Kyndryl has a competitive networking services unit, particularly in partnership with Cisco. Its focus has been on SD-WAN, campus networks, and network management as part of broader cloud services deals. This 5G partnership with Nokia is its first serious effort to work with one of the major carrier-grade vendors using cellular technology. It creates an opportunity for Kyndryl to position itself as a provider of services that underpin IoT and edge applications, rather than only cloud, which has until now been its main strength.
Prior to the Kyndryl announcement, Nokia was already developing private 5G solutions under the moniker Digital Automation Cloud (DAC). A key customer is Volkswagen, using the network to connect robots and wireless assembly tools. Over-the-air vehicle updates are also tested over the private network. Volkswagen operates in a dedicated 3.7-3.8 GHz band, which was allocated by the Federal Network Agency in Germany. This illustrates a third option for accessing spectrum, which will become an important consideration in private 5G rollouts.
Private 5G Use Cases
Private 5G has several benefits such as low latency, long-range, support for many users per access point, and provision for devices that are mobile due to handover. It is unlikely that it will completely replace other technologies, like wireless LAN, but it is very compelling for certain use cases.
Private 5G is useful on large sites, like mines, ports, farms, and warehouses where connected machines are moving about or some devices – like perimeter security cameras – are just out of reach. Utilities, like power, gas, and water, with infrastructure that needs to be monitored over long distances, will also start looking at it as a part of their predictive maintenance and resiliency systems. Low latency will become increasingly important as we see more and more customer-facing digital services delivered on-site and autonomous robots in the production environment.
Another major benefit of private 5G compared to operating on public service is that data can remain within the organisation’s own network for as long as possible, providing more security and control.
Private 5G Gaining Popularity
There has been a lot of activity over the last year in this space, with the hyperscalers, telecom providers and network equipment vendors developing private 5G offerings.
Last year, the AWS Private 5G was announced, a managed service that includes core network hardware, small-cell radio units, SIM cards, servers, and software. The service operates over a shared spectrum, like the Citizens Broadband Radio Service (CBRS) in the US, where the initial preview will be available. CBRS is considered a lightly licenced band. This builds on AWS’s private multi-access edge compute (MEC) solution, released in conjunction with Verizon to integrate AWS Outposts with private 5G operating in licenced spectrum. A customer reference highlighted was low latency, high throughput analysis of video feeds from manufacturing robots at Corning.
Similarly, Microsoft launched a private MEC offering last year, a cloud and software stack designed for operators, systems integrators, and ISVs to deploy private 5G solutions. The system is built up of components from Azure and its acquisition of Metaswitch. AT&T is an early partner bringing a solution to the market built on Microsoft’s technology and the operator’s licenced spectrum. Microsoft highlighted use cases such as asset tracking in logistics, factory operations in manufacturing, and experiments with AI-infused video analytics to improve worker safety.
Organisations are likely to begin testing private 5G this year for Industry 4.0 applications, either at single sites in the case of factories or in select geographic areas for Utilities. Early applications will mostly focus on simple connectivity for mobile machines or remote equipment. In the longer term, however, the benefits of private 5G will become more apparent as AI applications, such as video analysis and autonomous machines become more prevalent. This will require the full ecosystem of players, including telecom providers, network vendors, cloud hyperscalers, systems integrators, and IoT providers.
IT and Digital Leaders in Financial Services are aware of the benefits of IoT and there are some use cases that most of them think will help transform Financial Services (Figure 2).
However, there are many more potential use cases. Here are some use cases whose volume will only grow every day to fuel incessant data generation, consumption and processing at the Edge.
Smart Homes. IoT devices like Alexa/Google Home have capabilities to become “bank in a speaker” with edge computing.
In-Sync Omnichannels. IoT devices can be synced with other banking channels. A customer may start a transaction on an IoT device and complete it in a branch. Facial recognition can be used to identify the customer after he/she walks in and synced IoT devices will ensure that the transaction is completed without any steps repeated (zero re-work) thereby enhancing customer satisfaction.
Virtual Relationship Managers. In a digital branch, the customer may use Virtual Reality (VR) headsets to engage with virtual relationship managers and relevant experts. Gamification using VR can be amazingly effective in the area of financial literacy and financial planning.
Home and Auto Purchase. VR may also find use in home and auto purchase processes with financing built into it. The entire customer journey will have a much smoother experience with edge computing.
Auto and Health Insurance. Companies can use IoT (device installed in the vehicle) plus edge computing to monitor and improve driving behaviour, eventually rewarding safety with lower premiums. The growth in electric mobility will continue to provide the basis for auto insurance. Companies can use wearables to monitor crucial health parameters and exercising habits. The creation of real-time dynamic rewards around it can change behaviour towards a healthier lifestyle. Awareness, longevity, rising costs and pandemic will only fuel this sector’s growth.
Payments. Device to device contactless payment protocol is picking up and IoT and edge computing can create next-gen revolution in payments. Your EV could have an embedded wallet and pay for its parking and toll.
Branch/ATM. IoT sensors and CCTV footage from branches/ATMs can be utilised in real-time to improve branch productivity as well as customer engagement, at the same time enhancing security. It could also help in other situations like low cash levels in ATMs and malfunctions. Sending live video streams for video analytics to the cloud can be expensive. By processing data within the device or on-premises, the Edge can help lower costs and reduce latency.
Trading in Securities. Another area where response time matters is algorithmic trading. Edge computing will help to quickly process and analyse a large amount of data streaming real-time from multiple feeds and react appropriately.
Trade Finance. Real-time tracking of goods may add a different dimension to the risk, pricing and transparency of supply chains.
Cloud vs Edge
The decision to use cloud or edge will depend on multiple considerations. At the same time, all the data from IoT devices need not go to the cloud for processing and choke network bandwidth. In fact, some of this data need not be stored forever (like video feeds etc). As a result, with the rise in the number of IoT devices and increasing financial access, edge computing will find its place in the sun and complement (and not compete) with cloud computing.
The views and opinions mentioned in the article are personal.
Anupam Verma is part of the Leadership team at ICICI Bank and his responsibilities have included leading the Bank’s strategy in South East Asia to play a significant role in capturing Investment, NRI remittance, and trade flows between SEA and India.
Effective prescriptive maintenance only becomes possible after the accumulation and integration of multiple data sources over an extended period. Inference models should understand both normal and abnormal equipment performance in various conditions, such as extreme weather, during incorrect operation, or when adjacent parts are degraded. For many smaller organisations or those deploying new equipment, the necessary volume of data will not be available without the assistance of equipment manufacturers. Moreover, even manufacturers will not have sufficient data on interaction with complementary equipment. This provides an opportunity for large operators to sell their own inference models as a new revenue stream. For example, an electrical grid operator in North America can partner with a similar, but smaller organisation in Europe to provide operational data and maintenance recommendations. Similarly, telecom providers, regional transportation providers, logistics companies, and smart cities will find industry players in other geographies that they do not naturally compete with.
Employing multiple sensors. Baseline conditions and failure signatures are improved using machine learning based on feeds from multiple sensors, such as those that monitor vibration, sound, temperature, pressure, and humidity. The use of multiple sensors makes it possible to not only identify potential failure but also the reason for it and can therefore more accurately prescribe a solution to prevent an outage.
Data assessment and integration. Prescriptive maintenance is most effective when multiple data sources are unified as inputs. Identify the location of these sources, such as ERP systems, time series on site, environmental data provided externally, or even in emails or on paper. A data fabric should be considered to ensure insights can be extracted from data no matter the environment it resides in.
Automated action. Reduce the potential for human error or delay by automatically generating alerts and work orders for resource managers and service staff in the event of anomaly detection. Criticality measures should be adopted to help prioritise maintenance tasks and reduce alert noise.
It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).
#1 Establish a Data Culture
The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?
Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset, will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.
#2 Ingrain a Digital-First Mindset
Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.
A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.
AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.
#3 Demystify AI
The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.
They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.
#4 Drive Implementation Bottom-Up
AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.
As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.
The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.
Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.
At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.
The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.
We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.
#5 Invest in Upskilling
The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).
Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.
How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.
The Need to enable Foundational Shifts. The younger generation is more aware of environmental, social and governance issues that the world continues to face. Many of the countries in the region are emerging economies, where these issues become more apparent. COVID-19 has also inculcated an empathy in people and they are thinking of future success in terms of impact. The desire to enable foundational shifts is giving direction to the transformation journey in the region. The wonderful new paradigm that is the Digital Economy allows us to cut across all segments; and technology and its advancements has immense potential to create a more sustainable and inclusive future for the world.
Realising the Power of Momentum. The pandemic has caused major disruptions in the region. But every crisis also presents an opportunity to perhaps re-imagine a brighter world through a digital lens.The other thing that the pandemic has done is made people and organisations realise that to succeed they need to be open to change – and that momentum is important. As organisations had to pivot fast, they realised what I have been saying for years – we shouldn’t “let perfect get in the way of better”. This adaptability and the readiness to fail fast and learn from the mistakes early for eventual success, is leading to faster and more agile transformation journeys.
Where are we seeing the most impact?
Industries are Transforming. There are industries such as Healthcare and Education that had to transform out of a necessity and urgency brought about by the COVID-19 pandemic. This has led to a greater impetus for change and optimism in these industries. These industries will continue to transform as governments focus significantly on creating “Social Safety Nets” and technology plays a key role in enabling critical services across Health, Education and Food Security. Then there are industries, such as the Financial Services and Retail, that had a strong customer focus and were well on their digital journeys before the pandemic. The pandemic boosted these efforts.
But these are not the only industries that are transforming. There are industries that have been impacted more than others. There are several instances of how organisations in these industries are demonstrating not only resilience but innovation. The Travel & Hospitality industry has had several such instances. As business models evolve the industry will see significant changes in digital channels to market, booking engines, corporate service offerings and others, as the overall Digital Strategy is overhauled.
Technologies are Evolving. Organisations depended on their tech partners to help them make the fast pivot required to survive and succeed in the last year – and tech companies have not disappointed. They have evolved their capabilities and continue to offer innovative solutions that can solve many of the ongoing business challenges that organisations face in their innovation journey. More and more technologies such as AI, machine learning, robotics, and digital twins are getting enmeshed together to offer better options for business growth, process efficiency and customer engagement. And the 5G rollouts will only accelerate that. The initial benefits being realized from early adoption of 5G has been for consumers. But there is a much bigger impact that is waiting to be realised as 5G empowers governments and businesses to make critical decisions at the edge.
Tech Start-ups are Flourishing. There are immense opportunities for technology start-ups to grow their market presence through innovative products and services. To succeed these companies need to have a strong investment roadmap; maintain a strong focus on customer engagement; and offer technology solutions that can fulfil the global needs of their customers. Technologies that promote efficiency and eliminate mundane tasks for humans are the need of the hour. However, as the reliance on technology-led transformation increases, tech vendors are becoming acutely aware that they cannot be best-in-class across the different technologies that an organisation will require to transform. Here is where having a robust partner ecosystem helps. Partnerships are bringing innovation to scale in Asia.
We can expect Asia to emerge as a powerhouse as businesses continue to innovate, embed technology in their product and service offerings – and as tech start-ups continue to support their innovation journeys.
Ecosystm CEO Amit Gupta gets face to face with Garrett Ilg, President Asia Pacific & Japan, Oracle to discuss the rise of the Asia Digital economies, the impact of the growing middle class on consumerism and the spirit of innovation across the region.
The country clearly recognises the need for a robust infrastructure to accelerate innovation – Microsoft has also received approval from the New Zealand government in September last year to open a data centre region. To support the future of cloud services and to fulfil New Zealand progressive data centre demands, CDC Data Centres have also planned to develop two new hyperscale data centres in Auckland, New Zealand.
“The introduction of the Datagrid New Zealand data centre in Invercargill will be a welcome asset to the Southland region of New Zealand. With primary industries being farming, fishing and forestry, the region has done well throughout the COVID-19 pandemic. This initiative will further benefit the local economy by delivering opportunities for economic prosperity for local businesses.
With long-term growth at the data centre expected to consume up to 100MW of renewable energy, Meridian Energy is well equipped to provide renewable energy generated at the Manapouri hydroelectric power station, capable of generating 850MW. The potential closure of the 550MW Tiwai Point Aluminium Smelter is expected to put the country in a position of oversupply.
The data centre will be a critical piece of New Zealand’s infrastructure, supporting the roll-out of 5G networks by telecom providers and the need for low-latency cloud compute and data storage. Datagrid will provide a competitive alternative to the likes of Microsoft’s new data centre.
While the construction and opening of the data centre will possibly add more stress to New Zealand’s under-resourced construction sector, it will also create tech jobs in the Southland region, in the long term. Unique to the region is the Southern Institute of Technology that has a Zero Fees Scheme that has been confirmed until the end of 2022. The data centre will help to keep skilled tech workers in the region.“
Identifying emerging cloud computing trends can help you drive digital business decision making, vendor and technology platform selection and investment strategies.Gain access to more insights from the Ecosystm Cloud Study.
Hitachi announced their plans to acquire US based software development company GlobalLogic for an estimated USD 9.6 billion, including debt repayment. The transaction is expected to close by end of July, after which GlobalLogic will function under Hitachi’s Global Digital Holdings.
GlobalLogic was founded in 2000, and the Canada Pension Plan Investment Board and Swiss investment firm Partners Group have 45% of ownership; with the remainder owned by the company’s management.
Hitachi’s Business Portfolio Expansion
The acquisition of GlobalLogic is a part of Hitachi’s move to focus and extend the range of Hitachi’s digital services business. As Hitachi aims to expand from electronics hardware to concentrate on digital services, they are looking to benefit from GlobalLogic’s range of expertise – from chips to cloud services. Silicon Valley-based GlobalLogic has a presence in 14 countries with more than 20,000 employees and 400 active clients in industries including telecommunications, healthcare, technology, finance and automotive. This will also expand Hitachi’s network outside Japan by providing them access to a global customer base and will boost their software and solutions platforms, including Hitachi IoT portfolio and data analytics.
The GlobalLogic deal follows another big acquisition of ABB’s power grid business by Hitachi in July 2020 to focus on clean energy and distributed energy frontiers. This makes Hitachi one of the largest global grid equipment and service providers in all regions.
“Hitachi’s move to acquire GlobalLogic is very interesting and is in line with the growing trend of global Operation Technology (OT) vendors riding the wave of Industry 4.0 and ‘Product as a Service’ models – essentially, to move up the margin ladder with more digital services added on to their already established equipment business. Siemens, Schneider Electric, Panasonic, ABB, Hitachi and Johnson Controls are some of the prominent vendors who have taken pole positions in their respective industry domains, in this race to digitally transform their businesses and business models. Last year, Panasonic made a very similar move, taking a 20% equity stake in Blue Yonder, a leading supply chain software provider.
With rapid advancements in computing and communications (5G), it is now possible to converge the IT (Information Technology supporting enterprise information flows), the OT (Operational Technology – machine level control of the physical equipment), and the ET (Engineering Technology in the Product Design and Development space such as CAD, CAM, PDM etc.) domains. Three worlds that were separate till now. The convergence of these three worlds enables high impact use cases in automation, product, process, and business model innovation in almost all sectors, such as autonomous vehicles, energy efficient buildings, asset tracking and monitoring, and predictive and prescriptive maintenance. For the OT vendors therefore, it becomes critical to acquire IT and ET capabilities to become successful in the new cyber physical world. Most OT vendors are choosing to acquire these capabilities through strategic partnerships (such as Siemens with Atos and SAP; Panasonic with Blue Yonder) or acquisitions (such as Hitachi and GlobalLogic) rather than develop such capabilities organically in completely new domains.“