II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev - SAP, Ireland)

Predictive Analytics and the Big Data ChallengeAndrei Grigoriev, MBA, MScSr. Director, Custom Development EMEASAP

Nice, April 2014

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What is Predictive Analytics

Predictive analytics is about analyzing known facts and making predictions about unknown events.

Analyzing – algorithms

Known facts – (big) data

Unknown events – either in the past or in the future


Arrow of Time

Past

Cause

Future

Effect

Entropy (randomness)

increases

Observer

Known facts can be both in the past and future


Exabyte

Big Data – Will Be Just Data Soon

Big Data is about managing and analyzing large volumes of various types of data with great velocit y

Petabyte Age


Big Data – Are There Limits?

In the context of this paper: information about as many relevant events as possible with the highest possible resolution (granularity)

I believe there is no theoretical limit, i.e. indefinite data resolution is possible. Will we have enough energy to deal with that – that is not in the context of this paper.


Practical Considerations

We only need to predict with a reasonable accuracy – i.e. a good prediction means we always gain something. For example, price of shares.

What if everyone is able to predict?

Example: two people betting, both predict same results, no gain but commission is paid – resources drained, either betting will stop or hyper inflation will happen.

Not sure what effect it will have on the financial system but there are areas where benefits are clear – Life Sciences.


Hardware and Software Innovations

• Technology that allows the processing of massive quantities of real-time data in the main memory of the server to provide immediate results from analyses and transactions.

HW Technology Innovations

� Multi-core architecture

� Massive parallel scaling

� Cheap, commodity servers

� Huge data throughput performance

� Dramatic decline in price/performance

Software Technology Innovations

Row and column store

Compression

Partitioning & parallelization

No aggregate tables

� Column = Fast queries

� 5 – 30x ratio

� Analyze large data sets

� Complex computations

� Parallel processing

� Flexible modeling

� No data duplication


Life Sciences – Challenge

Creating better drugs and treatment is becoming more of a mathematical and engineering challenge.

Next generation sequencing is making genome data commodity but a lot more innovation should happen in algorithms and high performance computing to make the most of that.

We need to get results faster but we also need to be able to ask deeper and broader questions, look at many more scenarios before making a decision and analyze data from multiple sources.

We need to make it affordable.


Increased Data Value

Value of Information

DrugPathwayA molecular pathway is a

signaling cascade in a cell

with proteins as key

components

Compound designed to cure diseases

GENOMICS

PROTEOMICS

METABOLOMICS

Today 3500 known

diseases caused by DNA

changes (expected to be

7000)

TRANSCRIPTOMICS


Genome Sequencing

Annotation and Analysis

Raw DNA

Reads

Mapped

Genome

Discovered

Variants

Follow-up and

Validation

Patient

Samples

Sequencing Alignment Variant Calling

Sequencing Service/Lab

e.g. Biologist

Computational Pipeline

e.g. Bioinformatician

Computational Analysis

e.g. Clinicians AND Researchers

� Sequencing – hours

� Alignment and variant calling – weeks

� Find positions of reads within a reference genome� Find statistically relevant deviations from reference genome� Search for known and unknown patterns within reconstructed

genome


Big Data in Life Sciences

Research & Development

Planning Procure-ment

Storage & DeliveryProduction

Quality Assurance

Sales & Marketing

Analysis of nextgeneration

sequencing data

Global batch tracebility

Real-time complaint and sales reporting

Analysis of LIMS and recipe data

Predictive analytics

High Throughput Screening

Analysis of patents and documents

Margin Simulation with Raw Material

Prices

Sales & Operations Planning

Drug serialization

Real-time Analysis of

process engineering

data Social Media Analytics

Customer Segmen-tation Acceleration

Predictive Customer

Segmen-tation

© 2013 SAP AG or an SAP affiliate company. All rights reserved.

Thank you

Contact information:

Andrei Grigoriev, MBA, MScSr. Director, Custom Development EMEA

1A Waterside CityWest Business CampusDublin 24 Ireland

[email protected]

Backup


Abstract

Predictive analytics is about analyzing known facts to make predictions about unknown events.

What if we knew absolutely everything that ever happened and every bit of that data was available instantaneously – would that enable us to make more accurate predictions?

With examples from Life Sciences and Genes Expressions research this presentation explores the impact of Big Data and In-Memory technologies on analytics and discuses the possibility of a new reality where accurate predictions is an affordable commodity available to everyone – how will that change our world?


Biography

Andrei is Senior Director at SAP with expertise in big data, in-memory computing and analytics. He has over 16 years of diverse international career in development, product management and organizational leadership.

Andrei frequently speaks at industry events and conferences on big data, business intelligence, analytics. He hosts annual SAP Life Sciences Innovations Forums. He lectured and presented at leading universities in the UK and Ireland.

Andrei holds MBA and MSc degrees from University College Dublin.


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II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev - SAP, Ireland)

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