Disclaimer

• 4.5 days
• 2,200 session
• 25,000 people

Focussed on

• Cloud
• Watson

Neglected

• IoT
• Blockchain
• Security

Presentation agenda

• IBM vision
• IBM cloud platform
• Personal takeaways

Keynote Speech

Ginni Rometty

IBM CEO

IBM Cloud

• Enterprise-strong
• Data-first
• Cognitive to the core

Enterprise-strong

Scale (50 data centres in 19 countries)

Largest commercial IoT platform

Public, but designed for industry

Data sovereignty: choice and consistency

• On- or off-premise
• Level of privacy
• Range of services and applications

Support for hybrid solutions

• Data layer
• Application layer

Secure

Comprehensive innovation roadmap

Data-first

Value-adding strategy

• Democratisation (Facebook, Google)
• IP (IBM)

Governance

• Fine-grained access control
  • Public
  • Private
  • Licenced
• Locality
• Isolation

Cognitive to the Core

Range of data types

• Text
• Image
• Voice
• IoT sensors

Range of capabilities

• Analytics
• Machine learning
• Cognitive computing

Prepackaged, curated domain knowledge

IBM Strategic Overview

• Platform: Watson Data Platform (WDP)
• Ecosystem: Watson Data Platform Partners
• Methodology: DataFirst Method

Watson Data Platform "Experiences"

• Data Connect - data engineers
• Watson Analytics - business analysts
• Data Science Experience (DSx) - data scientists
• Bluemix - app developers

DSx Value Proposition

• System administration
• Best practices and staying current
• Collaboration
• Sharing (internally and wider community)
• Documentation
• Governance
• Granular access control (management and security)
• Auditing
• Model deployment and integration
• Scaling

Data Science Experience (DSx)

Ecosystem

Partnering with third-party vendors (mostly open source-based)

DataFirst Method

• Briefing & vision
• Discovery workshop
• Design & validate
• Implement
• Run & Maintain

Takeaways

IBM Weaknesses

• IBM is internally fragmented
• Watson's current capability ranges from weak to mediocre (evaluated against both marketing claims and competitor capabilities)

Cloud is big

API economy is big

Deep learning is big

DevOps is big

Container technology is big

Local industry lags by several years

New generation of core analytics technology is exclusively open source

IBM fully embraces and actively supports and contributes to open source

IBM explicitly supports a mix-and-match approach to using cloud services

IBM's strength is in integration and interoperability

• IBM provides tools
• Business partners
  • Build solutions
  • Market
  • Provide support

Cloud migration is a process rather than an event

IBM advocates and technologically supports hybrid architectures (on-premises cloud)

Hybrid cloud architecture is complex and requires significant and varied skills

Many, especially smaller, businesses are confused, uncertain, and apprehensive about cloud transition

Solutions have to be designed around client needs

Biggest cloud issues for clients

• Security
• Architecture
• Migration strategy

Cloud and cognitive strategy details are very sensitive to details regarding

• Time frames
• Costing

Cloud Strategy

Liz Herbert

from Forrester

fin

tags: miia cape town