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Big Data and Data Visualization

Big Data and Data Visualization Explained

Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The term is often used interchangeably with information graphics, information visualization, and statistical graphics. The Role of Data Visualization in Data Science Data visualization is a crucial step in the data science process. After data is collected, processed, and modeled, it must be visualized to draw meaningful conclusions. It’s also a key component of data presentation architecture, a discipline focused on efficiently identifying, manipulating, formatting, and delivering data. Importance Across Professions Data visualization is essential across various fields. Teachers use it to display student performance, computer scientists to explore AI advancements, and executives to communicate information to stakeholders. In big data projects, visualization tools are vital for quickly summarizing large datasets, helping businesses make informed decisions. In advanced analytics, visualization is equally important. Data scientists use it to monitor and ensure the accuracy of predictive models and machine learning algorithms. Visual representations of complex algorithms are often easier to interpret than numerical outputs. Historical Context of Data Visualization Data visualization has evolved significantly over the centuries, long before the advent of modern technology. Today, its importance is more pronounced, as it enables quick and effective communication of information in a universally understandable manner. Why Data Visualization Matters Data visualization provides a straightforward way to communicate information, regardless of the viewer’s expertise. This universality makes it easier for employees to make decisions based on visual insights. Visualization offers numerous benefits for businesses, including: Advantages of Data Visualization Key benefits include: Challenges and Disadvantages Despite its advantages, data visualization has some challenges: Data Visualization in the Era of Big Data With the rise of big data, visualization has become more critical. Companies leverage machine learning to analyze vast amounts of data, and visualization tools help present this data in a comprehensible way. Big data visualization often employs advanced techniques, such as heat maps and fever charts, beyond the standard pie charts and graphs. However, challenges remain, including: Examples of Data Visualization Techniques Early computer-based data visualizations often relied on Microsoft Excel to create tables, bar charts, or pie charts. Today, more advanced techniques include: Common Use Cases for Data Visualization Data visualization is widely used across various industries, including: The Science Behind Data Visualization The effectiveness of data visualization is rooted in how humans process information. Daniel Kahneman and Amos Tversky’s research identified two methods of information processing: Visualization Tools and Vendors Data visualization tools are widely used for business intelligence reporting. These tools generate interactive dashboards that track performance across key metrics. Users can manipulate these visualizations to explore data in greater depth, and indicators alert them to data updates or important events. Businesses might use visualization of data software to monitor marketing campaigns or track KPIs. As tools evolve, they increasingly serve as front ends for sophisticated big data environments, assisting data engineers and scientists in exploratory analysis. Popular data visualization tools include Domo, Klipfolio, Looker, Microsoft Power BI, Qlik Sense, Tableau, and Zoho Analytics. While Microsoft Excel remains widely used, newer tools offer more advanced capabilities. Data visualization is a vital subset of the broader field of data analytics, offering powerful tools for understanding and leveraging business data across all sectors. 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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Generative AI Regulations

Salesforce, Data Science, and Generative AI

Is Salesforce utilized in the field of data science? Salesforce data science and Generative AI Data Science-as-a-Service (DSaaS) democratizes access to machine learning through the Salesforce Data Management Platform, enabling widespread adoption of data science capabilities. Utilizing Salesforce for Data Science Empowerment: The integration of Salesforce into data science represents a transformative endeavor aimed at democratizing machine learning through Data Science-as-a-Service (DSaaS). By leveraging the Salesforce Data Management Platform, the objective is to empower individuals across various domains with the potential of data science. Democratization of Data Science: DSaaS introduces a versatile workbench that capitalizes on machine learning to refine segmentation, enhance activation strategies, and uncover deeper insights. Through robust analytics tools, users can gain profound insights into individual customer behaviors. Supported by a formidable 20-petabyte analytics environment and a real-time big data infrastructure, data-driven analytics are taken to unprecedented levels. Harnessing Modeling Resources: Data owners enjoy the flexibility to harness their data, algorithms, and models either within the Salesforce Data Management Platform or within their independent environments. Spearheading this initiative is the Salesforce Unified Intelligence Platform (UIP) team, constructing a centralized data intelligence platform aimed at enriching business insights, enhancing user experience, improving product quality, and optimizing operational efficiency, all while upholding the core value of trust embedded in the Salesforce platform. Salesforce Data Science and Generative AI Emphasizing Security and Design: Security stands as a cornerstone of the Salesforce platform, with the UIP’s evolution tracing back to a transition from a colossal Hadoop cluster to UIP in public clouds. The architectural journey prioritized data classification early on, engaging in meticulous reviews with legal and security experts to classify data intended for storage within UIP. Adopting the “zero-trust infrastructure” principle, the architecture is fortified against both internal and external threats, ensuring robust defense mechanisms against potential data breaches. Unlocking Data Science Potential through DSaaS: DSaaS serves as a catalyst in democratizing machine learning through the Salesforce Data Management Platform, spotlighting the pivotal role of data science in fostering generative AI and cultivating trustworthy AI. Data scientists play a critical role in ensuring data quality and organization to steer clear of issues such as biased or irrelevant outcomes. Navigating Data Science Challenges: Despite the transformative potential of data science, businesses encounter various challenges including managing diverse data sources, scarcity of skilled professionals, data privacy and security concerns, data cleansing complexities, and effectively communicating findings to non-technical stakeholders. Proposed Solutions: Addressing these challenges involves leveraging data integration tools, investing in the upskilling and reskilling of data professionals, implementing robust data privacy measures, employing data governance tools for data cleansing, and honing communication skills for reporting findings to non-technical stakeholders. The success of generative AI hinges on well-organized data, and data science is pivotal in achieving this. Whether utilizing AI tools built with the expertise of data scientists or building a data science team, businesses can navigate the evolving landscape of AI and data science with confidence. Content updated March 2024. 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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Digital Transformation for Life Sciences

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 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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Einstein Discovery

Einstein Discovery Analysis

Elevate Your Business Outcomes with Einstein Discovery Analysis Einstein Discovery revolutionizes your approach to predictive analytics, allowing you to effortlessly build reliable machine learning models without any coding. Reduce reliance on data science teams with an intuitive model-building wizard and streamlined monitoring process. Transition swiftly from data to actionable insights, ensuring every decision is guided by intelligence. Enhance Your Business Intelligence with Einstein Discovery Incorporate statistical modeling and machine learning into your business intelligence with Einstein Discovery. Seamlessly integrated into your Salesforce environment, operationalize data analysis, predictions, and enhancements with clicks, not code. Developers can utilize the Einstein Prediction Service to access predictions programmatically, while data specialists can predict outcomes within recipes and dataflows. Tableau users can also leverage Einstein Discovery predictions and improvements directly within Tableau. Advanced Analytics Made Simple with Einstein Discovery Einstein Discovery offers a comprehensive suite of business analytics tailored to your specific data needs. Licensing and Permission Requirements for Einstein Discovery To utilize Einstein Discovery, your organization needs the appropriate license, with user accounts assigned relevant permissions. Supported Use Cases and Implementation Tasks Einstein Discovery solutions effectively address common business use cases, typically involving a series of defined implementation tasks. Key Differentiation: Einstein Analytics vs. Einstein Discovery While Einstein Analytics integrates predictive and analytical capabilities within Sales, Service, and Marketing clouds, Einstein Discovery is specifically focused on providing actionable insights and data-driven stories. Key Benefits of Einstein Discovery Supported Data Integration and Functionality Einstein Discovery enables direct integration and import of data from external sources like Hadoop, Oracle, and Microsoft SQL Server. It extracts data from diverse sources, leveraging AI, ML, and statistical intelligence to identify patterns and generate informed predictions. Enhanced Features and Enhancements Einstein Discovery seamlessly integrates insights into Tableau workflows, unlocks insights from unstructured data, fine-tunes prediction accuracy with trending data, handles missing values in datasets, accelerates prediction processing with high-volume writeback, and offers enhanced settings panels for efficient prediction management. Partner with OMI for Expert Guidance Collaborate with experienced Salesforce services providers like OMI to maximize the benefits of Einstein Discovery, ensuring a seamless implementation process and ongoing support. Empower Your Business with Einstein Discovery Einstein Discovery delivers automated data analysis, interactive visualizations, and predictive insights to elevate decision-making and optimize business operations. Unlock the power of AI-driven analytics within your Salesforce ecosystem to accelerate growth and gain a competitive edge. 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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Opportunity Scoring with Einstein

Opportunity Scoring with Einstein

Salesforce has placed significant emphasis on developing their new Artificial Intelligence engine, Einstein, in recent years. One standout feature that has garnered attention is Einstein Opportunity Scoring, which will become available for free following the Summer ’20 release. This feature is offered to all Salesforce customers. So, what exactly is Einstein Opportunity Scoring? It’s a system that leverages data science and machine learning to score sales opportunities, enabling users to prioritize their actions effectively. Here’s how it works: The advantages of opportunity scoring are manifold: So, how does Einstein Opportunity Scoring operate? To implement Einstein Opportunity Scoring, certain prerequisites must be met: Building the prediction set involves defining positive and negative examples based on various criteria such as past outcomes, opportunity progress, and account activity. Implementation entails setting up the prediction using the Einstein Prediction Builder, defining the segment, specifying prediction outcomes, and selecting relevant fields. After deployment, users can monitor the prediction set status and review the predictions to ensure they align with business objectives. Over time, real-life data can be analyzed to assess the accuracy and effectiveness of the predictions. To enhance opportunity scores, users are advised to maintain accurate data, progress opportunities through stages promptly, and ensure completeness of opportunity records. Access to Einstein Opportunity Scoring is included in the Sales Cloud Einstein product suite at no additional cost, accessible through the Sales Cloud Einstein For Everyone permission set. By leveraging Einstein Opportunity Scoring, Salesforce customers can optimize their sales processes, improve efficiency, and make more informed decisions to drive business success. 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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