"Implementing AI for New Business Models and Efficiencies" - Parag Shrivastava, Mckesson
•
1 like•291 views
Dynamic talks Seattle: Artificial Intelligence (AI) and data are foundational to ideation of new business models that bring growth and efficiencies. This is in action in pharmaceutical supply chain for reducing costs, increasing volumes, and optimizing the contracts with suppliers. The implementation involves process and tools that make data usable, and overcome the challenges of culture, ethics, data scalability, compute and engineering, and. Learn about data collection, data management, and metadata management tools implementation and modern data architecture to support them. Discuss machine learning algorithms for growth and efficiency scenarios.
Report
Share
Report
Share
Download to read offline
Recommended
Big Data & Analytic: The Value Proposition
This document discusses big data and analytics and its value proposition. It outlines the 4 V's of big data - volume, variety, velocity and veracity. It then discusses how big data and analytics can be leveraged across different industries to drive key business imperatives like acquisition, personalization, profitability and retention. It also outlines 5 key analytical use cases and 5 key operational use cases for big data and analytics. Finally, it emphasizes the need to be proactive about privacy, security, governance and building a culture that infuses analytics.
Making Advanced Analytics Work for You
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
The Business Analytics Value Proposition
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
Cost to-Serve overview for Consumer Packaged Goods Companies
Learn about the challenges CPG companies face with understanding the costs to manufacture and sell products.
A Contrarian Look at HCIT Vendors
- The author is initiating coverage on enterprise electronic health record (EHR) vendors with a negatively biased view, giving Hold ratings to Cerner, Allscripts, and CPSI.
- The author believes the EHR industry faces significant disruption due to flaws like exorbitant costs, lengthy implementations, lack of interoperability, and poor return on investment.
- The healthcare industry is shifting from basic technology infrastructure investment to performance-based technology investment driven by value-added software services.
New Feature Spotlight: Company Scorecard 2.0
Get a bird's-eye-view of your employees' activity & productivity with new business insights from ConstructionOnline's Company Scorecard 2.0
HIMSS Analytics® - Healthcare IT State of the Market 2016
'Now that you have all this data, what do you do with it?'
By now, everyone's got an EMR. And most providers are also making use of ancillary technologies to help harness patient data toward more efficient care and better outcomes. But many components of health IT are still surprisingly underused in the U.S. hospital market. "While the EMR market itself is pretty saturated, and usage has really improved since the HITECH Act, the challenge for hospitals and health systems is, now that you have all this data, what do you do with it?" says Matt Schuchardt, Director of Market Intelligence Solutions Sales at HIMSS Analytics.
There's no shortage of technologies out there to help hospitals improve operations. But it may surprise you to realize how relatively untapped they often still are.
HIMSS Analytics LOGIC keeps tabs on all manner of IT products, and its list of the tools with biggest positive growth potential points to where the market will be heading in the coming years.
This deck will provide you a high level overview of the State of the Market. We encourage you take part in a webinar with the full presentation on the topic from Matt Schuchardt on May 2nd at 2pm EST.
Sign up for the webinar through the HIMSS Learning Center here: http://ow.ly/10szOD
Presentation4
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
Recommended
Big Data & Analytic: The Value Proposition
This document discusses big data and analytics and its value proposition. It outlines the 4 V's of big data - volume, variety, velocity and veracity. It then discusses how big data and analytics can be leveraged across different industries to drive key business imperatives like acquisition, personalization, profitability and retention. It also outlines 5 key analytical use cases and 5 key operational use cases for big data and analytics. Finally, it emphasizes the need to be proactive about privacy, security, governance and building a culture that infuses analytics.
Making Advanced Analytics Work for You
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
The Business Analytics Value Proposition
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
Cost to-Serve overview for Consumer Packaged Goods Companies
Learn about the challenges CPG companies face with understanding the costs to manufacture and sell products.
A Contrarian Look at HCIT Vendors
- The author is initiating coverage on enterprise electronic health record (EHR) vendors with a negatively biased view, giving Hold ratings to Cerner, Allscripts, and CPSI.
- The author believes the EHR industry faces significant disruption due to flaws like exorbitant costs, lengthy implementations, lack of interoperability, and poor return on investment.
- The healthcare industry is shifting from basic technology infrastructure investment to performance-based technology investment driven by value-added software services.
New Feature Spotlight: Company Scorecard 2.0
Get a bird's-eye-view of your employees' activity & productivity with new business insights from ConstructionOnline's Company Scorecard 2.0
HIMSS Analytics® - Healthcare IT State of the Market 2016
'Now that you have all this data, what do you do with it?'
By now, everyone's got an EMR. And most providers are also making use of ancillary technologies to help harness patient data toward more efficient care and better outcomes. But many components of health IT are still surprisingly underused in the U.S. hospital market. "While the EMR market itself is pretty saturated, and usage has really improved since the HITECH Act, the challenge for hospitals and health systems is, now that you have all this data, what do you do with it?" says Matt Schuchardt, Director of Market Intelligence Solutions Sales at HIMSS Analytics.
There's no shortage of technologies out there to help hospitals improve operations. But it may surprise you to realize how relatively untapped they often still are.
HIMSS Analytics LOGIC keeps tabs on all manner of IT products, and its list of the tools with biggest positive growth potential points to where the market will be heading in the coming years.
This deck will provide you a high level overview of the State of the Market. We encourage you take part in a webinar with the full presentation on the topic from Matt Schuchardt on May 2nd at 2pm EST.
Sign up for the webinar through the HIMSS Learning Center here: http://ow.ly/10szOD
Presentation4
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
HIMSS Analytics Essentials Briefs Research Agenda 2016
HIMSS Analytics publishes Essential Briefs, which are in-depth market research studies focused on identifying important topics in healthcare IT. The briefs provide insight into adoption trends for specific healthcare technologies by hospitals and ambulatory care providers. The 2016 research agenda outlines 12 Essential Briefs that will be published throughout the year on topics like population health, telemedicine, revenue cycle management, and security. The briefs are part of HIMSS Analytics' comprehensive market intelligence and advisory solutions for healthcare organizations and vendors.
Analytics Overview #Predictive Analytics
Predictive analytics uses data mining, statistics, modeling, machine learning and artificial intelligence to analyze current and historical facts to make predictions about future or otherwise unknown events. This presentation provides an overview of predictive analytics, including its business applications such as customer retention, risk management and operational optimization. Common predictive analytics methods and tools are also discussed.
Introduction to analytics
An introduction to analytics is a small presentation made for increasing awareness on analytics with some case studies of applying analytics in different functions.
These case studies are from informs.org which were openly available when the presentation was made. Due to confidentiality related obligations my personal experiences were shared - without naming clients - during the presentation. However, the case studies cannot be share on the PPT here. For more details or inputs on analytics you can reach me at twitter - @krdpravin or LinkedIn - https://in.linkedin.com/in/krdpravin
Business analytics in banking sector
Business analytics has become prominent in the banking sector as banks deal with large amounts of customer data. Analytics helps banks meet strategic goals beyond basic operations by providing insights into customer behavior from their data. This allows banks to better manage risks, identify fraud, improve customer retention through personalized offers, and place assets like ATMs more efficiently. Major banks in India like HDFC, Axis, and SBI are using analytics for applications like complete customer profiling, risk management, and incorporating social media data to make more informed decisions.
Pi cube banking on predictive analytics151
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
Approach and learnings data @de persgroep
The document outlines De Persgroep's approach to turning big data into smarter and actionable data through a 5 component program: collect, store, control, analyse, and act. It discusses collecting anonymous and logged in user data from multiple sources to create a 360 degree view of individual consumers. Data is stored centrally to avoid inconsistencies. Controls like data governance and privacy protection are applied. Data is analyzed through dashboards and modeling to profile consumers and predict behaviors. Actions are taken based on use cases to improve the omni-channel customer experience. Challenges in implementing such changes are organizational shifts, costs, evolving tools, and training skills.
How DePersgroep turns data into smart actions in 5 steps
De Persgroep collects and stores data from various sources to create a 360-degree view of individual consumers and turn it into actionable insights. Their big data program has 5 components: collect, store, control, analyze, and act. Data is collected both anonymously and through logins, social media, and user profiles to understand customer journeys, engagement, and lifetime value. Analytics turn the data into stories and models to score consumers. Insights are used to improve the consumer experience and move from multichannel to omnichannel engagement. Change is difficult and requires new skills, training, and starting with small use cases to prove value.
Big data: what multinational clients think
Some highlights from research amongst WFA's multinational member companies, conducted in partnership with The Customer Framework.
mHealth Summit EU 2015
Biobase is a bioinformatics company with 4 sites in Germany, US, India, and Japan and 110 employees. It has over 10 million in annual sales, serving customers in pharma, academia, and clinical settings. Cytolon is a company with 2 sites in Germany and US, 12 employees that manages patients in clinical studies for pharma companies and aims to become the market leader in this field within 2-3 years. Biobase has a large customer base and scientific excellence due to its low-cost operations in multiple countries and offices in key markets. It aims to become the number one provider of clinical diagnostic NGS interpretation worldwide through acquisition by Qiagen while maintaining 20% annual sales growth and high profit margins
Business analytics
Business analytics provides banks with tools to gain insights from data to inform decisions. Banking analytics helps improve customer segmentation, targeting, acquisition, and retention. It also enhances risk management, customer understanding, and fraud prevention. Big data analytics examines large datasets to uncover patterns and insights, allowing near real-time analysis. For banks, analytics enables efficient risk management, personalized services, regulatory compliance, and overall performance boosts.
Capgemini Smart Plant Supervision Solution
The document discusses smart plant supervision using Internet of Things (IoT) technology. It describes how IoT can be used to remotely monitor industrial equipment and processes using sensors, aggregate and analyze the sensor data using predictive analytics, and take actions like scheduling maintenance to prevent failures and optimize operations. Implementing such a smart plant supervision system using IoT could reduce costs through minimized downtime and waste while improving productivity, safety, and other benefits for manufacturing and other industries. The document provides examples of potential use cases and components of an IoT framework for smart plant supervision.
Analytics
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Predictive Analysis PowerPoint Presentation Slides
Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.
CDO_public
This document discusses how Chief Data Officers can show value within their organizations. It begins by listing common questions executives may ask about operational inefficiencies and issues. The document then argues that data problems are not necessarily the fault of the CDO. It discusses how CDOs can strengthen the case for data management by improving data quality, reducing costs and risks, and meeting regulatory demands. The document outlines how CDOs can develop an enterprise data management strategy and mission, and manage organizational change to make data a driver of the business. It envisions how companies can develop a "digital twin" and become truly data-driven and connected.
Business analytics
In order to make decisions in business, analysts drive data from various sources. it will be put to test. later forecasts are made. Thus, business analysis is a process to analyze the business data so that decisions can be made
Excel Datamining Addin Beginner
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
Assay Depot -- Pharmageddon
The Pistoia Alliance Conference in April 2011 included a series of 10-minute "lightning talks" from vendors about what they think pharma will look like in 2020. This presentation was delivered by Kevin Lustig of Assay Depot.
A Framework for Corporate Artificial Intelligence Strategy
Kajetan Schuler and Dennis Schlegel. A Framework for Corporate Artificial Intelligence Strategy. (ICDEc 2021)
Data Analytics with Managerial Applications Internship
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Challenges in adapting predictive analytics
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Analytics Across the Healthcare Ecosytem
Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
Big Data Analytics in light of Financial Industry
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
More Related Content
What's hot
HIMSS Analytics Essentials Briefs Research Agenda 2016
HIMSS Analytics publishes Essential Briefs, which are in-depth market research studies focused on identifying important topics in healthcare IT. The briefs provide insight into adoption trends for specific healthcare technologies by hospitals and ambulatory care providers. The 2016 research agenda outlines 12 Essential Briefs that will be published throughout the year on topics like population health, telemedicine, revenue cycle management, and security. The briefs are part of HIMSS Analytics' comprehensive market intelligence and advisory solutions for healthcare organizations and vendors.
Analytics Overview #Predictive Analytics
Predictive analytics uses data mining, statistics, modeling, machine learning and artificial intelligence to analyze current and historical facts to make predictions about future or otherwise unknown events. This presentation provides an overview of predictive analytics, including its business applications such as customer retention, risk management and operational optimization. Common predictive analytics methods and tools are also discussed.
Introduction to analytics
An introduction to analytics is a small presentation made for increasing awareness on analytics with some case studies of applying analytics in different functions.
These case studies are from informs.org which were openly available when the presentation was made. Due to confidentiality related obligations my personal experiences were shared - without naming clients - during the presentation. However, the case studies cannot be share on the PPT here. For more details or inputs on analytics you can reach me at twitter - @krdpravin or LinkedIn - https://in.linkedin.com/in/krdpravin
Business analytics in banking sector
Business analytics has become prominent in the banking sector as banks deal with large amounts of customer data. Analytics helps banks meet strategic goals beyond basic operations by providing insights into customer behavior from their data. This allows banks to better manage risks, identify fraud, improve customer retention through personalized offers, and place assets like ATMs more efficiently. Major banks in India like HDFC, Axis, and SBI are using analytics for applications like complete customer profiling, risk management, and incorporating social media data to make more informed decisions.
Pi cube banking on predictive analytics151
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
Approach and learnings data @de persgroep
The document outlines De Persgroep's approach to turning big data into smarter and actionable data through a 5 component program: collect, store, control, analyse, and act. It discusses collecting anonymous and logged in user data from multiple sources to create a 360 degree view of individual consumers. Data is stored centrally to avoid inconsistencies. Controls like data governance and privacy protection are applied. Data is analyzed through dashboards and modeling to profile consumers and predict behaviors. Actions are taken based on use cases to improve the omni-channel customer experience. Challenges in implementing such changes are organizational shifts, costs, evolving tools, and training skills.
How DePersgroep turns data into smart actions in 5 steps
De Persgroep collects and stores data from various sources to create a 360-degree view of individual consumers and turn it into actionable insights. Their big data program has 5 components: collect, store, control, analyze, and act. Data is collected both anonymously and through logins, social media, and user profiles to understand customer journeys, engagement, and lifetime value. Analytics turn the data into stories and models to score consumers. Insights are used to improve the consumer experience and move from multichannel to omnichannel engagement. Change is difficult and requires new skills, training, and starting with small use cases to prove value.
Big data: what multinational clients think
Some highlights from research amongst WFA's multinational member companies, conducted in partnership with The Customer Framework.
mHealth Summit EU 2015
Biobase is a bioinformatics company with 4 sites in Germany, US, India, and Japan and 110 employees. It has over 10 million in annual sales, serving customers in pharma, academia, and clinical settings. Cytolon is a company with 2 sites in Germany and US, 12 employees that manages patients in clinical studies for pharma companies and aims to become the market leader in this field within 2-3 years. Biobase has a large customer base and scientific excellence due to its low-cost operations in multiple countries and offices in key markets. It aims to become the number one provider of clinical diagnostic NGS interpretation worldwide through acquisition by Qiagen while maintaining 20% annual sales growth and high profit margins
Business analytics
Business analytics provides banks with tools to gain insights from data to inform decisions. Banking analytics helps improve customer segmentation, targeting, acquisition, and retention. It also enhances risk management, customer understanding, and fraud prevention. Big data analytics examines large datasets to uncover patterns and insights, allowing near real-time analysis. For banks, analytics enables efficient risk management, personalized services, regulatory compliance, and overall performance boosts.
Capgemini Smart Plant Supervision Solution
The document discusses smart plant supervision using Internet of Things (IoT) technology. It describes how IoT can be used to remotely monitor industrial equipment and processes using sensors, aggregate and analyze the sensor data using predictive analytics, and take actions like scheduling maintenance to prevent failures and optimize operations. Implementing such a smart plant supervision system using IoT could reduce costs through minimized downtime and waste while improving productivity, safety, and other benefits for manufacturing and other industries. The document provides examples of potential use cases and components of an IoT framework for smart plant supervision.
Analytics
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Predictive Analysis PowerPoint Presentation Slides
Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.
CDO_public
This document discusses how Chief Data Officers can show value within their organizations. It begins by listing common questions executives may ask about operational inefficiencies and issues. The document then argues that data problems are not necessarily the fault of the CDO. It discusses how CDOs can strengthen the case for data management by improving data quality, reducing costs and risks, and meeting regulatory demands. The document outlines how CDOs can develop an enterprise data management strategy and mission, and manage organizational change to make data a driver of the business. It envisions how companies can develop a "digital twin" and become truly data-driven and connected.
Business analytics
In order to make decisions in business, analysts drive data from various sources. it will be put to test. later forecasts are made. Thus, business analysis is a process to analyze the business data so that decisions can be made
Excel Datamining Addin Beginner
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
Assay Depot -- Pharmageddon
The Pistoia Alliance Conference in April 2011 included a series of 10-minute "lightning talks" from vendors about what they think pharma will look like in 2020. This presentation was delivered by Kevin Lustig of Assay Depot.
A Framework for Corporate Artificial Intelligence Strategy
Kajetan Schuler and Dennis Schlegel. A Framework for Corporate Artificial Intelligence Strategy. (ICDEc 2021)
Data Analytics with Managerial Applications Internship
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Challenges in adapting predictive analytics
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
What's hot (20)
HIMSS Analytics Essentials Briefs Research Agenda 2016
HIMSS Analytics Essentials Briefs Research Agenda 2016
How DePersgroep turns data into smart actions in 5 steps
How DePersgroep turns data into smart actions in 5 steps
Predictive Analysis PowerPoint Presentation Slides
Predictive Analysis PowerPoint Presentation Slides
A Framework for Corporate Artificial Intelligence Strategy
A Framework for Corporate Artificial Intelligence Strategy
Data Analytics with Managerial Applications Internship
Data Analytics with Managerial Applications Internship
Similar to "Implementing AI for New Business Models and Efficiencies" - Parag Shrivastava, Mckesson
Analytics Across the Healthcare Ecosytem
Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
Big Data Analytics in light of Financial Industry
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
Transforming Business with Smarter Analytics
Transforming Business with Smarter Analytics by Deb Mukherji @ BPT IBM Innovative Indonesia with Smarter Analytics, 12 June 2013, Shangri-La Hotel Jakarta
The Rise of Smart Operations
The document discusses research conducted by UPS and IDC on operational transformation in the manufacturing industry. The research found that while most companies use Lean/Six Sigma approaches, the increasing demands of customers have tested the limits of these methods. To achieve higher levels of operational excellence, companies must leverage new technologies like IoT and analytics (smart operations) to gain greater insights from data in real-time. The research showed that companies aggressively pursuing smart operations investments were better positioned for success and had made more progress in areas like connected products/assets and supply chain decision-making. Those lagging risk falling further behind industry leaders in competitiveness.
Life Sciences Executive Overview
Life sciences companies are in a complex market where healthcare costs have risen rapidly while reimbursements and margins have fallen to new lows. New genomic and large-molecule innovations are transforming therapies and improving patient outcomes. Targeted treatments for individuals with similar genetic markers continue to advance quickly. New collaboration models across partners, providers, and patients reinforce this trend.
Life Sciences Executive Overview
Life sciences companies are in a complex market where healthcare costs have risen rapidly while reimbursements and margins have fallen to new lows. New genomic and large-molecule innovations are transforming therapies and improving patient outcomes. Targeted treatments for individuals with similar genetic markers continue to advance quickly. New collaboration models across partners, providers, and patients reinforce this trend.
Faster and Cheaper Clinical Trials: The Benefit of Synthetic Data
Take the innovation leap: Four things pharma companies can do now for a synthetic data-driven approach to clinical trial design. https://accntu.re/3vjVjVs
EMC Perspective: Big Data Transforms the Life Science Commercial Model
The pharmaceutical industry faces many challenges that are forcing changes to business models. Customers now demand data on health outcomes in addition to drug efficacy, requiring companies to transform promotional strategies. Sales and marketing departments are under pressure to evolve traditional commercial models that define customers, messaging, and channels. To succeed in this new environment, life sciences firms will need to use analytics to quickly gain insights from both structured and unstructured internal and external data sources to inform optimized commercial models and adjust strategies in real time.
Data Governance for Clinical Information
This document summarizes a presentation on clinical information governance at GlaxoSmithKline (GSK). GSK is combining data modelling, master data management, enterprise service bus, data stewardship, and enterprise architecture to simplify managing clinical study information. They have established different levels of data stewardship accountability and are implementing a clinical data stewardship framework. Their goal is to transform how clinical trial data is collected, reported, archived and retrieved to make trials more efficient and enhance patient safety.
Solving Stagnated Business Growth with Data Mining
In today's data-driven world, leveraging insights from vast datasets can propel your business to new heights. From enhancing customer experiences to optimizing decision-making, data mining offers endless opportunities for growth and innovation.
Key benefits of data mining include:
Personalized customer experiences
Improved decision-making
New business models
Enhanced efficiency
Stronger cybersecurity
Achieving social goals
Discover how data mining can transform your sales strategies, marketing campaigns, healthcare services, customer service, HR management, fraud detection, and manufacturing processes.
Curious to learn more? Check out our comprehensive guide on how data mining can help solve stagnated business growth.
Ready to harness the power of data mining for your business? Let's connect and explore the possibilities.
Data science applications and usecases
The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.
Making a case for value based care in med tech
This document discusses the transformation of the medical technology industry towards value-based care models driven by data and digital platforms. It notes that healthcare expenditures are heavily weighted towards inpatient and outpatient care, while medical technology accounts for a smaller portion. It also summarizes strategies medical technology companies are using to adapt, including new commercial models, direct patient engagement, and partnering with payers and providers. The document advocates for placing data and digital platforms at the core to drive value-based outcomes and reduce total healthcare costs.
Pactera Big Data Solutions for Retail
1. Big data has the potential to significantly increase operating margins and productivity for retailers.
2. Retailers are investing in big data to improve merchandising, marketing, e-commerce, supply chain operations, and store operations.
3. Getting started with big data requires determining current maturity, identifying high-value use cases, assessing data and analytics capabilities, establishing data management processes, and anticipating business changes.
Business Intelligence, Data Analytics, and AI
The document discusses business analytics and its importance for businesses. It notes that while analytics was previously seen as only for large businesses, it is now important even for small businesses during the pandemic. The document provides predictions about the growth of machine learning, data management, and the use of prediction markets and data literacy initiatives by organizations. It also discusses trends in analytics like the focus on data strategy and democratizing data access. Finally, it provides a framework called the VIA model for conceptualizing analytics projects and an example of how it can be applied.
20150118 s snet analytics vca
The document discusses the growing importance and opportunities of analytics for businesses. It notes that there is a widening performance gap between top performers and bottom performers in their use of data and analytics. While the amount of data is growing exponentially, there is also a significant skills gap in having enough talent to effectively analyze and use data. The document outlines several major themes where businesses are applying analytics, including customer insights, risk management, operations, and product design. It argues that analytics can drive significant business value when integrated into operations and transformations.
Consumer Analytics A Primer
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
Better Business Outcomes with Big Data Analytics
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Healthcare Consulting Solutions | Mindtree
Mindtree provides healthcare services to overcome various challenges with its robust healthcare consulting services. Click here to know more about healthcare consulting.
yellowibm
1) The document discusses how big data and analytics can provide businesses with competitive advantages by generating insights from vast amounts of data to improve business outcomes.
2) It outlines three key steps for a successful big data and analytics implementation: building an analytics-driven culture, prioritizing privacy/security/governance, and investing in an integrated analytics platform.
3) Examples are provided of how various organizations have used big data and analytics to optimize operations, acquire and retain customers, manage risk, improve social services, and more.
Similar to "Implementing AI for New Business Models and Efficiencies" - Parag Shrivastava, Mckesson (20)
Faster and Cheaper Clinical Trials: The Benefit of Synthetic Data
Faster and Cheaper Clinical Trials: The Benefit of Synthetic Data
EMC Perspective: Big Data Transforms the Life Science Commercial Model
EMC Perspective: Big Data Transforms the Life Science Commercial Model
Solving Stagnated Business Growth with Data Mining
Solving Stagnated Business Growth with Data Mining
More from Grid Dynamics
Are you keeping up with your customer
Industry-leading brands have adapted to the ever-changing business environment and consumer expectations significantly faster than the competition. The key driving factor behind this speed-to-market is the adoption of intelligent cloud-native digital commerce architectures. In our talk, we will describe the best practices and case studies of achieving business agility that is imperative to excel in the post-pandemic era.
"Implementing data quality automation with open source stack" - Max Martynov,...
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open-source tools, and machine learning.
"How to build cool & useful voice commerce applications (such as devices like...
In this talk, we describe how to build conversational e-commerce applications for the growing market of voice-powered AI devices using Dialog Flow. This talk demonstrates the capabilities of "Flower Genie," a teaching-oriented chatbot that can recommend a bouquet for any occasion, then take an order and deliver the flower arrangement via Alexa. We present the overall architecture of a voice application developed with Dialog Flow, including dialog management and NLU, and then discuss the finer points of testing and publishing a voice application for Alexa.
"Challenges for AI in Healthcare" - Peter Graven Ph.D
Dynamic Talks Portland: The use of AI in many industries has revolutionized operations and efficiency. In healthcare, the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction?
Dynamic Talks: "Applications of Big Data, Machine Learning and Artificial Int...
The global HIV pandemic continues, particularly in Sub-Saharan Africa. By 2025, 40 million people will be living with HIV. The global cost of the pandemic is in the hundreds of billions of dollars. Better treatment means more people are living longer and costs will increase. The use of existing and emerging technologies is rare. Research institutions don’t share data. Data that drive US HIV policy in 2019 are from 2017 because of the time it takes for the CDC and NIH to combine data. The are many opportunities for big data, ML and AI to have a broader and continued impact on the HIV crisis. The use of these technologies can identify new avenues of research and help prioritize and focus efforts. We are starting to see these technologies used more and more. Several case studies will be presented. For example, advances in HIV vaccine research by Dr. David Heckerman; research at UCLA and Georgetown University looking at how social media can be used for tracking and predicting the spread of the epidemic; and work by researchers to improve care utilization in South Carolina. The opportunities for commercial and non-profit ventures to apply existing and emerging technologies like big data, ML and AI are countless. Tech4HIV is an organization working to drive these efforts into the tech sector and provides opportunities and resources for engagement.
Dynamic Talks: "Digital Transformation in Banking & Financial Services… a per...
Retail industry has gone through a significant transformation in the last decade; caused by the change in consumer behaviour and expectations which have been driven by the convenience and low cost offered by Amazon. There are clear signs of a similar disruption in the financial services industry now, driven by new entrants who are changing consumer expectations. In this talk, Rahul looks at the evolution of retailers and draws conclusions about the path players in the financial services have to take to compete and provide customer experiences that will be necessary to compete.
Dynamic Talks: "Data Strategy as a Conduit for Data Maturity and Monetization...
Organizations need to tap into the huge potential of their vast volumes of data, but a use case tactical approach is not going to work. Instead, they need to work in the definition of a data strategy linked to the most relevant goals for the enterprise.
Dynamics Talks: "Writing Spark Pipelines with Less Boilerplate Code" - Egor P...
Apache Spark is a general-purpose big data execution engine. You can work with different data sources with the same set of API in both batch and streaming mode. Such flexibility is great if you are experienced Spark developer solving a complicated data engineering problem, which might include ML or streaming. In Airbnb, 95% of all data pipelines are daily batch jobs, which read from Hive tables and write to Hive tables. For such jobs, you would like to trade some flexibility for more extensive functionality around writing to Hive or multiple days processing orchestration. Another advantage of reducing flexibility is creating "best practices", which can be followed by less experienced data engineers. In Airbnb, we've created a framework called "Sputnik," which tries to address these issues. In this talk, I'll show the typical boilerplate code, which Sputnik tries to reduce and concepts it introduces to simplify pipeline development.
"Trends in Building Advanced Analytics Platform for Large Enterprises" - Atul...
Dynamic Talks Dallas: In this talk, We will discuss some of the technology trends in the field of Advanced Analytics that are driving business trends.
The New Era of Public Safety Records Management: Dynamic talks Chicago 9/24/2019
For businesses large and small, cloud-based software platforms are breaking down data siloes between teams, adding new capabilities, and boosting efficiency. While not known as early adopters, there's a compelling case for public safety to do the same. In this session, we'll explore how a comprehensive records and evidence platform can simplify operations and streamline data management and access, securely and responsibly. We'll also highlight the role of artificial intelligence, and how it can help agencies respond more quickly and improve outcomes.
Dynamic Talks: "Implementing data quality automation with open source stack" ...
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open source tools, and machine learning.
Reducing No-shows and Late Cancelations in Healthcare Enterprise" - Shervin M...
Dynamic Talks Seattle: Patient No-shows and Late Cancelations is estimated to cost around $150B across the nation. Providence St. Joseph Health, Healthcare Intelligence Group has been working on this pain point for the past couple of years and has developed a software solution leveraging a predictive engine to identify high risk patients at risk of No-show and Late Cancellation on a daily basis. More than 25 clinics have been using this solution for about 2 years now to optimize their reminder call strategy and to have a meaningful reduction of No-shows and Late Cancellations. In addition, a call center pilot was designed recently to standardize this process end-end. Pilot results will be evaluated to prove the impact at scale.
Customer intelligence: a Machine Learning Approach: Dynamic talks Atlanta 8/2...
In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how advanced customer and content signals can be generated using predictive models, and how optimization and reinforcement learning techniques can be used for targeting, budgeting, and pricing decisions. This talk is for Data Scientists, Product Owners, and Software Engineers involved in marketing operations or development of marketing automation software and interested in ML-based decision automation techniques.
"ML Services - How do you begin and when do you start scaling?" - Madhura Dud...
Dynamic talks SF: So you have heard all the hype around how Machine Learning is going to change the world. But within your business context, where do you start? How do you get leadership buy-in for investment? And how and when you start scaling your ML Services?
In this session, you will walk away with an actionable framework to bootstrap and scale a machine learning services team. You will see the framework in action through an actual 0 to 1 product journey involving deep learning where we delivered value in record speed in-spite of not having a dataset when we started. You will get practical tips on how to make decisions about when and how to scale your capability to scale ML Services and platform. Some of the tips are pretty counterintuitive and revealed themselves with our experience of productizing ML services over the last 5+ years. (Using a diverse range of technologies - Vision, Language, Graph, Anomaly Detection, Search Relevance, Personalization)
Realtime Contextual Product Recommendations…that scale and generate revenue -...
Dynamic Talks SF: Recommendation systems are all around us. E-commerce companies like Amazon recommend products we are likely to buy based on our browsing behavior. Netflix suggests what shows we should watch based on our binging habits. Spotify builds a personalized playlist we would enjoy listening to, based on their understanding of what musical genre we are into.
In this talk, we will explore recent advances in the area of product recommendations in both research and practice. We will see how machine learning, design thinking and solid data engineering principles are combined to create an engaging customer experience that positively impacts the bottom line.
We will look at how we use various deep learning architectures to obtain image and text embeddings that supplement user and product based features to generate product recommendations that align closely with a consumer’s aesthetic preferences.
The talk would be of interest to data scientists, data engineers, product managers, UX designers and anyone interested in machine learning.
Decision Automation in Marketing Systems using Reinforcement Learning: Dynami...
- ML-based decision automation systems can make billions of micro-decisions in real-time to target customers, time promotions, and determine messaging and budgets.
- These systems use techniques like propensity scoring, recommendation algorithms, and multi-armed bandits to optimize for business objectives within complex environments.
- An example case study describes how a promotion targeting system for retailers and manufacturers can drive traffic, improve loyalty, and increase market share by automating decisions around targeting, timing, outreach, and promotion properties.
Best practices for enterprise-grade microservices implementations with Google...
When migrating to a cloud and microservices architecture, companies need to invest in foundational capabilities, such as a microservices platform, continuous delivery, and an immutable infrastructure. In this talk, we will discuss our experience implementing these capabilities on the enterprise-scale with Google Cloud, Kubernetes, Istio, Envoy, Spinnaker, and Hashicorp stack. We will also discuss best practices of onboarding the cloud to facilitate DevOps, SRE without sacrificing quality or control.
Attribution Modelling 101: Credit Where Credit is Due!: Dynamic talks Seattle...
Scott Burger presents on attribution modelling using multi-touch attribution and Markov chains. The presentation covers generating user journey data from Google Analytics in BigQuery, developing Markov models in R, and visualizing results in Tableau. Multi-touch attribution helps share credit across channels in a customer's journey rather than assigning all credit to the last touchpoint.
Building an algorithmic price management system using ML: Dynamic talks Seatt...
In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.
Customer intelligence: a machine learning approach- Dynamic talks Dallas Q2
In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how advanced customer and content signals can be generated using predictive models, and how optimization and reinforcement learning techniques can be used for targeting, budgeting, and pricing decisions. This talk is for Data Scientists, Product Owners, and Software Engineers involved in marketing operations or development of marketing automation software and interested in ML-based decision automation techniques.
More from Grid Dynamics (20)
"Implementing data quality automation with open source stack" - Max Martynov,...
"Implementing data quality automation with open source stack" - Max Martynov,...
"How to build cool & useful voice commerce applications (such as devices like...
"How to build cool & useful voice commerce applications (such as devices like...
"Challenges for AI in Healthcare" - Peter Graven Ph.D
"Challenges for AI in Healthcare" - Peter Graven Ph.D
Dynamic Talks: "Applications of Big Data, Machine Learning and Artificial Int...
Dynamic Talks: "Applications of Big Data, Machine Learning and Artificial Int...
Dynamic Talks: "Digital Transformation in Banking & Financial Services… a per...
Dynamic Talks: "Digital Transformation in Banking & Financial Services… a per...
Dynamic Talks: "Data Strategy as a Conduit for Data Maturity and Monetization...
Dynamic Talks: "Data Strategy as a Conduit for Data Maturity and Monetization...
Dynamics Talks: "Writing Spark Pipelines with Less Boilerplate Code" - Egor P...
Dynamics Talks: "Writing Spark Pipelines with Less Boilerplate Code" - Egor P...
"Trends in Building Advanced Analytics Platform for Large Enterprises" - Atul...
"Trends in Building Advanced Analytics Platform for Large Enterprises" - Atul...
The New Era of Public Safety Records Management: Dynamic talks Chicago 9/24/2019
The New Era of Public Safety Records Management: Dynamic talks Chicago 9/24/2019
Dynamic Talks: "Implementing data quality automation with open source stack" ...
Dynamic Talks: "Implementing data quality automation with open source stack" ...
Reducing No-shows and Late Cancelations in Healthcare Enterprise" - Shervin M...
Reducing No-shows and Late Cancelations in Healthcare Enterprise" - Shervin M...
Customer intelligence: a Machine Learning Approach: Dynamic talks Atlanta 8/2...
Customer intelligence: a Machine Learning Approach: Dynamic talks Atlanta 8/2...
"ML Services - How do you begin and when do you start scaling?" - Madhura Dud...
"ML Services - How do you begin and when do you start scaling?" - Madhura Dud...
Realtime Contextual Product Recommendations…that scale and generate revenue -...
Realtime Contextual Product Recommendations…that scale and generate revenue -...
Decision Automation in Marketing Systems using Reinforcement Learning: Dynami...
Decision Automation in Marketing Systems using Reinforcement Learning: Dynami...
Best practices for enterprise-grade microservices implementations with Google...
Best practices for enterprise-grade microservices implementations with Google...
Attribution Modelling 101: Credit Where Credit is Due!: Dynamic talks Seattle...
Attribution Modelling 101: Credit Where Credit is Due!: Dynamic talks Seattle...
Building an algorithmic price management system using ML: Dynamic talks Seatt...
Building an algorithmic price management system using ML: Dynamic talks Seatt...
Customer intelligence: a machine learning approach- Dynamic talks Dallas Q2
Customer intelligence: a machine learning approach- Dynamic talks Dallas Q2
Recently uploaded
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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).
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsLarge Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Building RAG with self-deployed Milvus vector database and Snowpark Container...
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
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
Communications Mining Series - Zero to Hero - Session 1
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
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
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.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Recently uploaded (20)
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
"Implementing AI for New Business Models and Efficiencies" - Parag Shrivastava, Mckesson
- 1. Implementing AI for New Business Models and Efficiencies Parag Shrivastava Senior Director, Enterprise Data Architecture McKesson Corporation 1
- 3. About McKesson Corporation* 3* Source: Investor Presentation| J.P. Morgan Healthcare Conference | Jan 8th 2019
- 4. AI brings strong total opportunity case It enables both, new revenue, and cost savings CUSTOMER FOCUS 4
- 5. Customer focus increases relevance 5 Belongingness Freedom Business to Consumer/ PatientBusiness to Business Growth Costs
- 6. Growth Business Use Cases AI enables new models of engaging consumers 6 Pharmacy Omnichannel experiences Product Planning Clinical Decision Support
- 7. Framework for AI Build AI solutions – lessons learned 7 Models AdoptionData Use Cases Pilot Ops Integration Data Hygiene Data Integration Executive Buy-in Value Driven Iterative Incremental win
- 8. Summary 8 Business Areas Sample Optimization/ Use Cases Lessons Learned Model Development • Procurement • Inventory • Distribution • Patient Retail • Cost Optimizations • Reducing Returns • Shipping and Tracking • Patient Engagement • Data Hygiene • Data Rights • Data Security • Large Data • Exploratory Data Analysis -EDA • Data Tagging • Generalized Linear Models, Clustering, Cross Validation, Model Optimizations • Ideation • Business Engagement • Prototyping • Using Python • Automation • Other tools • Models • Pipelines • Reusable models for tagging, matching Pharmaceutical Clinical Medical Surgical KeyOutputsServicesProducts
- 9. Thank you! Q & A parag.shrivastava@mckesson.com 9