austrade presentation - big data the new oil (microsoft draft)
TRANSCRIPT
Is the New
We need to find it, Extract it, Refine it, Distribute it and use it to drive Economic Prosperity Dr Andrew Seit
Healthy dose of Scepticism is always good in the mix.
Is the New
Or
Insight & Action
Wisdom
Knowledge
Information
Data
Valu
e of D
ata
Insight & Action
Wisdom
Knowledge
Information
Data
Valu
e o
f Data
Big Data
Insight & Action
Wisdom
Knowledge
Information
Data
Valu
e o
f Data
Big Data
Agenda
#1. Impact and Opportunity Big Data has created.
#2. Big Data Value Creation in Enterprise & Consumer
#3. Value Capture in Big Data for Government
Impact and Opportunity of Big Data
Start of the Revolution
Source: Mckinsey
Attack of The Exponential Age (Hockey Stick)
Attack of The Exponential Age (Hockey Stick)
Delta Cost has decrease:
• Storage• Computation• Mobile• Cloud (New Models)• SaaS, PaaS, IaaS
• Do More with Less …
The Hype Curve for Innovation
The internet of Things
Wearables
Australian burn 1,800 calories per day
These Data has gone from being highly macro …
… to very personal
Nick burns 1982 calories per day
.…to very Geospatially localized & Situational Aware
Big Data Just got Bigger …Recognised any of these Social Network?
Big Data Just got Bigger …Recognised any of these Social Network?
Big Data awareness goes all the way to the top:
Big Data awareness goes all the way to the top:
• Big Data Opens up new possibilities
• Privacy and securities needs to be revisited
• Talents and knowledge is short in supply
Key Questions C-Level asked most
Are we getting left behind by our competitors?
Can we use technology-enabled trends to change the
game?
What can we learn from other industries?
What is a good place to start in building this BD
technology position?
How can we sustain a pipeline of opportunities
over time?
How can our CompanyWin
With Technology?2
34
5
1
The ease of capturing big data’s value , and the magnitude of its potential, vary across sectors.
Source: Mckinsey
All Top Tier Consultants have their “Big Data Gigs”
When Big Data meets Analytics
Values&
Insights
AnalyticsAttributionAlgorithm
3A’s
VolumeVelocityVarietyVeracity
4V’s
From Data to Analytics to Result
Analytics
DeploymentCapture & ETL
Use case for Big Data
3.
4.
5.
1.2.
Real-time Common Operating Picture & Analytics
NSW RFS Common Operating Picture
Insurance (Flood, Fire, Hail, Earthquake)
Value Creation of Big Data in Enterprise & ConsumerVisualization of Business Outcome
Search and Recommendation is about to get a whole lot smarter ….
SocialVoiceMobile Search MAP (GIS)
Relevant Relevant
Profile, PreferencePersonalizationRecommendationsocial BehaviourTargeted Ads, Geospatial
SEARCH & DiscoveryFoundation Capabilities for Big Data
Matching Engine
Search And Discovery (Big Data scale)
Search And Discovery (Big Data scale)
• When Big Data Meet Machine Learning
= Deep Learning
• Google = Google Brain $$$$$
• Microsoft = Microsoft ML $$$$
• IBM = Watson (7 years) $$$$$$$
• LinkedIn = Image Machine learning $$
• Facebook = Deepface AI $$$
• DARPA = NLP (mixed languages) $$$$$
Deep Learning: Intelligence from Big Data
Augmented Intelligence (Watson) Computing
Augmented Intelligence (Watson) Computing
Visualization of Export Opportunities?
Visualization of Historical & Cultural Data
Visualisation & Interaction of Economic Complexity
Summary1. Exponential Growth in Data Created, Collected and Stored.
X10 increase in “Situation Awareness” with Big Data. Rethink intuitional approaches to decision process.
2. BD is not a “Silver Bullet” but a new philosophy of problem solving. It is not just a technology answer but a holistic approach we should be seeking.
3. Private sector and Enterprise are attacking this “Fog-of-Data” with “Evidence-Based Decisions” , increasing both the tempo and speed of decisions.
4. Big Data will have Top-line and bottom-line implications for companies and far reaching effects on the economy.
Key Issues need to be addressed
• Privacy concerns
• Data security issues
• Intellectual ownership and liability issues
Big Data
Policies
• Deployment of Technologies
• Legacy system or inconsistent data formats
• Ongoing innovation (Collaborative & Open)
Technology and Technique
• Access to “Foreign” data
• Integrating with own proprietary data
• It’s Social, IOT, mobile, Cloud,
Data Available
(Real-time)
• Talent Shortage, Stop looking start Grooming
• Leadership that understands Big Data
• Align workflows and Incentives
Organization Change and
Talent
Conclusion/Next steps
1. Big Data is the New Oil. Secret-sauce is in the extraction process
2. From Data to Wisdom, that journey is gain through learning, trying and experimenting.
3. Big Data offers New possibilities and enrichment of current opportunities while creating new ones.
4. Start Now, Start Small, Iterate Fast (not to lose sight on the Privacy and security issues)
(Democratization of tools, data, technology)
