The Big Trends in Big Data

Понравилась презентация – покажи это...

Слайд 0

The Big Trends in Big Data Timo Elliott, Global Innovation Evangelist, SAP @timoelliott

Слайд 1

Agenda Big Data Directions Using Big Data to Improve The Customer Experience Using Big Data to Empower Employees Using Big Data to Optimize Resource Use Using Big Data for Business Networks Wrap-up

Слайд 2

Big Data Directions

Слайд 3

Discuss “IT no longer supports your business strategy — it is your business strategy”

Слайд 4

The World Has Turned Upside-Down Transient, flexible Permanent, fixed OPERATIONS ANALYTICS

Слайд 5

Analytics Moves to the Core

Слайд 6

Information Becomes a Profit Center Real-time, highly personalized Business Ownership Product ? Customer Experience Iterative, ever-changing

Слайд 7

What Is Big Data? The Google Summary …

Слайд 8

Big Data Is Not Only About “Big” Data “My analytics are becoming more difficult because of the variety and types of data sources (not just the volume)” Source: Paradigm4 data scientist survey 2014 www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf

Слайд 9

Process data Human data Machine data Big Data Adds New Data Opportunities

Слайд 10

Big Data is “Signal” Data

Слайд 11

Descriptive: What happened? Diagnostic: Why did it happen? Predictive: What will happen? Prescriptive: How can we make it happen? Hindsight Insight Foresight Predictive Reaches Maturity

Слайд 12

Companies Don’t Use Most of Their Data Today Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012. Base: 634 business intelligence users and planners

Слайд 13

Transactions Are Still a Big Part of Big Data “Which types of data do you anticipate using in the next year?” Source: Paradigm4 data scientist survey 2014 www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf

Слайд 14

Big Data Is Heading for the “Trough of Disillusionment” Source: Gartner, August 2014, www.gartner.com/newsroom/id/2819918

Слайд 15

Benefits from Big Data Initiatives # 5 Identified new product opportunities (6%) #4 More reliable decision making (9%) #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 16

Hadoop and Other “NoSQL” Technology Enterprise “Data Lakes” and “Data Hubs”

Слайд 17

Hadoop is Complementary, Not a Replacement Source: Gartner

Слайд 18

A Typical Example of DW and Hadoop Integration

Слайд 19

OLTP + OLAP = HTAP “Hybrid transaction/analytical processing will empower application leaders to innovate via greater situation awareness and improved business agility. This will entail an upheaval in the established architectures, technologies and skills driven by use of in-memory computing technologies as enablers.” 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 20

With HTAP, the Operational Schema Looks Like a DW SAP HANA SAP HANA Live (Virtual Data Model) Customer Service Risk Management Team Finance and Operations Account Administration Executive Management Customers Channel Suppliers Accounting Forecasting Inventory Products Pricing Planning

Слайд 21

Data Warehouse HTAP Hadoop Big Data Architecture Directions: Short Term Where does data arrive? When does it need to move? Where does modeling happen? What can users do themselves? What governance is required?

Слайд 22

Metadata abstraction Increasingly automated Learning algorithms Content & Process Included Data Warehouse HTAP Hadoop Big Data Architecture Directions: Long Term 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 23

Opportunity Areas for Innovation Big Data initiatives are typically in one of the following areas: Hyper-personalize Customer Experience Plan & optimize Resources in Real Time Engage & empower Workforce of the Future Harness the intelligence of Networked Economy

Слайд 24

Using Big Data to Improve the Customer Experience

Слайд 25

80% of CEOs think they deliver a superior customer experience Source: The New Yorker – but only 8% of customers agree.

Слайд 26

Personalized Service

Слайд 27

Simplifying Systems

Слайд 28

Real-Time Retail Insights

Слайд 29

Social Data

Слайд 30

Unstructured Data “The improved information flow allows Medtronic to address product performance issues efficiently, accurately, and effectively and to detect trends at an earlier stage.”

Слайд 31

New Products and Services

Слайд 32

Network Analysis Churn model accuracy improved by 47% with social

Слайд 33

Sharing Data with Customers

Слайд 34

Слайд 35

Using Big Data to Empower Employees

Слайд 36

Worldwide, Only 13% of Employees Are Engaged at Work Source: Gallup State of the Global Workplace Report 2013

Слайд 37

Empowering Individual Performance Adapting to the analytics needs of your employees

Слайд 38

“Self-Service” Analytics

Слайд 39

Analytics Collaboration

Слайд 40

Collaborative Analytics

Слайд 41

Using Big Data to Optimize Resource Use

Слайд 42

Unilever “if we knew then what we know now, we would have started deploying SAP HANA much earlier, because it’s so important for business... We think it’s even more disruptive than we initially thought — we’ve only just started”  Marc Bechet, VP Global IT ERP, Unilever

Слайд 43


Слайд 44

Textile Rubber & Chemical Company 500 Employees, 4 internal IT staff Business Suite on HANA

Слайд 45

Textile Rubber & Chemical Company Primary Goal: Simplification One source for all data within SAP, no separate transactional and reporting databases, less infrastructure to maintain, no need to learn BW and ETL solutions 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 46

Big Data Process Mining

Слайд 47

Wearable devices have grown by 2x month over month since October 2012. Source: Mary Meeker’s Internet Trends, 2013 Photo: Intel Free Press

Слайд 48

The “Datafication” of Daily Life

Слайд 49

Unexpected Uses of Existing Data Source: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/

Слайд 50

Data, Data, Everywhere

Слайд 51

Sensors Allow Tracking of the Previously Untrackable

Слайд 52

Sensors + Cloud + Mobile + Analytics 1. Install flow sensors on your beer lines 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 53

Sensors + Cloud + Mobile + Analytics (cont.)

Слайд 54

Networked Crane Safety

Слайд 55

Crane Safety

Слайд 56

Sensors + Analytics + Predictive Maintenance

Слайд 57

Making It Easier to Add Sensors

Слайд 58

Using Big Data for Business Networks

Слайд 59

Networked economy: the next economic revolution All figures are in Trillions; 1990 international dollars; Source: Department of Economics, UC Berkeley, BAIN 8 MacroTrends Brief.

Слайд 60

Information Ecosystems 60

Слайд 61

Business Networks Are Becoming Information Networks Ariba Network More than 1M suppliers in more than 190 countries around the world   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 62

The SAP Big Data Strategy

Слайд 63

SAP Big Data Architecture Developer/Designer Data Analyst/Scientist

Слайд 64

Three Core Areas of Big Data Strategy

Слайд 65

Data Ingestion\ Acquisition Smart Data Access Transfer Datasets SAP IQ Web / Sensor Call Center Other Data Sources SAP SLT / Rep Server 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 66

Front-End Tools Adapted to Different Needs PREDICT Advanced Analytics ENGAGE Enterprise BI VISUALIZE Agile Visualizations

Слайд 67

Big Data Applications — E.g., Risk, Sensing, …

Слайд 68

Design Thinking

Слайд 69


Слайд 70

7 Key Points to Take Home Big Data is a huge opportunity 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

Слайд 71

Thank you Timo Elliott, SAP timo.elliott@sap.com Twitter: @timoelliott Blog: timoelliott.com

Слайд 72