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1© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Conquer Dark Data by Unleasing the Powerof SAP Analytics
Natasa Mastellou, Business Intelligence Solution Advisor, SAP Hellas, Cyprus & Malta
2© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Smart Cities
Smart EquipmentSmart Logistics
Smart Houses
Smart Vending
Connected Cars
Smart Automobile
IoT is everywhere
3© 2014 SAP SE or an SAP affiliate company. All rights reserved.
:-)Brand Sentiment
360O Customer View
ProductRecommendation
Propensity to Churn Real-time Demand/Supply Forecast
Predictive Maintenance
Fraud Detection
Network Optimization Insider Threats
Risk Mitigation,Real-time
Asset Tracking Personalized Care
Turn new signals into business value
Maximize Business & IT Benefits
Optimize Organizational Performance
Increase Sales & Improve Customer Service
4© 2014 SAP SE or an SAP affiliate company. All rights reserved.
From raw data to decision making
CollectData
Analysis
translate
extract ’noise’
IdentifyValuable
Information
EnhanceValuable
Information
DecisionMaking
5© 2014 SAP SE or an SAP affiliate company. All rights reserved.
of businesses embracing the IoTuse some form of analytics on their
harvested data.
18%
Nucleus Research, Gartner, Fortune Magazine
Use AnalyticsToday
NeedAnalytics by 2020
Ability to manage and consume all data is getting harder
Not utilizing all the information out
there
Not leveraging the power of collective insight
Missing new insights
IT is not agile enough and the business wants to get involved
=
10%
75%
Source: Competing on Analytics, Thomas Davenport
Low Performers
High Performers
77%
40%
36%
65%
33%
23%
8%
23%
Have significant decision-support/analytical capabilities
Value Analytical insights to a very large extent
Have above average analytical capability within industry
Use analytics across their entire organization
High performing companies are 50% more likely to use analytic information strategically
Competitive Edge is gained with Analytics
8© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Analytics will define the difference between the losers and winners going forward
9© 2014 SAP SE or an SAP affiliate company. All rights reserved.
RawData
CleanedData
Standard Reports
Ad Hoc Reports &
OLAP
Agile Visualization
PredictiveModeling
Optimization
What happened?
Why did it happen?
What will happen?
What isthe best that
could happen?
Use
rE
ng
ag
em
en
t
Maturity of Analytics Capabilities
SelfService BI
GenericPredictive Analysis
Co
llec
tive
Insi
gh
t
The evolution of BI
Agile Visualization
Advanced Analytics
Big Data
Mobile
Collaboration
Cloud
Enterprise Business Intelligence
Collective Insight requires a broad perspective
11© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Challenges for business users today
70% of effort in analytics
is preparing data for analysis
40% of IT managers required
more than two daysto prepare financial data for monthly reporting
25% of LoB end users indicate
that faster access to information would
have a significant impact on their organization
12© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Applications Analytics
La
nd
sc
ap
e m
an
ag
em
en
t & o
rch
estra
tion
HANA In MemoryM
od
elin
g &
lifec
yc
le m
an
ag
em
en
t
HADOOP
SAP IQ
Ro
les
, se
cu
rity, g
ov
ern
an
ce
, co
mp
lian
ce
, au
dits
TransactionalPlanning & Simulation
Graph Analytical
SAP Industry & LoB Apps
Partner & Custom Apps
Spatial
ESP
Extended Storage (IQ)
Tiered Storage (Hot-warm-cold)
Smart Data Access
Text, Social Media Processing
VisualizationsPredictiveMobile
Replication Framework
Data Services IM / MDW
Business Intelligence
SAP HANA Platform for Big Data
13© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Cicso UCS: The perfect fit for SAP HANA
© 2014 IDC, Document #250419
ROI Summary for Cisco UCS as a Platform for SAP HANA and Other SAP Mission-Critical Applications
Key Performance Improvements Realized from Customers Who Deployed Cisco UCS
14© 2014 SAP SE or an SAP affiliate company. All rights reserved.
HANA Tables & SMART DATA Access
Information Models
SAP HANA
Direct access/Universe
Other AppsLocationsReal-timeHADOOPMachineUnstructuredTransaction
Analytics on SAP HANA
15© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Challenges for business users today
70% of effort in analytics
is preparing data for analysis
40% of IT managers required
more than two daysto prepare financial data for monthly reporting
25% of LoB end users indicate
that faster access to information would
have a significant impact on their organization
16© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Pre-definedBusiness Intelligence content
No data movement =
Zero data latency=
Instant reporting & analysis on live, non-summarised operational data =
Game-changer
Lower TCO, through
collapsing the data management & BI layers (remove
daily ETL batch data loads, OLAP data stores, aggregations etc.)
Challenges can become benefits
17© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Traditional BI: “How many fraudulent credit card transactions occurred last week in Madrid?”
1 2 3 4 5 6 7 8 9
time
Complex Event Processing: “When three credit card authorizations for the same card occur in any five secondswindow, deny the requests and check for fraud.”
Why do I really need SAP HANA?
18© 2014 SAP SE or an SAP affiliate company. All rights reserved.
CANCERPATIENTSRECEIVE TREATMENT OPTIMIZEDTO THEIR DNA
BIG DATA GIVES SHOPPERSFASHIONADVICE TOFIT THEIR STYLE
100% ACCURACY THAT A SIGNAL IS POSITIVE AT 97% CONFIDENCE
21© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Demo
Predictive Maintenance
22© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Enable the business
Agile & IntegratedLandscape
Utilise the resources you
have
Future Proof,
Value for Money
Simple & Stable
Landscape
Key Takeaways
23© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Thank you
Natasa Mastellou, Business Intelligence Solution Advisor, SAP Hellas, Cyprus & Malta
