Thursday, October 2, 2014

Dominating Digital Disruption

The technology shifts that present opportunities to create new business models also disrupt existing systems in today's fast paced world.

As the pace of change accelerates, more and more countries and companies are struggling to stay afloat.

R “Ray” Wang who is the Principal Analyst, Founder and Chairman of Constellation Research discusses their 2014 report on what companies need to consider to manage change and dominate digital disruption.

Why is digitalization of business a disruptive factor?

Since 2000, 52% of the companies in the Fortune 500 have either gone bankrupt, been acquired or ceased to exist. The pace of change has increased, competition has intensified and business models have been disrupted.

Digitalization of business is a key factor in this accelerated pace of change as information flows faster. Most parties enjoy greater transparency, yet the digital divide makes transparency patchy. Every node reacts more quickly. The speed of execution as a differentiator has resulted in agility in delivering disrupting business models. Market leaders shift from selling products and services to promising outcomes and experiences.

Market leaders and fast followers want to know what trends will affect customer demand. How will these trends affect hiring decisions? Are there new and emerging technologies that will power disruptive business models? What factors will help organizations dominate digital disruption? How does one stay safe in a world of digital exhaust? What networks matter? Who are my competitors, collaborators and co-innovators? How does one make sense of the disparate and often contradictory trends pointed out by experts, pundits and analysts?

Using a tried and true futurist framework that looks at the political, economic, societal, technological, environmental and legislative (PESTEL) shifts ahead we synthesized the major trends and provide guidance on how Constellation approaches its research.

What are the tech trends that will boost Digital Business Disruption?

Important pillars of digital disruption begin with social, mobile, cloud, big data and analytics, and unified communications. The convergence of these pillars provides the key to future innovations. The technology shifts that present opportunities to create new business models are also the same opportunities that disrupt existing systems. While advanced materials, clean energy and personal genomics are key disruptive areas; Constellation identifies three technological shifts powering digital business disruption.

Mass personalization at scale drives relevancy and context. Systems of engagement shift to systems of mass personalization at scale that deliver relevancy. The shift from analog to transactional systems led to a wave of automation-driven efficiencies in the past century.

Today, engagement systems move toward experiential systems that deliver massive contextual relevancy at scale, create role-tailored communication styles, deliver bionic user experiences, and move at the right time and scale. The shift to systems that deliver mass personalization at scale is now underway. These systems start with an outcome-driven design point, solve delivery of massive individual scale, craft personalized conversations, interface with human APIs and enable people-to-people networks.

Can you give examples of key technologies that will impact all businesses?

Key technologies include 3D printers, ad technologies, augmented reality, context engines, crypto currencies, identity systems, facial recognition, payment technologies, digital wallets and personal clouds. Big data business models built around sensor and analytical ecosystem. Isolated networks shift to a connected world of sensor-based and analytical ecosystems that harness Big Data. In both the consumer and enterprise worlds, smart machines and wearables provide new types of sensors and add to the mix of Big Data available to create new insights.

Constellation estimates that as many as 200 million smart wearables will ship by 2017. These include bracelets, watches, eyewear and other devices with sensors. Data from equipment such as automobiles and trucks, medical devices, household appliances as well as power generators and building management systems can provide opportunities to improve operational efficiencies, create new business models and identify new usage patterns. These systems will not only communicate with each other, but also interface with people – overtly and covertly.

Much has been said about The Internet of Things. How does it evolve this year?

The Internet of Things moves from abstract concept to a living and breathing machine-to-machine mesh network interfaced with humanity. By 2020, a global market for a few billion cellphone Subscribe ID Modules (SIMs) swells to 100 billion Machine ID Modules (MIMs). Technologies include 100 gigabyte optical networks, advanced robotics and manufacturing, building management systems, MIMS, geo-location drones, self-driving cars, smart grids and software-defined networks. There is a quantum jump in the quantity and quality of information coursing through the digital economy. Big Data business models lie before us.

You spoke about cognitive systems and data intelligence. Can you elaborate?

Augmented humanity changes the future of work. Powerful yet static systems shift to cognitive systems that augment humanity. Cognitive computing is more than a new category. Cognitive systems represent a convergence of artificial intelligence, natural language processing, dynamic learning, and hypothesis generation to render vast quantities of data intelligible to help humans make better decisions. The ability to self-learn enables continuously reprogramming. These advancements represent a new class of technology to enable human and machine-guided decisions. Cognitive computing drives augmented humanity, where the sum of our collective insights and data can be served up at the right time in the right context. Technologies include facial recognition, human APIs, machine learning, natural language processing and self-learning algorithms.

(This post first appeared in my column in the Financial Chronicle dated February 28, 2014)

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