Tagged: Innovation

The rise of Digital Decision Support Systems and the Integrated Workspace


BI Summit Chicago

ABSTRACT

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.


ADDITIONAL INSIGHTS

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.


Nokia Keynote


BIO

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.

 

IEEE ETR 2018, Emerging Technologies Reliability Roundtable – Human Factors Session (2)


Following up on my last post about IEEE ERT 2018, here are a couple of charts for my “discussion brief,” which include a Human-Machine-System Capability Mapping chart (above) and concept illustrations of the Experiential Decision Support System (below.)  The charts’ text conveys context setting remarks, which I am also providing here.


Slide1


The goal of furthering machine intelligence is to make humans more able and smarter: the opposite engineering approach typically becomes a source of self-defeating technical myopia waiting to happen and missed opportunities. This simple mapping exercise can be customized to assess and roadmap capability levels.

The more sophisticated automation becomes, the more obvious the criticality of the human factor in both consumer and enterprise environments… rather than less. And, in any case, customer acceptance and adoption criteria remain Quality’s litmus test for emerging technologies.

Digitalization is fostering (a) XaaS,  (b) Self-Service, (c) the Shared Economy and the (d) Maker Movement. All elevate human involvement and drive the push for opening and democratizing technologies. These make (e) citizen science and citizen developers shape the next generation prosumers at mass market scale.

Digital Transformation initiatives embracing the above allow (f) nimbler enterprise teams to operate at far greater scale, scope and speed, and shift focus from routine operations to dynamic value creation coupled with extreme efficiencies.

This entails (g) interdisciplinary workstyles and collaborative organizational behaviors that include (h) customer co-creation models. In this new context, humans remain (i) the ultimate critical element in system reliability and safety. Left shifting Quality by Design (QbD) prioritizes Human-Centered-Design tools and processes to deliver high performance workforce automation systems.


Slide2


Cost-effective Lean Ops systems intertwine analytics, automation, programmability and flexible systems integration. All optimized for dynamic behaviors given Soft System’s perpetual motion. This means designing “for-ever” rapid and seamless reconfigurability instead of just engineering “day 1” implementations.

Operational Excellence dictates system-wide as well as subsystem level visualization, and a combination of centralized & distributed closed loop controls under user friendly operational modes. Cognitive models involve Situational Awareness (SA,) Sense Making (SM,) Root Cause Analysis (RCA,) Scenario Planning (SP,) and ROA (Real Options Analysis.)

The Experiential element is not just about programming known rules and policies but, most importantly, it grows by assimilating iterative learning in the context of cyclical automation: routine decisions and manual operations can be streamlined and collapsed, then switching to “exception” based management for that particular event.

Productivity calls for streamlining operations so that (a) waste can be eliminated & prevented, and (b) value based tasks can be performed effortlessly, in less steps, at speed & without error. High performance behaviors and sustainable competitiveness also call for the ability to (c) experiment and create new capabilities, as well as leveraging (d) process mining for customer journeys & value stream mapping (CJM & VSM) to continuously optimize them and guarantee service levels.

Service Operations Centers (SOC) should be equipped with Experiential Decision Support Systems (DSS) featuring (d) collaborative filtering, (e) actionable data stories conveying hindsight, insight & foresight and (f) adaptive cybernetics. Advanced visualization for both (f) intuitive & highly abstracted infographics and (g) scientific views is of the essence.

Quality is best addressed as a human experience, which determines (d) meaning and, therefore, the degree to which a system is lean vs. over-engineered or subpar (both being defective and carrying obvious and hidden costs.) A new take on QbD for Soft Systems, which are inherently fluid by definition, emphasizes acceptance testing probing for: usefulness & utility, usability & affectivity, consumability & serviceability and safety thru use cases and lifecycle events.

 

Being Digital in the DX Age


I am reviewing the Manifesto on Human Factors Engineering and making updates. In the meantime, what follows below was a draft introduction letter, which was left unpublished when releasing the Manifesto last year. Blue text shows new updates. As far as this post’s title is concerned, DX refers to Digital Experiences. The same acronym is also used for Digital Transformation initiatives.



 

Claude E. Shannon, the father of information theory, is credited with being the first to use the word “bit” in a ground-breaking paper published in the Bell Labs’ Research Journal in 1948. He defined a mathematical framework that defines information and how to encode and transmit it over communication networks.

John E. Karlin, the father of Human Factors Engineering and a peer of Shannon’s at Bell Labs, is credited with assembling the first business organization addressing the human side of the equation just a year earlier. His interdisciplinary team focused on how to interface and, therefore, best design communication systems that account for cognitive and behavioral matters, as well as form factor considerations for devices to be user friendly.

In the Age of Digital Transformation, the notion of “being digital” has transcended the sophisticated handling of binary digits and what we can do with tangible hardware. Data driven automation and the notion of zero-touch lead to the development of end-to-end digital systems that are largely software defined and autonomic. These are engineered to be highly efficient and to operate without human intervention… or so we thought.

That feat can only be accomplished by undertaking a holistic design approach which, paradoxically, highlights the larger context and the new nature of Human-Machine-Systems. Otherwise, we would incur a technical myopia where presumably good technology ends up addressing the wrong problems or causing new ones that offset the intended benefits. In the digital age, technical prowess alone does no longer guarantee success: impressive inventions can fail to “cross the chasm,” fall in the “valley of death,” and never become true innovations to their creators and investors’ dismay. Passing the Turing Test just to plunge into the uncanny valley paradox also reinforces that point.


DX HMS Capability Model

Note: the above draft chart is not self-explanatory, requires some updating and I will better address it on another post… but I’d like to leave this version here for ongoing discussions and feedback.


 

Being digital entails a new breed of jobs enabled by workforce automation. Any of us may become a citizen developer who can leverage self-service and intuitive decision support systems to create, blend and program services, because automation does the heavy lifting under the hood. Interdisciplinary collaboration is now within reach as teams involving individuals from different domains can effectively share these tools and the underlying resources to overcome the pitfalls and diminishing returns of organizational fragmentation. Enterprises can better innovate and further business opportunities by engaging in rapid experimentation with nimbler teams working at greater scale and scope, and by doing so at unprecedented speed.

At the time of writing this, and in the foreseeable future, no enterprise system is left alone without a human being accountable for its performance (or lack of thereof) since our skills and judgement remain key to critical and ultimate decision making. The more sophisticated the environment, the more obvious, as smaller agile teams become responsible for systems operating at greater scale, scope and speed.  Dr. Alonso Vera, Chief at NASA’s HSI (Human Systems Integration) Division, states that “humans are the most critical element in system safety, reliability and performance […] across the gamut of applications even as increasingly intelligent software systems come on-line,” Human-Centered Design and Operations of Complex Aerospace Systems 2017.

It should also be noted that best practices in A.I. are promoting the kind of augmented and collaborative intelligence that only Human-On-The-Loop and Human-In-The-Loop Computing can deliver. A.I. is also powering up Affective Computing to make day-to-day digital services be contextual, empathic and adaptive, and allowing for mass-personalization at scale. We are also leveraging Natural Language Processing coupled with Dataviz helping better search, discover and visualize insight and foresight with interactive infographic quality, instead of just rendering data overloading screens and overwhelming navigation.

These are all good reasons to further our understanding of how to best leverage analytics, automation and programmability to design enterprise and consumer systems driven by a human-centered approach. The desired outcome is greater utility, frictionless consumability, dynamic adaptation and, last but not least, extreme ease of use at any level throughout a service’s lifecycle. That’s the fertile ground that enables new cross-pollination opportunities to enable a better future, which continuous improvement sets in constant motion and, hence, always is in the making.

Being digital is a human experience and, as such, it involves human affects. That relates to how we perceive our predominantly analog world and the diversity of our social and cultural fabrics. We interact with a great deal of objects and services of all sizes which can, and will be, digitized and automated in some fashion. We will continue to lead our lives in a variety of changing contexts and perform numerous tasks throughout the day, some routinely and some exercising more demanding skills with both low and high tech in that mix. So, it pays to think of Human Factors Engineering as not only having pioneered human-centered-design, but as an endless source of serial innovation for Creative Technologists to address our evolving lifestyles and quality of life in the DX Age.