aws,-azure,-and-google-cloud-redefining-telecoms-commercial-models

AWS, Azure, and Google Cloud Redefining Telecoms Commercial Models

 

5G is the first ‘G’ in cellular technologies that paves the way for Communications Service Providers (CSPs) to operate in the software layer. Software represents a departure from transactional model characteristic of telecom equipment manufacturing. Software points toward a recurring revenue model that is more consistent and predictable akin to that of hyperscalers. According to global technology intelligence firm ABI Research, that has implications on the commercial models that underpin the industry.

“With a growing importance of software, the commercial imperative from a vendor’s perspective is stark: depart from a finite supply of (3G and 4G) equipment, characterised by scarcity, to monetization models based on (5G) software where the supply is essentially infinite,” states Don Alusha, Senior Analyst 5G Core & Edge Networks at ABI Research.

With 3G and 4G networks, commercial arrangements revolve around a CAPEX purchase model. CSPs pay a specific price to own an asset. It could be hardware (cellular antennas) or software predicated on perpetual licensing. The value can be paid in cash, financed, or leased. But what is most relevant, however, is that there is a set price. Once the deal is agreed, Network Equipment Vendors (NEVs) like Ericsson, Huawei, Nokia, and ZTE are guaranteed an upfront payment at the point of signing a contract. In a CAPEX model, NEVs have one stress point: winning the deal. The risk of implementing the purchased technology falls to CSPs. A key point to note is that, in general, by the time a product is adopted and used, the bulk of the budget has already been spent upfront for the installation, integration, and other professional services needed to get the product operational.

By contrast, in a 5G ecosystem, and by extension, cloud and software world, there may not be a ‘product’ sale. Technology suppliers still need to channel the required Research and Development (R&D) to build the technology and win a deal. They need to invest in marketing, execute the sales cycle in the hope they win the deal. In that respect, there is not much difference from the CAPEX model. The difference lies in the fact that OPEX models are associated with recurring (micro-) transactions—extra compute, more storage, more modules, etc. “Further, businesses built on OPEX models typically invest a significant amount of capital upfront and then try to make up with volume because of a superior cost structure that is associated with software; the marginal cost of producing an extra copy is very small. That underpins hyperscaler’s (Amazon, Google, and Microsoft) business model and strategy,” Alusha explains.

Though very subtle, there is an increasing consumerization of telecom technologies because of a growing adoption of cloud and software. The software business is a scale economic business. A considerable investment is made upfront to develop a software product and then the marginal cost of producing each one is very small. The fundamental difference between building software and manufacturing equipment is that the latter entails the creation and transfer of ownership of a product, while the former is much more intangible. There are advantages from a balance sheet perspective, as now CSPs pay for software in a rough approximation for their usage over time—an operational expense—as opposed to a fixed-cost basis in a CAPEX-centric world. This improves their Return-on-Invested Capital (ROIC) measurements.

In the new world of cloud and software, in addition to selling a transaction, NEVs must also make a material and positive impact on the recipient of the service to create value. “This enables the industry to explore new business models that look beyond where the money is in the value chain, to where it will be in the years to come. Cloudification of telecom equipment offers unprecedented opportunities (e.g., innovation, better economics, business agility, etc.) but it inherently constitutes new technologies for the industry and there is a risk attached. There will be challenges, given the lack of maturity and many unknowns around performance, best practices, and control of technology assets. But operators that rise to these challenges first may well gain a competitive market advantage,” Alusha concludes.

These findings are from ABI Research’s Cloudification of Telecom Technologies and Equipment application analysis report. This report is part of the company’s 5G Core & Edge Networks research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

oil-&-gas-iot-core-analytics-service-revenues-to-grow-to-us$712-million-in-2026

Oil & Gas IoT Core Analytics Service Revenues to Grow to US$712 Million in 2026

 

The Oil & Gas (O&G) IoT Analytics market garners heavy investment, yet it is deeply challenged by complex system integrations, siloed data, and  Supervisory Control and Data Acquisition (SCADA) management systems. In-house analytics is no longer a sustainable and cost-effective IoT option, and oil & gas firms have widely recognized the expertise of IoT cloud Platform-as-a-Service (PaaS)/SaaS vendors. According to global tech market advisory firm ABI Research, spending on big data and analytics in the oil and gas industry totaled US$156 million in 2020, an annual increase of 36.8% from 2018. Over the next 6 years, the oil & gas IoT core analytics service revenue will grow to US$712.7 million.

“Instead of developing analytics capabilities in-house, more and more enterprises are turning to supplier advanced analytics and Artificial Intelligence (AI) PaaS/SaaS offerings enabled through extensive cross-industry collaborations.  Partnership examples include Total Oil and Google Cloud, BP and Azure, and Seeq and Saudi Aramco. Simultaneously, the leading IoT vendors are continually competing for the top O&G contracts by offering an end-to-end solution and expanding their marketplace portfolios toward the edge,” explains Kateryna Dubrova, Research Analyst at ABI Research.

Azure and Amazon Web Services (AWS) are positioned as leading end-to-end solutions with basic public cloud toolkits. While Seeq, Foghorn, Falkonry, Manna, and Uptake, provide more advanced, specialized oil & gas analytics solutions. DataRobot, Noodle.ai, and Dataiku provide IoT ML integration services, with powerful AI engines and low-to-no-code solutions. Simultaneously, Nokia, C3.ai, Teradata, KX, and GE are positioning themselves firmly as offering system integration and overall digital transformation services for the oil & gas sector.

The diversification of the investment portfolios toward green energy and emission monitoring technologies is among the top trends in the oil and gas analytics market. The O&G enterprises and vendors are focusing  their efforts on the “green” market, driving the demand for “green” analytics use cases and applications. “Advanced analytics for upstream and downstream oil and gas operations is more or less solidified, so monitoring carbon emissions, lowering carbon footprints, and related green energy activities, are expected to become popular for advanced analytics monetization,” says Dubrova.

O&G enterprises are showcasing rapid adoption of the cloud and cloud-native analytics applications as components of their digital transformation model. “Cloud-based applications are available through subscription-based plans up to fully-managed services.. In such cases, there are considerable cost savings on infrastructure, including improving efficiencies and lowering the production costs,” Dubrova concludes.

These findings are from ABI Research’s IoT Analytics Services for Oil and Gas Markets application analysis report. This report is part of the company’s M2M, IoT & IoE research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

retina-ai-health,-inc.-welcomes-nwamaka-imasogie-as-chief-product-officer

RETINA-AI Health, Inc. Welcomes Nwamaka Imasogie as Chief Product Officer

 

Nwamaka Imasogie has joined RETINA-AI Health, Inc. as Chief Product Officer. She joins from Microsoft’s Healthcare AI and Research team where she was Principal Software Engineer. Nwamaka has a passion for healthcare and is driven to build technology that will save lives. Furthermore, she is a technology entrepreneur who has founded and sold two Tech startups. Prior to joining Microsoft, she was Co-Founder and Chief Technology Officer at GitLinks, a Tech Startup that provided and verified cybersecurity, regulatory compliance, and validation of open source software. She built the technology stack and led the company as CTO to its acquisition by the multi-billion dollar enterprise software firm, Infor. Before GitLinks, Nwamaka was an Engineering manager at Chevron.

Nwamaka obtained her first computer at the age 5 and has a passion for creating and stewarding products from conception to shipping. Her hands-on programming experience includes the following platforms and technologies: MLOps, Databricks, PyTorch, DevOps (AWS, GCP, Azure), Python, Javascript, Frontend, UI/UX, Continuous Integration, kubernetes, Docker, amongst others. She has published in Machine learning/Artificial Intelligence/Natural language Processing.

Nwamaka obtained a Master’s degree in Computer Science from Cornell University, and a Bsc. in Electrical Engineering from the University of Houston. She is the inventor on two technology U.S. patents.

Dr. Stephen G. Odaibo, RETINA-AI Health, Inc.’s CEO and Founder had the following to say: “We are thrilled that Nwamaka is joining us as Chief Product Officer. She has vast experience in software product development, regulatory compliance, and healthcare tech — including her most recent role as Principal Software Engineer on the Microsoft Healthcare AI team. We are assembling an exceptionally talented and multidisciplinary leadership team and are especially delighted that Nwamaka has chosen to join us in building RETINA-AI Health, Inc.”

ABOUT RETINA-AI Health, Inc:
RETINA-AI Health, Inc. is a privately-held Delaware C-Corp founded in 2017 and headquartered in Houston Texas. The company is focused on building artificial intelligence to transform healthcare and improve the outcomes of prevalent chronic diseases such as diabetes. More broadly, the company develops and deploys retina-based AI for detection of systemic chronic diseases at scale. RETINA-AI Health, Inc. has a strong unwavering commitment to adhere to the highest standards of quality, while continuously leading the world in healthcare AI innovation.