Digitalisation from the back office to the factory floorStephanie Locke
AI is a huge set of tools for making computers behave intelligently - Andrew Ng
70% of implementations fail to meet their stated aims. Following the holistic triple transformation approach taken by ‘lighthouses’ seems like a sensible approach to take. Industry 4.0: Reimagining manufacturing operations after COVID-19 McKinsey
Get started with AI:
- Start small with pilot projects to gain momentum
- Strong business case
- Build a team around this
- Provide broad training
- Longer-term develop an AI strategy that aligns with your business objectives
Top 5 Business Intelligence (BI) Trends in 2013Siva Shanmugam
Below are a few trends that we believe are going to gain momentum this year.
Agile IM
Cloud BI / SaaS BI
Mobile Business Intelligence
Analytics
Big Data
What is data, information & data analytics?
What is their importance & impact on the business and market?
Who is Incorta and how it adds great value as a new, unified, innovative & a market disruptive analytics platform?
Prepared by: QA Manager "Mohamed Elprince"
DevOps at ING Analytics: combining data engineering with data operations - Gi...Codemotion
ING was one of the early adopters of the DevOps movement. Currently, there is a lot of expertise in the organization: way of working, tools, and HR are all catered for DevOps. In the Analytics area, these best practices were the basis of a modern and stable architecture where data engineers, operations, and data scientists work together with business people on daily basis. The technology stack includes Hadoop, Spark, Flink, Kafka, Cassandra, and several IBM tools. In the talk I’m going to share tools evolution, skills and processes in place. Touching in the second part two use-cases.
6 Opportunities for In-House Studios to ExcelNuxeo
Today's in-house agencies and creative production studios impact business objectives beyond just marketing. They are creating content that is driving customer experience through the full, idea-to-shelf-to-performance lifecycle. This expansion and alignment with strategic business objectives positions in-house agencies at the center of innovation, growth, and speed to market.
This webinar outlines six opportunities to position your in-house creative studio for success in this expanded arena. We will explore how to optimize your creative operations and usher in a new foundation that drives efficiency, innovation, and growth.
An exciting talk on the main difficulties and how to overcome them when building and scaling data teams with Florian Douetteau
- Technological issues: What stack should they choose for the company’s architecture? And what about big data technologies; should they accept being a polyglot or rather assume being a ruthless dictator?
- HR issues: Who should they hire? Should they build their data team as an extension of the BI team? Or should they build it from scratch?
- Data issues: How are they supposed to get data inside his data lake? Which strategy should they adopt: the cicada, the spider or the fox one?
- Product issues: What is big data really about? And eventually, what are they willing to do with this bunch of data?
The talk aims at demonstrating how tough it can be to build and scale a data department, and at giving some insights about the strategy Florian thinks they should adopt.
Affecto Forum 2015 - Jaakko Ala-Paavola (Espotel): Teollisuuden Internet uus...Affecto
The document discusses how industrial internet and Internet of Things (IoT) enable new business models. It presents Gartner's four main IoT business models: optimize utilization, pay-per-use, remote operation, and provide digital services and content. It provides examples of companies using each model and discusses IoT service models. The document also introduces Espotel as an IoT integrator that brings data from the physical world to customers' IT systems and provides an example IoT business case using LoRa RF technology.
Data is that the fuel that launches firms earlier than the pack—often so much ahead. people who will collect, analyze, associated quickly build use of knowledge can have an ever-increasing competitive advantage. That’s the promise of huge knowledge.
Digitalisation from the back office to the factory floorStephanie Locke
AI is a huge set of tools for making computers behave intelligently - Andrew Ng
70% of implementations fail to meet their stated aims. Following the holistic triple transformation approach taken by ‘lighthouses’ seems like a sensible approach to take. Industry 4.0: Reimagining manufacturing operations after COVID-19 McKinsey
Get started with AI:
- Start small with pilot projects to gain momentum
- Strong business case
- Build a team around this
- Provide broad training
- Longer-term develop an AI strategy that aligns with your business objectives
Top 5 Business Intelligence (BI) Trends in 2013Siva Shanmugam
Below are a few trends that we believe are going to gain momentum this year.
Agile IM
Cloud BI / SaaS BI
Mobile Business Intelligence
Analytics
Big Data
What is data, information & data analytics?
What is their importance & impact on the business and market?
Who is Incorta and how it adds great value as a new, unified, innovative & a market disruptive analytics platform?
Prepared by: QA Manager "Mohamed Elprince"
DevOps at ING Analytics: combining data engineering with data operations - Gi...Codemotion
ING was one of the early adopters of the DevOps movement. Currently, there is a lot of expertise in the organization: way of working, tools, and HR are all catered for DevOps. In the Analytics area, these best practices were the basis of a modern and stable architecture where data engineers, operations, and data scientists work together with business people on daily basis. The technology stack includes Hadoop, Spark, Flink, Kafka, Cassandra, and several IBM tools. In the talk I’m going to share tools evolution, skills and processes in place. Touching in the second part two use-cases.
6 Opportunities for In-House Studios to ExcelNuxeo
Today's in-house agencies and creative production studios impact business objectives beyond just marketing. They are creating content that is driving customer experience through the full, idea-to-shelf-to-performance lifecycle. This expansion and alignment with strategic business objectives positions in-house agencies at the center of innovation, growth, and speed to market.
This webinar outlines six opportunities to position your in-house creative studio for success in this expanded arena. We will explore how to optimize your creative operations and usher in a new foundation that drives efficiency, innovation, and growth.
An exciting talk on the main difficulties and how to overcome them when building and scaling data teams with Florian Douetteau
- Technological issues: What stack should they choose for the company’s architecture? And what about big data technologies; should they accept being a polyglot or rather assume being a ruthless dictator?
- HR issues: Who should they hire? Should they build their data team as an extension of the BI team? Or should they build it from scratch?
- Data issues: How are they supposed to get data inside his data lake? Which strategy should they adopt: the cicada, the spider or the fox one?
- Product issues: What is big data really about? And eventually, what are they willing to do with this bunch of data?
The talk aims at demonstrating how tough it can be to build and scale a data department, and at giving some insights about the strategy Florian thinks they should adopt.
Affecto Forum 2015 - Jaakko Ala-Paavola (Espotel): Teollisuuden Internet uus...Affecto
The document discusses how industrial internet and Internet of Things (IoT) enable new business models. It presents Gartner's four main IoT business models: optimize utilization, pay-per-use, remote operation, and provide digital services and content. It provides examples of companies using each model and discusses IoT service models. The document also introduces Espotel as an IoT integrator that brings data from the physical world to customers' IT systems and provides an example IoT business case using LoRa RF technology.
Data is that the fuel that launches firms earlier than the pack—often so much ahead. people who will collect, analyze, associated quickly build use of knowledge can have an ever-increasing competitive advantage. That’s the promise of huge knowledge.
How a Data-Driven Culture Improves Organizational Performance Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this slideshare, learn from IDC research and Amaysim BI Manager about:
Why meeting the analytical needs of business users matter to organizational performance
What’s driving leaders in APAC enterprises towards a self-service paradigm?
How to encourage adoption of analytical tools in your organization
How leading Asia Pacific enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
This slideshare came from a full webinar delivered by Tableau. You can the full length webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
Sajit Joseph - The road to AI for the enterpriseHilary Ip
The document discusses how artificial intelligence is being used in various areas of enterprises including bots and virtual assistants, smart speakers, predictive analytics, and robotic process automation to improve customer experience and reduce costs. It provides examples of how each technology works and can be applied, as well as market trends and considerations for implementation. The focus is on harnessing AI technologies in the near term to generate business value for organizations.
How to ready your organization for Artificial IntelligenceCraig Milroy
This document discusses how organizations can prepare for artificial intelligence (AI) and the importance of data and machine learning. It provides examples of banks investing in AI and digital technologies. Sections include discussions of data science, different types of machine learning techniques (supervised, unsupervised, reinforcement, semi-supervised), and the need for data governance, skilled talent, and executive leadership to fully leverage AI.
SAP's Innovation Strategy for Public Sectorscoopnewsgroup
This document discusses SAP's strategy for digital government and public sector innovation. It notes trends impacting the public sector like changes in the workforce, rise of cloud computing and new technologies. SAP aims to help governments capture $1 trillion in annual value through digitizing citizen engagement, processes, work and data/decision-making. SAP's solution architecture supports digital transformation across business processes, intelligence, customer experience and more using technologies like blockchain, machine learning and SAP Leonardo. Case studies show how SAP helped Indiana address the drug crisis, South Africa improve emergency response, and Under Armour gain supply chain visibility.
Enable End-to-End Digital Government Transformation with SAP Solutionsscoopnewsgroup
The document discusses SAP solutions for digital government transformation. It introduces panelists from SAP who specialize in public sector solutions. It outlines the SAP S/4HANA ERP system and how it integrates with SAP Concur for expense management and travel. It also presents SAP Hybris for citizen experience management and how it can integrate with other SAP products and systems. The panelists provide their contact information for any questions.
Enhancing DAM Operation with Artificial IntelligenceNuxeo
This document discusses using artificial intelligence to enhance digital asset management operations. It makes the following key points:
1. AI can help identify and extract metadata from digital assets to improve searchability and reuse of content across systems. This reduces duplication and helps coordinate work among creative teams.
2. There are two types of AI: generic models that provide common services like classification and enrichment, and business-specific custom models that deliver more relevant insights tailored to a company's specific domains and use cases.
3. Business-specific models can be continuously trained on a company's own content and data to provide predictive outputs. This increases the business value and accuracy of metadata compared to generic models.
Developer Velocity Series in association with Quest
DevOps: A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.
DataOps: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics.
MLOps: MLOps […] enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.
DevSecOps: DevSecOps automatically bakes in security at every phase of the software development lifecycle, enabling development of secure software at the speed of Agile and DevOps.
ChatOps: ChatOps is a collaboration model that connects people, tools, process, and automation into a transparent workflow. This flow connects the work needed, the work happening, and the work done in a persistent location staffed by the people, bots, and related tools.
NoOps: NoOps is the idea that the software environment can be so completely automated that there’s no need for an operations team to manage it.
GitOps: GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools.
Developer Velocity is the Grand Unified Theory
Developer velocity: The ability to drive transformative business performance through software development
Top DVI companies are stronger financially
- 5x compound annual growth rate
- 60% more shareholder returns
- 20% higher operating margins
>Companies in the top quartile of the Developer Velocity Index (DVI) outperform others in the market by four to five times. Top-quartile companies also have 60 percent higher total shareholder returns and 20 percent higher operating margins.
Critical areas of focus
- People
Product management
Product management function
Product telemetry
Culture
Psychological safety
Collaboration and knowledge sharing
Continuous improvement culture
Talent management
Incentives
Capability building
- Processes
Working practices
Compliance practices
Security practices
Organisational enablement
Autonomous scoped teams
Dependency management
Culture
Continuous improvement
Talent management
Recruiting
Team health management
- Tooling
Planning tools
Collaboration tools
Development tools
DevOps tools
Cloud
Video at: https://www.quest.com/event/steph-lockes-developer-velocity-series-8148798/
Applying AI & Search in Europe - featuring 451 ResearchLucidworks
In the current climate, it’s now more important than ever to digitally enable your workforce and customers.
Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies.
In this webinar, we’ll discuss:
The top challenges and aspirations European business and technology leaders are solving using AI and search technology
Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe
What technology buyers should look for when evaluating AI and search solutions
Affecto Forum 2015 - Edward Mauser (Affecto): Get Ahead with IoT valueAffecto
Edward Mauser is a business advisor and value architect at Affecto who helps businesses evolve through technology. He has over 16 years of experience in product development and is a subject matter expert in IoT. The document discusses how IoT can enable companies to obtain operational savings, deliver new customer value, create new revenue streams, and change business models. It provides examples of how IoT can be used in areas like energy analysis, maintenance predictions, and digital advertising. The key is for companies to treat IoT projects as a phased approach and use prototypes to mitigate risks while learning. Properly utilizing IoT and big data can help optimize companies and disrupt industries by providing new customer value and business models.
SAP provides analytics solutions to help customers run their businesses better by accessing all relevant information, defining plans to align performance goals, and responding instantly to changing conditions. Their solutions provide capabilities for data analysis, business intelligence applications, and collaboration. Customers in various industries and regions have been able to increase financial and operational performance through SAP's analytics offerings.
CWIN17 san francisco-ai implementation-pubCapgemini
This document summarizes an AI presentation given by Michael Martin, an enterprise architect. It discusses various dimensions and applications of AI, including machine learning, deep learning, image analysis, and natural language processing. It provides examples of how AI can be used in legal research, medical research, fraud detection, and more. It also outlines considerations for implementing AI projects, such as identifying relevant data sources, deriving hypotheses, and measuring outcomes. Key implementation steps and an example logical architecture are presented. The document closes with some perspectives on challenges and directions for AI.
Disrupting Corporates with AI by Hicham MhannaData Con LA
Abstract:- Discussion of various applied cases of artificial intelligence solutions stemming from our venture work across verticals including consumer, financial and media.
NUS-ISS Learning Day 2018- Harnessing the power of cloud solutions in urban a...NUS-ISS
The document discusses harnessing the power of cloud solutions for urban applications. It provides an overview of key topics like internet of things (IoT), connected cities, industries impacted by IoT, applications of IoT, decentralized processing, monetizing IoT, architectural approaches, challenges, and maturity models. It also discusses specific examples of using AWS IoT services and developing smart urban IoT solutions.
ExistBI case study of Salesforce and Data Warehouse consulting for world leader in manufacturing wire rope company. The WireCo team approached ExistBI wanting to engage with certified consultants who could understand how they could move reports from Salesforce into a new Data Warehouse. Have a look at this case study as an example of ExistBIs consulting capabilities.
For more information contact us at www.existbi.com
Seminar & Talkshow : How Big Data & IoT Create Smart Environment and Business...Tunjung Utomo
- Big data is a large collection of datasets that were once difficult to process due to their size and complexity but can now be managed through tools and expertise.
- Big data changes business by allowing companies to use data as an asset to create new products/services, gain better customer insights, improve efficiency, and enable near real-time analysis for decision making.
- Examples of big data use cases include using location data to analyze visitor patterns at an F1 race, using predictive analytics for better sales and pricing recommendations, conducting root cause analysis to reduce costs, enabling real-time equipment monitoring for efficiency, and monitoring business performance metrics.
Pykih is a company that builds custom interactive data visualizations to provide intuitive overviews of data and address business problems. They have worked with over 50 brands across 7 countries to create dashboards, marketing visuals, and software integrations using real-time data. Pykih focuses on going beyond standard charts and technologies to internalize clients' domains and build long-term services with a product mindset.
Webinar: Smart answers for employee and customer support after covid 19 - EuropeLucidworks
The COVID-19 pandemic has forced companies to support far more customers and employees through digital channels than ever before. Many are turning to chatbots to help meet increasing demand, but traditional rules-based approaches can’t keep up. Our new Smart Answers add-on to Lucidworks Fusion makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve.
Learn ABC Again – Analytics and Business Intelligence on Cloud provides an overview of the evolution of business intelligence and analytics from the 1980s to present day. It discusses how business intelligence has shifted from on-premise implementations to cloud-based solutions that allow for greater collaboration, mobility, and real-time access to data. The document also introduces SmartPrise BI Suite, a cloud-based business intelligence product that offers features such as single interface access to reports, role-based security, and access to both structured and unstructured data.
- Data has been seen as a competitive advantage for some early adopter companies like Tesco, but many organizations have struggled to realize benefits from their AI investments.
- Developing a well-defined data and AI strategy is important to generate value and competitive advantages, but large organizations face challenges with change management, talent, data issues, and integrating models.
- High performing AI companies focus on vision, talent, governance processes, standardized platforms, understanding how to move from pilots to production, and measuring comprehensive metrics.
For the next 40 minutes, I’d like to share with you our experience leveraging AI for businesses.
We’ll first do a tiny little quiz to check your AI knowledge - don’t worry it’s not technical at all.
Then we discuss the common challenges that startups face and give examples on how you can navigate them.
From here, you can do a self-assessment of where you are in the AI maturity journey.
Then we go to through 3 case studies in detail based on their AI maturity. At the end, we also discuss how you can spot opportunities to use AI in your company!
Finally, we close off with a summary and a list of recommendations of no-code AI tools that you can take a look at :)
It’s a loot of content, but the idea is that you will be able to walk away with a renewed understanding of what it takes to build an AI-enabled business but more importantly, how you can be in the driver seat and do it yourself.
We’ll take Q&As at the end and if you have any questions please add them onto Slido :)
How a Data-Driven Culture Improves Organizational Performance Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this slideshare, learn from IDC research and Amaysim BI Manager about:
Why meeting the analytical needs of business users matter to organizational performance
What’s driving leaders in APAC enterprises towards a self-service paradigm?
How to encourage adoption of analytical tools in your organization
How leading Asia Pacific enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
This slideshare came from a full webinar delivered by Tableau. You can the full length webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
Sajit Joseph - The road to AI for the enterpriseHilary Ip
The document discusses how artificial intelligence is being used in various areas of enterprises including bots and virtual assistants, smart speakers, predictive analytics, and robotic process automation to improve customer experience and reduce costs. It provides examples of how each technology works and can be applied, as well as market trends and considerations for implementation. The focus is on harnessing AI technologies in the near term to generate business value for organizations.
How to ready your organization for Artificial IntelligenceCraig Milroy
This document discusses how organizations can prepare for artificial intelligence (AI) and the importance of data and machine learning. It provides examples of banks investing in AI and digital technologies. Sections include discussions of data science, different types of machine learning techniques (supervised, unsupervised, reinforcement, semi-supervised), and the need for data governance, skilled talent, and executive leadership to fully leverage AI.
SAP's Innovation Strategy for Public Sectorscoopnewsgroup
This document discusses SAP's strategy for digital government and public sector innovation. It notes trends impacting the public sector like changes in the workforce, rise of cloud computing and new technologies. SAP aims to help governments capture $1 trillion in annual value through digitizing citizen engagement, processes, work and data/decision-making. SAP's solution architecture supports digital transformation across business processes, intelligence, customer experience and more using technologies like blockchain, machine learning and SAP Leonardo. Case studies show how SAP helped Indiana address the drug crisis, South Africa improve emergency response, and Under Armour gain supply chain visibility.
Enable End-to-End Digital Government Transformation with SAP Solutionsscoopnewsgroup
The document discusses SAP solutions for digital government transformation. It introduces panelists from SAP who specialize in public sector solutions. It outlines the SAP S/4HANA ERP system and how it integrates with SAP Concur for expense management and travel. It also presents SAP Hybris for citizen experience management and how it can integrate with other SAP products and systems. The panelists provide their contact information for any questions.
Enhancing DAM Operation with Artificial IntelligenceNuxeo
This document discusses using artificial intelligence to enhance digital asset management operations. It makes the following key points:
1. AI can help identify and extract metadata from digital assets to improve searchability and reuse of content across systems. This reduces duplication and helps coordinate work among creative teams.
2. There are two types of AI: generic models that provide common services like classification and enrichment, and business-specific custom models that deliver more relevant insights tailored to a company's specific domains and use cases.
3. Business-specific models can be continuously trained on a company's own content and data to provide predictive outputs. This increases the business value and accuracy of metadata compared to generic models.
Developer Velocity Series in association with Quest
DevOps: A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.
DataOps: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics.
MLOps: MLOps […] enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.
DevSecOps: DevSecOps automatically bakes in security at every phase of the software development lifecycle, enabling development of secure software at the speed of Agile and DevOps.
ChatOps: ChatOps is a collaboration model that connects people, tools, process, and automation into a transparent workflow. This flow connects the work needed, the work happening, and the work done in a persistent location staffed by the people, bots, and related tools.
NoOps: NoOps is the idea that the software environment can be so completely automated that there’s no need for an operations team to manage it.
GitOps: GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools.
Developer Velocity is the Grand Unified Theory
Developer velocity: The ability to drive transformative business performance through software development
Top DVI companies are stronger financially
- 5x compound annual growth rate
- 60% more shareholder returns
- 20% higher operating margins
>Companies in the top quartile of the Developer Velocity Index (DVI) outperform others in the market by four to five times. Top-quartile companies also have 60 percent higher total shareholder returns and 20 percent higher operating margins.
Critical areas of focus
- People
Product management
Product management function
Product telemetry
Culture
Psychological safety
Collaboration and knowledge sharing
Continuous improvement culture
Talent management
Incentives
Capability building
- Processes
Working practices
Compliance practices
Security practices
Organisational enablement
Autonomous scoped teams
Dependency management
Culture
Continuous improvement
Talent management
Recruiting
Team health management
- Tooling
Planning tools
Collaboration tools
Development tools
DevOps tools
Cloud
Video at: https://www.quest.com/event/steph-lockes-developer-velocity-series-8148798/
Applying AI & Search in Europe - featuring 451 ResearchLucidworks
In the current climate, it’s now more important than ever to digitally enable your workforce and customers.
Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies.
In this webinar, we’ll discuss:
The top challenges and aspirations European business and technology leaders are solving using AI and search technology
Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe
What technology buyers should look for when evaluating AI and search solutions
Affecto Forum 2015 - Edward Mauser (Affecto): Get Ahead with IoT valueAffecto
Edward Mauser is a business advisor and value architect at Affecto who helps businesses evolve through technology. He has over 16 years of experience in product development and is a subject matter expert in IoT. The document discusses how IoT can enable companies to obtain operational savings, deliver new customer value, create new revenue streams, and change business models. It provides examples of how IoT can be used in areas like energy analysis, maintenance predictions, and digital advertising. The key is for companies to treat IoT projects as a phased approach and use prototypes to mitigate risks while learning. Properly utilizing IoT and big data can help optimize companies and disrupt industries by providing new customer value and business models.
SAP provides analytics solutions to help customers run their businesses better by accessing all relevant information, defining plans to align performance goals, and responding instantly to changing conditions. Their solutions provide capabilities for data analysis, business intelligence applications, and collaboration. Customers in various industries and regions have been able to increase financial and operational performance through SAP's analytics offerings.
CWIN17 san francisco-ai implementation-pubCapgemini
This document summarizes an AI presentation given by Michael Martin, an enterprise architect. It discusses various dimensions and applications of AI, including machine learning, deep learning, image analysis, and natural language processing. It provides examples of how AI can be used in legal research, medical research, fraud detection, and more. It also outlines considerations for implementing AI projects, such as identifying relevant data sources, deriving hypotheses, and measuring outcomes. Key implementation steps and an example logical architecture are presented. The document closes with some perspectives on challenges and directions for AI.
Disrupting Corporates with AI by Hicham MhannaData Con LA
Abstract:- Discussion of various applied cases of artificial intelligence solutions stemming from our venture work across verticals including consumer, financial and media.
NUS-ISS Learning Day 2018- Harnessing the power of cloud solutions in urban a...NUS-ISS
The document discusses harnessing the power of cloud solutions for urban applications. It provides an overview of key topics like internet of things (IoT), connected cities, industries impacted by IoT, applications of IoT, decentralized processing, monetizing IoT, architectural approaches, challenges, and maturity models. It also discusses specific examples of using AWS IoT services and developing smart urban IoT solutions.
ExistBI case study of Salesforce and Data Warehouse consulting for world leader in manufacturing wire rope company. The WireCo team approached ExistBI wanting to engage with certified consultants who could understand how they could move reports from Salesforce into a new Data Warehouse. Have a look at this case study as an example of ExistBIs consulting capabilities.
For more information contact us at www.existbi.com
Seminar & Talkshow : How Big Data & IoT Create Smart Environment and Business...Tunjung Utomo
- Big data is a large collection of datasets that were once difficult to process due to their size and complexity but can now be managed through tools and expertise.
- Big data changes business by allowing companies to use data as an asset to create new products/services, gain better customer insights, improve efficiency, and enable near real-time analysis for decision making.
- Examples of big data use cases include using location data to analyze visitor patterns at an F1 race, using predictive analytics for better sales and pricing recommendations, conducting root cause analysis to reduce costs, enabling real-time equipment monitoring for efficiency, and monitoring business performance metrics.
Pykih is a company that builds custom interactive data visualizations to provide intuitive overviews of data and address business problems. They have worked with over 50 brands across 7 countries to create dashboards, marketing visuals, and software integrations using real-time data. Pykih focuses on going beyond standard charts and technologies to internalize clients' domains and build long-term services with a product mindset.
Webinar: Smart answers for employee and customer support after covid 19 - EuropeLucidworks
The COVID-19 pandemic has forced companies to support far more customers and employees through digital channels than ever before. Many are turning to chatbots to help meet increasing demand, but traditional rules-based approaches can’t keep up. Our new Smart Answers add-on to Lucidworks Fusion makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve.
Learn ABC Again – Analytics and Business Intelligence on Cloud provides an overview of the evolution of business intelligence and analytics from the 1980s to present day. It discusses how business intelligence has shifted from on-premise implementations to cloud-based solutions that allow for greater collaboration, mobility, and real-time access to data. The document also introduces SmartPrise BI Suite, a cloud-based business intelligence product that offers features such as single interface access to reports, role-based security, and access to both structured and unstructured data.
- Data has been seen as a competitive advantage for some early adopter companies like Tesco, but many organizations have struggled to realize benefits from their AI investments.
- Developing a well-defined data and AI strategy is important to generate value and competitive advantages, but large organizations face challenges with change management, talent, data issues, and integrating models.
- High performing AI companies focus on vision, talent, governance processes, standardized platforms, understanding how to move from pilots to production, and measuring comprehensive metrics.
For the next 40 minutes, I’d like to share with you our experience leveraging AI for businesses.
We’ll first do a tiny little quiz to check your AI knowledge - don’t worry it’s not technical at all.
Then we discuss the common challenges that startups face and give examples on how you can navigate them.
From here, you can do a self-assessment of where you are in the AI maturity journey.
Then we go to through 3 case studies in detail based on their AI maturity. At the end, we also discuss how you can spot opportunities to use AI in your company!
Finally, we close off with a summary and a list of recommendations of no-code AI tools that you can take a look at :)
It’s a loot of content, but the idea is that you will be able to walk away with a renewed understanding of what it takes to build an AI-enabled business but more importantly, how you can be in the driver seat and do it yourself.
We’ll take Q&As at the end and if you have any questions please add them onto Slido :)
[DSC Europe 22] Next-Wave of Value – Operating Model for Scaling Data Science...DataScienceConferenc1
The generation of value using machine learning and artificial intelligence for companies and their customers is now undeniable. Many companies have successfully completed the first phase of data-driven transformation and are now facing the task of creating sustainable value for the organization through scaling. With the advance of hyperscalers, automation and democratization of AI, many skills that were previously relevant and difficult to access are becoming “commodities”. The interaction between the data scientists and business departments is becoming even more important. The central question of many companies is now: "What should the organization for efficient scaling of data-driven solutions look like in the future?" In this talk, the question will be considered from an organizational and technological point of view using "Lessons Learned" with the aim of outlining the essential foundations for a sustainable operating model.
World AI summit "barriers to scale" - Deloitte Maurice Fransen
The document discusses barriers to scaling artificial intelligence. It notes that AI is experiencing growth across three horizons: augmented intelligence which augments human decisions, assisted intelligence which requires human assistance, and autonomous intelligence where AI decides and executes autonomously. Common barriers to scaling AI include data and technology challenges, ensuring ethical and unbiased AI, and overcoming organizational barriers. The workshop aims to understand hurdles to leveraging value from AI. Common hurdles include lack of skilled talent, data issues, and an organizational culture not recognizing AI needs. The document outlines categories of barriers and provides guidance on overcoming barriers through a multidisciplinary approach involving different phases to generate value from AI.
This document summarizes a TDWI (The Data Warehouse Institute) meeting in Sydney, Australia in March 2015. The meeting agenda included a presentation on "Business Intelligence - A Self-Service Future?" by Patrick Spedding from Rocket Software. The presentation discussed the shift toward self-service business intelligence driven by factors such as mobility, analytics, and empowering individual users. It outlined how self-service BI is changing roles and organizations and what elements are imperative for self-service offerings. The document provided an overview and background of the meeting host, presenter, and topics discussed.
This report describes in detail the work done by me during my Summer Internship when I was doing my two year MBA at XIMB. The project was in the Digital Marketing domain, and broadly my work revolved around these topics-
1) SEO
2) Content Marketing
3) Creative Writing
4) Digital Tools
5) Digital Strategy
Summer Project Title: Implementing SEO and Content Marketing Strategies for Brand Awareness and Lead Generation
Beet Analytics Technology provides state-of-the-art diagnostic and analytical tools to improve manufacturing operations facing complex assembly challenges. Their software and consulting services give engineers and specialists visibility into production data to reduce downtime and improve productivity. Cappius is a digital transformation company focusing on renovating businesses using technologies like big data analytics, IoT, mobile, and cloud. Their Enterprise Speech Analytics solution analyzes customer service call audio in real-time to provide insights into sentiment, moods, and trends to enhance customer experience. Hackolade is a visual modeling tool for MongoDB schemas that assists with database design and documentation. Happiest Minds enables digital transformation through technologies like big data analytics, IoT, mobility, cloud, security
The document discusses artificial intelligence (AI) and Capgemini's approach to AI. It provides examples of how AI can be applied in different industries and business functions. It also outlines Capgemini's AI platform, principles, and offerings. Capgemini aims to help clients implement impactful and scalable AI solutions through a combination of technology, services, and ecosystem partnerships.
Nadine Schöne, Dataiku. The Complete Data Value Chain in a NutshellIT Arena
Dr. Nadine Schöne is a Senior Solutions Architect at Dataiku in Berlin. In this role, she deals with all aspects of the data value chain for all users – including integration of data sources, ETL, cooperation, statistics, modelling, but also operationalization, monitoring, automatization and security during production. She regularly talks at conferences, holds webinars and writes articles.
Speech Overview:
How can you get the most out of your data – while staying flexible in your choice of infrastructure and without having to integrate a multitude of tools for the different personas involved? Maximizing the value you get out of your data is a necessity today. Looking at the whole picture as well as careful planning are the key for success. We will have a look at the complete data value chain from end to end: from the data stores, collaboration features, data preparation, visualization and automation capabilities, and external compute to scheduling, operationalization, monitoring and security.
The document discusses several important considerations for companies looking to implement artificial intelligence, including developing an AI transformation playbook, assessing an organization's AI maturity, anticipating costs and timing, deciding whether to build or buy AI solutions, and addressing important legal and ethical issues around explainability, privacy, fairness, and safety. The document provides guidance on how companies can effectively lead their organization into the AI era by establishing the right strategies, processes, and safeguards.
This document discusses new ways of handling old data and unlocking value from unstructured content through cognitive systems. It provides predictions for big data and analytics spending and adoption through 2020. Key points include:
- 90% of digital information is unstructured content stored in separate repositories that don't communicate.
- By 2020, 50% of business analytics software will incorporate prescriptive analytics using cognitive computing.
- Organizations that can analyze all relevant data and provide actionable insights will gain $430 billion in productivity over less analytical peers.
- Cognitive software can support better decision-making by applying broader evidence without bias to situations.
- The cognitive software market is expected to grow rapidly over the next five
Business intelligence and analytics both refer to maximize the value of your data to make better decisions, ALTEN CAlsoft Labs helps
enterprises accelerate business intelligence by providing the most comprehensive, integrated and easy-to-use reporting and analytics features with its industry specific analytics solutions and best in-class technology.
Smarter Analytics: Supporting the Enterprise with AutomationInside Analysis
The Briefing Room with Barry Devlin and WhereScape
Live Webcast on June 10, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=5230c31ab287778c73b56002bc2c51a
The data warehouse is intended to support analysis by making the right data available to the right people in a timely fashion. But conditions change all the time, and when data doesn’t keep up with the business, analysts quickly turn to workarounds. This leads to ungoverned and largely un-managed side projects, which trade short-term wins for long-term trouble. One way to keep everyone happy is by creating an integrated environment that pulls data from all sources, and is capable of automating both the model development and delivery of analyst-ready data.
Register for this episode of The Briefing Room to hear data warehousing pioneer and Analyst Barry Devlin as he explains the critical components of a successful data warehouse environment, and how traditional approaches must be augmented to keep up with the times. He’ll be briefed by WhereScape CEO Michael Whitehead, who will showcase his company’s data warehousing automation solutions. He’ll discuss how a fast, well-managed and automated infrastructure is the key to empowering faster, smarter, repeatable decision making.
Visit InsideAnlaysis.com for more information.
The document discusses disruptive technologies and trends impacting Israeli CIOs in 2006 according to interviews and surveys conducted by the author. Some key points include:
1) Most CIOs saw increases in their 2006 budgets compared to 2005 and report directly to the CEO. Outsourcing and temporary staffing were common.
2) Emerging technologies of interest included SOA, enterprise architecture, analytics, and open source software. CIOs wanted skills in these areas.
3) Survey results found that asset management, business process mapping, and integrating systems were priorities to improve operations and decision making. This creates opportunities for vendors in these areas.
A Data Driven Roadmap to Enterprise AI Strategy (Sponsored by Contino) - AWS ...Amazon Web Services
AI is transforming every aspect of our daily lives and the data landscape is becoming increasing open and transparent, thanks to the Consumer Data Right, most notably Open Banking. Between the high level academia and low level algorithms, where should the modern business leader start on their AI journey and harness true value from their data? Let us show you a step by step, data-driven approach towards enterprise-wide AI adoption.
Many Organizations are struggling with the best way to govern and manage the use of Generative AI in the enterprise. There are many dimensions to this challenge ranging from ethical issues, data architecture and quality, legal and copywrite, operational and more.
This is why a governance framework needs to be carefully designed and put into place so the business can make the most use of this truly revolutionary technology, reduce and mitigate risks, control costs, maintain a positive employee and customer experience and most importantly, find competitive advantage in the marketplace.
7 Steps to Transform Your Enterprise Architecture Practicepenni333
Enterprise architecture has a critical role in driving business success. But enterprise architects often find that they must create a better understanding for IT and business leaders of the function’s place in strategic planning, application rationalization, and business/IT alignment.
In this slidecast, author Beth Bacheldor explains what steps enterprise architects can take to transform their practice and give colleagues a greater appreciation of its value. The result? The business will have a greater opportunity to profit from enterprise architecture as an essential component of its operations.
Originally posted on: http://smartenterpriseexchange.com/groups/smart-architect
IP&A109 Next-Generation Analytics Architecture for the Year 2020Anjan Roy, PMP
The document discusses next generation information architecture. It describes how traditional architectures are no longer sufficient to handle big data and varied sources. A next generation architecture features a data lake that stores all data in its native format without schema. It also includes an analytics fabric and cloud fabric to enable flexible, scalable analysis and lower costs. This architecture supports self-service analytics, predictive modeling, and real-time insights across diverse data.
Enterprise application integration (EAI) evolved in the early IT industry to allow information exchange between mainframe and minicomputer systems. Common integration methods included file transfers and shared databases. In the 1990s, messaging-oriented middleware (MOM) emerged as a new paradigm, using message queues to enable both real-time and non-real-time integration across unpredictable computer networks. This represented a shift towards loosely-coupled integration using queues rather than tightly-coupled methods like remote procedure calls. Today, service-oriented architectures and microservices are further advancing loosely-coupled integration approaches.
Similar to AI in the Enterprise: Hype vs. Reality (20)
Harness the power of Data using Incedo TM Lighthouse for Operational decision...Incedo
The document discusses using data and AI/ML to develop decision automation solutions. It describes Incedo Lighthouse, a platform that implements a 6-step workflow to harness data for operational decision making: 1) defining KPIs, 2) identifying problem areas, 3) analyzing root causes, 4) recommending actions, 5) testing actions, and 6) implementing and tracking actions. The platform uses techniques like anomaly detection, root cause analysis, and experimentation to provide targeted recommendations to improve key metrics.
USING INCEDO’S SENTIMENT ANALYSIS FRAMEWORK FOR KOL INSIGHTSIncedo
1) The document discusses gathering unstructured data from online sources like social media and patient registries and storing it in an Enterprise Data Hub.
2) An analytics engine is used to analyze the data, including mining text and determining keyword frequencies, and to analyze sentiments of key opinion leaders.
3) The results of the sentiment analysis are visualized using Incedo's sentiment analysis framework to provide insights into categories like drug performance, efficacy and safety issues.
Managing household water supply with internet of things (IOT)Incedo
Water supply at households can now be managed using IoT. Find out how you can digitally manage water supply using inter-connected devices, efficiently and effectively.
Complete Automation in Retail Banking – IncedoIncedo
Incedo researched how automation technology has evolved in the Retail Banking sector. Learn more about the key drivers and barriers to attaining complete automation in the sector.
Building the Future of Monitoring with Artificial IntelligenceIncedo
Incedo Inc is an artificial intelligence and technology firm that has experienced strong growth since its inception in 2011, growing from 1500 employees to over 671% in size. The company provides specialized product engineering and data analytics services with a focus on emerging technologies. Incedo has experience using machine learning and natural language processing for applications in various industries like monitoring industrial equipment, developing chatbots for customer service, and creating diagnostic services for connected vehicles.
The document discusses a 4 step process but provides no details on the actual steps or content of the process. It references numbered sections but provides no information within those sections. Overall, the document does not contain any substantive information that could be summarized due to the lack of details provided within the numbered sections.
1. Incedo provides forecasting, valuation, deal term development, and consulting services to help clients develop appropriate forecasting scenarios and find partners through the in- and out-licensing process.
2. Valuation and forecasting use commercial, financial, and market data to develop realistic estimates for negotiations. An asset's potential varies with different partners, so deal terms depend on each partnership's needs and industry dynamics.
3. Incedo's proprietary online platform RnDpipeline.com allows users to post opportunities, search matches, and model revenues, costs, valuations, and deal structures to facilitate business development and licensing activities across the life sciences industry.
Incedo is a young company looking to build its organization. It considers employees to be its greatest assets and is seeking people willing to take on extra responsibilities as the company is in its early stages of inception. Applicants should be ready to help build up the organization.
Now a day’s, pharma research is facing challenges in
deciphering molecular understanding of disease initiation,
progress and establishment as well as performance
assessment of drug molecule on such phases of disease
development. Emerging of next generation sequencing
bases molecular tools were found to be a key method for
creating genome wide genomics landscape of gene
mutations, gene expression and gene regulation events.
Although NGS is a powerful tool for molecular research but
same time it have its own technical challenges. Few major
challenges of NGS based pharmacogenomics is
summarized below
This document provides an overview of Incedo, a technology firm with operations across North America, South Africa, and India. It summarizes Incedo's services and expertise in areas like data and analytics, emerging technologies, product engineering, and IT services. It highlights Incedo's focus on long-term partnerships and measurable success for clients through solving critical business problems with technology. It also describes Incedo's expertise in key verticals like life sciences, financial services, and telecommunications.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
1. AI in the Enterprise:
Hype vs. Reality
Proprietary and confidential
September 12, 2019
2. AI in the Enterprise:
Hype vs. Reality
Proprietary and confidential
September 12, 2019
3. How
#2) Do you have the right
instrumentation integrated to monitor
the impact from AI projects?
What
#1) Are the AI projects focused on
delivering measurable business
outcomes?
Hype or Reality?
AI for the Sake of AI
4. How
#4) Are there long-term career paths
for AI/ML Data Scientists & Engineers?
What
#3) Is there a core AI capability under a
CDO/CTO? Or is it a ‘bolt-on’ as part of the
CIO org?
Hype or Reality?
Nurturing AI Talent
5. How
#6) Is the legacy BI/EDW environment
the main data platform for AI projects?
What
#5) Is there a Data Governance team
with an CXO commitment to truly
enable Data Democratization?
Hype or Reality?
Data as a Core Asset
6. How
#8) Does the business accept the idea
of Probabilistic Recommendations
(accompanied by Confidence Intervals)?
What
#7) Does the organization have an
appetite for Experimentation across
the Enterprise – not just cosmetic
website changes?
Hype or Reality?
The Legacy of Determinism
7. How
#10) Have AI projects been
integrated to Transaction systems
(e.g. ERP, RPA) in the last 12 months?
What
#9) Do you have an enterprise AI
platform infrastructure?
Hype or Reality?
Crossing the AI Scale Chasm
8. #1) Are the AI projects focused on delivering measurable business outcomes?
#2) Do you have the right instrumentation integrated to monitor the impact from AI
projects?
AI for the Sake of AI
#3) Is there a core AI capability under a CDO/CTO? Or is it a bolt-on as part of the CIO
org?
#4) Are there long-term career paths for AI/ML Data Scientists & Engineers?
Nurturing AI Talent
#5) Is there a Data Governance team with an CXO commitment to truly enable Data
Democratization?
#6) Is the legacy BI/EDW environment the main data platform for AI projects?
Data as a Core Asset
#7) Does the organization have an appetite for Experimentation across the Enterprise:
not just cosmetic website changes?
#8) Does the business accept the idea of Probabilistic Recommendations ?
The Legacy of
Determinism
#9) Does you have an enterprise AI platform infrastructure?
#10)Have AI projects been integrated to Transaction systems (e.g. ERP, RPA) in the last
12 months?
Crossing the AI
Scale Chasm
Hype or Reality: The 10 Questions
10. #1: Be Ruthless About Outcomes
● Quantify outcomes
from AI projects that
can be measured in
business KPI terms
● Integrate the
instrumentation
process and
methodology into the
AI project
Business Outcome KPI
Influence KPI: AI
Project success metric
11. ● Build a centralized AI/ML
team to work on cross-
functional problems
● Create an eco-system that
enables collaboration
among Citizen Data
Scientists
#2: Invest in Building Organizational Capability
Hat tip: ‘Team of Teams’ by Gen Stanley McChrystal
The Seals: Core AI
Competency
Decentralized AI
Teams
12. #3: Elevate Data to be a First-Class Citizen
● Invest in Data
Governance
leadership from
business outside of IT
● Build out a ‘Data as a
Service’ architecture
that extends beyond
data provisioning
Data Governance and Policy Office
Enterprise Data Architecture AI and Data Science
Foundation Data
Services
13. ● Monitor and evaluate
the ability of AI
systems to get better
over time
● Continuously
challenge AI
recommendations
with field experiments
#4: Integrate Probabilistic Systems into Operating
Processes
Naive Bayes isn’t all
that naïve!
14. #5: Invest in ‘AI Platform as a Service’
● Invest in an AI@Scale
Platform that enforces
standardized AI model
Lifecycle Management
● Move away from
monolithic systems to
‘Lego Blocks’ of modules
that can be assembled to
solve specific problems
15. The Manifesto
#1: Be Ruthless About Outcomes
#2: Invest in Building Organizational Capability
#3: Elevate Data to be a First-Class Citizen
#4: Integrate Probabilistic Systems into Operating
Processes
#5: Invest in ‘AI Platform as a Service’
17. Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
Enabled successful Customer Journeys across the
Lifecycle (NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies
18. Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
Enabled successful Customer Journeys across the Lifecycle
(NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies
Reduced ‘Time to Expertise’ by integration of Knowledge
assets into the Customer Support processes
‘Cognitive Automation’ integrated into the Workflow
#2: Autonomous Enterprise
Using AI Enabled
Knowledge Assets
19. Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
Enabled successful Customer Journeys across the
Lifecycle (NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies
#2: Autonomous Enterprise
Using AI Enabled
Knowledge Assets
#3: AI-Driven Experimentation
Reduced ‘Time to Expertise’ by integration of Knowledge
assets into the Customer Support processes
‘Cognitive Automation’ integrated into the Workflow
High-velocity of experiments with dynamic targeting
Go beyond the traditional A/B Testing to explore MAB
strategies
• Typography Choice Goals
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• Typography Choice Goals
• Goal: To help introduce the new proposal template layouts in Google Slides.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
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• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
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• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Typography Choice Goals
• Goal: To help introduce the new proposal template layouts in Google Slides.
• Note: Feedback, changes, and other design ideas are welcome!
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• Body Slide (Template #4 - Image with text)
• Goal: Helps client understand information with an image and text. Helps prevent the client from being overwhelmed with too much info at once.
• Required to Include: Header title; content itself; Incedo logo.
• Optionally include: Subheader; image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #6 - Text and image swapped)
• Goal: Helps client understand information with text and an image. Helps prevent the client from being overwhelmed with too much info at once.
• Required to Include: Header title; content itself; Incedo logo.
• Optionally include: Subheader; image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #4 - Image with text)
• Goal: Helps client understand information with an image and text. Helps prevent the client from being overwhelmed with too much info at once.
• Required to Include: Header title; content itself; Incedo logo.
• Optionally include: Subheader; image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #6 - Text and image swapped)
• Goal: Helps client understand information with text and an image. Helps prevent the client from being overwhelmed with too much info at once.
• Required to Include: Header title; content itself; Incedo logo.
• Optionally include: Subheader; image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Typography Choice Goals
• Goal: To help introduce the new proposal template layouts in Google Slides.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
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• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Body Slide (Template #9 - Wide image with two text columns)
• Goal: Gives the client a feeling of trustworthiness with a large graphic (ideally a non-busy photo) with text space for explanations.
• Required to Include: Header title; image; Incedo logo.
• Optionally include: Image descriptor.
• Note: Feedback, changes, and other design ideas are welcome!
• Typography Choice Goals
• Goal: To help introduce the new proposal template layouts in Google Slides.
• Note: Feedback, changes, and other design ideas are welcome!