Digital Transformation drives today’s Workforce Automation and Business Intelligence (BI) initiatives where nimbler agile teams undertake tasks and jobs of unprecedented scale, scope and speed.
Digitalization also involves “Self-Service” business models which are based on the direct involvement of end-users and a frictionless customer journey, all relaying on seemingly instantaneous and automated mass-personalization.
Given that digitalization has become pervasive and that ‘making tech human’ has become a critical success factor, the new field of Genuine Intelligence (GI,) addresses holistic Human-Machine-Systems (HMS) leveraging collaborative environments comprised of networked insights, tools and processes. GI’s signature deliverable is Digital Decision Support Systems involving Integrated Workspaces.
This construct adheres to LeanOps and Quality by Design (QbD) principles for emerging technologies and, therefore, optimizes for (a) quality outcomes as gauged by consumer and operational experiences performed under (b) highly efficient operations and (c) advantageous resource utilization and effort levels.
Both value generation and productivity gains are constantly audited and iteratively improved throughout event lifecycles and over the lifespan of the system.
Jose de Francisco is a Senior Design Director at Nokia Software Group. His 20+ year experience encompasses multi-disciplinary leadership responsibilities in strategy, product & portfolio management, research & development, marketing, partnerships and project & program management. Jose is a Distinguished Member of Technical Staff (DMTS) and has worked with Bell Labs on next generation platforms. He is a Member of the Advisory Board at MIT’s Institute for Data Systems and Society (IDSS) and is the recipient of an MBA in International Marketing and Finance (MBA/IMF) from Chicago’s DePaul University as a Honeywell Europe Be Brilliant Scholar. Jose also holds a postgraduate degree in Human Factors Engineering from BarcelonaTech (UPC) and can be followed on innovarista.org.
“The ultimate test of a practical theory, of course, is whether or not it can be used to build working systems. It is good enough to use in the real world? […] Almost uniquely among the social sciences, this new social physics framework provides quantitative results at scales ranging from small groups, to companies, to cities, and even to entire societies […] it provides people –e.g., government and industry leaders, academics, and average citizens- a language that is better than the old vocabulary of markets and classes, capital and production […] the engine that drives social physics is big data: the newly ubiquitous digital data now available about all aspects of human life. Social physics functions by analyzing patterns of human experience and idea exchange.” – Social Physics by Alex Pentland.
Back in 2010 I worked on the Amazing Learning Unit, a research project leading to a proof of concept demonstration. The anecdote behind it’s name was that by calling it A.L.U. we played with the fact that those same three letters formed Alcatel-Lucent’s stock ticker. On a more serious note, we partnered with Lego and the Illinois Math & Science Academy (IMSA) to unveil a simulation at Mobile World Congress in 2011, which was very well received.
The Amazing Learning Unit’s concept entailed “Lego robotics” equipped with Touchatag’s RFID readers and Android phones and tablets. As you can see in the above picture, these “mobile units” were designed to look, behave and roam around like autonomous screens, cameras and sensors with wheels.
Driven by human factors engineering principles, the thinking behind the project was centered not on technology, but on taking down the classroom’s physical walls, which can make today’s schools and school districts behave like “geofenced silos”. This is an environment that can constrain kids’ exposure to an outside world that’s growing more connected and diverse. The project’s main goal was to enable boundariless collaborative learning, our technologies being the means to that end.
The concept called for the robots to roam around the classroom and sense what a kid was playing with, or what book she/he was reading. Classroom’s objects and books would feature the Touchatag’s stickers to that end. The result is a mobile sensing network that falls in the IoT, Internet of Things, category.
Leveraging social analytics, we thought of a “serendipity engine” which would then connect the kid with another child from any other school who would be engaged in a similar activity, and whose skill and learning behaviors happened to be a good match for them to play together. The smartphone screens would prompt interactive online activities jointly with video calls engaging them in context-aware and “peer-to-peer collaborative learning”.
We discussed what’s now known as collaborative filtering and matchmaking options to promote role model behaviors and how to adequately display them to help realize everyone’s potential, and to do so in everyone’s best interest. We also looked into sensitive matters centered on behavioral analytics, privacy and the pros and cons of emotional and persuasive design features.
As part of the project’s research, gamification techniques were thought out to incentivize players, such as competitive challenges, progressive skill levels, in-game rewards and scoreboards. Circling back with a recent post on working with personas, the ones created for this project were modeled after our own children and my kid inspired and enjoyed participating in the project’s living lab.
The prototype unveiled at Mobile World Congress showcased some of the above concepts. It is worth sharing that the business goal was to help experience some as complex as the IP Multimedia Subsystem (IMS) in a new and radically light back in 2010. I strived to humanize what can otherwise come across as overlay technical and rather obscure sets of technologies behind network infrastructure, platforms and telecommunication services, the essence of our company’s product portfolio. Therefore, we purposely placed the emphasis on creating new experiences such as the one delivered by the Amazing Learning Unit. Our inventiveness and technologies became transparent and were in place to deliver the magic.
Interestingly enough, this research project led to discussions with MIT and a leading global network operator. That time around, we looked at how this kind of experiences can be applied in enterprise environments to raise productivity and foster collaborative and multi-disciplinary workstyles. Enabling new organizational and decision making cultures in other words. The following phase of the research was titled Immersive Mobile Systems, IMS in short : )
Pervasive “software defined smarts” and “ubiquitous connectedness” are set to change how we design and interact with day-to-day objects as well as sophisticated enterprise systems. The fact is that the rise of (1) behavioral and social analytics, (2) machine learning and recommender technologies and (3) a new generation of context-aware adaptive interfaces happen to redefine and elevate how Human Factors Engineering, HFE, can effectively deliver User Centered Design (UCD). HFE’s end goal is to humanize tech. The outcome grows the user base by democratizing technologies, which leads to serial innovation.
Just a couple of weeks ago I participated in very interesting discussions on user profiling and mass personalization during workshops hosted at the IBM Innovation Center in Cambridge, MA. In subsequent posts I will keep that conversation going by outlining a framework addressing users, personas and identities, which are not interchangeable terms.
Back in Boston we also exchanged insights on ease of consumability from an end-to-end systems engineering approach. This reinforces HFE’s holistic principle around the user experience journey spanning complete lifecycles, all the way from discoverability and experimentation to decommissioning, repurposing and/or recycling products and services upon End of Life (EOL).
Modeling personas is a well known research technique. Though, let’s keep in mind that it entails a proxy approach and is just one tool in HFE’s toolset. My experience is that solely relying on modeling personas alone is not enough. In any case, I would like to take this chance to retrieve a couple videos from a project I worked on about 10 years ago. Here is the context:
- The first iPhone was released in mid 2007, which took the market by storm with a fast growing ecosystem of applications.
- The wireless telecommunications sector had mostly relied on business models and revenue from voice services instead.
- The advent of a “data tsunami” put significant pressure on 2.5G wireless networks and accelerated 3G and 3.5G deployments.
Long story short, delivering Mobile Broadband meant going beyond 3.5G to improve and scale infrastructure, delivery platforms and services in more effective and cost efficient ways, which became 4G’s opportunity, LTE, WiMAX, UMB being the competing standards early on.
These two videos were released in 2008 with the objective to discuss scenarios envisioning a short term 2010 horizon, as well as a forward looking outlook for 2015 for 4G. Note that LTE has become the prevailing worldwide network technology and that the standard was finalized by the end of 2008.
We defined two personas (fictional characters) to synthesize a selective set of research findings and assumptions, which helped us visualize and explore 4G’s opportunity: Zoe – Millennial, and Ethan – Gen-X. The stories in this narrative were structured as a succession of persona-based scenarios assembled as a “Day-in-the-Life” journey. The emphasis was placed on network effects depicting social interactions and collaborative behaviors engaging others in a variety of sessions.
By conducting a retrospective assessment ten years later, we can now spot hits-and-misses and what specific forecasting frameworks and techniques worked better. It is also true that this project was leveraged as “thought leadership” initiative seeking to influence future developments: prompting LTE adoption by network operators in this particular case. Research wise, that means factoring a “confirmation bias” (a self-fulfilling prophecy effect) and
In the spirit of full disclosure, our customers asked about the availability of these applications as soon as we discussed our vision with them. So, we went on to form an extensive ecosystem initiative known as ng.connect to collaborate with third party partners and research institutions to “make things real”. I was involved in ng.connect as an internal consultant in its early days, mostly supporting the University Innovation Program on a project basis. As PARC’s, Palo Alto Research Center’s, Alan Kay put it, “the best way to predict the future is to invent it”.
By the way, there are other related quotes out there. I came across Alan K’s one when reading Walter Isaacson’s “The Innovators”. My favorite take was written by Antonio Machado, a Spanish poet:
Wayfarer, the only way
Is your footprints and no other.
Wayfarer, there is no way.
Make your way by going farther.
By going farther, make your way
Till looking back at where you’ve wandered.