Data Management Archives - gettectonic.com - Page 6
Salesforce Data Migration Tools
24Jul

Salesforce Data Migration

Salesforce Data Migration: A Key to CRM Success The migration of data into Salesforce is critical for the efficient functioning of Salesforce CRM. When executed correctly, it reduces data duplication, consolidates customer and operational data into a unified platform, and extends CRM capabilities beyond basic functionalities. Proper data migration serves as the foundation for advanced business intelligence and in-depth analytics. On the other hand, poorly managed migration can lead to transferring incorrect, duplicate, or corrupted data, compromising the system’s reliability. An efficient migration process safeguards data integrity, ensures a seamless transfer to Salesforce, and enhances overall organizational performance. What is Data Migration in Salesforce? Salesforce data migration is the process of transferring data from external systems, databases, or platforms into Salesforce. This process captures critical business information and integrates it into Salesforce’s CRM framework securely. The migration process also involves data cleansing, verification, and transforming data into formats compatible with Salesforce’s structure. Why You Need Salesforce Data Migration Importance Data migration is indispensable for companies looking to modernize their operations and enhance performance. With Salesforce, organizations can: Benefits Migrating Data from Legacy Systems to Salesforce Migrating data from legacy systems to Salesforce is essential for scalability and efficient data management. Key advantages include: Salesforce Data Migration Process Data migration involves transferring data into Salesforce to improve customer engagement and operational workflows. The process ensures data accuracy and compatibility with Salesforce’s architecture. Key Steps for Salesforce Data Migration Types of Salesforce Data Migration Top Salesforce Data Migration Tools Data Archiving in Salesforce Salesforce data archiving involves relocating unused or historical data to a separate storage area. This optimizes system performance and ensures easy access for compliance or analysis. Advantages Top Options for Data Archiving Best Practices for Salesforce Data Migration Conclusion Salesforce data migration is a pivotal step in transforming organizational processes and achieving CRM excellence. When done right, it improves efficiency, eliminates data duplication, and ensures accurate information storage. By following best practices, leveraging appropriate tools, and engaging migration specialists, organizations can unlock Salesforce’s full potential for scalability, automation, and advanced analytics. Successful migration paves the way for better decision-making and future growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 24, 2024in Data
AI in Drug Research
23Jul

AI in Drug Research

Insights on Leveraging AI in Biopharmaceutical R&D: A Discussion with Kailash Swarna Last month, Accenture released a report titled “Reinventing R&D in the Age of AI,” which explores how biopharmaceutical companies can harness artificial intelligence (AI) and other advanced technologies to enhance drug and therapeutic research and development. AI in Drug Research. Kailash Swarna, managing director and Accenture Life Sciences Global Research and Clinical lead, spoke with PharmaNewsIntelligence about the report’s findings and how AI can address ongoing challenges in research and development (R&D), while offering a return on technological investments. “Data and analytics are crucial in advancing drug development, from early research to late-stage clinical trials,” said Swarna. “The industry still faces significant challenges, including the time and cost required to bring a medicine to market. As a leading technology firm, it’s our role to leverage the best in data analytics and technology for drug discovery and development.” AI in Drug Research Accenture conducted detailed interviews with leaders from biopharma companies to explore AI’s role in drug development and discovery. These interviews were part of a CEO forum held just before the JP Morgan conference, where technology emerged as a major area of opportunity and concern. Key Challenges in R&D Understanding the challenges in the drug R&D landscape is crucial for identifying how AI can be effectively utilized. Swarna highlighted several significant challenges: 1. Scientific Growth “The rapid advances in biology and disease understanding present both opportunities and challenges,” Swarna noted. “While our knowledge of human disease has greatly improved, keeping pace with scientific progress in terms of executing and reducing the time and cost of bringing new therapeutics to market remains a major challenge.” He described the clinical trial process as “fraught with complexities,” including data management issues. Despite industry efforts to accelerate drug development, it often still takes over a decade and billions of dollars. 2. Macroeconomic Factors Drug R&D companies also face challenges from macroeconomic conditions, such as reimbursement issues and the Inflation Reduction Act in the US. “These factors are reshaping how companies approach their portfolios and the disease areas they target,” Swarna explained. “The industry is undergoing a retooling to address these economic impacts.” 3. Technology Optimization Many companies have made substantial technology investments, but integrating and systematically utilizing these technologies across the entire R&D process remains a challenge. “While individual technology investments have been valuable, there is a significant opportunity to unify these efforts and streamline data usage from early research through late-stage development,” Swarna said. Reinventing R&D with AI The report emphasizes that technological advancements, particularly generative AI and analytics, can revolutionize the R&D pipeline. “This isn’t about a single technology but about a comprehensive rethinking of processes, data flows, and technology investments across the entire R&D spectrum,” Swarna stated. He stressed that the reinvention of R&D processes requires an enterprise-wide strategy and implementation. Responsible AI Swarna also highlighted the importance of addressing potential challenges associated with AI. “At Accenture, we have a robust responsible AI framework,” he said. Responsible AI encompasses managing issues like bias and security. Accenture’s framework considers factors such as choosing appropriate patient populations and understanding how bias might impact research data. It also addresses security concerns, including intellectual property protection and patient privacy. “Protecting patient privacy and complying with global regulations is crucial when utilizing AI technology,” Swarna emphasized. “Without proper safeguards, we risk data loss or breaches.” Measuring ROI of AI in Drug Research To ensure that AI technologies positively impact the R&D lifecycle, Swarna described a framework for measuring return on investment (ROI). “Given the long cycle of our industry, we’ve developed objective measures to evaluate the impact of these technologies on cost and time,” he explained. Companies can use quantitative measures to track interim milestones, such as recruitment costs and speeds. “These metrics allow us to observe progress in smaller increments rather than waiting for end-to-end results,” Swarna said. “The approach varies by company and their stage in implementing these technologies.” Benefits of AI in Clinical Trials Incorporating AI into clinical trials has the potential to reduce research times and costs. While Swarna and Accenture cannot predict policy impacts on drug pricing, he offered a theoretical benefit: optimizing technology could lower development costs, potentially making medicines more affordable and accessible. Swarna noted that reducing R&D spending could lead to more effective drugs being available to larger populations without placing an excessive burden on the healthcare system. For further details, the original report and discussion were published by Accenture and can be accessed on their official site. AI in Drug Research. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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July 23, 2024in Generative AI
Metadata and Salesforce
14Jul

Metadata and Salesforce

How do I manage metadata in Salesforce? Search Setup for Custom Metadata Types. On the All Custom Metadata Types page, click Manage Records next to the custom metadata type for which you want to add or modify records. On the list of custom metadata records, click New, or click Edit to modify an existing custom metadata record. Fill out the fields. Use Metadata API to retrieve, deploy, create, update or delete customization information, such as custom object definitions and page layouts, for your org. This API is intended for managing customizations and for building tools that can manage the metadata model, not the data itself. Understanding Metadata Metadata encompasses various elements such as database objects and fields, screen layouts, automation rules, reporting structures, and access permissions. These elements, among others, form the overall configuration of an application for an organization. In large organizations, there can be over 100,000 metadata configurations with numerous dependencies between them. The Importance of Metadata Management As configurations grow, effective metadata management becomes crucial. Without it, the ability to make swift changes diminishes. Forrester describes this challenge as the “Salesforce@scale dilemma,” highlighting the complexities organizations face when scaling their use of Salesforce. This issue isn’t unique to Salesforce but applies to any metadata-driven enterprise application: “Every app change risks breaking one of hundreds of data and process customizations, integration links, and third-party add-ons. The result: Every change requires long and expensive impact-analysis and regression testing projects—killing the responsiveness that made Salesforce attractive at the start.” The Change Intelligence Report Series from Elements.cloud quantified Forrester’s observations: 50% of custom databases are never used, and 41% of custom-created fields are never populated. This leads to wasted effort and increased technical debt. Reducing this debt requires better metadata control. Establishing a Metadata Dictionary At the core of any metadata-driven app is a metadata dictionary, which AI can significantly enhance. A metadata dictionary is a comprehensive list of all metadata, grouped by type, with detailed attributes for each item such as version, usage, and dependencies. It can also include third-party content like business analysis, design specifications, and training materials. Managing applications without a metadata dictionary can be inefficient and risky. The common alternative, using spreadsheets, is hard to maintain and lacks support for impact analysis. Enterprise application vendors should include a metadata dictionary in their platforms, though many do not, often leaving it to third parties to create one via APIs. Steps to Establish a Metadata Dictionary Creating a metadata dictionary involves several actionable steps that streamline data management and boost productivity. Salesforce Metadata Salesforce metadata defines how objects and records behave and the “look and feel” of your Salesforce org. It includes: Metadata and Salesforce It’s essential to distinguish between metadata and data: For example, metadata can define schema properties (e.g., fields in a standard object like Address) and how business data is presented and processed. Metadata API Functionality The Metadata API allows you to manage and move metadata between Salesforce orgs during development. It supports: Use Metadata API for the final stages of development, and source push and pull commands for intermediate stages. These methods ensure efficient and accurate metadata management, enhancing the overall agility and scalability of your Salesforce environment. By effectively managing metadata, organizations can minimize technical debt, streamline operations, and maintain the flexibility and responsiveness that make platforms like Salesforce valuable. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 14, 2024in Salesforce
Generative AI for Tableau
11Jul

Generative AI for Tableau

Tableau’s first generative AI assistant is now generally available. Generative AI for Tableau brings data prep to the masses. Earlier this month, Tableau launched its second platform update of 2024, announcing that its first two GenAI assistants would be available by the end of July, with a third set for release in August. The first of these, Einstein Copilot for Tableau Prep, became generally available on July 10. Tableau initially unveiled its plans to develop generative AI capabilities in May 2023 with the introduction of Tableau Pulse and Tableau GPT. Pulse, an insight generator that monitors data for metric changes and uses natural language to alert users, became generally available in February. Tableau GPT, now renamed Einstein Copilot for Tableau, moved into beta testing in April. Following Einstein Copilot for Tableau Prep, Einstein Copilot for Tableau Catalog is expected to be generally available before the end of July. Einstein Copilot for Tableau Web Authoring is set to follow by the end of August. With these launches, Tableau joins other data management and analytics vendors like AWS, Domo, Microsoft, and MicroStrategy, which have already made generative AI assistants generally available. Other companies, such as Qlik, DBT Labs, and Alteryx, have announced similar plans but have not yet moved their products out of preview. Tableau’s generative AI capabilities are comparable to those of its competitors, according to Doug Henschen, an analyst at Constellation Research. In some areas, such as data cataloging, Tableau’s offerings are even more advanced. “Tableau is going GA later than some of its competitors. But capabilities are pretty much in line with or more extensive than what you’re seeing from others,” Henschen said. In addition to the generative AI assistants, Tableau 2024.2 includes features such as embedding Pulse in applications. Based in Seattle and a subsidiary of Salesforce, Tableau has long been a prominent analytics vendor. Its first 2024 platform update highlighted the launch of Pulse, while the final 2023 update introduced new embedded analytics capabilities. Generative AI assistants are proliferating due to their potential to enable non-technical workers to work with data and increase efficiency for data experts. Historically, the complexity of analytics platforms, requiring coding and data literacy, has limited their widespread adoption. Studies indicate that only about one-quarter of employees regularly work with data. Vendors have attempted to overcome this barrier by introducing natural language processing (NLP) and low-code/no-code features. However, NLP features have been limited by small vocabularies requiring specific business phrasing, while low-code/no-code features only support basic tasks. Generative AI has the potential to change this dynamic. Large language models like ChatGPT and Google Gemini offer extensive vocabularies and can interpret user intent, enabling true natural language interactions. This makes data exploration and analysis accessible to non-technical users and reduces coding requirements for data experts. In response to advancements in generative AI, many data management and analytics vendors, including Tableau, have made it a focal point of their product development. Tech giants like AWS, Google, and Microsoft, as well as specialized vendors, have heavily invested in generative AI. Einstein Copilot for Tableau Prep, now generally available, allows users to describe calculations in natural language, which the tool interprets to create formulas for calculated fields in Tableau Prep. Previously, this required expertise in objects, fields, functions, and limitations. Einstein Copilot for Tableau Catalog, set for release later this month, will enable users to add descriptions for data sources, workbooks, and tables with one click. In August, Einstein Copilot for Tableau Web Authoring will allow users to explore data in natural language directly from Tableau Cloud Web Authoring, producing visualizations, formulating calculations, and suggesting follow-up questions. Tableau’s generative AI assistants are designed to enhance efficiency and productivity for both experts and generalists. The assistants streamline complex data modeling and predictive analysis, automate routine data prep tasks, and provide user-friendly interfaces for data visualization and analysis. “Whether for an expert or someone just getting started, the goal of Einstein Copilot is to boost efficiency and productivity,” said Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group. The planned generative AI assistants for different parts of Tableau’s platform offer unique value in various stages of the data and AI lifecycle, according to Leone. Doug Henschen noted that the generative AI assistants for Tableau Web Authoring and Tableau Prep are similar to those being introduced by other vendors. However, the addition of a generative AI assistant for data cataloging represents a unique differentiation for Tableau. “Einstein Copilot for Tableau Catalog is unique to Tableau among analytics and BI vendors,” Henschen said. “But it’s similar to GenAI implementations being done by a few data catalog vendors.” Beyond the generative AI assistants, Tableau’s latest update includes: Among these non-Copilot capabilities, making Pulse embeddable is particularly significant. Extending generative AI capabilities to work applications will make them more effective. “Embedding Pulse insights within day-to-day applications promises to open up new possibilities for making insights actionable for business users,” Henschen said. Multi-fact relationships are also noteworthy, enabling users to relate datasets with shared dimensions and informing applications that require large amounts of high-quality data. “Multi-fact relationships are a fascinating area where Tableau is really just getting started,” Leone said. “Providing ways to improve accuracy, insights, and context goes a long way in building trust in GenAI and reducing hallucinations.” While Tableau has launched its first generative AI assistant and will soon release more, the vendor has not yet disclosed pricing for the Copilots and related features. The generative AI assistants are available through a bundle named Tableau+, a premium Tableau Cloud offering introduced in June. Beyond the generative AI assistants, Tableau+ includes advanced management capabilities, simplified data governance, data discovery features, and integration with Salesforce Data Cloud. Generative AI is compute-intensive and costly, so it’s not surprising that Tableau customers will have to pay extra for these capabilities. Some vendors are offering generative AI capabilities for free to attract new users, but Henschen believes costs will eventually be incurred. “Customers will want to understand the cost implications of adding these new capabilities,”

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July 11, 2024in Generative AI, Salesforce Data Cloud
Data Protection Improvements from Next DLP
11Jul

Data Protection Improvements from Next DLP

Insider risk and data protection company Next DLP has unveiled its new Secure Data Flow technology, designed to enhance data protection for customers. Integrated into the company’s Reveal Platform, Secure Data Flow monitors the origin, movement, and modification of data to provide comprehensive protection. Data Protection Improvements from Next DLP. This technology can secure critical business data flow from any SaaS application, including Salesforce, Workday, SAP, and GitHub, to prevent accidental data loss and malicious theft. “In modern IT environments, intellectual property often resides in SaaS applications and cloud data stores,” said John Stringer, head of product at Next DLP. “The challenge is that identifying high-impact data in these locations based on its content is difficult. Secure Data Flow, through Reveal, ensures that firms can confidently protect their most critical data assets, regardless of their location or application.” Next DLP argues that legacy data protection technologies are inadequate, relying on pattern matching, regular expressions, keywords, user-applied tags, and fingerprinting, which only cover a limited range of text-based data types. The company highlights that recent studies indicate employees download an average of 30 GB of data each month from SaaS applications to their endpoints, such as mobile phones, laptops, and desktops, emphasizing the need for advanced data protection measures. Secure Data Flow tracks data as it moves through both sanctioned and unsanctioned channels within an organization. By complementing traditional content and sensitivity classification-based approaches with origin-based data identification, manipulation detection, and data egress controls, it effectively prevents data theft and misuse. This approach results in an “all-encompassing, 100 percent effective, false-positive-free solution that simplifies the lives of security analysts,” claims Next DLP. “Secure Data Flow represents a novel approach to data protection and insider risk management,” said Ken Buckler, research director at Enterprise Management Associates. “It not only enhances detection and protection capabilities but also streamlines data management processes. This improves the accuracy of data sensitivity recognition and reduces endpoint content inspection costs in today’s diverse technological environments.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 11, 2024in Generative AI, Salesforce Data Cloud, Salesforce Optimization
Salesforce Data and AI Prevent Food Waste
05Jul

Salesforce Data and AI Prevent Food Waste

FareShare’s Tech-Driven Fight Against Food Waste and Insecurity Every year, around 10 million tons of perfectly edible surplus food goes to waste in the UK, while millions struggle to afford to have enough to eat. This waste not only exacerbates food poverty but also has a significant environmental impact, with greenhouse gases from rotten or wasted food accounting for about half of all global food system emissions. Salesforce Data and AI Prevent Food Waste. Charity FareShare is acutely aware of the severity of the situation. CEO George Wright emphasizes, “If food waste was a country, it would be the third biggest producer of global greenhouse gas emissions behind America and China.” Globally, 30% of food is wasted, and in the UK, it’s 25%, encompassing food thrown away at home, ploughed back into the ground, or wasted in the hospitality and retail industries. FareShare, which started 30 years ago, originally aimed to tackle this issue by redistributing surplus food that would otherwise go to waste. Now, FareShare operates 35 warehouses across the United Kingdom, employing around 600 people and 15,000 volunteers. They collect surplus food from 700 food companies and work with 8,500 charities to redistribute it to school clubs, community centers, and faith groups. Growth Amid Crisis Although FareShare had grown into a national organization, it was still relatively small when COVID-19 hit. Wright explains, “FareShare was about £3 million in terms of fundraising. COVID came and everything boomed.” During the pandemic, demand for FareShare’s services skyrocketed. Collaborations with high-profile figures like footballer Marcus Rashford brought more focus and support. The charity’s fundraising surged from £3 million to as high as £75 million before stabilizing at around £23 million. The amount of food distributed increased from 5,000 tons in the early days to 55,000 tons. The cost-of-living crisis has further exacerbated food insecurity, with the number of people in need more than doubling from six million to 13 million. Wright notes, “The bottom 20% of our society is economically cut adrift. Therefore, we’ve seen demand explode for more and more food. Last year, we did 55,000 tons, that’s 130 million meals. We could easily double or treble that if we had access to the food and the finance.” Salesforce Data and AI Prevent Food Waste To meet this growing demand, FareShare is ramping up its use of technology, particularly Salesforce. Over the past seven years, FareShare has utilized Salesforce’s Sales and Service Cloud to manage customer contacts and some food offers. Recently, FareShare conducted a full review of its operations and technology use, deciding to significantly increase its investment in Salesforce. FareShare is now exploring how Nonprofit Cloud and Data Cloud can benefit the organization. Wright explains, “Why reinvent the wheel? If there’s something great out there, use it and use it quickly.” Nonprofit Cloud provides FareShare with a unified view of its supporters, enabling better management of food and monetary donors. Data Cloud offers a centralized data source, replacing disparate spreadsheets, to improve data management. The aim is to have a holistic view of supporters, including donation history and preferences, to enhance their experience and demonstrate the impact of their contributions. AI components within Salesforce further boost productivity by suggesting tailored communications, drastically reducing the time required for tasks like crafting donor emails. Future Prospects FareShare is in the early stages of integrating Nonprofit Cloud and Data Cloud, aiming to establish these key systems before expanding into the full Salesforce ecosystem. Wright emphasizes the broader benefits of this partnership: “We’re not just getting the tools, we’re getting ways of working.” The primary objective for the additional Salesforce technology is improving fundraising. FareShare needs enhanced tech to scale its supporter base, generate more income, and effectively communicate the impact of donations. Wright envisions leveraging the wider Salesforce ecosystem to connect surplus food with charities in need, optimizing logistics to maximize social impact and minimize costs. The Bigger Picture FareShare sees AI playing a crucial role in tackling food waste and sustainability, potentially linking food sources and surplus across the country to charities in need. Wright concludes, “There’s more food wasted than we tackle and more charities that need more food. If we could connect those with a logistics solution, we could optimize for maximum use of food, minimum use of miles to get it to them. Maximum social impact, minimal cost. There’s a big tech opportunity there.” By harnessing the power of technology and strategic partnerships, FareShare aims to continue its mission to reduce food waste and food insecurity, creating a more sustainable and equitable future. Salesforce Data and AI Prevent Food Waste Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 5, 2024in Salesforce for Nonprofits
RevOps Sute for Salesforce Data Management
03Jul

RevOps Suite for Salesforce Data Management

Traction Complete Launches Revolutionary “RevOps Data Management Suite for Salesforce” In an era where artificial intelligence (AI) is revolutionizing business, Traction Complete is proud to announce the launch of “The RevOps Data Management Suite for Salesforce.” This groundbreaking suite is the first data management solution designed specifically for Revenue Operations professionals, empowering users to fully leverage their data by ensuring it is clean, connected, and ready for AI adoption. RevOps Sute for Salesforce Data Management. Importance of Data Integrity “The importance of clean and connected data can’t be stressed enough, especially in the age of AI. Without a solid foundation of reliable data, businesses are navigating in the dark, unable to make strategic decisions.” David Nelson, CEO of Traction Complete RevOps Suite for Salesforce Data Management Stephen Daniels, Vice President of Revenue Operations at Cresta, echoes this sentiment: “There is a cost to bad data. If we didn’t have a solution like Traction Complete in the first place to scale off, we would be building on a bad foundation that would cause hundreds of thousands of dollars of headaches in the future.” The Impact of Poor Data Poor data is more than just a nuisance; it’s a significant barrier to success. And it is expensive. It leads to distrust in CRM systems and lost productivity, ultimately impacting business revenue. Traction Complete’s mission is to tackle these challenges head-on, ensuring that data is accurate and seamlessly integrated across the Salesforce platform. This foundation enables businesses to adopt the latest technology seamlessly. Recent findings from McKinsey highlight the critical role of data quality in AI adoption: “Fifty-six percent of companies say ‘inaccuracy’ is the biggest risk posed by adopting generative AI. Yet only 32% of companies have systems in place for mitigating such inaccuracies.” Expertise and Innovation Traction Complete, born from the recently Salesforce-acquired Traction on Demand, draws on over 1.4 million hours of consulting experience to build trusted data management solutions that improve data quality. “At the pace that AI and machine learning are expanding and changing how we operate, businesses can’t afford to overlook the importance of data quality. Any businesses not setting their data foundation right now will be left behind,” adds Nelson. The Future of Data Management As organizations continue to navigate the complexities of digital transformation, the message is clear: the time to invest in data management is now. With the RevOps Data Management Suite for Salesforce, Traction Complete is leading the way, providing the tools necessary for businesses to thrive in the age of AI and beyond. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 3, 2024in Generative AI, Salesforce, Salesforce Tableau
Best Practices for RAG LLM
02Jul

Best Practices for RAG LLM

Best Practices for Building a RAG LLM Chatbot In today’s competitive landscape, a robust Retrieval-Augmented Generation (RAG) Large Language Model (LLM) chatbot can provide a significant edge. However, implementing such a system involves multiple steps and challenges. Below is a guide to help you navigate this journey effectively. 1. Define Clear Objectives Start by identifying your goals: A clear purpose will drive your design and implementation decisions. 2. Prepare Your Data Your chatbot’s success relies heavily on the quality and structure of your data. Assess Your Data Format: Enrich Your Data: 3. Select the Right Platform Choose a platform based on your goals and data type. Common architectures include: Standard RAG with Vector Databases: Relational Databases: Hybrid Text Search: Graph Databases: 4. Fine-Tune the Model While RAG often enriches data without altering the base model, fine-tuning may be essential in specific scenarios, such as: You can fine-tune open-source models directly or use OpenAI’s APIs for custom training. OpenAI also offers domain-specific GPTs, which can save development time. 5. Master Prompt Engineering Crafting precise prompts is critical to guide LLM responses. Prompts generally include: Example for Elasticsearch: javascriptCopy codeYour task is to create a valid Elasticsearch DSL query. Given the mapping: “`{mapping}“`, translate the query: “`{query}“` into JSON. – Use only fields from the mapping. – Ensure case-insensitivity and support fuzzy matches. – Compress the JSON output, removing spaces. For advanced prompt strategies, see Modern Advances in Prompt Engineering. 6. Iterative Testing Testing machine learning models is inherently complex. Key strategies: 7. Optimize the Front-End Collaborate with UI/UX teams to integrate LLM capabilities seamlessly. Consider: 8. Avoid Common Pitfalls Looking Ahead The rise of LLMs has transformed how we think about search and chatbot applications. While we’re in the early stages, the possibilities are immense. Businesses that adapt and strategically integrate these technologies will unlock innovative opportunities and thrive in the evolving landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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July 2, 2024in Data
Data Cloud - Facts and Fiction
30Jun

Data Cloud – Facts and Fiction

Salesforce Data Cloud: Debunking Myths and Unveiling Facts If you’ve been active on LinkedIn, attending recent Salesforce events, or even watching a myriad of sporting events, you’ve likely noticed that Salesforce has evolved beyond just CRM. It’s now CRM + DATA + AI. Although Salesforce has always incorporated these elements, with Einstein AI and data being integral to CRM, the latest innovation lies in the Data Cloud. Data Cloud – Facts and Fiction Data Cloud, formerly known as Salesforce Genie, represents Salesforce’s latest evolution, focusing on enabling organizations to scale and grow in an era where data is the new currency. It is the fastest-growing product in Salesforce’s history, pushing new boundaries of innovation by providing better access to data and actionable insights. As Data Cloud rapidly develops, potential clients often have questions about its function and how it can address their challenges. Here are some common myths about Data Cloud and the facts that debunk them. Myth: Data Cloud Requires MuleSoft Fact: While MuleSoft Anypoint Platform can accelerate connecting commonly used data sources, it is not required for Data Cloud. Data Cloud can ingest data from multiple systems and platforms using several out-of-the-box (OOTB) connectors, including SFTPs, Snowflake, AWS, and more. Salesforce designs its solutions to work seamlessly together, but Data Cloud also offers connector options for non-Salesforce products, ensuring flexibility and integration capabilities beyond the Salesforce ecosystem. Myth: Data Cloud Will De-Duplicate Your Data Fact: Harmonizing data in Data Cloud means standardizing your data model rather than de-duplicating it. Data Cloud maps fields to a common data model and performs “Identity Resolution,” using rules to match individuals based on attributes like email, address, device ID, or phone number. This process creates a Unified Individual ID without automatically de-duplicating Salesforce records. Salesforce intentionally does not function as a Master Data Management (MDM) system. Myth: Data Cloud Will Create a Golden Record Fact: Data Cloud does not create a single, updated record synchronized across all systems (a “golden record”). Instead, it retains original source information, identifies matches across systems, and uses this data to facilitate engagements, known as the Data Cloud Key Ring. For instance, it can recognize an individual across different systems and provide personalized experiences without overwriting original data. Myth: You Can’t Ingest Custom Objects from Salesforce Fact: During the data ingestion process, you can select which objects to ingest from your Salesforce CRM Org, including custom objects. The system identifies the API names of the objects and fields from the data source. Ensuring the Data Cloud integration user has access to the necessary information (similar to assigning Permission Sets) allows you to ingest and map custom objects accordingly. Myth: Data Cloud Requires a Data Scientist and Takes a Long Time to Implement Fact: While implementing Data Cloud involves ingesting, mapping data, running identity resolution, and generating insights, it does not necessarily require a data scientist. Skilled Salesforce Admins can often manage data integration from third-party applications. Effective Data Cloud implementation requires thorough planning and preparation, akin to prepping a room before painting. Identifying use cases and understanding data sources in advance can streamline the implementation process. Myth: Data Cloud is Expensive Fact: Data Cloud operates on a consumption-based pricing model. Engaging in strategic conversations with Salesforce Account Executives can help understand the financial implications. Emphasizing the value of a comprehensive data strategy and considering the five V’s of Big Data—Volume, Variety, Veracity, Value, and Velocity—ensures that your data supports meaningful business outcomes and KPIs. In Summary Salesforce Data Cloud represents a significant evolution in managing and leveraging data within your organization. It helps break down data silos, providing actionable insights to drive organizational goals. Despite initial misconceptions, implementing Data Cloud does not require extensive coding skills or a data scientist. Instead, thorough planning and preparation can streamline the process and maximize efficiency. Understanding the value of a comprehensive data strategy is crucial, as data becomes the new currency. Addressing the five V’s of Big Data ensures that your data supports meaningful business outcomes and KPIs. At Tectonic, our team of certified professionals is ready to assist you on this journey. We offer a Salesforce Implementation Solution package to help you get hands-on with the tool and explore its capabilities. Whether you need help understanding your data sources or defining use cases, our data practice can provide the expertise you need. Talk to Tectonic about Data Cloud and discover how our tailored solutions can help you harness the full potential of your data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 30, 2024in Salesforce Data Cloud
Gearset's Features Empower Salesforce Teams
30Jun

Gearset’s Features Empower Salesforce Teams

Gearset, a leading Salesforce DevOps platform, is introducing a new suite of features aimed at accelerating and improving reliable release management for Salesforce teams. Gearset’s Features Empower Salesforce Teams. These enhancements, including Archiving, expanded Compare and Deploy capabilities, and Flow Navigator, address critical user feedback and deliver tools for optimizing data and enhancing visibility throughout the DevOps lifecycle. According to Matt Dickens, CPO and co-founder of Gearset, effective DevOps strategies rely on accurate and consistent data to deploy applications at the pace of innovation. The latest features empower teams to manage and apply relevant data in real-time, supporting organizations in navigating continuous changes to drive business growth on Salesforce confidently. Gearset Archiving provides organizations with control over Salesforce data storage by consolidating backup, sandbox seeding, and monitoring solutions into a unified platform. By eliminating outdated data from Salesforce orgs, enterprises can reduce storage costs and prevent performance degradation. Key benefits of Archiving include: Dickens explains that Gearset Archiving allows teams to safely store obsolete or legacy data off-platform while maintaining accessibility for future use, without impacting org performance. Maintaining clean and consistent metadata across the organization is crucial for Gearset. Recognizing limitations in Salesforce deployment tools, Gearset’s enhanced Compare and Deploy capability now enables DevOps teams to quickly analyze and compare metadata, facilitating efficient issue identification and resolution during deployments. This update streamlines operations, ensuring faster delivery of business-critical functionality to maximize ROI. Gearset’s Flow Navigator tool introduces visual comparison capabilities for Salesforce deployments, addressing challenges with Flow versioning and enhancing transparency in deployment processes. By presenting flows in their native Salesforce format, Flow Navigator enables teams to better manage deployments and mitigate potential conflicts. Dickens emphasizes that Gearset remains committed to user-driven innovation, aiming to continuously address Salesforce challenges and deliver solutions that save time, reduce stress, and improve efficiency for all customers. In summary, Gearset’s latest release underscores its dedication to empowering Salesforce teams with robust tools tailored to enhance efficiency and reliability across the DevOps lifecycle. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 30, 2024in Data, Enterprise, Salesforce
iDataMasker for Salesforce FinTech
24Jun

iDataMasker for Salesforce FinTech

Safeguarding Data Privacy and Security in the Digital Age with iDataMasker In today’s digital transformation era, data privacy and security are paramount for organizations worldwide. As cloud-based platforms like Salesforce become integral to business operations, robust solutions to protect sensitive information are essential. iDataMasker for Salesforce FinTech powers security in Salesforce banking solutions. Introducing iDataMasker on Salesforce AppExchange IntellectAI has launched iDataMasker, an advanced data obfuscation application, now available on the Salesforce AppExchange marketplace. This innovative tool is set to revolutionize data security within Salesforce environments. Addressing the Threat of Data Breaches Data breaches and unauthorized access can lead to significant financial losses, reputational damage, and legal issues for organizations. With stringent data protection regulations such as GDPR and CCPA, companies must take proactive steps to ensure compliance. iDataMasker provides a comprehensive solution with advanced anonymization techniques to uphold the highest standards of data privacy and security. Key Features of iDataMasker Compliance and Data Security Compliance with industry regulations and standards is crucial for businesses. iDataMasker helps organizations achieve compliance effortlessly with its robust data masking capabilities. Whether handling personally identifiable information (PII), financial data, or healthcare records, iDataMasker ensures sensitive data remains protected and compliant. Enhancing Organizational Data Security By safeguarding sensitive information from unauthorized access and data breaches, iDataMasker enhances an organization’s overall data security posture. This instills confidence in both the company and its customers, knowing that their data is secure within the Salesforce environment. Usability and Operational Efficiency iDataMasker maintains data privacy while ensuring information remains usable for business processes. This allows companies to harness data-driven insights without compromising confidentiality. Rigorous data masking policies help maintain data integrity and foster a culture of responsible data management, strengthening data governance practices. Using obfuscated data that mirrors real-world scenarios, iDataMasker streamlines processes such as testing, training, and development. Organizations can work with realistic data without compromising confidentiality, leading to improved operational efficiency and faster time-to-market. Building Customer Trust Demonstrating a strong commitment to data privacy and security is vital for building customer trust and loyalty. By implementing iDataMasker, organizations can show their dedication to protecting customer data, fostering long-lasting relationships based on trust and transparency. Conclusion In today’s digital landscape, data privacy and security are non-negotiable. iDataMasker, developed by IntellectAI and available on the Salesforce AppExchange marketplace, offers a powerful solution to address these critical concerns. Leveraging advanced data masking techniques, flexible configuration options, seamless integration, and compliance readiness, iDataMasker empowers organizations to safeguard their sensitive data while fully embracing the potential of Salesforce. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 24, 2024in Salesforce
Changes in Advertising Changing CRMs
22Jun

Changes in Advertising Changing CRMs

Oracle announced last week that it is exiting the advertising business and will sunset its adtech by September 30. While the announcement is not surprising given the massive layoffs in 2022 affecting Oracle Advertising teams, the rapidity of Oracle Advertising’s decline is a clear indicator of how swiftly the digital advertising landscape can evolve. This move is likely just the first of many significant Changes in Advertising Changing CRMs. What happened? Oracle Advertising faced challenges beginning in 2018 and never managed to recover. Several forces related to data deprecation adversely impacted the business: Changes in Advertising Changing CRMs Retooling its acquisitions to function in a consent-driven and regulated environment would have required significant investment from Oracle. Given its track record with privacy law compliance, this would have been a daunting task, necessitating both rapid innovation and market trust in its solutions. What does this mean for the advertising ecosystem? Oracle’s exit from adtech marks a significant shift in the advertising ecosystem. The sharp decline in advertising revenue from $2 billion in 2022 to $300 million in 2024 suggests a major miscalculation by Oracle. Without demand- or supply-side platforms (unlike Google, Microsoft, and Amazon) and lacking a large audience base (unlike Meta, Disney, and Netflix), Oracle’s benefits as an adtech partner or acquirer were unclear. The key question now is whether Oracle’s intellectual property will find new ownership and continue in some form. What does this mean for the marketing ecosystem? The broader marketing ecosystem is likely to see more shifts as major players adapt to the new landscape. Leading martech vendors like Adobe and Salesforce have already transitioned from DMPs to CDPs. Adobe Real-Time CDP and Salesforce Data Cloud for Marketing are gaining market share, while Oracle has struggled in the B2C martech space. Oracle’s decision to cut investments in martech and adtech has significantly impaired its B2C market efforts, with products like Responsys failing to gain the traction that Eloqua has in the B2B space. Oracle also announced it will sunset related B2C marketing products like Oracle Maxymiser in the coming months. These changes are just the beginning of a broader transformation in digital advertising, driven by evolving privacy standards, consumer expectations, and technological advancements. This marks the dawn of a new era in which agility and compliance will be key to success in the digital advertising and marketing landscapes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 22, 2024in Salesforce Data Cloud
21Jun

AI and Consumer Goods Cloud

Salesforce’s latest “rolling thunder” of AI enhancements brings significant innovations to Consumer Goods Cloud, leveraging the power of the Einstein AI platform already integrated into Sales Cloud and Service Cloud. These enhancements are designed to optimize planning and execution for consumer goods companies. Salesforce Consumer Goods Cloud is an industry-specific solution that helps consumer goods companies streamline their route-to-market processes. By unifying trade promotion management and retail execution capabilities on a single platform, it enables seamless collaboration between headquarters and field teams. Utilizing Salesforce’s core CRM functionality and the Einstein AI platform, Consumer Goods Cloud empowers companies with data-driven insights and intelligent automation to drive profitable growth. “Consumer goods companies are laser-focused on profitable growth. With the latest Salesforce innovations for Consumer Goods Cloud, they can unify consumer and customer data to plan promotions precisely, equip every field rep with tools to increase sales and reduce downtime, and integrate trusted AI into every service agent’s workflow to solve problems and upsell more frequently,” explained Rob Garf, VP and GM of Retail and Consumer Goods at Salesforce. “In short, every consumer goods company can now transform into an AI Enterprise.” What’s New in Consumer Goods Cloud The latest updates in Consumer Goods Cloud focus on integrating Salesforce’s Data Cloud with Einstein generative AI capabilities, enhancing three key areas: Data Cloud for Consumer Goods: Account managers can now unify account and industry data to build rich customer profiles, segment accounts to the individual store level, and design hyper-localized assortment and promotion plans. For instance, a soft drink distributor can identify which citrus-flavored sodas are most popular in specific Mexican convenience stores and optimize replenishment accordingly. Einstein Copilot Account Summarization: Within the service console, agents can access AI-generated account summaries, eliminating the need to switch between screens and knowledge articles. Summaries include last interactions, order history, satisfaction scores, and promotion details, enabling agents to resolve inquiries quickly and upsell intelligently. Consumer Goods Cloud Einstein 1 for Sales: This AI-powered enhancement package provides sales managers, field reps, merchandisers, and delivery drivers with productivity and revenue-boosting insights. Real-time notifications and recommendations on stock levels, replenishment, special handling needs, and payment collection keep field teams responsive and effective. The Salesforce Embedded AI Difference Salesforce’s strategy of embedding AI via a unified Einstein platform offers several advantages: Consistency: With Einstein already integrated into Sales and Service Clouds, Salesforce can efficiently extend proven AI tools to industry-specific use cases, benefiting users with familiar interfaces and interaction paradigms. Completeness: Embedding AI at the platform level allows Salesforce to enhance the entire workflow from planning to execution. Consumer goods companies can apply intelligent insights to both back-office processes like promotion management and field activities like stock checks and payment collection. Continuous Innovation: The Einstein platform enables rapid deployment of Salesforce’s latest generative AI advancements across all clouds, ensuring customers always have access to state-of-the-art capabilities. Mars Snacking, one of the world’s largest consumer goods companies, is already benefiting from Salesforce’s AI-powered industry cloud. “At Mars Snacking, we are on an ambitious journey to rewire and almost double the size of our business by 2030,” said Bartek Kononiuk, Global Head of Product – Trade Promotion Management. “Consumer Goods Cloud and Trade Promotion Management will enable us to improve our business processes, data availability, and user experience in critical growth-enabling areas.” AI Innovation Comes at a Cost As the consumer goods industry strives to meet rapidly evolving buyer expectations, Salesforce’s embedded AI solutions for Consumer Goods Cloud offer timely advantages. By democratizing access to generative AI and data management capabilities, Salesforce enables companies of all sizes to optimize decision-making, boost field productivity, and drive profitable growth. However, these advanced functionalities come with significant costs. Salesforce’s Einstein AI enhancements often have substantial per-user surcharges, sometimes exceeding $100 per month. For large deployments involving thousands of employees, these expenses can quickly escalate. Consumer goods companies must carefully evaluate the productivity and revenue gains against the added licensing costs. Additionally, while Salesforce is leading the way in enterprise generative AI, the technology is still maturing. Early adopters may encounter instances where the AI delivers suboptimal results. Salesforce’s Trust Layer aims to mitigate these risks, but companies should approach generative AI with a clear understanding of its current limitations. The ongoing enhancements in Salesforce’s Einstein portfolio present a promising yet costly opportunity for customers to evolve into full-fledged AI Enterprises. As the costs and benefits become clearer, consumer goods companies will need to strategically decide where and how aggressively to deploy these advanced capabilities. Those that find the right balance could gain a significant competitive edge in the rapidly changing digital landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 21, 2024in Einstein 1 Platform, Generative AI, Salesforce

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