Rena is a Director of Custom Research at Current Analysis, specializing in Delivery Management. She is responsible for ensuring the delivery of actionable recommendations and guidance to clients to assist them in formulating their market development and execution strategies. Her expertise is in telecommunications and IT services, including business networking , communications, data center, security, and business continuity services.
• At IBM Think 2021, IBM released several capabilities that help customers craft what it calls an ‘intelligent data fabric’.
• IBM AutoSQL (Structured Query Language) for Cloud Pack for Data is designed to help streamline access to data stored in multiple locations.
Artificial intelligence (AI) is no longer the shiny new toy on the market that it was a few years ago. Many organizations have by now dabbled with the technology, and a large number have rolled up their sleeves and deployed multiple AI projects. As enterprises mature in their adoption of the technology, they are eager to deploy AI at scale, moving beyond one or two limited implementations to applying machine learning (ML) to more tasks and making it available to a larger number of business units.
• Domopalooza focused on the key themes of data agility, data literacy, and intelligent action.
• Domo announced multiple new products and features, including native integration with Amazon Redshift, availability of natural language generation, DDX Bricks, and Domo Everywhere.
Domo held its annual Domopalooza customer conference at the end of March, hosting a high energy event that outlined three key themes: data agility, data literacy, and intelligent action. Data agility refers to the need for organizations to respond quickly and easily to shifts in data demand and to make data accessible to all (including employees, partners, and customers). It requires a data architecture that integrates disparate data sets into a unified view, enabling the seamless flow of information. Data literacy is about empowering knowledge workers with analytical confidence, so they feel comfortable making data driven decisions. Intelligent action relates to the need to make data easy to use and engage with (such as via visualizations), so that line of business users can quickly use the insights to guide decisions.
• Custom Neural Voice can be trained to generate natural language that sounds like a specific person.
• Microsoft has considered the implications of Custom Neural Voice and prioritizes responsible use of the technology but the solution underscores the urgency for discussions related to Responsible AI.
Microsoft recently announced general availability, in limited access (use cases subject to Microsoft approval) of Custom Neural Voice, a service that uses artificial intelligence to generate natural language (enabling computers to ‘speak’). The achievement is quite impressive because of the level of customization it offers. Enabling computers to talk isn’t new, but what does raise eyebrows is that Custom Neural Voice can be trained to generate natural sounding speech that mimics a person. And not just a fictional person – but a specific individual. Continue reading “Microsoft’s Voice Mimicking Achievement Takes Natural Language Generation to New Levels, Albeit Controversial”→
• Corporate initiatives to promote greater environmentally responsible polices have become increasingly important to a broader audience in recent years
• Accenture and Salesforce plan to bring to market services that help enterprises better track and measure progress in implementing sustainability initiatives, including those related to diversity and governance.
Initiatives that promote environmental sustainability have been making headlines. In the fall of 2020, the state of California declared it would ban the sale of new gasoline-powered cars and trucks starting in 2035. At the end of January President Biden announced plans to replace all federal government vehicles with electric vehicles. And two days later, GM made front page news by announcing that it would stop manufacturing gasoline and diesel fueled cars and SUVs by 2035.
While announcements in the automotive space command widespread attention, efforts to promote greater sustainability are quietly being implemented in other industries as well, including the technology sector. Technology services providers have taken a two-pronged approach to promoting improved sustainability. As a first step, they have vowed to reduce their own carbon footprints, with many French IT services firms assuming a leadership role. Atos, a French IT services provider, has pledged to reduce its carbon emissions by 50% over the next five years, and to reduce the carbon emissions it influences by 50% over the next ten years. Similarly, France-based Capgemini has committed to being carbon neutral no later than 2025 and to be net zero by 2030. These IT services companies plan to reduce business travel, increase the use of renewal energy, utilize hybrid and electric cars, scrutinize supply chains, and participate in initiatives such as reforestation.
The second, and complementary, part of technology providers’ strategies is to help customers reduce their carbon footprints. For example, Capgemini uses AI and analytics to help companies analyze and optimize energy consumption and implement logistics solutions that reduce fuel consumption. Atos is making significant investments in sustainability and expects that decarbonization activities will generate a 1% revenue increase for the company in the three to four years post COVID-19. In 2020 the company acquired EcoAct, a 160 person strong carbon reduction strategy consulting firm. Atos plans to work with EcoAct to launch a global Decarbonization Excellence Center in H1 2021 and will work with customers on decarbonization assessments and roadmaps for achieving carbon neutrality, and will offer digital solutions that decarbonize business processes. Continue reading “Tech Services Providers Tackle Sustainability”→
Only slightly more than half of the respondents to GlobalData’s recent survey on emerging technologies felt that they fully understood artificial intelligence.
Before decision-makers act on the insights revealed by artificial intelligence, they need to have confidence in its findings, which requires an understanding of how they are obtained.
There is a widely accepted belief that artificial intelligence (AI) holds the potential to significantly alter the way organizations operate, vastly improving business outcomes. Businesses conceptually grasp that the technology’s benefits range from increasing efficiency and productivity to enhancing the customer experience. Yet, AI is still often viewed as a mysterious black box that yields little insight into how its findings are obtained. Without understanding what is happening under the covers, line-of-business leaders can be reluctant to act on the findings. Continue reading “Enterprises Require Tools That Explain AI Findings”→
• Telecom service providers are looking to up the role they play in customers’ advanced analytics initiatives by offering services related to data preparation and project deployment and management.
• Mobile operators are eager to explore opportunities in the monetization of user information, but this must be done carefully and with an eye to regional regulations and privacy-related concerns.
Analytics is a hot area, with many organizations looking to leverage the wealth of information they collect to improve efficiency, enhance productivity, make more informed decisions, and improve the customer journey. However, there are many steps that need to be taken before data can actually yield the intended results. It must be collected, transported, curated, processed, and then visualized in order to provide value to end users. It’s a process that requires an ecosystem of vendors, and telcos are looking to expand the role they play. Their contribution to the transport component is obvious, but they are now looking to insert themselves in other parts of the process as well by offering services related to data preparation and project deployment and management. Continue reading “Telecom Operators Look to Increase Their Role in Enabling Advanced Analytics for Enterprises”→
• Capgemini rolled out a suite of six services to help companies leverage the benefits of edge computing and 5G.
• Success will depend on Capgemini’s ability to demonstrate its industry expertise and experience in managing complex ecosystems, and customers’ appetite for new digital transformation projects during the pandemic.
Edge computing and 5G networking are hot technologies that claim to propel businesses into the future by supporting intelligent industry and digital transformation. There is no doubt that the proliferation of connected devices holds the potential to transform how businesses operate, leading to greater efficiency, increased automation, and more insightful and timely data-driven decision-making. Edge computing provides processing power closer to the source of data collection, improving latency and addressing privacy-related concerns. 5G networking enables businesses to transmit large volumes of data to an edge device quickly, reducing latency and costs associated with sending data to the cloud, and facilitating a more near real-time experience. Despite the benefits these technologies promise to deliver, identifying appropriate use cases and deploying the solutions can be a challenge for many organizations. Continue reading “Capgemini’s New Suite of Services Helps Customers Leverage Edge and 5G Technologies”→
A substantial portion of survey respondents revealed that IT budgets will remain relatively unchanged, and an encouraging 20% even indicated budget increases due to COVID-19.
Communication and collaboration, cloud services, security, networking, and mobility spending will be most resilient; at least 30% of respondents expect spending increases for these technologies.
Although infectious disease experts had warned of the potential of a pandemic for years, COVID-19 took most organizations by surprise. The majority of large enterprises had extensive business continuity and resiliency plans in place, but most were still scrambling to keep employees securely connected when governments mandated that all but the most essential workers stay home. This sudden change in normal business operations, combined with the economic impact of the global lockdown, has (not surprisingly) influenced IT spending plans for the remainder of the year – and most likely into 2021. Continue reading “COVID-19: GlobalData Survey Reveals Bright Spots for IT Spending”→
• AI was featured prominently during Microsoft Build 2020, with a tagline of ‘Putting AI Into Action’ and a goal of bringing state of the art AI to all developers.
• Microsoft made several announcements that supported this vision, including updates on Microsoft Project Turing model, investments in infrastructure for AI processing, and the preview of Project Bonsai.
Microsoft’s annual developer conference, Microsoft Build 2020, was held as a virtual event on May 19 and 20, with more than 200,000 people registered. When kicking off Microsoft Build 2020, CEO Satya Nadella noted that the technology industry is being called upon to address the world’s most acute needs, and that developers are now more important than ever. He pointed out that organizations need the ability to remote everything at a moment’s notice, and to simulate and automate everywhere to enable more agile responses. Continue reading “Build 2020: Microsoft ‘Goes Big’ on AI and Demonstrates Thought Leadership”→
In mid-May, AWS highlighted its portfolio of AI tools and solutions during its AWS Summit Online for the Americas region and announced the general availability of Amazon Kendra for enterprises.
Tools that support AI model development and management and pre-built solutions that can be easily deployed by developers who aren’t AI experts help streamline AI adoption.
AWS understands the challenges enterprises face when building their own machine learning models. The company notes that when scaling AI adoption, enterprises face wide-ranging complexities that can start as early as the data collection stage and continue throughout the model management lifecycle. At the beginning of a project, organizations face challenges related to data identification, storage, and curation as they pull together disparate data sources. Later, while building and training models, they need to manage numerous other complexities, such as sharing notebooks and pre-trained models. They need to ensure effective collaboration among what can be a growing number of individuals or teams, each with their own specializations. And, since machine learning models aren’t usually perfect the first time, team members need to communicate during the process of model tuning and optimization. They need to manage multiple versions of models, run experimental models in real time, and compare results. Even after deployment, machine learning algorithms need to be managed and monitored for concerns such as data drift, with newer versions deployed as additional data is collected or the factors that impact model results change. Managing these tasks can be challenging, and as AWS rightly points out, tools that help manage the complexities do much to streamline and speed AI deployments. Continue reading “AWS Aims to Make AI More Accessible for Both AI Specialists and Non-AI Experts”→