database camp 2016 @ united nations, nyc - amir orad, ceo, sisense

In Chip Biz AnalyticsInnovation & Disruption

Amir Orad Sisense CEOJuly 2016

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

What Do Five Data Geek Students Dream About

BEERS & CHIPS

Technology Disruption

Traditional BI

1995

In-Memory BI

2005

In-Chip BI

2015

Memory Bandwidth – Data Size vs Speed

Too SlowX50

X10

L1 cache L2 / L3 cache RAM DiskDistance from L1 = slowdown

Data Beer

IN ORDER TO UNDERSTAND IN-CHIP ANALYTICS

LET’S ASSUME THAT:

Memory bandwidth 2

L1 cache Home fridge Distance Immediate Customer

x1

L2 / l3 cache Shop Distance Bicycle Customer

x10

RamSupermarket Distance Car Customer

x50

Disk Brewery Distance Airplane Customer

If data equals beer then data storage units equals

Vectorization & Cache AwarenessL1

Cac

he

First

into

RAM

OP1004K

(Values) 1004K

(Values)1004K

(Values)

Result Vector

Push Back To RAM

1004K

(Values)

SIMD REGISTERApply Operation On 4/8 Data Elements Simultaneously

OPOP

Colu

mn

4

1004K

(Values)

Resu

lt Ve

ctor

1004K

(Values)

Colu

mn

1

1004K

(Values)

Colu

mn

21004K

(Values)

Colu

mn

3

1004K

(Values)

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

SPEED!STRATA AWARD

Analyzing 10TB of data In 10 seconds

On a single node on a standard Dell Server

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

Perfo

rman

ce In

dex

Sisense

Industry

Are We Solving the Real Problem?

Breaking an Assembly Line Tradition

Need DBA to build database

Define what data will be queried

Join tables upfront

Normalize and create a star schema

Why?

Surprising BenefitsHandle 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

Technology Disruption Results

DW, OLAPComplex“Expensive” mash-upTB ScaleMonths

Traditional BI

1995

In-MemorySimple for BizManual mash-upGB ScaleWeeks

In-Memory BI

2005

In-ChipSimpler for Biz & ITAd-hoc mash-upTB ScaleDays

In-Chip BI

2015

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

Analytics CapabilitiesPRESCRIPTI

VEHow Can We Make it Happen?

PREDICTIVEWhat 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

Exam

ples

A Dream Comes True – 1000+ Clients

Lessons Learned

Dream BIG

Refine, refine, refine benefits

Don’t automate, obliterate!

Disrupt, don’t improve

Thank You Demo

Free Trialwww.sisense.com