Private Health Insurance Exchange for ProvidersHealthPocket
Hospitals, clinics and doctors, don’t lose patients due to changes in insurance. Make sure your patients can find health plans you accept.
One of the biggest problems patients face is finding a cost-effective plan that allows them to get the medical care they need from the team they want. Hospitals and clinics to solve this problem for their patients with HealthPocket Private Exchange.
Configure and brand Private Exchange for your hospital or clinic. This free resource can be embedded in your patient portals to ensure patients choose a health plan that lets them get care from your hospital.
Interoperability between electronic health record systems will continue to be a major issue in 2016. Healthcare consumerism is expected to grow as consumers demand more convenient access to healthcare information and services through digital technologies. However, the value of big data analytics may be difficult to prove as healthcare organizations struggle with data integration and a lack of analytics talent. There is also a risk that some healthcare startups could run afoul of regulators or have their apps shut down if they do not properly address privacy, safety and licensing issues. The role of technology leadership in healthcare organizations may broaden beyond the traditional CIO to include other executives focused on specific areas like analytics, security and digital transformation.
The document discusses how government data is increasingly connected but remains segmented and disconnected. It introduces GraphGrid, a fully-managed platform that uses graph databases and technologies to connect government data and enable agencies to discover relationships and gain insights. This allows agencies to solve complex problems, improve security, increase intelligence sharing between agencies, and improve mission outcomes.
Data monopolists like google are threatening the economy hbrankiteny
Big data holds risks beyond threats to consumer privacy, including threats to free market competition from data monopolies. As companies build proprietary data sets and use them to create new products and markets, data becomes an unfair barrier to entry that prevents new competitors. This hurts competition and the economy. The search market provides an example where Google's vast historical search data gives it an insurmountable advantage over competitors. Regulators should consider whether a company's exclusive ownership of certain data that blocks market entry constitutes a monopoly problem requiring antitrust intervention, similar to Standard Oil in oil or Northern Securities in railroads.
Big data refers to the collection and analysis of extremely large data sets to reveal patterns, trends, and associations. It allows for more accurate analyses, confident decision making, and greater efficiencies. Some key points:
- Big data comes from various sources like web browsing, social media, sensors, and can provide insights into customer engagement, retention, and optimizing marketing programs.
- Many large companies are using big data to reduce costs, optimize operations, develop new data-driven products and services, and support internal business decisions.
- Technologies used for big data include Hadoop, HDFS, NoSQL, MapReduce, MongoDB, and Cassandra.
- Challenges remain around overreliance
Prediction Markets is a valuable tool for executive decision making, lowering cost and increasing accuracy. Smartly applied, the information gleaned from prediction markets help managements listens to the voices in the company that might otherwise go unheard.
Did you know that on average, it takes 5 days to provision a new employee or contingent worker? Are you traversing complex digital environments, growing regulatory requirements, and exploding personnel growth?
What if you could onboard employees, have them provisioned, access approved, and onboard in half the time? With SailPoint’s automated identity solutions, you can.
Private Health Insurance Exchange for ProvidersHealthPocket
Hospitals, clinics and doctors, don’t lose patients due to changes in insurance. Make sure your patients can find health plans you accept.
One of the biggest problems patients face is finding a cost-effective plan that allows them to get the medical care they need from the team they want. Hospitals and clinics to solve this problem for their patients with HealthPocket Private Exchange.
Configure and brand Private Exchange for your hospital or clinic. This free resource can be embedded in your patient portals to ensure patients choose a health plan that lets them get care from your hospital.
Interoperability between electronic health record systems will continue to be a major issue in 2016. Healthcare consumerism is expected to grow as consumers demand more convenient access to healthcare information and services through digital technologies. However, the value of big data analytics may be difficult to prove as healthcare organizations struggle with data integration and a lack of analytics talent. There is also a risk that some healthcare startups could run afoul of regulators or have their apps shut down if they do not properly address privacy, safety and licensing issues. The role of technology leadership in healthcare organizations may broaden beyond the traditional CIO to include other executives focused on specific areas like analytics, security and digital transformation.
The document discusses how government data is increasingly connected but remains segmented and disconnected. It introduces GraphGrid, a fully-managed platform that uses graph databases and technologies to connect government data and enable agencies to discover relationships and gain insights. This allows agencies to solve complex problems, improve security, increase intelligence sharing between agencies, and improve mission outcomes.
Data monopolists like google are threatening the economy hbrankiteny
Big data holds risks beyond threats to consumer privacy, including threats to free market competition from data monopolies. As companies build proprietary data sets and use them to create new products and markets, data becomes an unfair barrier to entry that prevents new competitors. This hurts competition and the economy. The search market provides an example where Google's vast historical search data gives it an insurmountable advantage over competitors. Regulators should consider whether a company's exclusive ownership of certain data that blocks market entry constitutes a monopoly problem requiring antitrust intervention, similar to Standard Oil in oil or Northern Securities in railroads.
Big data refers to the collection and analysis of extremely large data sets to reveal patterns, trends, and associations. It allows for more accurate analyses, confident decision making, and greater efficiencies. Some key points:
- Big data comes from various sources like web browsing, social media, sensors, and can provide insights into customer engagement, retention, and optimizing marketing programs.
- Many large companies are using big data to reduce costs, optimize operations, develop new data-driven products and services, and support internal business decisions.
- Technologies used for big data include Hadoop, HDFS, NoSQL, MapReduce, MongoDB, and Cassandra.
- Challenges remain around overreliance
Prediction Markets is a valuable tool for executive decision making, lowering cost and increasing accuracy. Smartly applied, the information gleaned from prediction markets help managements listens to the voices in the company that might otherwise go unheard.
Did you know that on average, it takes 5 days to provision a new employee or contingent worker? Are you traversing complex digital environments, growing regulatory requirements, and exploding personnel growth?
What if you could onboard employees, have them provisioned, access approved, and onboard in half the time? With SailPoint’s automated identity solutions, you can.
Innovation and Transformation in Financial ServicesCertus Solutions
Historically, financial services firms have struggled to target and tailor their product offerings to the customer journey. Often only traditional demographic information – gender, age, occupation – is collected with no real insight as to what life stage a customer is in and how this could influence their financial activity.
To compete in a consumer-empowered economy, it is increasingly clear that financial services firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more.
Let’s Build aSmarter Planet: Re-thinking the way Insurance works!IBMAsean
The document discusses how the insurance industry is being transformed by new technological and economic forces, and outlines a vision for how insurers can build a "smarter" approach. It argues that insurers must leverage the growing volumes of available data through more integrated, intelligent, and dynamic systems in order to streamline operations, develop new customer-centric products and services, and build a more sustainable infrastructure. The document provides examples of how some leading insurers are already taking steps to work smarter through automation, analytics, and virtualization.
Data Quality Management (DQM) impacts a number of key business drivers, ranging from regulatory
compliances, to customer satisfaction, to building new business models. Quality is one of the key functions
under Data Governance, as unverified/unqualified data has little value to the organization. One of the leading
global research and advisory firm estimates that an average Fortune 500 enterprise loses about $9.7mn
annually over data quality issues. Although the true intangible cost of poor data is much higher, the sad truth
is that data quality has not been paid the attention it deserves.
How to Leverage Increased Data Granularity in the ICD-10 Code SetPerficient, Inc.
A webinar designed for healthcare professionals. We explore how to leverage the increased data granularity in the ICD-10 code set. While there are risks, a properly executed ICD-10 implementation will deliver plentiful rewards.
This document discusses how life insurance companies can leverage big data analytics across their value chain. It begins by explaining how data sources have expanded dramatically in recent years due to factors like the growth of digital devices and the internet of things. It then outlines how big data can be used in various parts of the insurance lifecycle from product development to claims processing. The document presents a four stage framework for life insurers to adopt big data analytics and provides examples of how some companies have realized benefits. It concludes by noting that while insurers recognize big data's potential, many challenges remain in analyzing diverse and voluminous unstructured data.
About Data Quality And Regulatory Compliance at FI - ShieldShield
Shield explained the importance of data quality, data completeness and regulatory compliance for financial institutions. Read more about Severe Fines for Non-Compliance and solutions to data completeness for compliance. Have a look at this presentation or visit our website at: https://bit.ly/3JTxSsn
The document discusses how organizations are facing challenges managing the growing amounts of data and information from various sources. This includes extracting insights from large amounts of structured and unstructured content across the enterprise. It also talks about the need to connect people and processes to improve collaboration, decision making and customer service. New approaches to enterprise information management are needed to gain control of data, drive business optimization and enable organizations to adapt quickly to changes.
Data Modernization: The Foundation for Digital TransformationCognizant
The document discusses how leading organizations are transforming their digital cores to enable artificial intelligence and leverage data as a strategic asset. It provides case studies of companies in various industries that have implemented data modernization initiatives with quantifiable results, such as increased revenues, decreased costs, and improved customer experiences. Specifically, it describes how one utility was able to use drone imagery and AI to predict and service remote insulators, saving time and resources.
The document discusses how big data is being adopted across various industries with healthcare and commerce having the highest potential for growth. It provides examples of how big data is being used in healthcare for clinical decision making, operations, preventative medicine, and customer service. Challenges to healthcare big data adoption include a lack of skills, high costs, data security, and different data formats. Banking examples show big data use for fraud prevention, compliance, risk management, and customer insights. Challenges for banking include legal and privacy issues, data quality, and skills shortages. Future trends will include more personalized care and monitoring through interconnected devices and data sources.
3 Steps to Turning CCPA & Data Privacy into Personalized Customer ExperiencesJean-Michel Franco
Your company’s success lies in your capacity to keep your customers’ trust while offering them a personalized experience. With the right Data Privacy framework and technology for your data governance project you will maintain compliance and prosper.
CCPA isn’t the first privacy regulation to impact virtually every organization that does business in the United States – it’s simply the one starting in 2020. As these regulations continue to expand and change, what if there was a way to turn compliance into your advantage? Attend this session and learn how a strong, carefully considered data governance program can help you stay ahead of new regulations like CCPA, and also enhance customer experiences with trusted data.
Learn how a 3-step approach can help you:
Ensure regulatory compliance at scale
Deliver advanced analytics with trusted data
Enable customer personalization for more accurate business insights targeted offers, and behavioral knowledge
Big data in banking allows banks to build contextual and mutually beneficial relationships with customers by understanding their spending habits, lifestyle preferences, risk profiles, and savings goals. However, poor data quality costs the US economy trillions each year, with many business leaders and individuals unsure of how accurate the data they use really is. The amount of data being created is growing exponentially across industries like healthcare, social media, the internet of things, and on the stock exchange, but making sense of this unstructured data requires new database architectures beyond traditional relational databases.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
1) Data management is crucial for financial firms to manage risk and generate returns, but new regulations have increased the amount of data firms must handle.
2) The document discusses challenges financial firms face in data management, including legacy systems, changing a focus to data quality, and establishing consistent data definitions across business units and regulations.
3) Interviewees note key processes like risk management, compliance, and reporting require clean, consistent data without room for error, but data transformations across systems introduce reconciliation issues and inconsistencies.
Marketing and sales teams, today, rely on data. They use analytics to forecast and track performance. They require current, actionable company and contact data to find leads, target campaigns, prioritize and engage with prospects, and ultimately win deals. High performance companies work with data providers who deliver current external company and contact data into their CRM and Marketing Automation applications. The question is, how do you choose the right data provider?
Infographic | Quality of Data & Cost of Bad Data | Sapience AnalyticsSapience Analytics
As the quality of data becomes more and more crucial to the success of an organization, the cost of bad data goes staggeringly high.
Read this Infographic and understand the dependence of organizations on data in terms of:
Importance of data
Quality of data
Cost of bad data
Reasons for bad data quality
The document discusses the challenges facing the healthcare industry including spiraling costs, consumer dissatisfaction, and regulatory changes. It highlights opportunities for healthcare organizations to leverage technology like CRM and business analytics to enhance customer service, control costs, comply with regulations, and gain a competitive advantage in an uncertain environment. The key is recognizing that patients are the ultimate beneficiaries and focusing business strategies and processes around their needs.
FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions.
Key findings include:
Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority.
The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality.
Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
Is Dirty Data Clogging your Marketing Engine? Do what high performance companies do; implement a data management program with InsideView. The sooner you do, the lower the cost.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Innovation and Transformation in Financial ServicesCertus Solutions
Historically, financial services firms have struggled to target and tailor their product offerings to the customer journey. Often only traditional demographic information – gender, age, occupation – is collected with no real insight as to what life stage a customer is in and how this could influence their financial activity.
To compete in a consumer-empowered economy, it is increasingly clear that financial services firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more.
Let’s Build aSmarter Planet: Re-thinking the way Insurance works!IBMAsean
The document discusses how the insurance industry is being transformed by new technological and economic forces, and outlines a vision for how insurers can build a "smarter" approach. It argues that insurers must leverage the growing volumes of available data through more integrated, intelligent, and dynamic systems in order to streamline operations, develop new customer-centric products and services, and build a more sustainable infrastructure. The document provides examples of how some leading insurers are already taking steps to work smarter through automation, analytics, and virtualization.
Data Quality Management (DQM) impacts a number of key business drivers, ranging from regulatory
compliances, to customer satisfaction, to building new business models. Quality is one of the key functions
under Data Governance, as unverified/unqualified data has little value to the organization. One of the leading
global research and advisory firm estimates that an average Fortune 500 enterprise loses about $9.7mn
annually over data quality issues. Although the true intangible cost of poor data is much higher, the sad truth
is that data quality has not been paid the attention it deserves.
How to Leverage Increased Data Granularity in the ICD-10 Code SetPerficient, Inc.
A webinar designed for healthcare professionals. We explore how to leverage the increased data granularity in the ICD-10 code set. While there are risks, a properly executed ICD-10 implementation will deliver plentiful rewards.
This document discusses how life insurance companies can leverage big data analytics across their value chain. It begins by explaining how data sources have expanded dramatically in recent years due to factors like the growth of digital devices and the internet of things. It then outlines how big data can be used in various parts of the insurance lifecycle from product development to claims processing. The document presents a four stage framework for life insurers to adopt big data analytics and provides examples of how some companies have realized benefits. It concludes by noting that while insurers recognize big data's potential, many challenges remain in analyzing diverse and voluminous unstructured data.
About Data Quality And Regulatory Compliance at FI - ShieldShield
Shield explained the importance of data quality, data completeness and regulatory compliance for financial institutions. Read more about Severe Fines for Non-Compliance and solutions to data completeness for compliance. Have a look at this presentation or visit our website at: https://bit.ly/3JTxSsn
The document discusses how organizations are facing challenges managing the growing amounts of data and information from various sources. This includes extracting insights from large amounts of structured and unstructured content across the enterprise. It also talks about the need to connect people and processes to improve collaboration, decision making and customer service. New approaches to enterprise information management are needed to gain control of data, drive business optimization and enable organizations to adapt quickly to changes.
Data Modernization: The Foundation for Digital TransformationCognizant
The document discusses how leading organizations are transforming their digital cores to enable artificial intelligence and leverage data as a strategic asset. It provides case studies of companies in various industries that have implemented data modernization initiatives with quantifiable results, such as increased revenues, decreased costs, and improved customer experiences. Specifically, it describes how one utility was able to use drone imagery and AI to predict and service remote insulators, saving time and resources.
The document discusses how big data is being adopted across various industries with healthcare and commerce having the highest potential for growth. It provides examples of how big data is being used in healthcare for clinical decision making, operations, preventative medicine, and customer service. Challenges to healthcare big data adoption include a lack of skills, high costs, data security, and different data formats. Banking examples show big data use for fraud prevention, compliance, risk management, and customer insights. Challenges for banking include legal and privacy issues, data quality, and skills shortages. Future trends will include more personalized care and monitoring through interconnected devices and data sources.
3 Steps to Turning CCPA & Data Privacy into Personalized Customer ExperiencesJean-Michel Franco
Your company’s success lies in your capacity to keep your customers’ trust while offering them a personalized experience. With the right Data Privacy framework and technology for your data governance project you will maintain compliance and prosper.
CCPA isn’t the first privacy regulation to impact virtually every organization that does business in the United States – it’s simply the one starting in 2020. As these regulations continue to expand and change, what if there was a way to turn compliance into your advantage? Attend this session and learn how a strong, carefully considered data governance program can help you stay ahead of new regulations like CCPA, and also enhance customer experiences with trusted data.
Learn how a 3-step approach can help you:
Ensure regulatory compliance at scale
Deliver advanced analytics with trusted data
Enable customer personalization for more accurate business insights targeted offers, and behavioral knowledge
Big data in banking allows banks to build contextual and mutually beneficial relationships with customers by understanding their spending habits, lifestyle preferences, risk profiles, and savings goals. However, poor data quality costs the US economy trillions each year, with many business leaders and individuals unsure of how accurate the data they use really is. The amount of data being created is growing exponentially across industries like healthcare, social media, the internet of things, and on the stock exchange, but making sense of this unstructured data requires new database architectures beyond traditional relational databases.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
1) Data management is crucial for financial firms to manage risk and generate returns, but new regulations have increased the amount of data firms must handle.
2) The document discusses challenges financial firms face in data management, including legacy systems, changing a focus to data quality, and establishing consistent data definitions across business units and regulations.
3) Interviewees note key processes like risk management, compliance, and reporting require clean, consistent data without room for error, but data transformations across systems introduce reconciliation issues and inconsistencies.
Marketing and sales teams, today, rely on data. They use analytics to forecast and track performance. They require current, actionable company and contact data to find leads, target campaigns, prioritize and engage with prospects, and ultimately win deals. High performance companies work with data providers who deliver current external company and contact data into their CRM and Marketing Automation applications. The question is, how do you choose the right data provider?
Infographic | Quality of Data & Cost of Bad Data | Sapience AnalyticsSapience Analytics
As the quality of data becomes more and more crucial to the success of an organization, the cost of bad data goes staggeringly high.
Read this Infographic and understand the dependence of organizations on data in terms of:
Importance of data
Quality of data
Cost of bad data
Reasons for bad data quality
The document discusses the challenges facing the healthcare industry including spiraling costs, consumer dissatisfaction, and regulatory changes. It highlights opportunities for healthcare organizations to leverage technology like CRM and business analytics to enhance customer service, control costs, comply with regulations, and gain a competitive advantage in an uncertain environment. The key is recognizing that patients are the ultimate beneficiaries and focusing business strategies and processes around their needs.
FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions.
Key findings include:
Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority.
The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality.
Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
Is Dirty Data Clogging your Marketing Engine? Do what high performance companies do; implement a data management program with InsideView. The sooner you do, the lower the cost.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Big Data Issues Today
1. BIG DATA
CHALLENGES
VOLUME
SCALE OF DATA
It’s estimated that
of data are created each day
2.5
QUINTILLION BYTES
(2.3 TRILLION GIGABYTES)
VARIETY
DIFFERENT FORMS
OF DATA
ACCURACY
UNCERTAINTY
OF DATA
have mobile phones
6 BILLION
PEOPLE
Most companies
have at least
of data stored
100 TERABYTES
(100,000 GIGABYTES)
CALCULATING THE TRUE COST
OF DATA QUALITY
that monitor items such as fuel
level and tyre pressure
100 SENSORS
420 MILLION
WEARABLE, WIRELESS
HEALTH MONITORS
There are
150 EXABYTES
(161 BILLION GIGABYTES)
As of 2011, the global size
of data in healthcare was
estimated to be
BUSINESS LEADERS
There are
who don’t trust
the information
they use to
make decisions
$3.1 TRILLION A
YEAR
Poor data quality costs the US
economy around
OF
RESPONDENTS
in one survey were unsure of
how much of their data was
inaccurate
1 IN 3
to manage
production
risk and
compliance
27%
OPERATIONS &
FINANCE
for customer
acquisition +
loyalty
SALES &
MARKETING
UNHAPPY
CUSTOMERS
IDENTIFYING THE
WRONG OPPORTUNITIES
MISSED OPPORTUNITIES
Deal with your data quality challenges using the best tools and trusted
frameworks with the data quality framework from certus solutions.
for growth
+ future
opportunity
STRATEGY &
SUSTAINABILITY
WITH EACH AREA OF THE BUSINESS WANTING MEANINGFUL,
ACCURATE AND TIMELY DATA FOR....
Information Managers
are at the centre of balancing the competing
needs of the business to deliver on their
Information Management requirements.
Depending on your industry, big data now encompasses information from quite
diverse sources – both internal and external. From transactional and social data, to
enterprise content, as well as contextual data derived from sensors and mobile devices.
The volume, sources and types are driving the need to improve the accuracy and veracity
of information – so that correct and meaningful insight can be obtained reliably.
A financial organisation deploying their initial Data
Quality measurements against only 5 elements of
member data measured a business impact in excess of
$1.5M with a cost to remediate of less than 5K.
This is just one aspect of what poor data
quality might be costing you...
QUANTIFYING THE VALUE OF YOUR DATA
AND UNLOCKING ITS TRUE POTENTIAL
THROUGH DATA QUALITY IS NOW A MAJOR BUSINESS IMPERATIVE.
Find out more at certussolutions.com/data-quality
Modern cars have close to
Insurance Company Example