TBR believes that creating common terminology and understanding around data is key to successfully implementing an evolutionary digital transformation strategy, one that enables the organization to transform incrementally, as it capitalizes on new opportunities and deals with new challenges. Essential to this approach to digital transformation is an organizational cultural transformation, one that embraces continual innovation and ongoing collaboration across departments and disciplines and that enlists all parties in the process of harnessing organizational data assets to move the organization forward.

The many uses and users of data

It is commonly accepted that IoT represents the intersection of IT with operations technology (OT). This is true, but only part of the story. Business management is another key player in many projects, and TBR believes it should be a component in all IoT projects. In fact, potential users of data from IoT projects extend beyond these three stakeholders, including many of the departments throughout an organization, such as marketing and sales. To deliver the greatest possible value from any project, including but not confined to IoT, all the potential users of the data should be considered in the design and evolution of each project.

In the early stages of the recent surge in IoT, three to four years ago, the different stakeholders were often brought together for workshops or ideation sessions to invent new solutions made possible by IoT. As IoT has become more common and relevant players are more familiar with common use cases such as status monitoring and asset tracking, there has been less need for this challenging and expensive invention phase of IoT projects. Instead, new projects are often undertaken entirely or almost entirely by OT, sometimes working with IT to ensure compliance with company standards. These projects can confidently deliver a positive ROI while only using data for a single purpose, usually operational efficiency. Potential other uses for the data, or from data that could be generated by the solution, are often not considered in the design. This can be a waste.

The data generated from an IoT project often have value beyond the immediate purpose of the project. For instance, data from a status monitoring solution can be used to identify patterns that could predict service-related incidents. Similarly, comparing status reports across different assembly lines or factories might help identify superior or deficient configurations. Status reports could be correlated with operations speed to help identify either capacity problems or the potential for greater capacity. Capacity limitations or windfalls affect both marketing and sales.

The same kind of potential repurposing of data can be found for most IoT projects. Data has multiple uses. Different people within the organization are able to recognize different potential uses. Uses can be classified into short term and long term. Status data is valuable immediately. Indeed, for the purpose of reacting quickly to status deviations, the data has no long-term value. A solution built for only that purpose would often discard the data to minimize project cost, resulting in a loss of the potential value of the data for long-term analyses. To extract the greatest value and meet broader organizational needs, other people in the organization should be involved in the project design.

Manufacturing scale matters less as we pivot to a knowledge economy

Economies of scale as a barrier to entry have been a fundamental precept taught for years in economics classes worldwide. Capital had to be invested ahead of being able to create value, and then people could be hired to staff the capital equipment to produce goods. Having both capital assets and existing volume gave companies a distinct competitive advantage. It drove both vertically integrated companies as well as horizontal holding company models, with the latter made famous by Jack Welch’s oversight of U.S. blue chip company General Electric.

Technology today has greatly reduced scale as a competitive advantage. Virtualization and abstraction have led to business theorists talking increasingly about asset-lite business models and asymmetric competition. Clouding this pivot is the emerging discussion around consumer scale. This is a competitive edge gained not necessarily from capital scale, but by capturing consumer brand loyalty that generates the scale. This concept is often discussed as the “force multiplier” or the network effect of the ecosystem. It is giving rise to additional new terminology, such as multi-enterprise business networks, in which partnering and the joining of complementary assets enable all participants to benefit from the aggregation of intellectual property, which is fed to the entire ecosystem of loyal customers.

Humans have the big ideas; curating those ideas into scalable advantage requires technical skills, automation and patent protection

When consumer loyalty generates cash, that cash can be deployed to fund projects, such as small-scale, smaller-dollar-volume projects akin to becoming an internal venture capital (VC) arm for any future product and service innovations. This concept manifests itself in the notion of fast failure and rapid iterations that are anathema to scaled manufacturing best practices. Being successful requires having people who are insightful about what businesses or consumers want and how to turn those wants into an automated piece of software — in short, algorithms.

As virtualization and software abstraction move the economy ever closer to utility computing, first discussed in the late 1980s by technology futurists, and as quantum nears economic advantage, the mission-critical business competency will be writing algorithms to apply against the ubiquitous data traffic being generated and stored throughout the computing utility network. Faster compute leads to faster exploration and discovery. Faster discovery leads to shorter product and service cycles and therefore shorter competitive advantage windows.

As such, algorithms that generate these new insights will increasingly become the way enterprises generate wealth, as well-skilled individuals push the limits of conventional wisdom and then deliver these new insights. Preserving that ever-shortening advantage will come from increased vigilance in protecting intellectual property. Thinking and creativity provide the advantage. We hear time and again at analyst conferences about how skills are in short supply and how people are a firm’s greatest asset. TBR expects to hear more frequently about the patent protections around these automated ideas.

Clean blockchain data fed to quantum will accelerate the value of algorithm patents

Accurate data will be available in real time for these algorithms to run against to generate real-time decision-making guidance. As automation removes more and more human toil from the economy, only individuals at the point of creation or the point of consumption will be critical to the business, with the algorithms mining the consumer demand to test against the next big idea to come from well-skilled humans and converted into competitive advantage through an automated algorithm run against real-time, accurate data.

As explored further in TBR’s Quantum Computing Market Landscape, in the quantum computing realm, where insights and actions can be obtained exponentially faster, the IP advantage is also exponentially greater. Think of the traveling salesman example that comes up regularly in quantum conversations: If a delivery company can patent an algorithm that speeds up delivery rounds and makes deliveries more efficient overall, that could swiftly create extinction events in the delivery market. If we extrapolate this, emerging technology has the potential to fundamentally alter competitive landscapes by generating faster and more accurate insights.

TBR analysts will be attending the Quantum.Tech conference Sept. 10-11 in Boston. Please contact your account executive to coordinate a conversation with TBR analysts at the event.

5G will push CSPs to accelerate and broaden their NFV/SDN initiatives

According to Technology Business Research, Inc.’s (TBR) latest NFV/SDN Telecom Market Forecast, covering 2018 to 2023, 5G will push CSPs to adopt a new network architecture and both NFV and SDN will be critical aspects of that architecture going forward. As such, TBR expects NFV/SDN-related spend growth will correlate with 5G deployments. Since CSPs will need to upgrade their networks from an end-to-end perspective to realize the full potential of 5G, this will naturally drive CSPs toward the virtualization and cloudification of their networks. This trend will impact most, if not all, of the major network domains from an NFV/SDN perspective over the next five years. TBR notes that 5G core is inherently virtualized and that this will also naturally push CSPs deeper into the NFV/SDN space over the next five years as they transition to a stand-alone 5G network.

Rakuten’s legitimization of vRAN will also drive NFV/SDN market growth

Though significant skepticism remains in the industry that Rakuten will be able to make the vRAN model work, should this scenario occur, TBR believes it would embolden CSPs to double down on their own NFV/SDN initiatives, especially as it relates to vRAN. RAN is one of the costliest domains in the construction of a network, and it is a key area CSPs will be keen to virtualize to reap cost savings.

White-box adoption will proliferate, portending significant OEM disruption

TBR expects the use of white-box hardware in NFV/SDN environments will proliferate through the forecast period, accounting for 60% of NFV/SDN hardware spend in 2023, up from 15% in 2018. This industry shift toward white-box hardware will significantly disrupt incumbent OEMs’ business models, prompting them to evolve into software-centric companies. Industry organizations such as the Open Compute Project (OCP) and initiatives spearheaded by leading CSPs such as AT&T will fuel the rapid uptake of white boxes during the forecast period.

With the launch of BullSequana Edge and investments in quantum computing, Atos takes a pragmatic approach to executing digital transformation initiatives

For a few years, the impact of big data has created ebbs and flows in buyers’ behavior in end-consumer interactions and IT purchasing patterns, as is typical with any technology. Consumerization of business applications, demand for data quality and governance, and the adoption of connected technologies compel vendors such as Atos to explore opportunities around managing customer data and to invest in solutions that can help them protect their competitive edge.

During the Atos Technology Days 2019 opening keynote, Atos CEO Thierry Breton announced BullSequana Edge, the company’s first edge server to power AI everywhere. Built with the goal of delivering lots of power, including 165-teraflop capacity, that is stored locally, the appliance enables Atos to address the upcoming shift in data localization. Breton stated that while 80% of data globally is stored in data centers and in the cloud, that percentage is expected to shrink to 20% by 2025 as clients seek ways to analyze data in real time at the edge, where it is created. The addition of AI capabilities amplifies Atos’ position as an end-to-end services provider within the IoT space and closes gaps in the asymmetrical relationship between IT and operational technology (OT) departments.

Additionally, BullSequana Edge helps Atos address challenges of exponential data volumes and heterogenous data complexities due to the advent of AI and machine learning (ML), necessary blocks supporting the data economy foundation. With optimized security capabilities including infusion detection, disc encryption and secure boot, the BullSequana server enables Atos to alleviate common pain points of IT and OT, especially as the company builds and offers vertical-centric solutions with the hardware. Although offering a hardware appliance drifts Atos further from pure systems integrators (SIs), which typically manage asset-light portfolios, it brings Atos closer to key IT buyers, which remain the centralized governing body of the final IT purchases, even in discussions that include the C-Suite.

Atos also continues to enhance its quantum computing capabilities. As TBR wrote in its May 2019 Digital Transformation Insights Report, which focused on quantum, “Atos took its strengths in design computing for appliances and programming and emulation environments and announced several quantum research initiatives, including the opening of a global R&D lab in Yvelines, France, and Atos Quantum Learning Machine (QLM) implementations in Europe and the U.S. to enable clients to experiment with disruptive technologies, tackle the explosion of data and accelerate the number of practical use cases across industries. Additionally, about a year ago, Atos developed a consulting practice around quantum computing to educate and advise clients on whether it is possible to use quantum to accelerate business applications. During Atos Technology Days 2019, Atos announced myQLM, a light version of a QLM, which is an on-premises environment designed for quantum software developers. Users can download myQLM on their desktops and use a set of algorithms to train remotely, at home, at universities and research centers, and simulate the actual QLM. A Phyton-based language, QLM allows students and researchers to develop and share code within the community, creating additional entry points for Atos’ broader services portfolio. With customers ranging from universities and research centers to high-performing computer ecosystems and commercial clients, Atos, is building one use case at a time. For France-based oil and gas company Total, Atos is using a QLM simulator to accelerate the analysis of seismic activities, helping Total stay ahead of competitors. Atos is also working with Bayer and RWTH Aachen University in Germany to evaluate the use of quantum computing to research and analyze human disease patterns.“

Our analysis further states that we expect Atos to “monitor the underlying scientific and engineering breakthroughs of the competing architectural investments and accelerate the commercial utility through its development of leading-edge use case applications” in concert with its business partners as it looks to quantum to gain a competitive advantage. For Atos, partner ecosystems, such as with Fujitsu, 1QBit, Rigetti, IBM Q and D-Wave, will play a critical role in its ability to accelerate the development of business use cases.

Navigating the dynamics of the IT services markets demands vendors demonstrate innovation, which allows them to gain trust with new buyer personas. While many of Atos’ SI peers have invested heavily in areas such as marketing services to better appeal to the CMO buyer for customer experience opportunities, the company relies on its full technology stack to expand its addressable market. While some investments, such as in quantum, may not have sizable, tangible impact on its performance, Atos will benefit from being first to market once economic advantage is achieved.

According to a TBR special report on quantum and economic advantage, “What we do not know is how fast quantum computing will take off and what impact it will have on our knowledge as a society. What we do know is that it will take off — algorithm by algorithm, as economic advantage is achieved incrementally — and fundamentally change what we know.” TBR’s Digital Transformation Insights Report further states, “As vendors continue to battle business process and technological hurdles across all three phases of digital transformation — substitution, extension and transformation — quantum will be the one technology that will fundamentally transform enterprises and the way they go to market and sell products and services,” placing Atos in the spotlight through its innovation investments.

Atos Technology Days 2019, held in Paris on May 16 and 17, displayed myriad practical applications of emerging technologies. Held alongside one of the largest events centered on startups in Europe — Viva Technology, with 124,000 attendees, 13,000 startups and 3,300 investors — Atos Technology Days presented an excellent platform for Atos to showcase its innovation capabilities across its entire portfolio stack, including hardware, applications and services. Running under the theme Welcome to the Post-cloud Era, the presentations walked over 200 clients, partners, executives and industry analysts through Atos’ vision of the IT of tomorrow, centered on the edge, quantum, IoT and cybersecurity as well as Atos’ ability to stitch it all together to deliver business outcomes to its customers. Data is exploding, and Atos is preparing to accommodate this phenomenon by effectively managing, storing, securing and analyzing data.

A small but influential group from EY’s leadership team, including incoming Chairman and CEO Carmine Di Sibio, were on hand in a newly redesigned wavespace to recognize the winners of the EY NextWave Data Science Challenge. An extension of the program deployed in Australia last year, this global challenge resulted in 12,000 submissions from 4,500 participants from 477 universities in 15 countries.

The basic challenge: Predict human traffic patterns

The overarching goal of the project was to take a data set provided by EY partner Skyhook of citizens in the greater Atlanta area. The challenge was to take the citizens’ locations as of 3 p.m. and predict where those citizens would be located at 4 p.m. EY Global Analytics Program Director Antonio Prieto, who spearheaded this effort that will be expanded on in November, stated the intention was to connect students to a challenge that resonates with EY’s mission of building a better working world, which can be done through analytics-optimized smart cities.

Participants were allowed to enter multiple submissions as their models evolved and as they generated new “what if” scenarios. The award winners received cash prizes, EY badges and EY internships. The winners and their locations were:

First Place: Sergio Banchero is studying electronics in Australia and is a native of Brazil.

Second Place (shared): Katherine Edgley and Philipp Barthelme shared the second-place prize and are both studying applied mathematics at the University of Edinburgh.

Third Place: Chia Yew Ken of Singapore has an affinity for natural language processing and finds the parallels to AI pattern recognition interesting.

Each participant presented their basic findings and discussed the underpinning mathematical calculations and manipulations in ways that challenged this mature worker with a liberal arts background to comprehend. The incremental improvements on the algorithm scores seemed slight until put into context by Banchero, who translated his algorithm’s net improvement over the average of all submissions as ultimately capable of reducing 3,200 pounds of CO2 emissions, which would require rain forest acreage equivalent to 16 football fields to remediate naturally.