In Chip Biz Analytics - Innovation & Disruption Amir Orad, CEO of Sisense Video of this session at the Database Camp conference at the UN is on http://www.Database.Camp

1. In Chip Biz Analytics
Innovation & Disruption
Amir Orad
Sisense CEO
July 2016

2. Quick Background
Inventor/co-founder of Cyber Analytics company Cyota (RSA)
CEO of $200M Financial Crime analytics company Actimize (NICE)
Leading Sisense to Simplify Complex Data Analytics
Columbia University MBA

3. What Do Five Data Geek
Students Dream About

4. BEERS & CHIPS

5. Technology Disruption
Traditional BI
1995
In-Memory BI
2005
In-Chip BI
2015

7. Memory Bandwidth – Data Size vs Speed
Too SlowX50
X10
L1 cache L2 / L3 cache RAM DiskDistance from L1 = slowdown

8. Data Beer
IN ORDER TO
UNDERSTAND
IN-CHIP
ANALYTICS
LET’S ASSUME THAT:

9. Memory bandwidth 2
L1 cache Home fridge Distance Immediate Customer
x1
L2 / l3 cache Shop Distance Bicycle Customer
x10
Ram
Supermarket Distance Car Customer
x50
Disk Brewery Distance Airplane Customer
If data equals beer then data storage units equals

10. Vectorization & Cache Awareness
L1Cache
FirstintoRAM
OP
100
4K
(Values) 100
4K
(Values)
100
4K
(Values)
Result Vector
Push Back To RAM
100
4K
(Values)
SIMD REGISTER
Apply Operation On
4/8 Data Elements
Simultaneously
OP
OP
Column4
100
4K
(Values)
ResultVector
100
4K
(Values)
Column1
100
4K
(Values)
Column2
100
4K
(Values)
Column3
100
4K
(Values)

11. Our Technology In memory columnar
execution mode
CACHE aware query
kernel
CACHE aware
decompression
Instruction recycling &
learning algorithms
LLVM based compiler
with SIMD support
Full 64BIT support
Columnar storage

12. SPEED!
STRATA AWARD
Analyzing 10TB of data In 10 seconds
On a single node on a standard Dell Server

13. Empowering growth, anywhere everywhere, on affordable HW
300% improvement in efficiency and
speed with every new chip Intel
releases vs. 30% industry average
1
3
9
Intel Chip Generation
PerformanceIndex
Sisense
Industry

14. Are We Solving the Real Problem?

15. Breaking an Assembly Line Tradition

17. Need DBA to build database
Define what data will be queried
Join tables upfront
Normalize and create a star schema
Why?

18. Surprising Benefits
Handle complex data faster, cheaper, easier
Boost performance 10X-100X; Cut HW reqs
Eliminate & simplify data preparations
Save precious DBA/IT time
Eliminate manual Join, Index, Star Schema
Fast to deploy; Agile to change
Self service for everyone - Biz & IT
Shrink TCO & time to insight

19. Technology Disruption Results
DW, OLAP
Complex
“Expensive” mash-up
TB Scale
Months
Traditional BI
1995
In-Memory
Simple for Biz
Manual mash-up
GB Scale
Weeks
In-Memory BI
2005
In-Chip
Simpler for Biz & IT
Ad-hoc mash-up
TB Scale
Days
In-Chip BI
2015

20. Sisense Technology Disruption: Single StackTM and In-ChipTM
Single-StackTM
Replace 4 layers with 1 tool
Database, ETL
Analytics, Visualization
For Biz Users, no IT/DBA
NO hodgepodge of tools
In-ChipTM
Patent pending Proprietary tech
In-Memory  In-Chip analytics
NO need to:
Prepare data, schema
Define indexes, joins

21. Analytics Capabilities
PRESCRIPTIVE
How Can We Make it Happen?
PREDICTIVE
What Will Happen?
DIAGNOSTIC
Why Did it Happen?
DESCRIPTIVE
What Happened?
FINANCIALS GROWTH
Compare monthly
financials current to
previous year
ANOMALY DETECTION
Identify anomalies in
attacks resulting in
cyber incidents
MORTALITY RISK
Predict and segment
patients based on risk
of diabetes
RECOMMENDATION
Optimize sales by
recommending
products purchased
Examples

22. A Dream Comes True – 1000+ Clients

23. Lessons Learned
Dream BIG
Refine, refine, refine benefits
Don’t automate, obliterate!
Disrupt, don’t improve

24. Thank You  Demo
Free Trial
www.sisense.com