Ilker Tasdemir, MDS ap, Business Analytics Director, presentation

1. SAP FORUM ANKARA
Dijital Dönüşüm
Verinin Merkezine Seyahat
Konuşmacı Adı : İlker Taşdemir
Firma Adı : İş Analitik Çözümleri Grup Direktörü – MDS ap
© 2017 SAP AG or an SAP affiliate company. All rights reserved.

2. 2
Eastern
Europe
Middle East
Africa
 49+ years of Experience in IT (Since 1967)
 4500+ Employees in 30 countries across 3 continents
 150+ companies unified under the group
 100+ top resellers awards from global IT Leaders
 A 4 billion USD Leader offering stability & high Integrity
in Technology & Solutions
 SAP Partner Centre of Excellence
MDS AP Tech Overview
a MIDIS Group Company
➢ Over 24 Years of in depth experiences helping customers Manage, Integrate,
Analyze and Mobilize Business Mission critical Data across the enterprise;
Exceptional track record providing Turnkey IT Solutions across Turkey, Middle
East & Europe.
➢ A Unique Partnership with SAP; Implementing Excellence; Optimizing
Application Management
➢ Strategic long term partnerships with our customers; Focusing on Customer
Satisfaction and Technology Innovation
➢ Help customers better use their data assets to improve business performance
and make smarter decisions

3. 3
Data Management Enterprise
Performance
Management
Business Analytics Enterprise
Architecture
Omnichannel and
Cloud Solutions
• OLTP & Real Time Database
• High Performance Analytic
database
• Data Archiving
• Data Replication
• Data Connectivity
• Security & Auditing
• Disaster Recovery
• Big Data
• Strategy Management
• Budgeting and Planning
• Financial Consolidation
• Profitability and Cost
Management
• Disclosure management
• IFRS9 Reporting
• Operational & Analytical
Reporting
• Executive Dashboards
• Predictive Analysis
• Data Discovery &
Visualization
• Enterprise Data
Warehousing
• Data Modelling
• Complex Event Processing
• Data Quality Management
• Master Data Management
• Meta Data Management
• Enterprise Integration
• Analytical LOB Apps
• Data Modelling
• Business Process Modelling
• Zachman framework
• TOGAF methodology
• e-Commerce
• e-Marketing
• CRM
• HRMS
• e-Banking
• Mobile Banking
• Omnichannel Solutions
MDS AP Tech
Practices Overview

4. 4
4
MDS AP with the best of breed SAP Business Analytics Platform
provides complete Agile Visualization & Advanced Analytics
Solutions that optimize Any Data Variety, regardless of its
structure, at Real-Time Velocity, to deliver next generation analytics
Our Mission is to provide high quality service to our strategic
customers by delivering world class solution offerings with high
integrity, commitment and professionalism.
Our Vision is to become the leading partner that brings value
to our customer base.
Our Differentiators

5. 5
Rich Ecosystem
Over 25 Partners

6. 6
Customer Base
Over 400 Enterprise Customers

7. Verinin Merkezine
Seyahat
DATA

8. 8
Verinin Tarihçesi
40,000 BCE
Cave drawings and tally(bone) sticks. These are used to track trading activity and record inventory
2400 BCE
The abacus is developed, and the first libraries are built in Babylonia
300 BCE – 48 AD
The Library of Alexandria is the world’s largest data storage center
100 AD – 200 AD
The Antikythera Mechanism – the first mechanical computer – is developed in Greece
1928
Fritz Pfleumer creates a method of storing data magnetically, which forms basis of modern digital data storage technology.
1970
Relational Database model developed by IBM mathematician Edgar F Codd. The Hierarchal file system allows records to be accessed using a simple index system. This means
anyone can use databases, not just computer scientists.
1991
The birth of the internet. Anyone can now go online and upload their own data, or analyze data uploaded by other people.
2005
Hadoop – an open source Big Data framework now developed by Apache – is developed. The birth of “Web 2.0 – the user-generated web”.
2008
Globally 9.57 zettabytes (9.57 trillion gigabytes) of information is processed by the world’s CPUs.
An estimated 14.7 exabytes of new information is produced this year.
2010
Eric Schmidt, executive chairman of Google, tells a conference that as much data is now being created every two days, as was created from the beginning of human
civilization to the year 2003.
2016
1.3B people on business and social networks today. 1 Yottabyte has been reached and it’s predicted
that by 2020 we’ll reach 6 Yottabytes (6,000 exabytes).

9. 9
Verinin Değişimi
Data Type Range Max Precision
CHAR(n)
CHARACTER(n)
1<=n<=32K-1
1<=n<=32K-1
n/a
n/a
VARCHAR(n)
CHARACTER
VARYING(n)
1<=n<=32K-1
1<=n<=32K-1
n/a
n/a
INTEGER/INT
UNSIGNED INT
-2(^31)<=n<=2(^31)-1
Or –2,147,483,648 and 2147483647
0 TO 4294967294
10
TINYINT 0<=n<=255 3
SMALLINT -2(^15)<=n<=2(^15)-1
Or
-32,768 to 32,767
5
BIGINT(n)
UNSIGNED
BIGINT(n)
-9.2(^18)<=N<=9.2(^18)-1
Or
0 and 1.8(^19)-1
20
FLOAT(precision) Platform-dependant 16
REAL(precision) Platform-dependant 7
DATE Jan 1, 0001 to Dec 31, 9999 n/a
DATETIME
SMALLDATETIME
TIMESTAMP
0001-01-01 00:00:00.000000 to
9999-12-31 23:59:59.999999
n/a
TIME 00:00:00.000000 to
23:59:59.999999
n/a
DECIMAL(p,s)
NUMERIC(p,s)
-1038 to 1038-1 126
DOUBLE 2.22(^-308) to 1.79(^308) 15
BIT 0, 1 n/a
MONEY 19
SMALLMONEY 10
BINARY (length) n/a
UNIQUEIDENTIFIERSTR 36 n/a
VARBINARY (length) n/a

10. 10
Semi/Unstructured Data Grows 7x Faster Than Structured!

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What is Semi/Unstructured Data?
Are these data sets?
“Unstructured” data

12. 12
What is Semi/Unstructured Data?
Are these data sets?
“Unstructured” data

13. 13
Semi/Unstructured sources
Consider:
Web pages, E-mail, news & blog articles, forum postings, and other social media.
Contact-centre notes and transcripts.
Surveys, feedback forms, warranty claims.
And every kind of corporate documents imaginable.
Date: Sun, 13 Mar 2005 19:58:39 -0500
From: Adam L. Buchsbaum <alb@research.att.com>
To: Seth Grimes <grimes@altaplana.com>
Subject: Re: Papers on analysis on streaming data
seth, you should contact divesh srivastava,
divesh@research.att.com
regarding at&t labs data streaming technology.
adam
SURVEY
EMAIL

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Semi/Unstructured Data
Date Time Location Type Officer Report
02/02/2007 15:30 Phoenix Robbery John Smith Officer Smith arrested Pat Fitzgerald near Safeway on Tuesday. He was driving a green
Acura TL. Pat Fitzgerald stole Nintendo wiis on 01/02/2007 from Walmart. He was
transporting the Nintendo wiis to Mike Lewis in Phoenix.

15. 15
Understanding the data is key to success
Sense & Respond Predict & Act
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Generic
Predictive
Analytics
Predictive
Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that
could happen?
CompetitiveAdvantage
Analytics Maturity

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Your mobile device knows more and more about you each day
Where you are
Where you’ve been
Where you are likely to be going to
Who you’ve met
Who you are going to meet
Who you call
Who calls you
Who you TXT-IM-link
All your contacts and their details
All your URLs
All your passwords
What you are doing now
What you’ve done in the past
What music you listen to
What movie you watch
What news/books you read
What apps you use
How much you’ve slept
How much you’ve exercised

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Complex Data and Lightning Action
Traditional BI: “How many negative comments/fraudulent
transactions occurred last week in Istanbul?”
Complex Event Processing: “When 3 negative comments or
suspicious transactions occur in any 5 seconds in Istanbul,
check the comment/transaction and execute the workflow”

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The New ROI: Return on Data
Source: IDC Study: Realizing the Data Dividend, 2014.
The formula
[data + analytics + people ]
@
speed
Key Opportunity
Areas
Organizations can realize
Return on Data in several
key areas…
Productivity
Includes strategic
planning, human
capital management,
IT optimization
Operations
Includes demand
and supply chain
management,
logistics
Return
on Data
$674
billion
Return
on Data
$486
billion
Return
on Data
$158
billion
$235
billion
$1.6 trillion
Return on Data
18
Return
on Data
Customer
Facing
Includes customer
acquisition, retention,
support and pricing
Innovations
Includes service,
research and
development
innovation

19. Veri Teknolojileri

20. 20
Imagine If You Could…
Reduce recall times
from 8 months to 8
minutes
Identify new fraud
patterns in seconds
vs. days
Access and
manage petabytes
of data
Avoid criminal
liability for
improper data use
in regulated
industry scenarios
And save up to
$20M in risk
exposure
And cut potential
fraud costs by
$71M
And reduce
costs by $2M
Ensure data
lineage, access
rights, and security
in Hadoop

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What’s Stopping Us?
The Digital Divide between Enterprise and Big Data
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 21
Too Complex Too Slow Unable to
Work Together
ENTERPRISE BIG DATA

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ENTERPRISE BIG DATA
Bridging the Digital Divide
Introducing
SAP HANA Vora
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 22

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Why SAP HANA Vora?
Bridging the Digital Divide for Analysts, Developers, DBAs, and Data Scientists
Simplify Big Data
Ownership
Democratize Data Access
For data science discovery
Precision Decision Making
In enterprise apps + analytics
Business coherence
On-demand correlation
New insights from
aggregated data
Interactive data
Enrich the candidate
data sets
Simplified landscape
Improved correlation with
historical data

24. 24
SAP HANA Vora
What’s Inside and What Does It Do?
Democratize
Data
Access
Make
Precision
Decisions
Simplify
Big Data
Ownership
SAP HANA Vora is an in-memory query engine which leverages
and extends the Apache Spark execution framework to provide
enriched interactive analytics on Hadoop.
Drill Downs on HDFS
Mashup API Enhancements
Compiled Queries
HANA-Spark Controller
Unified Landscape
Open Programming
Any Hadoop Clusters

25. 25
YARN
HDFS
Enable Precision Decisions
With Contextual Insights In Enterprise Systems
Other Apps
Files Files Files
HANA-Spark Controller for improved
performance between distributed systems
Gain business coherence with business data and big data
Compiled queries enable applications &
data analysis to work more efficiently
across nodes
Familiar OLAP experience on Hadoop
to derive business insights from big data
such as drill-down into HDFS data
Compiled
Queries
Spark
Controller
Drill Downs
SAP HANA in-memory platform
Vora
Spark
Vora
Spark
In-Memory
Store
Application Services
Database Services
Integration Services
Processing Services
SAP HANA Platform
Vora
Spark
HANA Smart Data
Access Spark
Controller

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Democratize Data Access for Data Science Discovery
Extensive programming support for
Scala, python, C, C++, R, and Java allow
data scientists to use their tool of choice,
Pursue new inquiries without compromise on data and
easily integrate these insights with all data
Enable data scientists and developers
who prefer Spark R, Spark ML to mash
up corporate data with Hadoop/Spark
data easily
Optionally, leverage HANA’s multiple
data processing engines for developing
new insights from business and
contextual data.
Mashup
Enhancements
Open
Programming
Optional Use of SAP HANA for
Delegated, multi-engine pre-processing
Spark Data-source
API enhancement
In-Memory
Store
SAP HANA Platform
YARN
HDFSFiles Files Files
Vora
Spark
Vora
Spark
Vora
Spark
Application Services
Database Services
Integration Services
Processing Services

27. 27
Imagine How SAP HANA Vora Can Change Your Business…
Adjust your sales force
and business
resources on the fly
based on real-time info
Understand what
customers are saying
about your marketing
messages
Find fresh opportunities
for product distribution
from detailed and
archived data
And simplify your
big data
management
And work in real
time with access to
all the data
And react quickly
with fresh insights
from the data lake
And beat your
competition to new
markets with
precision targeting
Manage data as a
single system, not a
collection of fragments

28. 28
SAP HANA Vora: Financial Services Use Case
Fraud
Detection
Get access to all your data
including historical and
contextual trends and
current business data
to analyze anomalies
Risk
Mitigation
Be assured of more
precise data to perform
Monte Carlo simulations to
produce distributions of
possible outcome values
with more precise context
Targeted Marketing
Campaigns
React rapidly to customer
sentiment and pinpoint
targeting for sales and
marketing campaigns with a
more complete view of
customer needs and wants
360° Customer
Service
Ensure a more complete
picture of the customer with
analysis of unstructured
customer data, such as social
media profiles, emails, calls,
complaint logs, discussion
forums, and website history

29. 29
SAP HANA Vora: Utilities Use Case
Predictive
Maintenance
Access complete historical
information and optimize
maintenance of critical
components and low tension
devices
Grid Planning
Generate more precise
simulations with access to
more data and forecast
consumption and production
based on weather, load
profiles, and other criteria
Smart Meters
Capture and analyze real-
time data from smart meters
to quickly respond to
emergency situations with
drill-down analysis.
Pipeline Monitoring
Protect pipelines through
continuous monitoring
across the regions.
Perform root-cause
analysis in case failure.

30. 30
SAP HANA Vora: Telecommunications Use Case
Network Capacity
Planning
Access unified data from
multiple sources for a
unified OLAP view and
analyze call detail records
(CDRs) and network load
and plan infrastructure
expansions with greater
precision
Real-time Bandwidth
Allocation
Unify big data with business
data in-memory for real-time
response, and steer traffic
and optimize network quality
of service (QoS) to ensure
high service levels
Cellphone Service
Improvement
Combine ERP, CRM,
billing, and quality data in
one place for real-time
analysis to better manage
equipment placements,
leases, and services,
reduce costs, and increase
customer satisfaction
Targeted Network
Maintenance/Upgrades
Analyze machine data for
precise decisions about system
placement and reveal
opportunities for greater
incremental revenue

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SAP HANA Vora: Healthcare Use Case
360 Patient
View
Gain a more complete
picture of the patient by
bringing together test,
genomic, archive, patient
care, and patient history
data into one unified view
Insurance/Medicare
Fraud Analysis
Search claims data from
many raw sources and
uncover previously hidden
fraud patterns
Managing Recalls &
Adverse Events
Quickly track affected
persons, reduce exposure,
and limit risk by leveraging
combined contextual and
business data to proactively
deliver information in
minutes, not weeks
Diagnosis and
Research
Synthesize data from many
raw sources, in real time,
such as combining patient
data with up-to-date
journal/medical data, and
speed diagnoses and
research efforts

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Why SAP?
Technology designed from the
ground up to work with
distributed data at scale
The leading worldwide provider of
business applications with more than 40
years experience in delivering enterprise-
class applications
Leading-edge platform,
applications and business
network on premise and in the
cloud
SAP Enables
The Digital Enterprise
SAP Knows
Big Data
SAP Delivers
Worldwide Support

33. 33
Try It Today
Access the Cloud Trial >
bit.ly/1K1qLyo
Read More About it >
http://go.sap.com/product/data-
mgmt/hana-vora-hadoop.html
Talk to Your SAP/MDSAP Rep >

34. © 2017 SAP AG or an SAP affiliate company. All rights reserved.
Teşekkürler
İletişim Bilgileri:
İlker Taşdemir
İş Analitik Çözümleri Grup Direktörü – MDS ap
+90 532 5499392
+971 50 7129169
ilker.tasdemir@mdsaptech.com