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The Big Trends in Big Data презентация
Содержание
- 2. Agenda Big Data Directions Using Big Data
- 3. Big Data Directions
- 4. Discuss “IT no longer supports your business strategy — it is your business strategy”
- 5. The World Has Turned Upside-Down Transient, flexible Permanent, fixed OPERATIONS ANALYTICS
- 6. Analytics Moves to the Core
- 7. Information Becomes a Profit Center Real-time, highly
- 8. What Is Big Data? The Google Summary …
- 9. Big Data Is Not Only About “Big”
- 10. Process data Human data Machine data Big Data Adds New Data Opportunities
- 11. Big Data is “Signal” Data
- 12. Descriptive: What happened? Diagnostic: Why did
- 13. Companies Don’t Use Most of Their Data
- 14. Transactions Are Still a Big Part of
- 15. Big Data Is Heading for the “Trough
- 16. Benefits from Big Data Initiatives # 5
- 17. Hadoop and Other “NoSQL” Technology Enterprise “Data Lakes” and “Data Hubs”
- 18. Hadoop is Complementary, Not a Replacement Source: Gartner
- 19. A Typical Example of DW and Hadoop Integration
- 20. OLTP + OLAP = HTAP “Hybrid transaction/analytical
- 21. With HTAP, the Operational Schema Looks Like
- 22. Data Warehouse HTAP Hadoop Big Data
- 23. Metadata abstraction Increasingly automated Learning algorithms Content
- 24. Opportunity Areas for Innovation Big Data initiatives
- 25. Using Big Data to Improve the Customer Experience
- 26. 80% of CEOs think they deliver
- 27. Personalized Service
- 28. Simplifying Systems
- 29. Real-Time Retail Insights
- 30. Social Data
- 31. Unstructured Data “The improved information flow allows
- 32. New Products and Services
- 33. Network Analysis Churn model accuracy improved by 47% with social
- 34. Sharing Data with Customers
- 36. Using Big Data to Empower Employees
- 37. Worldwide, Only 13% of Employees Are Engaged
- 38. Empowering Individual Performance Adapting to the analytics needs of your employees
- 39. “Self-Service” Analytics
- 40. Analytics Collaboration
- 41. Collaborative Analytics
- 42. Using Big Data to Optimize Resource Use
- 43. Unilever “if we knew then
- 44. Nope
- 45. Textile Rubber & Chemical Company
- 46. Textile Rubber & Chemical Company Primary Goal:
- 47. Big Data Process Mining
- 48. Wearable devices have grown by 2x
- 49. The “Datafication” of Daily Life
- 50. Unexpected Uses of Existing Data Source: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
- 51. Data, Data, Everywhere
- 52. Sensors Allow Tracking of the Previously Untrackable
- 53. Sensors + Cloud + Mobile + Analytics
- 54. Sensors + Cloud + Mobile + Analytics (cont.)
- 55. Networked Crane Safety
- 56. Crane Safety
- 57. Sensors + Analytics + Predictive Maintenance
- 58. Making It Easier to Add Sensors
- 59. Using Big Data for Business Networks
- 60. Networked economy: the next economic revolution
- 61. Information Ecosystems
- 62. Business Networks Are Becoming Information Networks Ariba
- 63. The SAP Big Data Strategy
- 64. SAP Big Data Architecture Developer/Designer Data Analyst/Scientist
- 65. Three Core Areas of Big Data Strategy
- 66. Data Ingestion\ Acquisition
- 67. Front-End Tools Adapted to Different Needs
- 68. Big Data Applications — E.g., Risk, Sensing, …
- 69. Design Thinking
- 70. Wrap-Up
- 71. 7 Key Points to Take Home
- 72. Thank you Timo Elliott, SAP timo.elliott@sap.com Twitter: @timoelliott Blog: timoelliott.com
Слайд 2Agenda
Big Data Directions
Using Big Data to Improve The Customer Experience
Using Big
Using Big Data to Optimize Resource Use
Using Big Data for Business Networks
Wrap-up
Слайд 7Information Becomes a Profit Center
Real-time, highly personalized
Business Ownership
Product ? Customer Experience
Iterative,
Слайд 9Big Data Is Not Only About “Big” Data
“My analytics are becoming
Source: Paradigm4 data scientist survey 2014
www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf
Слайд 12
Descriptive:
What happened?
Diagnostic:
Why did it happen?
Predictive:
What will happen?
Prescriptive:
How can we make it
Hindsight
Insight
Foresight
Predictive Reaches Maturity
Слайд 13Companies Don’t Use Most of Their Data Today
Source: Forrsights Strategy Spotlight:
Слайд 14Transactions Are Still a Big Part of Big Data
“Which types of
Source: Paradigm4 data scientist survey 2014
www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf
Слайд 15Big Data Is Heading for the “Trough of Disillusionment”
Source: Gartner, August
Слайд 16Benefits from Big Data Initiatives
# 5 Identified new product opportunities (6%)
#4
#3 Improved operational efficiency (11%)
#2 Identified new business opportunities (31%)
#1 “DON’T KNOW” (51%)
Source: Information Difference Research Study Dec 2013: “Big Data Revealed” http://helpit.com/us/industry_articles/big_data_revealed.pdf
Слайд 20OLTP + OLAP = HTAP
“Hybrid transaction/analytical processing will empower application leaders
Gartner, 2014
Source: Gartner 2014, “Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation”
HTAP = Hybrid transaction/analytical processing
A single system for both OLTP (operational) and OLAP (analytical) processing. Data is stored once, in-memory, and so instantly available for analytics.
Слайд 21With HTAP, the Operational Schema Looks Like a DW
SAP HANA
SAP HANA
Customer Service
Risk Management Team
Finance and Operations
Account Administration
Executive Management
Customers
Channel
Suppliers
Accounting
Forecasting
Inventory
Products
Pricing
Planning
Слайд 22Data Warehouse
HTAP
Hadoop
Big Data Architecture Directions: Short Term
Where does data arrive?
When
Where does modeling happen?
What can users do themselves?
What governance is required?
Слайд 23Metadata abstraction
Increasingly automated
Learning algorithms
Content & Process Included
Data Warehouse
HTAP
Hadoop
Big Data Architecture
Where does data arrive?
When does it need to move?
Where does modeling happen?
What can users do themselves?
What governance is required?
Integrated Data “System” (cloud & on-premise)
Слайд 24Opportunity Areas for Innovation
Big Data initiatives are typically in one of
Hyper-personalize
Customer Experience
Plan & optimize
Resources in
Real Time
Engage & empower
Workforce of the Future
Harness the intelligence of
Networked Economy
Слайд 26
80% of CEOs think they deliver a superior customer experience
Source: The
– but only 8% of customers agree.
Слайд 31Unstructured Data
“The improved information flow allows Medtronic to address product performance
Слайд 37Worldwide, Only 13% of Employees Are Engaged at Work
Source:
Gallup State
Слайд 43Unilever
“if we knew then what we know now, we would have
Marc Béchet, VP Global IT ERP, Unilever
Слайд 46Textile Rubber & Chemical Company
Primary Goal: Simplification
One source for all data
SAP BusinessObjects and SAP Suite on HANA considered “better fit” than BW
Live in four weeks. 65 SAP users today, growing to 150
Speed increases “were just a bonus”, but real-time KPIs a big hit
"With HANA Live, in 5 mins we could see more information than we could in the last 7 months”
“Administration of HANA is minimal at this point — it’s really been a pressure to work with it”
”Users just felt speed increase: 4x faster is the slowest we've seen compared to ASE”
Recently implemented SAP Suite on HANA
Слайд 48
Wearable devices have grown by 2x month over month
since October 2012.
Source:
Photo: Intel Free Press
Слайд 50Unexpected Uses of Existing Data
Source: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
Слайд 53Sensors + Cloud + Mobile + Analytics
1. Install flow sensors on
2. The sensors beam data to box plugged into the internet
3. Data sent to HANA in the cloud
4. Mobile interfaces to analyze consumption
http://weissbeerger.com/
Слайд 60
Networked economy: the next economic revolution
All figures are in Trillions; 1990
Слайд 62Business Networks Are Becoming Information Networks
Ariba Network
More than 1M suppliers in
Transact with suppliers – The Network handles over $460 billion per year in commerce
Reduce supply costs – Customers save a combined total of $82M daily
Слайд 66
Data Ingestion\ Acquisition
Smart Data Access
Transfer Datasets
SAP IQ
Web / Sensor
Call
Center
Other
Data Sources
SAP
SAP Data Services
SAP SQL Anywhere
SAP ESP
Hadoop Adapter
Hadoop Hive
SAP ERP
BW
Hortonworks Data Platform
Intel Distribution for Hadoop
Partner Hadoop Distributions
The SAP HANA Platform and Hadoop
Слайд 67Front-End Tools Adapted to Different Needs
PREDICT
Advanced Analytics
ENGAGE
Enterprise BI
VISUALIZE
Agile Visualizations
Слайд 717 Key Points to Take Home
Big Data is a huge
Get closer to your customers through better insight and hyper-personalization
Use “datafication” to make better use of resources
Empower your employees to make better decisions
Leverage your business networks
Big data is the heart of your next IT platform — simplicity and flexibility are essential
The biggest barriers are ideas and culture — use design thinking to help
