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Potential impact of future technologies 2 21-18


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Presentation given at Premier Concepts CIO Conference, February 2018, Davie, FL

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Potential impact of future technologies 2 21-18

  1. 1. Potential Impact of Future Technologies Jim Rutt, CISSP, CISM, CISA, CGEIT, C|CISO, CRISC, CCSK CIO, The Dana Foundation
  2. 2. Obligatory Background • CIO of the Dana Foundation • President/Chairman of the Board of Technology Affinity Group (TAG) • Advisor to multiple VCs and dozens of startups • VCs: Lightspeed, Work-Bench • Minerva Labs-advanced evasive malware platform • PIXM- Anti PHISHing leveraging Computer Vision • Stealth-Edge Computing • Few others: advanced SIEM, streaming analytics, email inoculation, etc etc • Key focus on practical application and governance of emerging enterprise technology.
  3. 3. Putting a name to the future • Deloitte: “Symphonic Enterprise” • Gartner: “Digital Mesh” • Work-Bench: “Digital Transformation” The Key conveyance is the continuance of CONVERGANCE, or.. Business intimacy as opposed to Business Alignment: “It’s about the synchronicity of action that becomes possible between individuals and groups who are acting from a shared and common perspective and purpose.” Marc Shiller, “The 11 Secrets of Highly Influential IT”
  4. 4. Lots of media hype about the impact of future tech…
  5. 5. Gartner’s Hype Cycle
  6. 6. Dire predictions for the human workforce: • “Humans Need Not Apply” YouTube video claims 45% of currently employed people will be out of a job thanks to AI ( • There will be changes in the workforce, but the more likely scenario: what Deloitte calls “No-Collar Workforce”, where AI-enabled components work with humans in both an augmented and automated fashion.
  7. 7. ……but in terms of enterprise impact, ……it may take some time • We’re still dealing with slightly less stimulating issues…
  8. 8. (Practical) Tech in The Enterprise with (Practical) Future Impact • AI • Cloud Native • Cybersecurity/Quantum Encryption • IoT/Edge Computing • Blockchain/Smart Contracts • Serverless Computing • Smart Dust
  9. 9. Artificial Intelligence Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more
  10. 10. AI right now • Vendor oversaturation similar to late 90’s Dot Com era. • Too many entrants, too many scattershot approaches. • Location of data (to avoid lock-in) biggest concern for enterprises
  11. 11. Present Applications • Chatbots • Cognitive agents • Robotics • Self-Driving Cars/Trucks • Good progress on all fronts here.
  12. 12. Where is AI’s true future impact In the Enterprise? Behind the scenes • In operations, most notably business process re-engineering. • Augmentation of current work processes as well as Automation. • “Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals, recommendations, merchandising placements, fraud detection, translations, and much more”- Jeff Bezos
  13. 13. AI’s True Impact in Enterprise: Powering Systems of Intelligence From Jerry Chen, “The New Moats”
  14. 14. Cloud vs. Systems of Intelligence • Cloud moat =unbundling “capabilities” into individually deployed “microservices” for scale advantage • Systems of intelligence moat =bundling “capabilities” into processes advantage • SCALE ADVANTAGE (broad) vs. PROCESS ADVANTAGE (competitive advantage)
  15. 15. Systems of Intelligence examples • Manufacturing: predictive maintenance on equipment • Insurance: Automation of insurance filing claims • Pharmaceuticals: Optimization of R&D resource allocation for a portfolio of drug candidates in clinical trial • Financial services: Automated customer interactions with chatbots • Process advantage drives Systems of Intelligence
  16. 16. AI/ML: Future Impact • There will be a Github of AI*, as we are already seeing with shared algos and models similar to plain vanilla source code on GitHub. • Vertical impact Drivers: • Finance: Compliance has become the prime mover. AI will augment this discipline • Pharma: Increase in efficiency of predicting successful compounds • Talent Alignment Challenges/Opportunities • Today most talent is oriented towards consumer applications • A path to exploiting the (much larger) opportunities in enterprise will need a solid plan for talent development/realignment. *Work-Bench 2017 Enterprise Almanac
  17. 17. AI/ML: Future Impact Continued • Deep Learning and more exotic forms of AI are great in theory, but difficult to implement in practice due to the intensive parameter tuning and amount of data required to train an algorithm… • New trend: make AI methods that require less data more accessible by adding representation schemes from “traditional” ML
  18. 18. Corporate Attitude on AI • Ambitious attitudes: “AI is a competitive differentiator. We want to own the model, we don’t want Palantir to own it.” • •Smart recruiting tactics: Avoiding talent wars with the webscales by sourcing data scientists in India rather than US, and Masters-levels rather than PhDs. • •…But some skepticism, particularly around deep learning: • “We have lots of existing regression models that are finely tuned. Deep learning is just going to be incremental and more expensive right?”
  19. 19. Biggest challenge: Root cause analysis
  20. 20. Other challenge: Siloed efforts • Line of business vs. CDO/governance oversight • Enterprise data science functions are decentralizing to get more funding/buy-in from across the enterprise. • Most organizations lack culture of collaborative data exchange, and data governance teams slow projects down. • Value will be in tight integration of ML workflows spanning the entire pipeline
  21. 21. CLOUD NATIVE: THE NEXT GENERATION • 2017 is shaping up to be a pivotal year for Fortune 1000 deployments of cloud native infrastructure. • Container orchestration is the ‘VMware’ anchoring the cloud native ecosystem. Exactly who will play this critical role will become clearer this year. • Cloud native is reshaping databases, middleware, big data, developer tools, and business models
  22. 22. Legacy to future impact: Containers are the key • Apps on VM’s • Containers on VM’s • Containers on Bare metal • Containers are lighter — 10’s-100’s of MBs vs. multiple GBs, just the right size for component based microservices • Containers are faster — they can be spun up and down in seconds vs. minutes to realize the true agility, resilience, and portability of cloud computing • Containers are more efficient — you can fit 4-8 times as many app components (or microservices) on a bare metal container server than you can on a VM because of the way containers share OS resources to free up space
  23. 23. Cloud Native Computing Foundation
  24. 24. Microservices • Microservices extends service-oriented architecture by decoupling apps into single-purpose services that communicate with other microservices via APIs or messages. Source: PWC “Agile coding in enterprise IT: Code small and local”
  25. 25. Cloud Native Adoption • Driven by developer demand for containers. • Container Orchestration is the new data center operating system: • AWS: Mesos • Microsoft Azure: Kubernetes, Docker Swarm, and Mesos, • Google :Kubernetes • New development paradigms like functional programming and pipeline-to- serverless fit perfectly towards a cloud native approach.
  26. 26. Future Impact of Cloud Native • Middleware will be disintermediated • Former ESB functions will be taken over by pipelines and code distributed within app containers • Application servers reduced to function level actors • Database Paradigm shifting from single point of non-agility to a disruption of the CAP Theorem.
  27. 27. Serverless Computing • Middleware, once a core layer of the IT stack, is shedding significant weight as middleware functions now reside in distributed code. • Functional pipelines (aka “serverless”) = no more complex event processor • Ephemeral snippets of code govern app data traffic, rendering the need for dedicated services for business rules obsolete.
  28. 28. Streaming: Data and Apps in one stack • STREAMING IS THE NEW COMPLEX EVENT PROCESSING SERVER & ESB • Data and app stacks have been separate until now… Container orchestrators like Kubernetes and Mesos distribute data workloads better than Hadoop’s Yarn. Spark, Kafka, Herron and other new school stream processing engines all integrate directly with container orchestrators
  29. 29. Cybersecurity • SecDevOps blurs the lines between networking and application security as the race for cloud-native security products intensifies. • Beyond the 1%: SOIs as consumable microservices will bring advanced security technology to the 99% of companies who previously couldn’t afford. • Systems of Intelligence (SOI) is where its at.
  30. 30. Cybersecurity’s place in the System Framework
  32. 32. IoT • Distributed analytics will be critical for remote/low-bandwidth industrial IoT operations.**Edge Computing • IoT is potent for competitive advantage amongst industrials like gunpowder was for kingdoms of the 1200s. • Industrial IoT = earlier than most of us think because distributed infrastructure remains in its infancy. • Security for IoT will spawn directly from distributed analytics architectures. • The next frontier is systems management software bridging disparate IoT software systems.
  33. 33. Real value of IoT • IoT = new era of industrial competition • IoT requires highly distributed infrastructure • Industrials are building IoT platforms — highly specialized PaaS not available from traditional cloud giants
  34. 34. Edge Computing and IoT • Compute resources will slowly shift from the cloud to devices
  35. 35. Future Impact: Edge Computing • 3 Key impacts • Compliance localization • Reduction of chatter needed to aggregate data • Bring intelligence to static CDN paradigm
  36. 36. Benefits of edge • Bridges gaps in: • •Networking throughput that render real-time data processing too slow for the cloud • •Compute for local data preprocessing that may be too resource intensive for endpoints • •Intermediary data store for efficient, spoke-hub distribution of sensor data • AWS Greengrass • Governing data flow will be a new analytics architecture as ANALYTICAL TOOLS BUILT FOR THE INTERNET DON’T WORK FOR IOT
  37. 37. Impact • Distributed analytics architectures instrument deeply into endpoints in the gateway, and thus will be the providing data to security solutions focused on device anomaly detection and distributed policy-based prevention.Traditional security vendors talk a big game about IoT but they are going to struggle to get into the industrial space because operators aren’t going to want to instrument connected assets 10 ways like IT does in the data center. • Because of this dynamic, distributed analytics vendors have an opportunity to become security vendors themselves.
  38. 38. Finally IoT • Vertical AI software is highly specialized, and creating a full stack solution tuned to a particular use case often means developing proprietary hardware to obtain data from older, non-IoT enabled physical assets. • These startups intend to convince OEMs to manufacture the devices on their behalf. We believe this wishful thinking because OEMs will not be able to extract enough value from hardware purpose-built to serve even the largest of vertical application markets
  39. 39. Blockchain • Smart Contracts • ICO • Consensus, provenance, immutability, finality
  40. 40. Blockchain-Future Impact • Privacy • Disintermediation of previous trust brokers • Tokenization: trading of assets • Harvard Business Review “The Truth About Blockchain”
  41. 41. Quantum Cryptography Source:
  42. 42. Smart Dust • “But perhaps most mind-bending of all is considering what happens when sensors, antennas, and even computing equipment can be combined into information gathering devices on the microscale. This is a concept known as “smart dust,” ..what will happen if miniaturization continues on its current trajectory—the point at which devices can be scattered to the winds inthe millions, billions, or even trillions to measure the world in breathtaking detail. Big data and the Internet of Things gone wild.”-Jason Dorrier, Singularity University
  43. 43. Computer Vision • Visual and spatial analysis • Paired with AI, powerful future impact
  44. 44. Honorable Mention • Robotic Process Automation (RPA)-”Robobosses”
  45. 45. Thank You!!