Cloud Computing Archives - gettectonic.com

09Sep

Predictive Analytics

Industry forecasts predict an annual growth rate of 6% to 7%, fueled by innovations in cloud computing, artificial intelligence (AI), and data engineering. In 2023, the global data analytics market was valued at approximately $41 billion and is expected to surge to $118.5 billion by 2029, with a compound annual growth rate (CAGR) of 27.1%. This significant expansion reflects the growing demand for advanced analytics tools that provide actionable insights. AI has notably enhanced the accuracy of predictive models, enabling marketers to anticipate customer behaviors and preferences with impressive precision. “We’re on the verge of a new era in predictive analytics, with tools like Salesforce Einstein Data Analytics revolutionizing how we harness data-driven insights to transform marketing strategies,” says Koushik Kumar Ganeeb, a Principal Member of Technical Staff at Salesforce Data Cloud and a distinguished Data and AI Architect. Ganeeb’s leadership spans initiatives like AI-powered Salesforce Einstein Data Analytics, Marketing Cloud Connector for Data Cloud, and Intelligence Reporting (Datorama). His expertise includes architecting vast data extraction pipelines that process trillions of transactions daily. These pipelines play a crucial role in the growth strategies of Fortune 500 companies, helping them scale their data operations efficiently by leveraging AI. Ganeeb’s visionary work has propelled Salesforce Einstein Data Analytics into the forefront of business intelligence. Under his guidance, the platform’s advanced capabilities—such as predictive modeling, real-time data analysis, and natural language processing—are now pivotal in transforming how businesses forecast trends, personalize marketing efforts, and make data-driven decisions with unprecedented precision. AI and Machine Learning: The Next Frontier Beginning in 2018, Salesforce Marketing Cloud, a leading engagement platform used by top enterprises, faced challenges in extracting actionable insights and enhancing AI capabilities from rapidly growing data across diverse systems. Ganeeb was tasked with overcoming these hurdles, leading to the development of the Salesforce Einstein Provisioning Process. This process involved the creation of extensive data import jobs and the establishment of standardized patterns based on consumer adoption learning. These automated jobs handle trillions of transactions daily, delivering critical engagement and profile data in real-time to meet the scalability needs of large enterprises. The data flows seamlessly into AI models that generate predictions on a massive scale, such as Engagement Scores and insights into messaging and language usage across the platform. “Integrating AI and machine learning into data analytics through Salesforce Einstein is not just a technological enhancement—it’s a revolutionary shift in how we approach data,” explains Ganeeb. “With our advanced predictive models and real-time data processing, we can analyze vast amounts of data instantly, delivering insights that were previously unimaginable.” This innovative approach empowers organizations to make more informed decisions, driving unprecedented growth and operational efficiency. Real-World Success Stories Under Ganeeb’s technical leadership, Salesforce Einstein Data Analytics has delivered remarkable results across industries by leveraging AI and machine learning to provide actionable insights and enhance business performance. In the past year, leading companies like T-Mobile, Fitbit, and Dell Technologies have reported significant improvements after integrating Einstein. Ganeeb’s proficiency in designing and scaling data engineering solutions has been critical in helping these enterprises optimize performance. “Scalability with Salesforce Einstein Data Analytics goes beyond managing data volumes—it ensures that every data point is converted into actionable insights,” says Ganeeb. His work processing petabytes of data daily underscores his commitment to precision and efficiency in data engineering. Navigating Data Ethics and Quality Despite the rapid growth of predictive analytics, Ganeeb emphasizes the importance of data ethics and quality. “The accuracy of predictive models depends on the integrity of the data,” he notes. Salesforce Einstein Data Analytics addresses this by curating datasets to ensure they are representative and free from bias, maintaining trust while delivering reliable insights. By implementing rigorous data quality checks and ethical considerations, Ganeeb ensures that Einstein Analytics not only delivers actionable insights but also fosters transparency and trust. This balanced approach is key to the responsible use of predictive analytics across various industries. Future Trends in Predictive Analytics The future of predictive analytics looks bright, with AI and machine learning poised to further refine the accuracy and utility of predictive models. “Success lies in embracing technological advancements while maintaining a human touch,” Ganeeb notes. “By combining AI-driven insights with human intuition, businesses can navigate market complexities and uncover new opportunities.” Ganeeb’s contributions to Salesforce Einstein Data Analytics exemplify this balanced approach, integrating cutting-edge technology with human insight to empower businesses to make strategic decisions. His work positions organizations to thrive in a data-driven world, helping them stay agile and competitive in an evolving market. Balancing Benefits and Challenges – Predictive Analytics While predictive analytics offers vast potential, Ganeeb recognizes the challenges. Ensuring data quality, addressing ethical concerns, and maintaining transparency are crucial for its responsible use. “Although challenges remain, the future of AI-based predictive analytics is promising,” Ganeeb asserts. His work with Salesforce Einstein Data Analytics continues to push the boundaries of marketing analytics, enabling businesses to harness the power of AI for transformative growth. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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September 9, 2024in Google Analytics, Salesforce Data Cloud, Salesforce Einstein, Salesforce Marketing Cloud

07Sep

Adopting Salesforce Security Policies

Data breaches reached an all-time high in 2023, affecting more than 234 million individuals, and there’s no sign of the trend slowing down. At the center of this challenge is how organizations allocate resources to safeguard customer data. One of the most critical systems for managing this data is CRM platforms like Salesforce, used by over 150,000 U.S. businesses. However, security blind spots within Salesforce continue to pose significant risks. To address these concerns, the National Institute of Standards and Technology (NIST) offers a strategic framework for Salesforce security teams. In February 2024, NIST released Version 2.0 of its Cybersecurity Framework (CSF), marking the first major update in a decade. Key improvements include the introduction of a new “Govern” function, streamlining of categories to simplify usability, and updates to the “Respond” function to enhance incident management. This framework now applies across all industries, not just critical infrastructure. For Salesforce security leaders, these changes will significantly affect how they manage security, from aligning Salesforce practices with enterprise risk strategies to strengthening oversight of third-party apps. Here’s how these updates will influence Salesforce security going forward. What is the NIST Cybersecurity Framework 2.0? The NIST Cybersecurity Framework, first launched in 2014, was developed after an executive order by President Obama, aiming to provide a standardized set of guidelines to improve cybersecurity across critical infrastructure. The framework’s objectives include: The newly updated NIST CSF 2.0, released in 2024, expands on the original framework, providing organizations with structured, yet flexible, guidance for managing cybersecurity risks. It revolves around three core components: the CSF Core, CSF Profiles, and CSF Tiers. Key Components of NIST Cybersecurity Framework 2.0 These components help organizations understand, assess, and improve their cybersecurity posture, forming the basis for risk-informed strategies that align with organizational needs and the evolving threat landscape. Key Updates in the NIST Cybersecurity Framework 2.0 and Their Impact on Salesforce Security The 2024 updates to NIST CSF offer insights that Salesforce security leaders can use to align their strategies with evolving cybersecurity risks. Implementation Strategies for Salesforce Security Leaders To incorporate CSF 2.0 into Salesforce security operations, leaders should: Conclusion: Embracing NIST CSF 2.0 to Strengthen Salesforce Security The 2024 NIST Cybersecurity Framework updates offer crucial insights for Salesforce security leaders. By adopting these practices, organizations can enhance data protection, strengthen incident response capabilities, and ensure business continuity—critical for those relying on Salesforce for managing sensitive customer data. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

September 7, 2024in Data, Enterprise, Salesforce, Salesforce Shield, Technology

06Sep

AI Senate Bill 1047

California’s new AI bill has sparked intense debate, with proponents viewing it as necessary regulation and critics warning it could stifle innovation, particularly for small businesses. Senate Bill 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, mandates that developers of advanced AI systems costing at least $100 million to train must test their models for potential harm and put safeguards in place. It also offers whistleblower protections for employees at large AI firms and establishes CalCompute, a public cloud computing resource aimed at startups and researchers. The bill is awaiting Governor Gavin Newsom’s signature by Sept. 30 to become law. Prominent AI experts, including Geoffrey Hinton and Yoshua Bengio, support the bill. However, it has met resistance from various quarters, including Rep. Nancy Pelosi and OpenAI, who argue it could hinder innovation and the startup ecosystem. Pelosi and others have expressed concerns that the bill’s requirements might burden smaller businesses and harm California’s leadership in tech innovation. Gartner analyst Avivah Litan acknowledged the dilemma, stating that while regulation is critical for AI, the bill’s requirements might negatively impact small businesses. “Some regulation is better than none,” she said, but added that thresholds could be challenging for smaller firms. Steve Carlin, CEO of AiFi, criticized the bill for its vague language and complex demands on AI developers, including unclear guidance on enforcing the rules. He suggested that instead of focusing on AI models, legislation should address the risks and applications of AI, as seen with the EU AI Act. Despite concerns, some experts like Forrester Research’s Alla Valente support the bill’s safety testing and whistleblower protections. Valente argued that safeguarding AI models is essential across industries, though she acknowledged that the costs of compliance could be higher for small businesses. Still, she emphasized that the long-term costs of not implementing safeguards could be greater, with risks including customer lawsuits and regulatory penalties. California’s approach to AI regulation adds to the growing patchwork of state-level AI laws in the U.S. Colorado and Connecticut have also introduced AI legislation, and cities like New York have tackled issues like algorithmic bias. Carlin warned that a fragmented state-by-state regulatory framework could create a costly and complex environment for developers, calling for a unified federal standard instead. While federal legislation has been proposed, none has passed, and Valente pointed out that relying on Congress for action is a slow process. In the meantime, states like California are pushing ahead with their own AI regulations, creating both opportunities and challenges for the AI industry. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

September 6, 2024in Salesforce

15Aug

Deepfake Detection With New Tool

Pindrop Expands Deepfake Detection with New Tool On Thursday, voice authentication vendor Pindrop expanded its deepfake detection capabilities with the preview release of Pindrop Pulse Inspect, a tool designed to detect AI-generated speech in digital audio files. This new tool builds on Pindrop’s earlier launch of Pindrop Pulse at the start of the year. While Pindrop Pulse initially targeted call centers, Pulse Inspect broadens its reach, catering to media organizations, nonprofits, government agencies, and social networks. Pindrop Pulse is already integrated with the company’s fraud protection and authentication platform. The new Pulse Inspect tool allows users to upload audio files to the Pindrop platform to determine if they contain synthetic speech, providing deepfake scores in the process. The introduction of Pulse Inspect is timely, coinciding with heightened concerns over deepfakes as the U.S. general election in November approaches. In recent months, Pindrop has tested its technology on high-profile cases. The company analyzed a deepfake audio clip of presidential candidate Kamala Harris, posted on X by Elon Musk, and discovered partial deepfakes in the audio. Pindrop also examined a deepfake of Elon Musk, released on July 24, identifying voice cloning technology from vendor ElevenLabs as the source. Additionally, Pindrop detected a fake robocall, generated using ElevenLabs’ technology, impersonating President Joe Biden before the January Democratic presidential primary. ElevenLabs has publicly stated its commitment to preventing the misuse of audio AI tools. “The human ear can no longer reliably distinguish between real and synthetically generated audio,” said Rahul Sood, Pindrop’s Chief Product Officer, during a discussion on the risks deepfakes pose for the upcoming election. “It’s almost impossible to have a high level of confidence without assistance.” Fighting AI with AI Analysts emphasize the necessity of tools like Pulse Inspect in the age of generative AI. “They’re fighting AI with AI,” said Lisa Martin, an analyst at the Futurum Group, highlighting the importance of Pindrop’s technology. According to Pindrop, their detection technology is trained on over 350 deepfake generation tools, 20 million unique utterances, and more than 40 languages. “We know how powerful generative AI is—it can be used for good, but it can also be weaponized, as we’re seeing,” Martin noted. She added that with the increasing ease of creating deepfakes, the demand for detection tools like Pulse Inspect will only grow. As deepfakes continue to proliferate, companies like Pindrop and competitors such as Resemble AI are racing to develop these detection solutions. With Pulse Inspect, Pindrop is extending its technology’s application beyond call centers. Pindrop has also partnered with Respeecher, a voice cloning vendor that collaborates with Hollywood. “Respeecher is working with Pindrop to ensure their synthetic voice technology for Hollywood is not misused,” said Martin, stressing the importance of ethical development and use of AI voice cloning technology. Pulse Inspect is positioned to assist media companies, social media networks, nonprofits, and government organizations in navigating the challenges of AI-generated audio. The Challenge of Scaling Deepfake Detection While Pindrop is well-equipped to detect deepfakes, scaling this technology could be costly and complex, according to Forrester Research analyst Mo Allibhai. “Implementing this technology at scale is expensive, even from an integration standpoint,” said Allibhai. “We need to be selective in how we deploy it.” Allibhai suggested that edge AI, such as Apple’s upcoming generative AI system for iPhones, could ease these challenges by reducing the reliance on cloud computing, making solutions like Pulse Inspect more viable in the long term. Pindrop Pulse Inspect offers an API-driven batch-processing platform and user interface, designed to meet the evolving needs of organizations facing the growing threat of deepfake audio. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

August 15, 2024in Generative AI

12Jul

Smithsonian Data Cloud

The Smithsonian already embraces technology through its Open Access Initiative. ousing 2D and 3D renders of collections it provides access to over 20 museums. Enter Salesforce and Smithsonian Data Cloud. The world’s largest network of research, museum, and education facilities, the Smithsonian Institution, manages over 150 million collections across its 21 museums, the National Zoo, and eight research centers. Navigating this vast array of artifacts can be overwhelming, even for dedicated history enthusiasts. To enhance accessibility, the Smithsonian Institution is collaborating with cloud computing giant Salesforce. The goal is to streamline the user experience by integrating disparate data sources, such as ticketing systems and donation histories, into a unified system. This initiative aims to provide a clearer blueprint of Smithsonian’s diverse locations and offerings, according to Lori Freeman, Salesforce’s Vice President and General Manager of Nonprofit Industry Solutions and Strategy. “Smithsonian is so progressive. They have all this content, they have all this history, they have incredible tools,” Freeman told Technical.ly. “So this technology is going to enable them to reach audiences they would never get to.” For instance, this system will allow museum staff and volunteers to assist visitors in locating exhibits across different Smithsonian locations. Becky Kobberod, the Smithsonian’s Head of Digital Transformation, illustrated this by describing how a visitor at the Hirshhorn could ask about a piece of art at the National Museum of American History. “It’s connecting the dots and creating a Smithsonian ecosystem that we currently don’t have. If you want to engage in our various museums, you go to each of them separately,” Kobberod said. “Whereas now, we’re providing you a front door, so to speak, that can help you navigate across all of the many different museums and resources that we have.” Although specifics about the technology and user interface have not been disclosed, Freeman emphasized that the main objectives are to keep visitors engaged and to build lifelong relationships with both in-person and virtual visitors. Building on Current Tech Resources The Smithsonian’s Open Access initiative, launched in early 2020, offers 2D and 3D renderings of its collections, totaling around 5 million items to date. Users can interact with 3D images to get a 360-degree view of fossils, sculptures, and artifacts like Neil Armstrong’s spacesuit. This initiative began with 2.8 million pieces and continues to grow, said Kobberod. In addition to Open Access, the Smithsonian has other digital initiatives. In 2022, the National Museum of African American History and Culture, in collaboration with Baltimore-based digital services firm Fearless, launched a searchable online platform to make certain collections and stories more accessible. Kobberod noted that only about 1% of the collections are physically displayed at any given time. Digitizing and uploading these collections not only preserves them but also makes them accessible to people who might not have the means to visit in person. “Smithsonian exists as a service to all of the American public,” she said. “We know that this is core to our future, and to making what we have available to the nation and the world.” Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

July 12, 2024in Data, Salesforce, Salesforce Data Cloud, Salesforce Experience Cloud, Salesforce Hospitality Travel and Transportation, Salesforce Optimization, Salesforce Public Sector Solutions, Technology

02Jul

Salesforce AI Journey

“We’re on an Incredible Journey”: Why Salesforce’s AI Push is Just Beginning This article originally appeared in TechRadar by Mike Moore. Salesforce AI Journey. When it comes to leveraging AI to enhance global workforces, some companies are leading the charge, particularly when their technology is at the forefront. Salesforce, known for its robust AI tools, is one such company pushing the boundaries. At its recent World Tour London event, Salesforce emphasized its commitment to AI, showcasing how its Einstein tools are already benefiting customers worldwide. TechRadar Pro spoke with Paul O’Sullivan, SVP, Solution Engineering & UKI, CTO, Salesforce, about the company’s vision for enhancing efficiency and productivity across all markets, particularly in the UK. A Wave of Innovation “We’re on an incredible journey,” O’Sullivan stated, referencing Salesforce’s $4 billion investment in the UK and Ireland in 2023. “We’re well-positioned in the UK to maximize AI’s potential and help our customers achieve true value.” This ambition is epitomized by Salesforce’s new AI center in London. The 40,000 square foot facility is set to be a hub for AI collaboration and development, addressing the growing demand for AI technology. O’Sullivan hinted that this is just the beginning. “We’re an innovation-led company—always looking ahead,” he said, highlighting the UK’s history of driving innovation as a positive indicator for AI’s future in the capital. Growing Demand and Education As demand for AI tools and services increases among businesses of all sizes, O’Sullivan acknowledged the rapid pace of change in the AI landscape. “It starts with education—at all levels,” he noted, recognizing the varying degrees of AI knowledge among business leaders. O’Sullivan compared the current AI momentum to past technological revolutions like cloud computing, websites, and ecommerce. Companies had to adapt quickly to avoid falling behind, and he noted that the window for catching up with AI might be even smaller. He predicted a “steady wave of innovation” in AI before it becomes ubiquitous in the business world, with various models and platforms vying for dominance. “It feels like everyone is in a race for AI,” he added, “and there’s a collective agreement that AI will enhance productivity and efficiency, benefiting both the bottom and top lines of big enterprises.” Human Jobs and AI Looking forward, O’Sullivan dismissed concerns that AI would replace human jobs. He suggested that AI would instead create new opportunities for human workers. “I think human nature is inherently curious,” he said. “We will continue to explore new ways of doing things and offer different levels of connection and service.” Drawing parallels to the industrial revolution, O’Sullivan pointed out that machines didn’t eliminate jobs; they increased productivity and efficiency. He believes AI will have a similar impact. “We’re going to see a new level of productivity and efficiency with AI, just as we did with the industrial revolution,” he concluded. Salesforce’s AI journey is only just beginning, promising exciting advancements and opportunities for businesses and workers alike. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

July 2, 2024in Salesforce, Salesforce Einstein

24May

The Growing Role of AI in Cloud Management

AI technologies are redefining cloud management by automating IT systems, improving security, optimizing cloud costs, enhancing data management, and streamlining the provisioning of AI services across complex cloud ecosystems. With the surging demand for AI, its ability to address technological complexities makes a unified cloud management strategy indispensable for IT teams. Cloud and security platforms have steadily integrated AI and machine learning to support increasingly autonomous IT operations. The rapid rise of generative AI (GenAI) has further spotlighted these AI capabilities, prompting vendors to prioritize their development and implementation. Adnan Masood, Chief AI Architect at UST, highlights the transformative potential of AI-driven cloud management, emphasizing its ability to oversee vast data centers hosting millions of applications and services with minimal human input. “AI automates tasks such as provisioning, scaling, cost management, monitoring, and data migration,” Masood explains, showcasing its wide-ranging impact. From Reactive to Proactive Cloud Management Traditionally, CloudOps relied heavily on manual intervention and expertise. AI has shifted this paradigm, introducing automation, predictive analytics, and intelligent decision-making. This evolution enables enterprises to transition from reactive, manual management to proactive, self-optimizing cloud environments. Masood underscores that this shift allows cloud systems to self-manage and optimize with minimal human oversight. However, organizations must navigate challenges, including complex data integration, real-time processing limitations, and model accuracy concerns. Business hurdles like implementation costs, uncertain ROI, and maintaining the right balance between AI automation and human oversight also require careful evaluation. AI’s Transformation of Cloud Computing AI has reshaped cloud management into a more proactive and efficient process. Key applications include: “AI enhances efficiency, scalability, and flexibility for IT teams,” says Agustín Huerta, SVP of Digital Innovation at Globant. He views AI as a pivotal enabler of automation and optimization, helping businesses adapt to rapidly changing environments. AI also automates repetitive tasks such as provisioning, performance monitoring, and cost management. More importantly, it strengthens security across cloud infrastructure by detecting misconfigurations, vulnerabilities, and malicious activities. Nick Kramer of SSA & Company highlights how AI-powered natural language interfaces simplify cloud management, transforming it from a technical challenge to a logical one. With conversational AI, business users can manage cloud operations more efficiently, accelerating problem resolution. AI-Enabled Cloud Management Tools Ryan Mallory, COO at Flexential, categorizes AI-powered cloud tools into: The Rise of Self-Healing Cloud Systems AI enables cloud systems to detect, resolve, and optimize issues with minimal human intervention. For instance, AI can identify system failures and trigger automatic remediation, such as restarting services or reallocating resources. Over time, machine learning enhances these systems’ accuracy and reliability. Key Applications of AI in Cloud Management AI’s widespread applications in cloud computing include: Benefits of AI in Cloud Management AI transforms cloud management by enabling autonomous systems capable of 24/7 monitoring, self-healing, and optimization. This boosts system reliability, reduces downtime, and provides businesses with deeper analytical insights. Chris Vogel from S-RM emphasizes that AI’s analytical capabilities go beyond automation, driving strategic business decisions and delivering measurable value. Challenges of AI in Cloud Management Despite its advantages, AI adoption in cloud management presents challenges, including: AI’s Impact on IT Departments AI’s growing influence on cloud management introduces new responsibilities for IT teams, including managing unauthorized AI systems, ensuring data security, and maintaining high-quality data for AI applications. IT departments must provide enterprise-grade AI solutions that are private, governed, and efficient while balancing the costs and benefits of AI integration. Future Trends in AI-Driven Cloud Management Experts anticipate that AI will revolutionize cloud management, much like cloud computing reshaped IT a decade ago. Prasad Sankaran from Cognizant predicts that organizations investing in AI for cloud management will unlock opportunities for faster innovation, streamlined operations, and reduced technical debt. As AI continues to evolve, cloud environments will become increasingly autonomous, driving efficiency, scalability, and innovation across industries. Businesses embracing AI-driven cloud management will be well-positioned to adapt to the complexities of tomorrow’s IT landscape. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

May 24, 2024in Data, Enterprise, Generative AI

17Feb

Salesforce Unveils Einstein Copilot

Salesforce Unveils Einstein Copilot: A New Era in AI-Powered CRM Salesforce, the world’s leading customer relationship management (CRM) platform, has introduced Einstein Copilot, an advanced AI-powered assistant designed to revolutionize business interactions with CRM systems. Seamlessly integrated within Salesforce’s ecosystem, including Data Cloud, Einstein Copilot automates complex workflows, enhances operational efficiency, and delivers highly personalized customer insights. By leveraging AI, it empowers sales, marketing, and customer service teams while ensuring compliance with data privacy and governance standards. The Challenge: Enhancing CRM Efficiency with AI As customer relationships became more intricate, businesses sought a CRM solution that could: Salesforce responded by integrating cutting-edge AI capabilities into its CRM platform, ensuring a seamless, secure, and intelligent customer experience. The Einstein Copilot Solution Salesforce has embedded Einstein Copilot across its CRM applications, transforming customer relationships through AI-driven automation. By harnessing Data Cloud, Einstein Copilot accesses unified, reliable data to generate real-time insights, helping businesses make smarter decisions and enhance customer interactions. With customization tools like Copilot Builder, Prompt Builder, Skill Builder, and Model Builder, businesses can tailor Einstein Copilot to meet their unique needs. Integration with Salesforce extensions and external APIs further optimizes workflows, automating tasks and streamlining operations. Key Features of Einstein Copilot Results: Transforming CRM with AI Since its launch, Einstein Copilot has significantly improved customer satisfaction and operational productivity by: Challenges & Considerations Despite its success, deploying Einstein Copilot presented some challenges, including: The Future of Einstein Copilot Salesforce is committed to expanding Einstein Copilot’s capabilities, focusing on: Salesforce envisions Einstein Copilot setting a new benchmark in AI-driven CRM, enabling businesses to foster stronger customer relationships, drive growth, and streamline operations. About Salesforce Salesforce is the world’s leading CRM platform, empowering businesses with innovative solutions that blend cloud computing, artificial intelligence, and big data analytics. Since its founding in 1999, Salesforce has been at the forefront of digital transformation, helping organizations optimize operations and elevate customer engagement. Conclusion Einstein Copilot marks a transformative shift in CRM, leveraging AI to enhance efficiency, automate workflows, and deliver superior customer experiences. With AI-driven automation and intelligent decision-making, businesses can now focus on building long-lasting customer relationships that drive loyalty and success. Einstein Copilot is now Agentforce. Content updated January 2025. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

February 17, 2024in AI Tools, Artificial Intelligence, Data, Salesforce

07Feb

Healthcare Cloud Marketplace

Healthcare Cloud Computing Market: A Comprehensive Overview and Future Outlook Vantage Market Research Report: Insights into Healthcare Cloud Computing by 2030 WASHINGTON, D.C., February 6, 2024 /EINPresswire.com/ — The global Healthcare Cloud Marketplace was valued at USD 38.25 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 18.2% from 2023 to 2030, reaching approximately USD 145.86 billion by 2030, according to Vantage Market Research. This technology allows healthcare organizations to utilize cloud-based services for data storage, management, and analysis, providing numerous benefits such as cost efficiency, scalability, flexibility, security, and interoperability. It enhances healthcare delivery by enabling seamless data access and sharing across various locations, devices, and networks. Additionally, cloud computing supports the integration of advanced technologies like artificial intelligence, big data analytics, telehealth, and mobile health, driving progress in disease diagnosis, treatment, and prevention. Market Dynamics The market’s growth is fueled by several key factors, including the increasing demand for healthcare IT solutions, the rising prevalence of chronic diseases, the widespread adoption of electronic health records (EHRs), and evolving payment models and regulatory frameworks. The exponential increase in healthcare data, encompassing patient records, imaging scans, and research findings, necessitates scalable storage and analysis solutions. Cloud computing meets this need by providing flexible and scalable infrastructure, accommodating data growth without overburdening IT systems. The rise of telehealth and remote patient monitoring further boosts the demand for secure, cloud-based platforms that facilitate efficient data exchange. However, stringent data privacy regulations like HIPAA and GDPR require robust security measures, compelling healthcare organizations to seek cloud providers that offer strong compliance and access controls. This need for a balance between agility and security shapes the healthcare cloud computing market’s future trajectory. Leading Companies in the Global Healthcare Cloud Computing Market Market Segmentation By Product: By Deployment: By Component: By Pricing Model: By Service Model: Key Trends and Opportunities The healthcare cloud computing market is witnessing significant trends, including the adoption of hybrid and multi-cloud models, which combine the benefits of both public and private clouds. The integration of artificial intelligence (AI) and machine learning (ML) into cloud-based healthcare applications is opening new avenues for personalized medicine, clinical decision support, and drug discovery. Moreover, blockchain technology is emerging as a solution to enhance data security and patient privacy, addressing critical industry concerns. Key Findings: Opportunities: Healthcare Cloud Marketplace The healthcare cloud computing market is poised for robust growth, driven by the increasing demand for scalable and secure data management solutions. As healthcare organizations navigate challenges related to data privacy and security, robust cloud solutions and supportive government policies will be essential in unlocking the full potential of cloud computing in healthcare. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

February 7, 2024in Salesforce

15Jul

Salesforce Analytics Acronyms

Here is a helpful list of analytics acronyms you may encounter when discussing Salesforce. Salesforce Analytics Acronyms Acronym Meaning Definition AWS Amazon Web Services AWS offers “reliable, scalable, and inexpensive cloud computing services…the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally.” (source)Multiple Salesforce products use AWS; however, it’s prominent in the analytics arena, where organizations rely on AWS to:”Extract, prepare, and analyze Salesforce.com data using Amazon AppFlow, AWS Glue DataBrew, and Amazon Athena.” (source) BYOAI Bring Your Own AI ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. BYOM Bring Your Own Model ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. CRMA CRM Analytics This is the analytics/business intelligence (BI) solution on the Salesforce platform, integrating Salesforce data with external data and provides the ability to analyze large datasets while performing predictive analytics. (Previously known as Wave/Einstein Analytics/Tableau CRM). EA Einstein Analytics The former name for CRM Analytics (also formerly Tableau CRM). You may still come across EA in outdated documentation. GPT Generative Pre-trained Transformer GPT is a technology concept, although it’s often mistaken for a product name (with the rise of the wildly popular ChatGPT). LLM Large Language Model An AI model that has been trained using lots of data. Salesforce has their own LLM that outperformed expectations during testing. Salesforce’s LLM leverages Data Cloud, or can be plugged into a different data lake. Organizations can also opt to their own LLM alongside their Salesforce data. NBA Einstein Next Best Action An AI-driven Salesforce product that makes recommendations to users when they are working with records in Salesforce. It appears within a Lightning component that’s typically placed on the record page sidebar. The user can choose to Accept or Reject the recommendation. NLP Natural Language Processing “Artificial intelligence that focuses on how computers can understand, interpret, and generate human language. NLP allows customers to interact with systems using normal human language rather than complex commands.” (source) SAQL Structured Query Language “Use SAQL to access data in Tableau CRM dataset. Tableau CRM uses SAQL behind the scenes in lenses, dashboards, and explorer to gather data for visualizations.” (source) TCRM Tableau CRM Tableau CRM is a self-service data visualization and business intelligence (BI) platform that integrates Salesforce data with external data. Formerly known as Einstein Analytics, and now CRM Analytics, this product boasts some of the most powerful data analysis capabilities in the market. XMD Extended Metadata “Enables you to customize the formatting of dataset fields and their values in Analytics dashboards and lenses.” (source) Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

July 15, 2023in Salesforce

30May

Cloud Analytics Explained

Understanding Cloud Analytics Cloud analytics refers to leveraging cloud computing resources to conduct data analysis more efficiently. It involves using advanced analytical tools to extract insights from vast datasets, presenting information in a user-friendly format accessible via web browsers. Core Concepts of Cloud Analytics Explained Cloud analytics shifts traditional data analytics operations, such as processing and storage, to public or private cloud environments. Similar to on-premises analytics, cloud solutions facilitate pattern identification, predictive modeling, and business intelligence (BI) insights. They leverage cloud technologies and algorithms, notably artificial intelligence (AI), including machine learning (ML) and deep learning (DL). Operational Framework of Cloud-Based Analytics Cloud analytics platforms offer capabilities to build, deploy, scale, and manage data analytics solutions in a cloud-based infrastructure. Examples include cloud enterprise data warehouses, data lakes, and on-demand BI and marketing analytics. Users can subscribe to services under flexible pricing models, alleviating concerns about scalability, performance, and maintenance. Types of Cloud Analytics Cloud-based analytics solutions vary by deployment model: Key Features and Benefits Cloud analytics offers several advantages: Applications and Use Cases Cloud analytics supports diverse applications, including: Comparing Cloud Analytics with Traditional Data Analytics Cloud analytics leverages cloud infrastructure for scalable and flexible data processing, contrasting with traditional analytics tools deployed on-premises. This shift enhances agility and accessibility while reducing operational complexities and costs. Why Cloud Analytics Matters Cloud analytics empowers organizations to harness actionable insights efficiently, driving informed decision-making and competitive advantage. It streamlines operations, fosters collaboration, and enhances data reliability and strategic planning capabilities. Adopting cloud-based analytics enables businesses to transform data into valuable intelligence, fueling innovation and growth. By leveraging cloud-based resources, organizations can achieve operational excellence, secure data-driven insights, and maintain a competitive edge in today’s dynamic business landscape. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

May 30, 2023in Salesforce

02Dec

Cloud Technology Providers

Exploring Cloud Computing and the Leading Cloud Technology Providers The concept of cloud computing has evolved significantly over the past 60 years, with its modern form emerging in 2002 when Amazon, seeking efficient marketplace expansion, laid the groundwork for what we know today. Amazon Web Services (AWS), its cloud services subsidiary launched in 2006, now stands as the largest Cloud Service Provider (CSP) globally. This insight digs into the cloud infrastructure market landscape, spotlighting dominant players and more. What is a Cloud Service Provider? A Cloud Service Provider (CSP) leases technology, infrastructure, and expertise to businesses and individuals for cloud computing purposes, spanning online storage, compute, and networking accessible via the Internet. CSPs manage and operate multiple global data centers housing physical infrastructure like servers, hard drives, and cooling systems. Users worldwide can access this cloud infrastructure anytime, anywhere, purchasing capacity on a pay-as-you-go basis (usage-based pricing). Key Benefits of Using a CSP vs. Building On-Premises Companies often opt against building in-house cloud infrastructure due to cost, expertise requirements, and ongoing maintenance. Leveraging a robust CSP delivers numerous advantages, such as: CSPs also handle infrastructure testing, updates, security, and optimization, freeing businesses to focus on core operations. However, some challenges come with this cloud model. Challenges of Using a Cloud Technology Providers Primary challenges of CSPs include: Market Share of the Big Three CSPs (AWS, Azure, GCP) Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) collectively dominate the cloud infrastructure market, holding 66% market share globally, according to Synergy Research Group. Key Cloud Technology Providers in 2023 Leading cloud providers enabling global innovation include: Cloud computing continues to evolve, with market leaders and niche players alike driving innovation and transformation across industries. Content updated December 2023. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

December 2, 2022in Salesforce

19Aug

Digital Transformation for Life Sciences

In hindsight, one remarkable aspect of the COVID crisis was the speed with which vaccines passed through regulatory approval processes to address the pandemic emergency. Approvals that would typically take years were expedited to mere months, a pace not usually seen in the life sciences industry. It was an extraordinary situation, as Paul Shawah, Senior Vice President of Commercial Strategy at Veeva Systems, notes: “There were things that were unnaturally fast during COVID. There was a shifting of priorities, a shifting of focus. In some cases, you had the emergency approvals or the expedited approvals of the vaccines that you saw in the early days, so there was faster growth. Everything was kind of different in the COVID environment.” Today, the industry is not operating at that same rapid pace, but the impact of this acceleration remains significant: “What it did do is it challenged companies to think about why can’t we operate faster at a steady state? There was an old steady state, then there was COVID speed. The industry is trying to get to a new steady state. It won’t be as fast as during COVID because of unique circumstances, but expectations are now much higher. This drives a need to modernize systems, embrace the cloud, become more digital, and improve efficiency.” Companies like Veeva, alongside enterprise giants such as Salesforce, SAP, and Oracle, specialize in this market and play crucial roles in life sciences digitization. According to a McKinsey study, about 45% of tech spending in life sciences goes to three key technologies: applied Artificial Intelligence, industrialized Machine Learning, and Cloud Computing. Over 80% of the top 20 global pharma and medtech companies are operating in the cloud to some extent. However, a study by Accenture found that life sciences firms are among the lowest in achieving benefits from cloud investments, with only 43% satisfied with their results and less than a quarter confident that cloud migration initiatives will deliver the promised value within expected time frames. This presents both a challenge and an opportunity. Frank Defesche, SVP & GM of Life Sciences at Salesforce, sees it as the latter, stating: “The life sciences industry faces increased competition, evolving patient expectations, and ongoing pressure to bring devices and drugs to market faster. With rising drug costs, frustrated doctors, and varying regulatory scrutiny, life sciences organizations must find ways to do more with less.” The industry also contends with an unprecedented influx of data and disparate systems, making it difficult to move quickly. Addressing changes one by one is too slow and costly. Defesche believes that a systemic solution, fueled by connected data and Artificial Intelligence (AI), is key to overcoming these challenges. Paul Shawah of Veeva emphasizes the unique challenges of the life sciences sector: “Life sciences firms primarily do two things: discover and develop medicines, and commercialize them by educating doctors and getting the right drugs to patients. The drug development cycle includes clinical trials, managing everything related to drug safety, the manufacturing process, and ensuring quality. They also manage regulatory registrations. On the commercial side, it’s about reaching out to doctors and healthcare professionals.” Veeva’s Vault platform is designed for life sciences, with customers like Merck, Eli Lilly, and Boehringer Ingelheim. Shawah acknowledges it’s “still relatively early days” for cloud computing adoption but notes successes in areas like CRM, where Veeva achieved over 80% market share by standardizing processes and reducing technical debt. Other areas, like parts of the clinical trials process, remain largely untapped by cloud computing. Shawah sees opportunities to improve patient experiences and make the process more efficient. AI represents a significant area of opportunity. Shawah explains Veeva’s approach: “I’ll break AI into two categories: traditional AI, Machine Learning, and data science, which we’ve been doing for a long time, and generative AI, which is new. We’re focusing on finding use cases that create sustainable, repeatable value. We’re building capabilities into our Vault platform to support AI.” Joe Ferraro, VP of Product, Life Sciences at Salesforce, emphasizes AI’s critical role: “We are born out of the data and AI era, and we’re taking that philosophy into everything we do from a product standpoint. We aim to move from creating a system of record to a system of insight, using data and AI to transform how users interact with software.” Ferraro highlights the need for change: “Organizations told us, ‘Please don’t build the same thing we have now. We are mired in fragmented experiences. Our sales and marketing teams aren’t talking, and our medical and commercial teams don’t understand each other.’ Life Sciences Cloud aims to move the industry from these fragmented experiences to an end-to-end, AI-powered experience engine.” The COVID crisis highlighted the critical role of the life sciences industry. There’s a massive opportunity for digital transformation, whether through specialists like Veeva or enterprise players like Salesforce, Oracle, and SAP. Data must be the foundation of any solution, especially amidst the current AI hype cycle. Ensuring this data is well-managed is a crucial starting point for industry-wide change. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

August 19, 2022in Generative AI, Life Sciences Cloud

26Apr

Cloud Based Managed Services

At Tectonic, our aim is to enable you to concentrate on your business operations while we handle the intricacies of technology. Our team oversees all technology assets and evaluates your IT systems to ensure your employees have the necessary equipment to support their work. We work diligently in the background to maintain the seamless functioning of your business, encompassing tasks such as consistent backups, 24/7 server monitoring, event logging, and more. Cloud Based Managed Services. Managed Cloud Cloud managed services, or managed cloud, refer to applications, services, or ecosystems in the cloud managed by a third-party organization. These services include IT tasks like engineering on demand, operations management, 24/7 help desk support, hosting, and implementation. Cloud-based service involves the organized administration of cloud computing products and services. It encompasses processes, strategies, policies, and technology used to control and maintain public and private clouds, hybrid clouds, or multicloud environments. Cloud infrastructure managed services involve companies like Microsoft, AWS, HubSpot, IBM, and Google Cloud. This allows businesses to receive personalized tech support from specialists in specific software providers. Examples of cloud-based services include file storage and backup, web-based email, and project management tools. SaaS cloud service providers include Dropbox, G Suite, Microsoft Office 365, and Slack. While cloud service providers are cost-effective, they lack the robustness of fully managed IT services. With Tectonic, managed cloud services provide scalable solutions, offering benefits like consistent backups, disaster recovery, and flexibility to adapt to changing business requirements without downtime. Managed services differ from SaaS as they go beyond software provision, often handling networking and hardware requirements. Managed cloud services offer hybrid IT and cloud administration, with providers offering unique values in areas like migration, optimization, security, and configuration. Advantages of Managed Cloud Service Providers (MCSPs) include resource optimization, cloud service integration, and predictable spending. However, potential drawbacks include high costs, optimization impacting performance, and security risks due to multi-tenancy. Amid the global COVID-19 pandemic, cloud adoption has surged, with companies embracing remote work models. Businesses increasingly partner with cloud managed service providers to optimize IT infrastructure, address regulatory compliance, and ensure security in the cloud environment. Local servers, physically located on-premises or in private data centers, are not considered cloud services. They host applications, websites, or services within a specific organization’s infrastructure. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

April 26, 2022in Data, Google, Salesforce, Salesforce Managed Services, Technology

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