1. Predictive Analytics and
the Big Data Challenge
Andrei Grigoriev, MBA, MSc
Sr. Director, Custom Development EMEA
SAP
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

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Arrow of Time
Past
Cause
Future
Effect
Entropy
(randomness)
increases
Observer
Known facts can be both in the past and future

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Exabyte
Big Data – Will Be Just Data Soon
Big Data is about managing and analyzing large
volumes of various types of data with great velocity
Petabyte
Age

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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.

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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.

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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

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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.

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Increased Data Value
Value of Information
DrugPathway
A 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

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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

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Big Data in Life Sciences
Research &
Development
Planning
Procure-
ment
Storage &
Delivery
Production
Quality
Assurance
Sales &
Marketing
Analysis of next
generation
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

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Thank you
Contact information:
Andrei Grigoriev, MBA, MSc
Sr. Director, Custom Development EMEA
1A Waterside CityWest Business Campus
Dublin 24 Ireland
andrei.grigoriev@sap.com

13. Backup

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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?

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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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