problems that other analytics solutions couldn’t solve Fixed Granularity of Data Inflated Results Nov 2017 West 355,000 $5,600,000 Nov 2017 Southwest 240,000 $3,100,000 Nov 2017 Midwest 412,000 $3,400,000 Nov 2017 South 138,000 $4,500,000 Nov 2017 East 445,000 $6,800,000 Monthly Sales & Foot Traffic by Region What were weekly sales and foot traffic in October for New York City? Sales Foot Traffic 1 2 3 1 2 3
- 19. Definition: Chained Chasm Trap … … A data model consisting of two or more pairs of fact tables that have one or more shared dimensions. Fact Fact Fact Dim Dim
- 20. F500 telecom marketers search across chasm trap to analyze campaign performance Use Case: Marketing campaign influence on churn and sales Scale: 75M monthly customer communications, 5TBs of point-of-sale store data Subscriptions Campaign Performance POS Systems Marketing Campaigns Customers gold subscriptions email open rate phone sales 18-29 Apple campaigns
- 21. Q & A
- 22. DON’T BI. JUST SEARCH.