What is innovation? Is there a right time for innovation?

To put it simply, innovation is continuous renewal. It’s about finding, creating, and utilizing better ways to expand your business model, to grow your operational excellence, and to optimize customer intimacy.

Hear our experts, Frank Wammes – Chief Technology Officer, Continental Europe- Capgemini, and Jaap Bloem, Principal Analyst- Sogeti share their insights on how and when innovation should be applied.

Frank Wammes

FrankWammesJAAP: Hi Frank. Innovation keeps topping the charts. I don’t know if the holiday season makes a difference but 13 pages long the Harvard Business Review tries to explain when it’s time for companies to reinvent themselves. Their article in the December 2015 issue “Knowing When to Reinvent” features examples from Nestlé, Adobe, Xerox, Netflix and Aetna. As a CTO and industry consulting veteran, what isyour take on innovation these days? 

FRANK: Innovation is all about continuous renewal. A most practical thing! It’s about finding, creating, and utilizing better ways to expand your business model, to grow your operational excellence, and to optimize customer intimacy. Innovation in terms of continuous renewal indeed is a continuous beauty contest to amaze the outside world, to build credibility, and to reflect on self-consciousness. Everything comes down to putting the 4 T’s to work: TransformingTransport and Trade – of goods, information, and everything in between; atop and underneath – with Technology of all kinds


JAAP: I agree. Once upon a time, you were a grocery, a government, a bicycle repair shop, a newspaper, a teacher, an engineer, an accountant, a physician, a bank, an insurance company, an army, a music label, a car maker, or whatever. It was quite clear what your professional role meant in relation to others – to other roles, to other people, and to other organizations. Staying on the job as a dedicated practitioner and consciously moving with the times in terms of skills, machines, and techniques would have you a life long occupied, and your experience, products and services would remain most valuable. Those days are gone!

Jaap Bloem

FRANK: Today, all knowledge, frameworks and worksheets are out on the Internet as are any conceivable necessary and desirable free and easy-to-use supporting applications. Business structures are readily available as web service packages, while sensor and app laden smart devices as well as cloud services assist people 24/7. They take over functions like talking, monitoring, understanding and anticipating, blending human and artificial intelligence.

Every part of life, every industry and business ecosystem is humming with ever newer, faster and smarter digital dynamics. The building blocks and business templates are being designed and developed for you by startups and so-called unicorns. Traditional businesses just need to follow suit, like for instance General Electric, the company that has declared itself to be the biggest startup ever by adopting FastWorks, a set of techniques, based upon and guided by Lean Startup guru Eric Ries himself.

JAAP: Go kick in, and disrupt. Dare to dream and do it! That’s the bottom line of today’s entrepreneurial attitude: playful, open, direct, collective and continuous. Innovation has become childishly practical – at least the ways we these days talk about it – judging by the success of impactful disrupters. Lessons can be taken from The Billion Dollar Startup Club of The Wall Street Journal and Dow Jones VentureSource:


Besides such open-book wisdom there are books that can and even must be judged by their cover, especially when the message on it changed in the process towards publication. A famous example is one of the bestsellers in Eric Ries’s O’Reilly Lean Series that saw the light in December 2014. Its main title simply reads “Lean Enterprise.”


FRANK: Central to the point we want to make is the message in the two subtitles which is sort of a minimal viable Q&A. Today, even wannabe High Performance Organizations Innovate by Adopting Continuous Delivery, DevOps, and Lean Startup at Scale, period! My take: ground breaking thought should come in two book covers with subtitles that illuminate each other.

Indeed, you just read the new definition of business innovation itself. Eric Ries published the book on startuply lean in 2011[1], at the age of 33. Not convinced of the value? Mr Ries helped General Electric become the Biggest Startup ever[2] via “FastWorks,” just another “technique” as it is called. The method was exposed to everyone in the April 2014 Harvard Business Review[3]. Now, that’s not an April fool’s joke – any organization can and should embrace Lean, Agile, DevOps, and the like . . .

JAAP: Co-author Barry O’Reilly explains why it is so hard to embrace a culture of continuous experimentation and learning, to transform your business to an adaptable Lean Enterprise by quoting the Economist Intelligent Unit’s answer: “the main obstacles to improved business responsiveness are slow decision-making, conflicting departmental goals and priorities, risk-averse cultures and silo-based information.[4] [5]” And then there is Digital Disruption . . .

Gartner predicts that most organizations in 2017 will have implemented a two-speed strategy for working with digital technologies. It has everything to do with leveraging ongoing Digital Disruption. Think of APIs, apps and digital platforms; think of big data, cloud and analytics. All of this you must do yourself because it determines your competitive edge, and the agility of your business.

FRANK: Over time, we have become familiar with the digital technology angle and focus that is supposed to say it all: Business Technology (BT). BT is not so much about replacing IT; instead this “undichotomy” reflects the two-speed paradigm related to the disruptive potential of digital technology that organizations should fully and continuously leverage.

JAAP: Any kind of overengineering should be abolished through “Agile”, “Lean”, and MVPs: “Minimal Viable Products”. These notions are at the core of modern IT approaches, and equally apply to business environments that all must iterate from IT to BT in order to foster and harness competitive advantage.

FRANK: Two-speed digital business technology is all a matter of Designing to Disupt, or Daring to Dream. Realizing business dreams requires abolishing former overengineering practices, and having business development and your operations organized and function as one – physically and digitally. We used to call this “Alignment” but that is far too reactively and weakly put. Therefore new idioms and semantics have emerged: on both the IT side and the BT side should development (Dev) and operations (Ops) interact as a well-oiled machine. That’s the rationale behind DevOps: lean en agile!

JAAP: This in short is the roadmap for today’s inventive entrepreneurship and delivery. Knowledge and innovation centers have been supplanted by co-creation labs, while applying innovation, or continuous renewal, has become the ongoing business beauty contest.

FRANK: Correct, and in addition there is this IOT Innovation of Technology taking place: the marriage of Information Technology, Operational Technology, and the Internet Of Things. We have begun calling this the IOT Tech Triad, and it sure needs dedicated attention from C-level executives and Lean Enterprise Architects.

JAAP: What we’re seeing already Frank, is that CIO’s and CTO’s alike are ever more acting as the company Chiefs who step up to the tasks that come with this threefold Innovation Of Technology. How they go about doing this will be discussed in a next episode of our Capgemini podcast series.

Listen to the full podcast here:  podcaster_full








Jaap Bloem AUTHOR:
Jaap Bloem is in IT since the PC and now a Research Director at VINT, the Sogeti trend lab, delivering Vision, Inspiration, Navigation and Trends.

Posted in: API, Big data, Capgemini Group, Cloud, Developers, DevOps, Digital strategy, Human Behaviour, Innovation, Internet of Things, IT Security, IT strategy, Quality Assurance, Smart, Transformation      
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It’s that time of the year when we look back and celebrate some of the best contributions throughout the year. Here’s the No.9 Blog of the year!

By late 2014, Shivon Zilis took the semantic bull by the horns. She explains: “MI is a unifying term for what others call Machine Learning (ML) and Artificial Intelligence (AI). When I called it AI, too many people were distracted by whether certain companies were ‘true AI,’ and when I called it ML, many thought I wasn’t doing justice to the more ‘AI-esque,’ like the various flavors of Deep Learning. People have immediately grasped Machine Intelligence, so here we are.”

Experience the power of Machine Intelligence and its flavors, and listen to Part I of Let Supercharged Machine Intelligence Grow Your Company Culture of Big Data Analytics in Capgemini’s new podcast series called “Data and The Hunch.”

Latest: A New Wave of Automation: a new report on Machine Intelligence


Jaap Bloem AUTHOR:
Jaap Bloem is in IT since the PC and now a Research Director at VINT, the Sogeti trend lab, delivering Vision, Inspiration, Navigation and Trends.

Posted in: Automation Testing, Big data, Business Intelligence, Capgemini Group, Digital strategy, Marketing, Quality Assurance, Research, Security      
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When did you have a truly clarifying conversation around IT-related high-order concepts? I mean for instance Big Data or the Internet of Things? Since June 2011, the website is dedicated to this specific moving target. It’s a pet project of Mr. Gil Press, Forbes columnist and former Senior Director Thought Leadership Marketing at EMC Corporation. In October 2015, Dell bought EMC – famous for its Data Lake 2.0 approach – at a staggering 67 billion dollar, which qualifies as the largest deal in tech history ever. Image credit : google images

This conclusively indicates that Big Data means Big Money, and that its importance is only about to grow. One month later however, Textio and BloombergBusiness noted that Big Data in 2015 had lost over 35% as a prominent buzzword in tech job postings. Gil Press commented:

‘Two years ago Big Data was everywhere. Companies bragged about using it. New investment funds and top 40 bands were named after it. Engineering job listings containing Big Data were significantly more popular than those that did not. But today, Big Data has become so highly saturated that its use has passed into cliché.’

Textio and BloombergBusiness’ top winners were Artificial Intelligence and Real-time Data, which serves to show that Big Data has made it to the next level of Insight and Analytics. The ever so famous World Wide Web, dub-dub-dub, or simply Web went the same path and is today being overwhelmed by the mysterious Internet of Things; more accurate of Things and Sensors and Actuators (Vint Cerf); and on a high level of Things and Services (Bosch). That is quite correct since many other protocols have enriched TCP/IP and overtook the dominance of simple HTTP. Most non-technical people don’t care – which means don’t want to understand this – sometimes to the effect that IoT is being ridiculed as the Internet of Thingamajigs.

Around 2006, after Pervasive Computing, Ubiquitous Computing and indeed the Internet of Things, the notion of Cyber-Physical Systems (CPS) was introduced to eradicate confusion, to repair all damage done and to reconcile viewpoints. But terminology is hard to get rid of, and CPS developed as just another buzzword, next to the Industrial Internet, the Industrial Internet of Things, Industry 4.0, and – let’s not forget – the amazing Internet of Everything.

Even to experts it has become a quite confusing landscape. In May 2014, Sathish AP Kumar of Coastal Carolina University, a Senior IEEE Member and a University Senator raised this question on the scientific community website ‘I am hearing the terms Internet of Things and Cyber-Physical Systems interchangeably and wondering what exactly the difference between these terms are?’

Afer a few weeks, Mr. Kumar, who works in the fields of Cyber Security, Cloud Computing, Big Data Analytics, and Bio&Health Informatics, was the first to answer his own question by referencing slide #10 from a presentation called the ‘Internet of Things towards Ubiquitous and Mobile Computing.’ It was delivered by Professor Dr. Guihai Chen in October 2010 at the Microsoft Research Faculty Summit in Shanghai.

Dr. Chen’s explanation goes like this: the Internet addresses the Cyber World and the Internet of Things the Physical World, aiming at Ubiquitous Connections. Now, as the notion suggests, Cyber-Physical Systems combine the two, aiming at what Mr. Chen calls Harmonious Interactions. Go draw these relations and you have slide #10. The distinctions, overlap and directions are very clear, especially when considering what Mr. Chen had pointed out just before.

He said: ‘Smart Planet, Pervasive Computing – all such buzzwords refer to the same balloon. When blown to large size it is called Smart Planet; when to middle size it is called Cyber-Physical Systems; when to small size it is called pervasive or embedded system. And IoT and CPS are actually a pair of twins.’

More than a year later, in August 2015, Mr. Nikos Pronios from Innovate UK, brings Industry 4.0 and the Industrial Internet of Things to the table in the ResearchGate discussion thread. Thereafter, Dr. Imre Horvath, a professor at Delft University of Technology, further enlightened matters with a short remark: ‘We may consider in our discussion the growing level of synergy between the physical hardware (both analogue and digital), the digital software (control, middleware, application programs), and the cyberware contents (media, data/info, codified knowledge, concept ontologies, learnt agency).’

Eventually, in November 2015, our attention was drawn to a special Elsevier science magazine issue on the combined topic of ‘Cyber-physical Systems (CPS), Internet of Things (IoT) and Big Data. The special issue of ‘Future Generation Computer Systems – The International Journal of eScience’ will be published in October 2016, and the webpage contains this extensive clarification:

‘Cyber-physical Systems (CPS) are emerging from the integration of embedded computing devices, smart objects, people and physical environments, which are typically tied by a communication infrastructure. These include systems such as Smart Cities, Smart Grids, Smart Factories, Smart Buildings, Smart Homes and Smart Cars.

The Internet of Things (IoT) refers to a world-wide network of interconnected heterogeneous objects that are uniquely addressable and are based on standard communication protocols. These include sensors, actuators, smart devices, RFID tags, embedded computers, mobile devices, et cetera.

The design of Cyber-physical Systems and the implementation of their applications need to rely on IoT-enabled architectures, protocols and APIs that facilitate collecting, managing and processing large data sets, and support complex processes to manage and control such systems at different scales, from local to global. The large-scale nature of IoT-based CPS can be effectively and efficiently supported and assisted by Cloud Computing infrastructures and platforms, which can provide flexible computational power, resource virtualization and high-capacity storage for data streams and can ensure safety, security and privacy.

The integration of networked devices, people and physical systems is providing such a tantalizing vision of future possibilities that IoT is expected to become a vibrant part of the digital business landscape.’

All in all this is inspiring guidance for coming to grips with Information Technology and ‘Anything Internet’ in 2016.

You can also listen to the podcast on


Jaap Bloem AUTHOR:
Jaap Bloem is in IT since the PC and now a Research Director at VINT, the Sogeti trend lab, delivering Vision, Inspiration, Navigation and Trends.

Posted in: Big data, Cloud, communication, Digital strategy, Innovation, Internet of Things, IT strategy, mobile testing, Security, Smart      
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Hi! I am back again with the transcript of a new episode of Capgemini’s “Data and The Hunch” podcast series. I am a Principal Analyst for VINT, the Sogeti Trend Lab, and work on anything Analytics related to the Connected Service Experience. My Twitter handle is @BLO2M – B-L-O-Numeric2-M – so if you like, feel free to follow me. Here’s the transcript:

Today, Fenny has joined me in the studio. Hi Fen, how can I help you?

Well Jaap, I’m here to fire some questions. Now first, you talk about Machine Intelligence – but where does that leave AI, Artificial Intelligence?          

Right! Well, mainly in business and IT environments, there is a growing tendency of abandoning the 60-year old notion of Artificial Intelligence (AI) – all the more since it has become feasible to deploy practical machine learning in the cloud, on offer from respectable vendors like Microsoft and Amazon on a pay-as-you-go basis.

Cloud-based machine learning – and deep learning definitely will be next – can now be attached seamlessly as a sophisticated and practical productivity accelerator to your Business Intelligence and Business Analytics practice (BI and BA), and also to Big Data efforts.

But we’re not done with AI, on the contrary. At the same time, so-called Integrative AI is on the rise: vision, speech, natural language, machine learning and planning are brought together to create systems, capable of seeing, understanding, and having meaningful conversations with people. Now the question isn’t “to AI or not to AI,” but how to do both – machine learning, deep learning and Integrative AI – everything in a practical and impactful fashion.

Sounds cool, but where does it leave notions like Narrow AI and Super AI? Where do they fit in?          

Good question, thank you! Now, since the field is developing at a frantic pace, and already hosts numerous successful startups with fancy names like Numenta, MetaMind, AI.ONE, WIT.AI, Clarifai, Jibo, and Palantir – after the seeing stones in Lord of the Rings – it is even more important to pay attention to the official main AI domains of Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).

I won’t go into any detail here but new advances and viewpoints today literally beg for the integration of Natural and Artificial Intelligence, and also of Human and Machine Intelligence or MI. Numenta already touts “the new era of Machine Intelligence.” MI recently was even proposed by BloombergBETA investor Shivon Zilis to replace AI as the new overarching notion. But, more importantly, instead of intelligence, we’d better simply talk about specific faculties or competencies, for that’s what it all comes down to!

I’m sure people are wondering how all this new intelligence stuff relates to daily business technology practice . . .        

Sure! On the administrative business side, we’ve been witnessing ever more informed and intelligent decision making, starting with Relational Database Management Systems (RDBMS), Data Warehouses (DW), and online analytical processing (OLAP) – blending over into Business Intelligence (BI), which is a matter of sense & respond), into Business Analytics (BA), a matter of anticipate & shape, and finally into Big Data, Predictive Analytics, Machine Learning, and Deep Learning. That’s the line of development for many organizations in further optimizing their value chain.

Okay, so if I understand you properly, you mean there are two seemingly competing strands while new horizons open up: AI on the one hand and MI on the other. Now, what would you consider the main takeaways?

Indeed Fenny – there’s no real difference between AI and MI. It’s all mainly a matter of taste as former well-defined subfields are integrating and expanding. Overseeing all this, there are three main messages. First, we should carefully distinguish between the two ends of the AI/MI spectrum, and make sure to not mix them up – that is the fundamental one and the practical one. Second, the most sensational developments are definitely more on the AI side, where advanced robotics, self-driving cars, brain-like processors, and wetware rank among the achievements that tend to wipe out the sci-fi category within our decade. Now, this pace of change is the third and probably the most important message!

But what are the chances, Jaap, of this being yet another technology bubble? 

Breakthrough innovations on the AI side already have started to bear relevance for vertical and horizontal business domains, to the effect of heavy investment and startup activity. Partly, this might be yet another AI or MI bubble but at least the impact of practical machine learning, algorithms, and deep learning on informed decision making continues to reshape the data first mindset and analytics culture in organizations. It means that practical AI and MI techniques are a proven lever for lifting the bottom line of your Corporate IQ.

This should be evangelized, guided and monitored from the top of the organization. Bill Gates, the guy who from his point of view firmly believes that a breakthrough in machine learning is worth ten Microsofts, also states that “the CEO’s role in raising a company’s Corporate IQ is to establish an atmosphere that promotes knowledge sharing and collaboration.” Corporate IQ today implies a data first, data science attitude – and that means: throughout the whole organization.

Sounds great Jaap, apparently there’s a lot going on on the practical side!         

Most certainly! Recently, I met Carlos Guestrin. He is the Amazon Professor of Machine Learning at the University of Washington, and also the founder and CEO of Dato, formerly GraphLab. Dato delivers ultra-fast data analytics, best-in-class predictive modeling, and production-ready data science. In this way, Mr. Guestrin’s Dato is taking the next step. The company’s “mission is to accelerate the creation of intelligent applications by making sophisticated machine learning as easy as ‘Hello World’!”

Carlos Guestrin points at posterchilds like Amazon, Google, Netflix, Pandora, Uber, Fitbit, LinkedIn and a few others, which he calls: “disruptive companies differentiated by intelligent applications using machine learning.” Now, with practical cloud-based machine learning, every company today already can organize their own level of disruption, or fend off competition.

My final advice: the pace of change is furious, so stay up to speed and prepare for driving in the fast lane by supercharging your company culture of data analytics with artificial and/or machine intelligence.

Thank you Jaap; see you next time!

Related posts:

  1. Data Hunch – How can Machine Intelligence Grow Your Big Data Analytics Culture?
  2. Machine Intelligence to Grow Your Company Culture of Big Data Analytics (Part I)
  3. Business Analytics 2014: Yes to 5 Qs, and a Need for Culture and Speed
  4. Machine learning: the next big thing in big data for our everyday life?

Jaap Bloem AUTHOR:
Jaap Bloem is in IT since the PC and now a Research Director at VINT, the Sogeti trend lab, delivering Vision, Inspiration, Navigation and Trends.

Posted in: Business Intelligence, Human Interaction Testing, Innovation, Microsoft, Sogeti Studio, SogetiLabs, Testing and innovation      
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e, Cyber-Physical systems, Cybersecurity vulnerabilities, digital development, Digital Disruption, dotcom, Web Stra

From brutal aggressors to confident incumbents — everyone, these days, seems to be perfectly comfortable with the notion of Digital Disruption. Centered around competitive advantage, its application ranges from the abrupt and irreversible stealth takeover of market dominance to a mere irritating interference of “fireflies before the storm” or even “wampum,” as the dotcom insurgents were famously depreciated by competitive advantage,’s Lou Gerstner and GE’s Jack Welch, at the end of the 20th century. In our modern Cyber-Physical Systems universe, however, where the digital mycelium has been pervasively mushroomed, Cybersecurity vulnerabilities and threats rank among the most dangerous disruptive forces, because they are inextricably linked to the omnipresent phenomenon of competitive Digital Disruption.

Image credit: www.industryweek.comImage credit: 

Digital Disruption Beyond the Buzz
By the end of June 1999, seven months before the dotcom crash set in, BusinessWeek devoted an issue to the then prevalent “Internet Anxiety.” Its symptoms were on the cover: “You’re Merrill Lynch when comes along. You’re Barnes & Nobles when hits big. You’re Toys “R” Us when eToys shows up. What would you do?” The response, then, was the cover story’s caption: “Part in envy, part in fear, Corporate America is embracing a radically new business model.” Although Mr. Welch surely didn’t have to fear or envy any competitor, his stance toward the Internet was utterly respectful: “I don’t think there’s been anything more important or more widespread in all my years at GE. Where does the Internet rank in priority? It’s No. 1, 2, 3, and 4.” At that time, many mainstream corporate giants were racing to solidify and build out what was called a company’s “Web Strategy,” while digital development exploded.

The year 2000 not only saw the dotcom bubble burst, but also the birth of Web Services (“a software system designed to support interoperable machine-to-machine interaction over a network”), as a new defining mechanism for what was commonly called the Digital Economy. Now, fifteen years later, Amazon has matured from a pure-play search / recommendation engine around books to the poster child of modern retail, Amazon Web Services is a 5 billion dollar business, and much of the attention has shifted to APIs, i.e. to programmable flexibility.

The Internet has expanded to the “Internet of Things” – the phrase that Kevin Ashton had coined in 1999, as he couldn’t think of something better. Radically new business models, once again, are transforming the way in which companies and industries operate. Sensor-laden smartphones and Smartphones On Wheels (aka Connected Cars) have followed the well-known application of RFID tags for Collaborative Planning, Forecasting and Replenishment (CPFR) purposes at Procter & Gamble; where Mr. Ashton – who headed the MIT Auto-ID Center – implemented, for the first time and successfully, his Connected Things That Talk & Think .

We have traded in dotcoms for lean startups, GE’s FastWorks proudly touting itself as The Biggest Startup Ever, and added both “Industrial” and “of Things (and Services)” to the Internet. Ours is the Age of Exponential Organizations where new entrants may well be ten times faster, better, and cheaper than incumbents. Increasingly, enterprises organize themselves around embedded automated sense & respond data feedback loops, which enable better operations, faster product innovation, new service models, and vastly-enhanced customer-targeting and retainment. The “Anything Internet” phase that we have entered is based on three mutually dependent “C” pillars: Cloud Computing or simply digital infrastructure, Cognitive Computing or digital intelligence, and last but not the least Cybersecurity.

cybersecurityCybersecurity Beyond the Buzz
The security of products and services is a key element of the overall security of cyber-physical systems, but a number of things are affecting organisations’ ability to put in place a solid digital defense system. These include an expanded attack surface, inefficiencies in the development process, a weak security architecture of the entire system, lack of specialised security skill sets, and insufficient use of third-party support. Securing a cyber-physical system is a challenge, because of its multiple points of vulnerability. These include the products and the services involved, the embedded software and the data residing within, plus the data aggregation platform, the data centers used for analysis, and of course, the communication channels.

The current Top 10 list from the Open Web Application Security Project (OWASP), covers the following alarming basic issues:

1 – Insecure Web Interface
2 – Insufficient Authentication/Authorisation
3 – Insecure Network Services
4 – Lack of Transport Encryption
5 – Privacy Concerns
6 – Insecure Cloud Interface
7 – Insecure Mobile Interface
8 – Insufficient Security Configurability
9 – Insecure Software/Firmware
10 – Poor Physical Security

Probably, Target, Home Depot, Sony, JP Morgan Chase, the U.S. Postal Service, the Office of Personnel Management, the White House, and many other organisations and institutions around the globe could have done more to prevent their breaches. On top of security fundamentals, we badly need more sophisticated data-handling techniques: access control management, tracking and auditing; anonymisation; encryption; separation of data; plus well defined and enforced data destruction policies. We simply cannot afford Internet Anxiety Disorder to disrupt economic progress and technological trustworthiness.

Want to know more? Then check out the new edition of Beyond the Buzz, and all other ones.

To read the original post and add comments, please visit the SogetiLabs blog: Competitive Disruption and Cybersecurity Beyond the Buzz

Related Posts:

  1. Coming Up: My Webinar on “Cybersecurity Kill Chain” theory
  2. UX and Cybersecurity – Seemingly Unrelated, Inextricably Linked
  3. 5 Platform Disruption Design Lessons
  4. Disruption and disruptors


Jaap Bloem AUTHOR:
Jaap Bloem is in IT since the PC and now a Research Director at VINT, the Sogeti trend lab, delivering Vision, Inspiration, Navigation and Trends.

Posted in: Behaviour Driven Development, Big data, Business Intelligence, communication, Developers, Digital strategy, Enterprise Architecture, Innovation, Internet of Things, IT strategy, mobile applications, Open Innovation, Opinion, Security, Technical Testing, Technology Outlook, Testing and innovation, Transformation, Transitioning      
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