This document discusses how AI can enable process innovation in organizations. It begins by explaining that while AI is associated with innovation, AI itself does not innovate - it enables innovators by handling information and tasks in new ways. The document then discusses how process innovation focuses on improving how work gets done through methods like Lean and Six Sigma. It provides examples of how AI can enable process innovation in areas like healthcare resource optimization, accelerating medical research, and improving manufacturing productivity. The document concludes by noting that the delivery of AI is also an opportunity for process innovation through methods like ModelOps.
This AI business checklist is a tool that provides an easy-to-use structure for strategic discussions, goal setting and critical decisions in your leadership team. A structure that you can use as a business leader to guide your decisions towards getting full value out of AI technology in your organisation. It is meant to be a tool that you can return to to guide your progress.
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. Here is a five-step process for navigating it successfully.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Accelerate Business Growth and Outcomes with AICognizant
An artificial intelligence company developed several AI solutions to help 10 organizations accelerate business growth and outcomes. The solutions included an AI-powered tool to help a professional services firm automate complex international due diligence searches. An AI platform optimized mining company worker accommodations and transportation. And an AI system analyzed clinical trial data to help fast-track cancer drug development for a pharmaceutical company. The document provides case studies on how each organization leveraged AI to improve processes, enhance customer experiences, and drive business results.
Why Data Science is Getting Popular in 2023?kavyagaur3
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
The document discusses six analytics trends that are likely to influence business in coming years:
1. Analytics is expanding across enterprises as organizations move towards becoming insight-driven.
2. Cognitive technologies and machines are evolving to work alongside humans in complementing roles.
3. Cybersecurity is becoming more predictive and proactive to anticipate threats.
4. The Internet of Things is enabling new innovations through aggregating and analyzing sensor data.
5. Companies are taking creative steps to address the shortage of analytics talent.
6. Analytics success requires a mix of both new and familiar topics as analytics becomes embedded in decision making.
Business analytics uses data to help organizations make better decisions and craft business strategies. As companies generate vast amounts of data, there is a need for professionals with data analysis skills. Leading companies are using analytics not just to improve operations but launch new business models. While some industries and digital natives have captured opportunities, much potential value from analytics remains untapped, especially in manufacturing, healthcare, and the public sector. For companies to succeed in an increasingly data-driven world, analytics must be incorporated strategically and supported by the right talent, processes, and infrastructure.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
This AI business checklist is a tool that provides an easy-to-use structure for strategic discussions, goal setting and critical decisions in your leadership team. A structure that you can use as a business leader to guide your decisions towards getting full value out of AI technology in your organisation. It is meant to be a tool that you can return to to guide your progress.
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. Here is a five-step process for navigating it successfully.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Accelerate Business Growth and Outcomes with AICognizant
An artificial intelligence company developed several AI solutions to help 10 organizations accelerate business growth and outcomes. The solutions included an AI-powered tool to help a professional services firm automate complex international due diligence searches. An AI platform optimized mining company worker accommodations and transportation. And an AI system analyzed clinical trial data to help fast-track cancer drug development for a pharmaceutical company. The document provides case studies on how each organization leveraged AI to improve processes, enhance customer experiences, and drive business results.
Why Data Science is Getting Popular in 2023?kavyagaur3
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
The document discusses six analytics trends that are likely to influence business in coming years:
1. Analytics is expanding across enterprises as organizations move towards becoming insight-driven.
2. Cognitive technologies and machines are evolving to work alongside humans in complementing roles.
3. Cybersecurity is becoming more predictive and proactive to anticipate threats.
4. The Internet of Things is enabling new innovations through aggregating and analyzing sensor data.
5. Companies are taking creative steps to address the shortage of analytics talent.
6. Analytics success requires a mix of both new and familiar topics as analytics becomes embedded in decision making.
Business analytics uses data to help organizations make better decisions and craft business strategies. As companies generate vast amounts of data, there is a need for professionals with data analysis skills. Leading companies are using analytics not just to improve operations but launch new business models. While some industries and digital natives have captured opportunities, much potential value from analytics remains untapped, especially in manufacturing, healthcare, and the public sector. For companies to succeed in an increasingly data-driven world, analytics must be incorporated strategically and supported by the right talent, processes, and infrastructure.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
How Companies Can Move AI from Labs to the Business CoreCognizant
APAC and Middle East organisations have big expectations from AI, but they’re only just getting started. To realise the full potential of AI-led innovation, they must rapidly, but smartly, scale their deployments and embrace a strong ethical foundation, keeping a close eye on the human implications and cultural changes required to convert machine intelligence from lofty concept to business reality.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Apervita received Frost & Sullivan's 2015 New Product Innovation Award for its secure, self-service analytics platform that allows healthcare organizations to easily publish, access, and commercialize clinical decision support rules, quality measures, and other analytics. The platform addresses the growing need for affordable, customizable analytics solutions. Apervita received high scores in Frost & Sullivan's evaluation for its strong match to customer needs, ease of use, and ability to empower sharing of best practices.
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
This document provides a summary of key strategies for successfully scaling artificial intelligence (AI) within an organization. It discusses the importance of having a clear business strategy that AI supports, focusing AI projects on delivering tangible business value. It also emphasizes having the right data strategy to power AI initiatives and taking a portfolio view of AI projects that balances experimentation with alignment to strategic goals. The document recommends challenging assumptions about how work gets done and preparing employees for how AI will change and augment their roles. It argues that organizations must think holistically about scaling AI to realize its full potential for driving business outcomes.
Organizational change is closely tied to digitization. Economists have not appreciated how firms are employing AI besides employing it to deploy robots. In fact, many firms have innovated by creating intelligent analytic systems that change their ability to create new products and manage processes and production.
In these firms, AI helps firms understand complex operations, connect with customers on a personalized level, and obtain a far more sophisticated analysis of processes and services. The result is that AI and ML are creating many new economic advantages at the firm level and for customers.artifici
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
- Analytics leaders have integrated analytics across their entire organizations and achieved substantial benefits like improved financial performance and faster decision making. They display characteristics like a data-driven culture and transparency in decision making.
- While most organizations recognize the need for better decision making, many lack formal, consistent processes. Nearly half report a lack of transparency in key decisions.
- Individuals and organizations are evolving to meet increasing demands for timely, data-driven decisions. People are enhancing their analytics skills and forging closer relationships with analytics professionals to leverage data insights.
1) The document discusses the evolution of data-driven decision making in leading organizations. It finds that 11% of surveyed organizations have integrated analytics across the entire company and are considered "analytics leaders".
2) These analytics leaders report greater benefits from analytics like improved financial performance and faster decision making. They also display characteristics like a data-based culture and transparent decision processes.
3) As pressure increases to make decisions more quickly, individuals and organizations are developing new skills to leverage analytics tools and forge closer relationships with analytics professionals to make evidence-based decisions.
4 Pillars For Creating A Winning Enterprise AI StrategyKartikChoudhary58
The past decade, Artificial Intelligence has made rapid strides from boardroom discussions to fueling multi-billion dollar success stories. Now, more and more businesses are realizing the potential benefits and are aiming to make this a top priority for investments.
As we are entering the new decade, we predict that this will be the decade when AI will come of age, and this will mean that it will be adopted to improve day-to-day processes.
Presenting our second part of this series, where we aim to ready enterprises to ride this technology-led wave of growth.
We have also included some case studies to share crucial insights into how successful enterprises are moving on this complex but rewarding journey.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
MphasiS provides various big data offerings including analytics on unstructured data like text, social media, images and logs. It also offers solutions to integrate structured and unstructured data for 360-degree insights. MphasiS has experience applying advanced analytics techniques like data mining and predictive modeling to solve problems in optimization, employee retention, and fraud prevention. It can help clients migrate to big data platforms like Hadoop, Hive, HBase, Vertica, and SAP HANA.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
Predictive analytics uses historical data and machine learning to identify future trends and outcomes, helping businesses make better decisions. Data science plays a key role by collecting, analyzing, and modeling large datasets to build accurate predictive models. Pursuing a data science course offers hands-on training and networking opportunities to learn skills in high demand. It is important for data scientists to consider ethics and ensure predictions are used responsibly and for the benefit of society.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
How Companies Can Move AI from Labs to the Business CoreCognizant
APAC and Middle East organisations have big expectations from AI, but they’re only just getting started. To realise the full potential of AI-led innovation, they must rapidly, but smartly, scale their deployments and embrace a strong ethical foundation, keeping a close eye on the human implications and cultural changes required to convert machine intelligence from lofty concept to business reality.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Apervita received Frost & Sullivan's 2015 New Product Innovation Award for its secure, self-service analytics platform that allows healthcare organizations to easily publish, access, and commercialize clinical decision support rules, quality measures, and other analytics. The platform addresses the growing need for affordable, customizable analytics solutions. Apervita received high scores in Frost & Sullivan's evaluation for its strong match to customer needs, ease of use, and ability to empower sharing of best practices.
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
This document provides a summary of key strategies for successfully scaling artificial intelligence (AI) within an organization. It discusses the importance of having a clear business strategy that AI supports, focusing AI projects on delivering tangible business value. It also emphasizes having the right data strategy to power AI initiatives and taking a portfolio view of AI projects that balances experimentation with alignment to strategic goals. The document recommends challenging assumptions about how work gets done and preparing employees for how AI will change and augment their roles. It argues that organizations must think holistically about scaling AI to realize its full potential for driving business outcomes.
Organizational change is closely tied to digitization. Economists have not appreciated how firms are employing AI besides employing it to deploy robots. In fact, many firms have innovated by creating intelligent analytic systems that change their ability to create new products and manage processes and production.
In these firms, AI helps firms understand complex operations, connect with customers on a personalized level, and obtain a far more sophisticated analysis of processes and services. The result is that AI and ML are creating many new economic advantages at the firm level and for customers.artifici
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
- Analytics leaders have integrated analytics across their entire organizations and achieved substantial benefits like improved financial performance and faster decision making. They display characteristics like a data-driven culture and transparency in decision making.
- While most organizations recognize the need for better decision making, many lack formal, consistent processes. Nearly half report a lack of transparency in key decisions.
- Individuals and organizations are evolving to meet increasing demands for timely, data-driven decisions. People are enhancing their analytics skills and forging closer relationships with analytics professionals to leverage data insights.
1) The document discusses the evolution of data-driven decision making in leading organizations. It finds that 11% of surveyed organizations have integrated analytics across the entire company and are considered "analytics leaders".
2) These analytics leaders report greater benefits from analytics like improved financial performance and faster decision making. They also display characteristics like a data-based culture and transparent decision processes.
3) As pressure increases to make decisions more quickly, individuals and organizations are developing new skills to leverage analytics tools and forge closer relationships with analytics professionals to make evidence-based decisions.
4 Pillars For Creating A Winning Enterprise AI StrategyKartikChoudhary58
The past decade, Artificial Intelligence has made rapid strides from boardroom discussions to fueling multi-billion dollar success stories. Now, more and more businesses are realizing the potential benefits and are aiming to make this a top priority for investments.
As we are entering the new decade, we predict that this will be the decade when AI will come of age, and this will mean that it will be adopted to improve day-to-day processes.
Presenting our second part of this series, where we aim to ready enterprises to ride this technology-led wave of growth.
We have also included some case studies to share crucial insights into how successful enterprises are moving on this complex but rewarding journey.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
MphasiS provides various big data offerings including analytics on unstructured data like text, social media, images and logs. It also offers solutions to integrate structured and unstructured data for 360-degree insights. MphasiS has experience applying advanced analytics techniques like data mining and predictive modeling to solve problems in optimization, employee retention, and fraud prevention. It can help clients migrate to big data platforms like Hadoop, Hive, HBase, Vertica, and SAP HANA.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
Predictive analytics uses historical data and machine learning to identify future trends and outcomes, helping businesses make better decisions. Data science plays a key role by collecting, analyzing, and modeling large datasets to build accurate predictive models. Pursuing a data science course offers hands-on training and networking opportunities to learn skills in high demand. It is important for data scientists to consider ethics and ensure predictions are used responsibly and for the benefit of society.
Similar to MPCA-SAS-innovators-flight-plan-ai.pdf (20)
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
2. Table of contents
3 | Introduction: the role of AI in innovation
4 | Process innovation, here we come:
why AI is a great way to fly
7 | A scenic tour of processes enabled by AI
10 | Conclusion: the rewards of process innovation
3. Introduction: the role
of AI in innovation
As an emerging technology, AI is naturally
associated with innovation. Many organizations
are using AI to develop new products and
business models, and to improve their
business processes.
In the 2022 AI Momentum Survey1
, 56% of
worldwide executives reported “increased
innovation” as a current benefit of their AI
deployments. Dr. Iain Brown, Head of Data
Science at SAS UK&I, says that “we can expect
to see even more innovation, at a larger scale,
and with bigger results.” The survey findings
bear out this prediction, with “increased
innovation” as the most frequently cited
expected future benefit at 43%.
But it’s important to recognize that AI in and
of itself doesn’t innovate. Instead, AI enables
the innovators – people who are creating flight
plans for their organizations. In the same way
that a pilot using instruments can fly to more
destinations and in more inclement conditions
than a pilot relying solely on visuals, AI enables
a wider range of innovation by handling
information and tasks in new and more
powerful ways.
1
AI Acceleration and the Future of Innovation: 2022 AI Momentum Survey Report,
SAS, Accenture and Intel with Forbes Insights, 2022.
3
4. Process innovation,
here we come: why AI
is a great way to fly
When developing your innovative flight plan,
process innovation presents as perhaps the
most covert type of innovation. It tends to be
less vaunted, because it isn’t about changing
where you’re going; it’s about changing how
you get there.
Process innovation addresses the “how” of an
organization. How is the work completed?
How is a product or service delivered? How
do customers experience or engage with it?
Methodologies such as Lean, Six Sigma and
Kanban have emerged to support organizations
in adopting new ways of working, new skills
and capabilities, and new tools and
technologies to reimagine the steps
taken to accomplish a defined outcome.
In this way, process innovation is foundational
to both product innovation and business model
innovation, which dictate the “why” or the
objectives of the organization. Whether it’s
a product innovation like a feature change
or a business model innovation like a new
sales channel, these developments are only
as good as the processes that support them.
4
5. Leaders who feel overwhelmed by the myriad
opportunities for AI and unsure how to
bring it all together can begin by looking at
process innovation. It’s an excellent place for
organizations to start with AI, because it can
present fewer barriers in terms of skills, culture
and change management – but can still yield
significant impact.
Every organization has incumbent processes
with some legacy weight or cost embedded
in them. These processes could be in customer
service, fraud detection, finance, IT management
or quality assurance.
In fact, almost every function in every area
of an organization could be a candidate
for some type of process innovation.
What’s the relationship
between process innovation
literacy and AI literacy?
Ready for takeoff with AI innovation?
Process innovation literacy is the
place to start. Learn how to identify
the problems to solve. What could
be done differently? What issues could
be addressed, avoided or eliminated?
Process innovation requires curiosity
and a willingness to question and learn.
Then move to AI literacy, learning
how to identify which problems can
be solved with AI and what type of
AI will solve them. Understand what,
where and how AI can be deployed,
as well as its limitations and boundaries,
so your organization can apply AI when
it’s the best tool for the job.
Expedite
How can we do things faster or more efficiently?
Eliminate
Can steps or waste be reduced? Can
dependencies or redundancies be cut back?
Explore
Can the information available be expanded
or reframed to help with decision-making?
5
6. When considering process
innovation, internal improvements
(like increased automation and
operational efficiency) may
come to mind.
Process innovation is often
thought of as invisible, taking
place in the back office. However,
process innovation can generate
improvements in user experience
that both customers and
employees will notice, and that
contribute to engagement and
retention. It can help businesses
build resilience and adapt to rapid
change. It can also generate better
insights that lead to improved
decision-making.
The value of composite AI
Processes aren’t individual disjointed actions: They’re logical
sequences of actions and steps. So, there’s no single AI or
analytics technique to rule them all. Instead, there’s a range
of AI tools and techniques that can be applied to process
innovation. Multiple techniques are often used in sequence
to accomplish and support a process.
For example, working with the process of predictive
maintenance, you might use four different AI techniques:
1. Analytics to identify trends and patterns.
2. Predictive analytics to identify and predict issues
that might emerge.
3. Optimization techniques to decide what should
be fixed by whom, and in what sequence.
4. Reporting to track the process.
In other words, you’re applying a portfolio of tools
to enable process innovation, so it’s important to
understand and invest in applying them holistically.
AI enables process innovation
in two ways:
1. By providing the capabilities
to reinvent how work gets
done: visualizing, analyzing
and presenting data in ways
that weren’t possible before.
2. By identifying areas of interest
or potential improvement.
AI can point you in the
right direction, highlighting
unusual data that needs more
investigation and orienting you
to different ways of thinking.
6
7. A scenic tour of processes
enabled by AI
With so many opportunities for process
innovation across industries and organizations,
the examples of AI-enablement are myriad.
Here’s a brief but exciting look at a few of
the cutting-edge developments that are
now within reach.
Health care resource optimization
Hospitals and health departments have
operational and health-related data that can be
used to predict future resource demands. Using
this data, they can plan to have staff, facilities and
supplies in place to handle patient needs. Without
AI, these projections may not be timely or reliable,
and they are not as responsive because they are
often made based on a single set of assumptions.
Scenario modeling is an excellent application
of AI innovation, because the model
development is guided by the data available
and adapts to new inputs over time. These
models can generate predictions based on
large, complex data sets, adjusting in response
to changing conditions and information.
Cleveland Clinic and SAS collaborated
to produce publicly available predictive
models to help health systems respond
to the COVID-19 pandemic.
Hospitals can use these models to
optimize resources, understand cost
and quantify the need for supplies
such as beds, ventilators and
personal protective equipment.
The models project worst-case,
best-case and most-likely scenarios
that factor in the impact of policies
such as masking and social
distancing on disease spread.
7
8. Accelerating medical research
Clinical trial data, electronic health records,
claims data and adverse event reports are only
snapshots of patients at random points in time.
To better understand the most effective therapies
for certain medical conditions or to detect drug
safety signals, health researchers turn to data
collected in the real world during patient visits
to health care systems. These vast data sets are
often unstructured and heterogenous, requiring
significant data manipulation before revealing
the critical insights within.
AI can significantly improve the efficiency of
organizing associated data sets for analysis,
allowing researchers to focus on improving
health outcomes.
The University of Alberta has launched a health
data management and analysis platform, the
Data Analytics Research Core (DARC), that
increases research capacity. Researchers can
access a robust platform for advanced analytics
and secure data storage.
One example of how DARC is accelerating
health research is in studies of children with
sudden neurologic symptoms. Researchers are
developing an algorithm that would help cut
down on the number of CT scans required for
diagnosis by at least 30%, which would reduce
patient exposure to radiation.
8
9. Improving productivity
in manufacturing
Factories have an abundance
of data that captures everything
from raw material usage to supply
chain dynamics to camera imagery
from production lines.
Advanced analytics can help
increase productivity by identifying
opportunities to fine-tune the
balance between speed and quality.
Pulp and paper manufacturer
Georgia-Pacific uses AI and machine
learning to optimize production.
Applying process innovation to AI
The delivery of AI and analytics is itself a product or
service, whether presented as a dashboard, model or
micro service. AI models and systems have development
life-cycle processes that are opportunities for improvement.
Platforms like SAS Viya represent process innovation in the
development of AI models and applications. Data scientists
and others without deep technical coding knowledge
can make use of these new capabilities, developing and
delivering AI and analytics tools more quickly and easily.
ModelOps is a specific process innovation that makes
it easier to deploy, monitor and maintain AI models and
analytics. One massive online gaming platform uses
ModelOps to roll out and manage hundreds of thousands
of models every second for online players. The platform’s
ability to innovate on product enablement has had
a significant impact on the gaming experience.
The company was able to increase
overall equipment efficiency during
the pandemic by 10%, getting more
toilet paper and cleaning supplies
into stores.
Data volumes have grown by five
times in recent years, but they are
able to maximize profitability by
analyzing that data and determining
how to operate better.
9