Conquer Dark Data by Unleasing the Power of SAP Analytics

Transcription

1 Conquer Dark Data by Unleasing the Power of SAP Analytics Natasa Mastellou, Business Intelligence Solution Advisor, SAP Hellas, Cyprus & Malta 1

2 IoT is everywhere Connected Cars Smart Equipment Smart Logistics Smart Automobile Smart Vending Smart Cities Smart Houses 2

3 Turn new signals into business value :-) Brand Sentiment Predictive Maintenance Network Optimization Insider Threats Asset Tracking Personalized Care Product Recommendation Risk Mitigation, Real-time Propensity to Churn Real-time Demand/ Supply Forecast 360 O Customer View Fraud Detection Maximize Business & IT Benefits Optimize Organizational Performance Increase Sales & Improve Customer Service 3

4 From raw data to decision making Collect Data Analysis translate extract noise Identify Valuable Information Enhance Valuable Information Decision Making 4

5 18% of businesses embracing the IoT use some form of analytics on their harvested data. 5

6 10% Use Analytics Today Missing new insights IT is not agile enough and the business wants to get involved 75% Need Analytics by 2020 Not utilizing all the information out there = Ability to manage and consume all data is getting harder Not leveraging the power of collective insight Nucleus Research, Gartner, Fortune Magazine

7 Competitive Edge is gained with Analytics High performing companies are 50% more likely to use analytic information strategically Have significant decisionsupport/analytical capabilities 23% 65% High Performers Low Performers Value Analytical insights to a very large extent 8% 36% Have above average analytical capability within industry 33% 77% Use analytics across their entire organization 23% 40% Source: Competing on Analytics, Thomas Davenport

8 Analytics will define the difference between the losers and winners going forward 8

9 User Engagement Collective Insight The evolution of BI Optimization Predictive Modeling What is the best that could happen? Agile Visualization Generic Predictive Analysis Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Self Service BI Why did it happen? What will happen? What happened? Maturity of Analytics Capabilities 9

10 Collective Insight requires a broad perspective Cloud Mobile Agile Visualization Advanced Analytics Big Data Enterprise Business Intelligence Collaboration

11 Challenges for business users today 40% of IT managers required more than two days to 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 70% of effort in analytics is preparing data for analysis 11

12 SAP HANA Platform for Big Data Applications SAP Industry & LoB Apps HANA In Memory Planning & Simulation Graph Spatial Partner & Custom Apps Transactional Analytical Text, Social Media Processing Extended Storage (IQ) Analytics Mobile Predictive Smart Data Access Tiered Storage (Hot-warm-cold) Visualizations Business Intelligence HADOOP SAP IQ Modeling & lifecycle management Landscape management & orchestration Roles, security, governance, compliance, audits ESP Data Services IM / MDW Replication Framework 12

13 Cicso UCS: The perfect fit for SAP HANA 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 2014 IDC, Document #

14 Analytics on SAP HANA Direct access/ Universe SAP HANA Information Models HANA Tables & SMART DATA Access Transaction Unstructured Machine HADOOP Real-time Locations Other Apps 14

15 Challenges for business users today 40% of IT managers required more than two days to 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 70% of effort in analytics is preparing data for analysis 15

16 Challenges can become benefits Pre-defined Business Intelligence content No data movement = Zero data latency= Instant reporting & analysis on live, nonsummarised operational data = Game-changer Lower TCO, through collapsing the data management & BI layers (remove daily ETL batch data loads, OLAP data stores, aggregations etc.) 16

17 Why do I really need SAP HANA? Traditional BI: How many fraudulent credit card transactions occurred last week in Madrid? time Complex Event Processing: When three credit card authorizations for the same card occur in any five seconds window, deny the requests and check for fraud. 17

18 CANCER PATIENTS RECEIVE TREATMENT OPTIMIZED TO THEIR DNA 18

19 BIG DATA GIVES SHOPPERS FASHION ADVICE TO FIT THEIR STYLE

20 100% ACCURACY THAT A SIGNAL IS POSITIVE AT 97% CONFIDENCE

21 Demo Predictive Maintenance 21

22 Key Takeaways Enable the business Simple & Stable Landscape Agile & Integrated Landscape Future Proof, Value for Money Utilise the resources you have 22

23 Thank you Natasa Mastellou, Business Intelligence Solution Advisor, SAP Hellas, Cyprus & Malta 23