Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Dell hans timmerman v1.1
Similar to Dell hans timmerman v1.1 (20)
More from BigDataExpo
More from BigDataExpo (20)
Recently uploaded
Recently uploaded (20)
Dell hans timmerman v1.1
- 1. 1
- 2. 2 © Copyright 2017 Dell Inc.2 Building a data driven organisation Hans Timmerman CTO Dell EMC The Netherlands Data is zowel de olie als het goud voor een organisatie: • olie als smeermiddel, • goud wanneer die data waarde en bescherming nodig heeft. Die twee tegenstrijdige waarderingen moeten gelijktijdig in een data- architectuur worden geborgd. Net zoals Big Data en Fast Data daarin een plaats moeten krijgen.
- 3. 3 © Copyright 2016 Dell Inc.3 A few words about Dell EMC Hans Timmerman CTO Dell EMC The Netherlands
- 4. 4 Strategically Aligned Businesses Dell Technologies Dell Inc. Client Solutions Group Global ServicesInfrastructure Solutions Group Dell Technologies - World’s largest privately controlled Tech Company 74 B$ revenue – 140.000 team members - 20.000+ patents - $4.5 billion on R&D/year
- 5. A data driven organisation . . . in a data driven world . . . © Copyright 2017 Dell Inc.
- 6. 6 FEEDING 9 BILLION PEOPLE BY 2050
- 7. 7 THE PRECISION FARMING REVOLUTION
- 8. 8 THE NEW OPPORTUNITY… Product DATA About The Product INTENT CONTEXT REAL-TIME
- 9. 9 MULTI-SOURCE DATA NEEDS DELIVERING ACTIONABLE INSIGHT IN REAL-TIME WEATHER FORECASTING SOIL ANALYSIS MACHINE DATA $100 MORE PROFIT/ACRE$43K INCREASE IN ANNUAL PROFIT 20% GREATER YIELD REAL TIME OPERATIONS
- 10. INFRA As a Service Realtime RISK management Realtime Enterprise SERVICES Flash/vSAN/vLANHyper Converged Infrastructures Hyperscale Elastic Cloud Services Software Defined Enterprise-grade Hybrid Cloud Analytics Mobile / Apps Cloud based Agile DevOps Micro services Distributed/compartmented Real time Analytics Intelligence Quick response Transparency REALTIME Organization Collective and Prescriptive Intelligence Become Digital (always on, anywhere, anytime) Drive Change (disrupt instead of being disrupted) Ecosystem & Alliance versatility Realtime Customer Engagement Automated Shared, open, reliable data BUSINESS IMPERATIVE #1
- 11. 11 Back Office Front Office System Centric User/device Centric Systems of Record • IT driven, Robust • Transactional oriented • Data centric • Safe & secure • Structured Systems of Insight • Data Governance • Analytics • Big Data • Business Intelligence Systems of Engagement • Interaction oriented • Contact & collaboration • User experience • Mobile • Unstructured Shared Data Shared, open, reliable data . . . . . . . demand well organized data management BIG DATA SMART DATA FAST DATA SUPPORTED BY HYBRID CLOUD PLATFORMS (as a utility)
- 12. 12 © Copyright 2017 Dell Inc.12 Changing value of data over time Value Time us sec minms dayhr yearmonth decade Value of data sets Value of the collected data sets Big DataFast Data • Processes • Models • Logics • Algoritms • Trends Challenge is combining them Predictive Capability Real Time Analysis on collected Intelligenge
- 13. 13 13 Growth acceleration of the digital universe The essence of Data Gravity © Copyright 2017 Dell Inc. Data and applications are attracted to each other . . . An application eats data
- 14. 14 14 Data gravity with equal data mass Data Application Data and applications are attracted to each other . . . Data is nothing without an application An application is nothing without data © Copyright 2017 Dell Inc.
- 15. 15 15 But when data masses differ . . . (1) Data Typical 2nd platform behaviour Large Applications billion lines of code Small data sets Data Data Data Data Data Data Data Application Application Centric Architectures STRUCTURED Non movable application Application is critical about the data to be presented . .
- 16. 16 But when data masses differ . . . (2) Application App App App App App App App Data Typical 3rd platform behaviour Big Datalakes billion bytes Small applications micro services Data Centric Architectures Non movable data © Copyright 2017 Dell Inc.
- 17. 17 But when data and app shrink. . . (3) ApplicationData Typical 4th platform behaviour Fast streaming data Intelligent micro services movable data © Copyright 2017 Dell Inc. movable services The Internet of Things Application Application
- 18. 18 Dawn of a new era © Copyright 2017 Dell Inc.18
- 19. 19 © Copyright 2017 Dell Inc.19 The cycle of the cloud Central Local Mainframe Closed Source Client Server Cloud Edge Intelligence Open Source Deep learning Machine learning Fast Data Big Data Product (HW + SW) Service Terminal use Smart device Big Applications
- 20. © Copyright 2017 Dell Inc.20 The internet of things Edge intelligence Fog Computing Mist Computing
- 21. 21 © Copyright 2017 Dell Inc.21 7 billion connected people 44 zettabytes of data 30 billion connected devices Scale magnitudes modernized Software driven
- 22. 22 22 IOT: HISTORY & DEVELOPMENT 1969 1966 In just a few decades, computers will be embedded into almost every industrial product. Karl Steinbuch (Cybernetic Engineer) Arpanet 1984 DNS 1991 WWW online 1993 Coffee pot in the University of Cambridge computer lab 1998 Google 1999 Kevin Ashton from MIT uses the term "Internet of Things" for the first time LG announces first online refrigerator 2008 IoT Conference Zurich 2011 IPv6 2005 Arduino Open source platform for microcontrollers Nest Sources: MIT, Gartner, PostScapes 2000 2010 Turing,Tesla,Morse 2014 Industrial Internet Consortium 2017 8.4 Billion Connected “Things” in use worldwide (world population: 7.5 Billion) Gartner EdgeX Foundry vendor-neutral open source project hosted by the Linux Foundation. Building a common open framework for Industrial IoT edge computing. The Industrial Internet Consortium publishes the First industrial internet connectivity framework
- 23. NIST definition*) publicatie 800 – 191 ‘Fog computing is een horizontaal, fysiek of virtueel resourceparadigma dat zich bevindt tussen slimme end-devices en traditionele cloud- of datacenters. Dit paradigma ondersteunt verticaal geïsoleerde en latency-gevoelige applicaties door een alom vertegenwoordigde, schaalbare, gelaagde, gefedereerde en gedistribueerde computing, storage en netwerkconnectiviteit.’ Kort gezegd is fog computing een cloud-gebaseerd-ecosysteem dat slimme end-devices in gekoppelde netwerken kan ondersteunen. Mist computing is een lichte variant en rudimentaire vorm van fog computing. *) NIST (US National Institute for Standards and Technology)
- 24. 24 And at the beginning there was one lake… Centralization As the traditional approach to gain analytical insight from data in disparate sources Batched data gathered in a single repository or cloud in preparation for analysis
- 25. 25 Now a new class of problems emerge . . .
- 26. 26 To one lake, or to multi-lake . . .
- 27. The perfect data i-mobility storm IoT effect Migration to the edge Regulatory compliance Bandwidth contraints Legacy data
- 28. 28 Technologies enabling distributed architectures Machine Learning at the Edge Deep Learning in the Cloud Fast Data Big Data © Copyright 2017 Dell Inc. Smart Data Because the Future is Federated
- 29. 29 © Copyright 2017 Dell Inc.29 Where some common denominators exist Meaningful data sets cross data zones Cloud SDDC SDN Application Array User space Edge In a multi-cloud world
- 30. 30 © Copyright 2017 Dell Inc.30 Computing nodes become virtual nodes in a virtual computing cluster at scale Connecting clusters at the analytics layer WWH World Wide Herd YARN Hadoop framework Virtual Computing Cluster
- 31. 31 Select relevant Datasources (Sensor) source node Datazone (locked) Edge gateway functionality Open source Edge software IoT Fog Metadata datasources repository Data source Datasource Datasource Datasource Datasource Datasource Datasource Analytic needs Push analytic microservices result Retrieve distibuted analytic results IoT node Provides small amount of intelligence or analytics at the edge to provide early (edge) detection capability Challenge of Edge Gateways is to address key Interoperability where “south meets north” in a distributed IoT architecture analytic engine Industrial Clouds Compliance IoT Fog Master Data Management © Copyright 2017 Dell Inc. Datasource
- 32. 32 © Copyright 2016 Dell Inc.32 Joint Data Management, GRC and Analytics Intelligence metadata IaaS PaaS SaaS iPaaS Link to the architecture intelli gence Worldwide analytics admin Master Data Management legal Governance Risk Mgt & Compliance
- 33. 33 © Copyright 2016 Dell Inc.33 World wide, integrated, open sourced iPaaS solutions SaaS SaaS SaaS SaaS iPaaS workbench Boomi admin WWH intelligence Archer legal Meta data Orient Decide Observe Act OODA cycle Saas execution Distributed data environments or zones users IaaS IaaS IaaS IaaS IaaS
- 34. 34 Let the transformation begin . . .
- 35. 35 Twitter: @hansdellemc blog: www.datacentered.nl
Editor's Notes
- Dell Technologies.
- @Presenter: The need for unconventional thiing when creating and defining the computational models of tomorrow, that Can embrace big data, That can be conducive to iot and That can be friendly to unprecedent scale abilities….
- @Presenter: The need for unconventional thiing when creating and defining the computational models of tomorrow, that Can embrace big data, That can be conducive to iot and That can be friendly to unprecedent scale abilities….
- Let’s keep going with this analogy. By the time we get to 2050, the UN predicts 9 Billion people by 2050. That is 9 billion people that need to be fed. If you look at the analysis and our current practices of farming the land, we see that we are incapable of feeding this many people. How are things going to change? <CLICK>
- This is a human challenge and a data challenge. What we’re going to see is a precision farming revolution. This is not your father’s farm equipment. There is a myriad of devices piping information both to and from the equipment The don’t just take information from external sources about where to put the crops and how to plant, they also sense the ground: how fertile is it, how has it been watered, what is the terrain. This information is being sent back, and used to improve the planting plan for next year, driving a higher yield. <CLICK>
- <CLICK> What if we could take this to the next level? What if we could take context and intent and do that in real time? The context of the situation: planting seeds The intent: drive the highest yield But instead of driving the plan from last year’s information, what if we could dynamically do it in real-time That’s where we are headed: a trend of what we like to call “catching in the act” Can we catch the tractor “in the act” of planting a seed and use data to alter the planting plan? But can equally apply to other use cases Can I catch a consumer in the act of doing something, and direct them or cross sell them to another product? Can I catch somebody malicious in the act doing something fraudulent? This idea of determining context, and intent, and then changing action based on large amounts of data in real-time, is where we are headed. <CLICK>
- So we see this in the world of agriculture. People taking advanced weather forecasting, historical yields, satellite imagery, soil analysis – combining that and delivering a real-time feed to plant equipment so that it can more efficiently farm the land. This is happening now, it will be happening everywhere by the time we get to 2050, and that’s how we are going to feed those 9 billion people. What does it deliver in real terms? Not just a science project, but tangible business results <CLICK> 20% greater yield <CLICK> $100 more profit per acre to the farmer, which means that the average U.S. farmer can not only help the world feed the exploding population, <CLICK> but also generate an additional $43,000 in annual profit. And we see companies like John Deere transforming from selling products to selling services, transforming how they make money. The product, the tractor, becomes a platform for a new business as a service model where they sell services and insurance, instead of products. This is the promise of big data and what we’re here to talk about. <CLICK>
- @Presenter: The need for unconventional thiing when creating and defining the computational models of tomorrow, that Can embrace big data, That can be conducive to iot and That can be friendly to unprecedent scale abilities….
- @Presenter Technology is truly accelertaing the growth of a digital universe that brings unforeseen and unexplored opportunities ready to be unveiled.
- @Presenter Technology is truly accelertaing the growth of a digital universe that brings unforeseen and unexplored opportunities ready to be unveiled.
- @Presenter Technology is truly accelertaing the growth of a digital universe that brings unforeseen and unexplored opportunities ready to be unveiled.
- @Presenter Technology is truly accelertaing the growth of a digital universe that brings unforeseen and unexplored opportunities ready to be unveiled.
- @Presenter Technology is truly accelertaing the growth of a digital universe that brings unforeseen and unexplored opportunities ready to be unveiled.
- It is a pleasure to be here today to celebrate with you, the dawn of a new era! [Welcome/opening remarks] Dell has been on a 30+ year journey to democratize technology and the power of information Evolved IT from a bookkeeping tool to the very center of business and life Looking ahead, realize we’ve barely begun
- It is a pleasure to be here today to celebrate with you, the dawn of a new era! [Welcome/opening remarks] Dell has been on a 30+ year journey to democratize technology and the power of information Evolved IT from a bookkeeping tool to the very center of business and life Looking ahead, realize we’ve barely begun
- @Presenter And it is the fact that all of these technology driven events are enabling and being augmented by the internet of things… And together they are transforming every aspect of our lives. While many of us focus on more scientific aspects of these technology advances, the great mass is bombarded with changes on all aspects of their daily activities in things as simple as…. For example… Ordering and waiting for a pizza….
- @PRESENTER: And IT NEEDS TO SCALE … to unforeseen dimensions… In 2020, only 5 years away, WE will be living in a world where…….. MORE THAN 7 BILLION WILL BE CONNECTED… THIS IS ALMOST THE SAME NUMBER OF PEOPLE WE HAVE TODAY ON EARTH… AND THESE PEOPLE WILL NOT BE ALONE… THEY WILL BE JOINED BY ANOTHER 30 BILLION DEVICES CONNECTED TO THE 7 BILLION PEOPLE AND MORE IMPORTANTLY CONNECTED TO EACH OTHER … AND TOGETHER THEY WILL GENERATE MORE THAN 44 ZETTABYTES OF DATA …. …… and Software will be the key enabler to create compelling outcomes The challenges we face today in essence…
- 22
- @Presenter: Model later deployed for monitoring/scoring at the edge (Lambda architecture)
- “To One Lake, or to Multi-Lake”... We can also copy some of teh fun lake picture and teh creation of the world slide before ....
- “To One Lake, or to Multi-Lake”... We can also copy some of teh fun lake picture and teh creation of the world slide before ....
- @Presenter: But then... A perfect storm came along caused by the convergence of five very disruptive functions and parallalized the data…. IoT Effect Regulatory Compliance Legacy data Bandwidth Contraints Migration to the edge
- @Presenter: changing the paradigm of data science from a centralized science to data science at the edge! Machine Learning can reduce the complexity of deploying edge computing. Machine-learning can be described as the study of algorithms that are able to learn from experience and examples. This can enable companies to easily automate repetitive tasks and processes enabling analytics to be performed at the edge of the network. As a result, the flow of information will be significantly more efficient while cutting down on network latency and associated costs. Artificial intelligence in the cloud enables deeper learning on available data through image analysis, language comprehension, and text recognition to name a few. These insights along with the machine learning at the edge offer an ever smarter and more precise rules engine for IoT data Edge/Fog computing compliments existing cloud/data center advantages that Dell Technologies offers. The rules engine at the edge to cleanse the data streaming off the things should be constantly refined with deep learning and artificial intelligence leveraging big data analytics in the cloud/Datacenter. By pulling the right data back to the core based on the rules engine at the edge you can then integrate it into existing IT systems such as your ERP (Enterprise Resource Planning), CRM (customer relationship management), and SCM (Supply Chain Management) systems. This is how you convert all the data you are gathering from a cost center to a profit center with business insights. Finally, constantly updating your edge rules engine ensures that you are passing the right insights over the network to the cloud/datacenter to enable remote access from anywhere and also compare the data across multiple sites which are not co-located. For instance, if you have a portfolio of building, you would pull the critical data back from to determine which building is most efficient and how those learnings could improve the efficiency of the rest of your portfolio.
- PRESENTER NOTE: What follows is a set of materials to help Dell Technologies spokespersons understand one unified story to carry to the market on Day One. Communications will hold a series of calls with global communications and spokesperson teams shortly before close. In addition, a video will be posted internally of Jeremy Burton delivering the pitch for on-demand viewing.
- Dell Technologies.