This presentation was give by Dave Read and Michael Delaney from Blue Slate Solutions at the Semetech Technology and Business Conference in NYC on October 2nd 2013.
This presentation, When to Consider Semantic Technology for Your Enterprise, was presented by Mike Delaney and David Read at the 2013 Semantic Technology and Business Conference.
Curlew Research Brussels 2014 Electronic Data & Knowledge ManagementNick Lynch
Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Integrating the CDO Role Into Your Organization; Managing the Disruption (MIT...Caserta
The role of the Chief Data Officer (CDO) has become integral to the evolution needed to turn a wisdom-driven company into an analytics-driven company. With Data Governance at the core of your responsibility, moving the innovation meter is a global challenge among CDOs. Specifically the CDO must:
• Provide a single point of accountability for data initiatives and issues
• Innovate ways to use existing data and evangelize a data vision for the organization
• Support & enforce data governance policies via outreach, training & tools
• Work with IT to develop/maintain an enterprise data repository
• Set standards for analytical reporting and generate data insights through data science
In this session, Joe Caserta addresses real-word CDO challenges, shares techniques to overcome them, manage corporate disruption and achieve success.
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
This presentation, When to Consider Semantic Technology for Your Enterprise, was presented by Mike Delaney and David Read at the 2013 Semantic Technology and Business Conference.
Curlew Research Brussels 2014 Electronic Data & Knowledge ManagementNick Lynch
Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Integrating the CDO Role Into Your Organization; Managing the Disruption (MIT...Caserta
The role of the Chief Data Officer (CDO) has become integral to the evolution needed to turn a wisdom-driven company into an analytics-driven company. With Data Governance at the core of your responsibility, moving the innovation meter is a global challenge among CDOs. Specifically the CDO must:
• Provide a single point of accountability for data initiatives and issues
• Innovate ways to use existing data and evangelize a data vision for the organization
• Support & enforce data governance policies via outreach, training & tools
• Work with IT to develop/maintain an enterprise data repository
• Set standards for analytical reporting and generate data insights through data science
In this session, Joe Caserta addresses real-word CDO challenges, shares techniques to overcome them, manage corporate disruption and achieve success.
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
Moving Past Infrastructure Limitations Presented by MediaMath
This presentation was given at a Big Data Warehousing Meetup with Caserta Concepts, MediaMath and Qubole. You can learn more about the event here: http://www.meetup.com/Big-Data-Warehousing/events/228372516/
Event description:
At Caserta Concepts, we are firm believers in big data thriving on the cloud. The instant-on, nearly unlimited storage and computing capabilities of AWS has made it the defacto solution for a full spectrum of organizations needing to process large amounts of data.
What's more, an ecosystem of value-added platforms has emerged to further ease and democratize the implementation of cloud based solutions. Qubole has developed a great platform for easily deploying and managing ephemeral and long-lived Hadoop and Spark clusters on AWS.
Moving Past Infrastructure Limitations: Data Warehousing at MediaMath
Over the past year and a half, MediaMath has undertaken a “data liberation” effort in an attempt to leave their bigbox, monolithic data warehouse behind. In this talk, Rory Sawyer, Software Engineer at MediaMath, will describe how this effort transformed MediaMath’s legacy architecture and legacy mindset, which imposed harsh inefficiencies on data sharing and utilization. The current mindset removes these inefficiencies and allows them to say “yes” to more projects and ideas.
Rory will also demo how MediaMath uses Amazon Web Services and Qubole so that infrastructure is no longer a limiting factor on what and how users query. This combination allows them to scale their resources up and down as needed while bridging different data sources and execution engines. Using and extending MediaMath’s data warehousing is no longer a privileged activity but an ability that every employee and client has.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
There is an overwhelming list of expectations – and challenges – in this new, emerging and evolving role. In this presentation, given at the 2016 CDO Summit, Joe Caserta focuses on:
- Defining the CDO title
- Outlining the skills that enhance chances for success
- Listing all the many things the company thinks you are responsible for
- Providing an overview of the core technologies you need to be familiar with and will serve to ultimately support your success
- Presenting a concise list of the most pressing challenges
- Sharing insights and arguments for how best to meet the challenges and succeed in your new role
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
GraphTour 2020 - Customer Journey with Neo4j ServicesNeo4j
This document provides an overview of Neo4j's customer journey and solutions for working with graph databases. It includes sections on problem identification and modeling sessions, desired business outcomes and data integration. It also shows examples of graph queries and discusses architecture, sizing and implementation considerations. The document aims to illustrate Neo4j's full end-to-end process for helping customers adopt graph databases from initial problem assessment through solution delivery.
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Building a New Platform for Customer Analytics Caserta
Caserta Concepts and Databricks partner up to bring you this insightful webinar on how a business can choose from all of the emerging big data technologies to figure out which one best fits their needs.
Data-Driven is Passé: Transform Into An Insights-Driven EnterpriseDenodo
This document summarizes a presentation on transforming companies into insights-driven enterprises. It discusses how most companies are currently data-driven but struggle to consistently turn data into effective actions. An insights-driven approach involves building multidisciplinary insights teams, establishing good data governance foundations, and combining the right tools and processes into systems of insight. Data virtualization is highlighted as a key technology enabler for systems of insight by providing agile data access and logical abstraction across structured and unstructured data sources. Examples are provided of how data virtualization has helped customers achieve single customer views and build logical data warehouses.
Machine learning - What they don't teach you on Coursera ODSC London 2016Harvinder Atwal
I’ll show some example of live models at MoneySuperMarket. However, the main theme will be that there is far more to successful implementation of Machine Learning than just creating good algorithms. There needs to be just as much effort, if not more, put into selling the benefits to the business, working with developers and engineers to put the model into production, building testing into the process and ongoing maintenance of the solution.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the enterprise data hub (EDH) as a new approach for data management. The EDH allows organizations to bring applications to data rather than copying data to applications. It provides a full-fidelity active compliance archive, accelerates time to insights through scale, unlocks agility and innovation, consolidates data silos for a 360-degree view, and enables converged analytics. The EDH is implemented using open source, scalable, and cost-effective tools from Cloudera including Hadoop, Impala, and Cloudera Manager.
Using Machine Learning & Spark to Power Data-Driven MarketingCaserta
Joe Caserta provides a statistically-driven model to understanding the customer path to purchase, which combines online, offline and third-party data sources. He shows how customer data is fed to machine learning, which assigns weighted credit to customer interactions in order to give insight to what marketing activities truly matter. This presentation is from Caserta's February 2018 Big Data Warehousing Meetup co-hosted with Databricks.
Large Scale Search, Discovery and Analytics in ActionGrant Ingersoll
The document discusses large scale search, discovery, and analysis. It describes how search has evolved beyond basic keyword search to require a holistic view of both user data and user interactions. It provides examples of use cases where advanced search, discovery, and analytics can provide insights from large amounts of data. Key challenges discussed include balancing performance, relevance, and operations across computation and storage systems.
ATAAS2016 - Big data analytics – data visualization himanshu and santoshAgile Testing Alliance
Data visualization can transform big data challenges by telling stories with data. It allows large amounts of complex data to be understood quickly through visual representations like charts and graphs. Effective data visualization improves communication, helps identify patterns and trends, and enables faster decision making. The right visualizations should be chosen based on the type of data to ensure the most insightful analysis.
Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) a...Patrick Van Renterghem
Digital ethics and ensuring fair and unbiased AI systems are important priorities for VDAB. They have developed principles of trust, transparency and benefit and are working to operationalize them. This includes qualitative and quantitative assessments of AI systems to identify any biases and ensure fair treatment of all users. VDAB aims to be a leader in the ethical development and use of AI to best serve citizens and employers.
Moving Data Science from an Event to A Program: Considerations in Creating Su...Domino Data Lab
This document discusses how organizations are increasingly experiencing information crises due to their inability to effectively govern and trust enterprise data across silos. It argues that data governance needs to expand its scope to support both transactional data and business decisions by integrating data sources into a robust infrastructure and data hub. Implementing effective data governance early is important to allow data reuse, maximize value, and help organizations avoid repeating past mistakes of working in silos.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
Moving Past Infrastructure Limitations Presented by MediaMath
This presentation was given at a Big Data Warehousing Meetup with Caserta Concepts, MediaMath and Qubole. You can learn more about the event here: http://www.meetup.com/Big-Data-Warehousing/events/228372516/
Event description:
At Caserta Concepts, we are firm believers in big data thriving on the cloud. The instant-on, nearly unlimited storage and computing capabilities of AWS has made it the defacto solution for a full spectrum of organizations needing to process large amounts of data.
What's more, an ecosystem of value-added platforms has emerged to further ease and democratize the implementation of cloud based solutions. Qubole has developed a great platform for easily deploying and managing ephemeral and long-lived Hadoop and Spark clusters on AWS.
Moving Past Infrastructure Limitations: Data Warehousing at MediaMath
Over the past year and a half, MediaMath has undertaken a “data liberation” effort in an attempt to leave their bigbox, monolithic data warehouse behind. In this talk, Rory Sawyer, Software Engineer at MediaMath, will describe how this effort transformed MediaMath’s legacy architecture and legacy mindset, which imposed harsh inefficiencies on data sharing and utilization. The current mindset removes these inefficiencies and allows them to say “yes” to more projects and ideas.
Rory will also demo how MediaMath uses Amazon Web Services and Qubole so that infrastructure is no longer a limiting factor on what and how users query. This combination allows them to scale their resources up and down as needed while bridging different data sources and execution engines. Using and extending MediaMath’s data warehousing is no longer a privileged activity but an ability that every employee and client has.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
There is an overwhelming list of expectations – and challenges – in this new, emerging and evolving role. In this presentation, given at the 2016 CDO Summit, Joe Caserta focuses on:
- Defining the CDO title
- Outlining the skills that enhance chances for success
- Listing all the many things the company thinks you are responsible for
- Providing an overview of the core technologies you need to be familiar with and will serve to ultimately support your success
- Presenting a concise list of the most pressing challenges
- Sharing insights and arguments for how best to meet the challenges and succeed in your new role
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
GraphTour 2020 - Customer Journey with Neo4j ServicesNeo4j
This document provides an overview of Neo4j's customer journey and solutions for working with graph databases. It includes sections on problem identification and modeling sessions, desired business outcomes and data integration. It also shows examples of graph queries and discusses architecture, sizing and implementation considerations. The document aims to illustrate Neo4j's full end-to-end process for helping customers adopt graph databases from initial problem assessment through solution delivery.
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Building a New Platform for Customer Analytics Caserta
Caserta Concepts and Databricks partner up to bring you this insightful webinar on how a business can choose from all of the emerging big data technologies to figure out which one best fits their needs.
Data-Driven is Passé: Transform Into An Insights-Driven EnterpriseDenodo
This document summarizes a presentation on transforming companies into insights-driven enterprises. It discusses how most companies are currently data-driven but struggle to consistently turn data into effective actions. An insights-driven approach involves building multidisciplinary insights teams, establishing good data governance foundations, and combining the right tools and processes into systems of insight. Data virtualization is highlighted as a key technology enabler for systems of insight by providing agile data access and logical abstraction across structured and unstructured data sources. Examples are provided of how data virtualization has helped customers achieve single customer views and build logical data warehouses.
Machine learning - What they don't teach you on Coursera ODSC London 2016Harvinder Atwal
I’ll show some example of live models at MoneySuperMarket. However, the main theme will be that there is far more to successful implementation of Machine Learning than just creating good algorithms. There needs to be just as much effort, if not more, put into selling the benefits to the business, working with developers and engineers to put the model into production, building testing into the process and ongoing maintenance of the solution.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the enterprise data hub (EDH) as a new approach for data management. The EDH allows organizations to bring applications to data rather than copying data to applications. It provides a full-fidelity active compliance archive, accelerates time to insights through scale, unlocks agility and innovation, consolidates data silos for a 360-degree view, and enables converged analytics. The EDH is implemented using open source, scalable, and cost-effective tools from Cloudera including Hadoop, Impala, and Cloudera Manager.
Using Machine Learning & Spark to Power Data-Driven MarketingCaserta
Joe Caserta provides a statistically-driven model to understanding the customer path to purchase, which combines online, offline and third-party data sources. He shows how customer data is fed to machine learning, which assigns weighted credit to customer interactions in order to give insight to what marketing activities truly matter. This presentation is from Caserta's February 2018 Big Data Warehousing Meetup co-hosted with Databricks.
Large Scale Search, Discovery and Analytics in ActionGrant Ingersoll
The document discusses large scale search, discovery, and analysis. It describes how search has evolved beyond basic keyword search to require a holistic view of both user data and user interactions. It provides examples of use cases where advanced search, discovery, and analytics can provide insights from large amounts of data. Key challenges discussed include balancing performance, relevance, and operations across computation and storage systems.
ATAAS2016 - Big data analytics – data visualization himanshu and santoshAgile Testing Alliance
Data visualization can transform big data challenges by telling stories with data. It allows large amounts of complex data to be understood quickly through visual representations like charts and graphs. Effective data visualization improves communication, helps identify patterns and trends, and enables faster decision making. The right visualizations should be chosen based on the type of data to ensure the most insightful analysis.
Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) a...Patrick Van Renterghem
Digital ethics and ensuring fair and unbiased AI systems are important priorities for VDAB. They have developed principles of trust, transparency and benefit and are working to operationalize them. This includes qualitative and quantitative assessments of AI systems to identify any biases and ensure fair treatment of all users. VDAB aims to be a leader in the ethical development and use of AI to best serve citizens and employers.
Moving Data Science from an Event to A Program: Considerations in Creating Su...Domino Data Lab
This document discusses how organizations are increasingly experiencing information crises due to their inability to effectively govern and trust enterprise data across silos. It argues that data governance needs to expand its scope to support both transactional data and business decisions by integrating data sources into a robust infrastructure and data hub. Implementing effective data governance early is important to allow data reuse, maximize value, and help organizations avoid repeating past mistakes of working in silos.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
This document describes a platform called Iyka dataSpryng that provides comprehensive analytics capabilities. It removes the need for complex and siloed analytic processes by allowing direct access and analysis of disparate data sources. Key features include a unified view of all data, knowledge portability to leverage ontologies and dictionaries, and self-service analytics. This empowers users and provides 2x more productivity and faster results compared to traditional analytic methods.
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
A7 getting value from big data how to get there quickly and leverage your c...Dr. Wilfred Lin (Ph.D.)
The document discusses how organizations can get value from big data quickly by leveraging their current infrastructure. It outlines Oracle's big data reference architecture and services for strategy, implementation, and optimization. Case studies show how Land O' Lakes optimized sales performance and a consumer goods company gained insights into shopper behavior to increase revenue.
This document discusses data analytics and big data. It begins with definitions of data analytics and big data. It then discusses perceptions of data analytics from different perspectives within an organization. It outlines the data analytics evolution and maturity cycle, highlighting that excellence is about gaining business insights using available data and collaborating across teams. The rest of the document provides examples of how data analytics can be applied and help business strategies in areas like human resources and sales/marketing.
Modernizing Integration with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3CMqS0E
Today, businesses have more data and data types combined with more complex ecosystems than they have ever had before. Examples include on-premise data marts, data warehouses, data lakes, applications, spreadsheets, IoT data, sensor data, unstructured, etc. combined with cloud data ecosystems like Snowflake, Big Query, Azure Synapse, Amazon S3, Redshift, Databricks, SaaS apps, such as Salesforce, Oracle, Service Now, Workday, and on and on.
Data, Analytics, Data Science and Architecture teams are struggling to provide the business users with the right data as quickly and efficiently as possible to quickly enable Analytics, Dashboards, BI, Reports, etc. Unfortunately, many enterprises seek to meet this pressing need by utilizing antiquated and legacy 40+ year-old approaches. There is a better way. Proven by thousands of other companies.
As Forrester so astutely reported in their recent Total Economic Impact Study, companies who employed Data Virtualization reported a “65% decrease in data delivery times over ETL” and an “83% reduction in time to new revenue.”
Join us for this very educational webinar to learn firsthand from Denodo Technologies and Fusion Alliance how:
- Data Virtualization helps your company save time and money by eliminating superfluous ETL pipelines and data replication.
- Data Virtualization can become the cornerstone of your modern data approach to deliver data faster and more efficiently than old legacy approaches at enterprise scale.
- How quickly and easily, Data Virtualization can scale, even in the most complex environments, to create a universal abstraction semantic model(s) for all of your cloud, on premise, structured, unstructured and hybrid data
- Data Mesh and Data Fabric architecture patterns for maximum reuse
- Other customers have used, and are using, Data Virtualization to tackle their toughest data integration and data delivery challenges
- Fusion Alliance can help you define a data strategy tailored to your organization’s needs and requirements, and how they can help you achieve success and enable your business with self-service capabilities
Gartner predicts that by 2026, 75% of organizations will adopt a digital transformation model predicated on cloud as the fundamental underlying platform. It is clear that cloud is here to stay and will continue to be top of mind for organizations of all sizes for years to come. To have a successful cloud strategy, not only is it important to know how other organizations are successfully migrating their architecture, but also how they are handling operations once they make the switch.
However, moving to and operating in the cloud successfully is not as easy as purchasing some public cloud credits and calling it a day. There are many common challenges that organizations face as they move to be cloud-first. By understanding more about these challenges, organizations can avoid expensive consequences.
Join this session to learn about:
Top trends in cloud migration and computingCommon challenges that organizations face as they move to a cloud-first approachConsequences that organizations face when they mishandle cloud adoption
Building enterprise advance analytics platformHaoran Du
Raymond Fu gave a presentation on building an enterprise analytics platform at the SoCal Data Science Conference. He has over 16 years of experience in big data, business intelligence, and enterprise architecture. He discussed how big data disrupts traditional architecture and requires new skills. Advanced analytics involves creating predictive models through machine learning to enable strategic and operational decisions. An enterprise analytics strategy involves data management, modernizing data platforms, and operationalizing advanced analytics models. Fu outlined the key capabilities needed for data management, analytics creation, and analytics operationalization. He provided examples of reference architectures and services that can be used to build an enterprise analytics platform.
Hadoop is a Java framework for managing large datasets distributed across clusters of commodity hardware. It allows for the distributed processing of large datasets across clusters of computers using simple programming models. Hadoop features distributed storage and processing of data and is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It provides reliable, scalable, and distributed computing and storage for big data applications.
Business-centric data models are key to gaining a clear view of the data that drives the business – from customers to products to invoices and more. They offer a clear, visual way for both business and technical stakeholders to communicate around the crucial business rules and definitions that drive both operational usage of data as well as analytics and reporting. This webinar will provide practical, concrete steps in creating valuable, business-centric data models that can show immediate value to the organization, while at the same time building towards a full-enterprise view.
Ensuring Data Quality and Lineage in Cloud Migration - Dan PowerMolly Alexander
Dan Power, Managing Director and Head of Data Governance at State Street Global Markets, gave a presentation on ensuring data quality and lineage when migrating to the cloud. He discussed how moving to the cloud presents both benefits like scalability and cost savings, but also challenges for maintaining data quality. Power recommended using the cloud migration as an opportunity to strengthen data governance strategies and automate quality checks. He also emphasized the importance of building collaborative frameworks between analytics, data, and governance teams to optimize how data is managed and used across cloud environments.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Belgium & Luxembourg dedicated online Data Virtualization discovery workshopDenodo
Watch full webinar here: https://bit.ly/33yYuQm
Data virtualization has become an essential part of enterprise data architectures, bridging the gap between IT and business users and delivering significant cost and time savings. This technology revolutionizes the way data is accessed, delivered, consumed and governed regardless of its format and location.
This 1.5 hour discovery session will show help you identify the benefits of this modern and agile data integration and management technology for your organisation.
Patterns for Successful Data Science Projects (Spark AI Summit)Bill Chambers
Running data science workloads is challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies option for open source data science tools like scikit-learn and Apache Spark’s MLlib in order to balance both functionality and cost.
However, even if a project succeeds at a point in time with any set of tools, these projects become harder and harder to maintain as data volumes increase and a desire for real-time pushes technology to its limit. New projects also struggle as new challenges of scale invalidate previous assumptions.
This talk will discuss some patterns that we see at Databricks that companies leverage to succeed with their data science projects. Key takeaways will be:
– Striving for simplicity
– Removing cognitive load for you and your team
– Working with data, big and small
– Effectively leveraging the ecosystem of tools to be successful
Data Modelers Still Have Jobs: Adjusting for the NoSQL EnvironmentDataStax
The document discusses how relational database management systems and relational modeling have dominated in the past but are declining with the rise of NoSQL databases. It argues that data modelers can save their careers by returning to focus on conceptual modeling rather than assuming relational modeling. Conceptual modeling involves communicating with users to understand entities, attributes, and relationships without implementation details. This will help data modelers choose the appropriate logical data model and adapt to changes in technologies.
All Together Now: A Recipe for Successful Data GovernanceInside Analysis
The Briefing Room with David Loshin and Phasic Systems
Slides from the Live Webcast on July 10, 2012
Getting disparate groups of professionals to agree on business terminology can take forever, especially when big dollars or major issues are at stake. Many data governance programs languish indefinitely because of simple hang-ups. But a new approach has recently achieved monumental results for the United States Navy. The detailed process has since been codified and combined with a NoSQL technology that enables even the most complex data models and definitions to be distilled into simple, functional data flows.
Check out this episode of The Briefing Room to hear Analyst David Loshin of Knowledge Integrity explain why effective Data Governance requires cooperation. Loshin will be briefed by Geoffrey Malafsky of Phasic Systems who will tout his company's proprietary protocol for extracting, defining and managing critical information assets and processes. He'll explain how their approach allows everyone to be "correct" in their definitions, without causing data quality or performance issues in associated information systems. And he'll explain how their Corporate NoSQL engine enables real-time harmonization of definitions and dimensions.
Visit us at: http://www.insideanalysis.com
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Similar to When to Consider Semantic Technology for Your Enterprise (20)
This week Chris Garber was at the EXL LifePRO conference in Naples FL presenting "How to Win Friends and Save Money". The presentation talks about how companies can improve efficiency by 40%+ using process improvement, technology, and data solutions.
“How to win friends and save money”
Improve your efficiency by 40%+ using process improvement, technology, and data solutions.
The competitive landscape, economic environment, and information technology continue to show up on top of the major challenges life insurers will face in the coming years. EXL Consulting will demonstrate that with process improvement, technology, and data solutions you can not only survive, but thrive, in this ever changing environment.
Key topics include:
1) Streamlining processes and improving turnaround time
2) Enhancing staff productivity while reducing errors
3) Improving pricing and customer insights with data federation
4) Modernizing legacy systems
5) Enabling customer self-services and mobile
This presentation was give by Dave Read at the Semantic Technology and Business Conference in June 2012. The goal of the presentation talks about using semantic technology to mitigate mobile platform constraints at runtime.
See more at www.blueslate.net
The document discusses transforming a healthcare payer's processes and rules to meet its business vision. It describes how Blue Slate Solutions helps clients (1) improve operations through process optimization and technology modernization, (2) has experience across healthcare payer functions, and (3) can help ensure an organization's current architecture and rules are flexible to meet future needs.
How to Succeed with Process Automation: The Zen of AutomationBlue Slate Solutions
David Read, CTO of Blue Slate Solutions, discusses the value created from “real” process automation and provides actionable insight into how to succeed with your own business process automation projects. Dave discusses the various challenges businesses run into, along with Blue Slate’s technique of evaluating which automation technique to apply to different business needs.
Topics discussed will include:
• The benefits of service oriented architecture
• When to automate business rules, workflow or both
• Understanding the importance of work flow structure and organization
• When to leverage industry specific point solutions that leverage pre-built workflows and rules
• The unique value of a rule engine which you might be overlooking today
The Road to Transformation: Ensuring your enterprise infrastructure will meet your business vision both today and tomorrow.
Blue Slate won the attendee's choice award for best case study at the Health IT Insight Summit, in Boston, 2010.
The document describes the architecture for building a JAX-WS web service that calculates order subtotals using JBoss Drools and Apache jUDDI integration. The proposed solution includes major components like a business rule engine (BRE) to house rules for calculations, a service registry for discovery, and connectors. The requirements involve calculating order subtotals based on items, taxes, discounts, and shipping. The architecture is designed around loose coupling, with interfaces, canonical definitions, and externalization of business rules and data formats.
Insurance companies face several hurdles to success, including new channels for growth, a competitive environment, data management challenges, and aging applications. Blue Slate Solutions helps clients address these issues through process improvement, technology solutions, and data management to optimize operations and gain competitive advantages.
This document discusses how healthcare payers can get ahead by optimizing processes, connecting technology solutions, and managing data. It provides a case study of how one healthcare payer client used process improvement, prioritization, and impactful solutions to reduce costs and meet requirements for a competitive bid to become a Medicare Administrative Contractor. The client was able to define a strategy, prioritize program elements, gather detailed requirements, and build impactful solutions to help them win the bid and take on twice the workload with over 30% lower cost per claim.
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
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
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.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...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 automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
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.
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.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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.