Decision Intelligence: a new discipline emerges

•Als PPTX, PDF herunterladen•

6 gefällt mir•4,662 views

This document discusses decision intelligence and how technology can help solve complex business problems through better decision making. It provides examples of different technologies that can be used for decision intelligence like predictive analytics, agent-based models, and natural language processing. The document emphasizes that decision intelligence aims to understand what data, expertise, and other assets are relevant to make decisions that help achieve desired outcomes. It also shares perspectives from industry experts on the importance of decision intelligence in today's complex world.

Technologie

AI
System
Dynamics
Operations
Research Big
Data
Predictive
Analytics
Complexity
Computational
Neuroscience
Statistical
Analysis Agent based
models
Simulation
Game Throey
Design
Thinking
Copyright © 2018 Quantallia LLC
1) Explosion of tech…
3) This is why we are
here today: to crystallize
technology into solutions
…we are (6) just
at the beginning
of the journey
Lots of complex
business problems
Decision Intelligence
… (5) and
decisions are
the unifying
principle
Human
Computer
Interaction
*Sustainable Development Goals are from the UN 2030 Agenda

In 2009, we asked:
“If technology could solve just one problem for
you that it has never solved before, what would
it be?”

THE DECISION ARCHETYPE
Decision Lever
Outcome
Outcome
Outcome
External Factors
Decision Lever
Intermediates
External Factors
Externals
Intermediates
Intermediates
Goal
Goal
Goal
Each dependency may require
various kinds of tech

How much should we invest in
clean, energy-efficient
manufacturing equipment, given
that we have to pay a carbon tax?

“WHY?”
Decision Lever
Outcome
Outcome
Outcome
External Factors
Decision Lever
Intermediates
External Factors
Externals
Intermediates
Intermediates
Goal
Goal
Goal

“HOW?”
Decision Lever
Outcome
Outcome
Outcome
External Factors
Decision Lever
Intermediates
External Factors
Externals
Intermediates
Intermediates
Goal
Goal
Goal

“IDEOLOGY”
Decision Lever
Decision Lever

Traditional View
What will
be the
outcome?
What
decisions
can we
make?
Data, Analytics, Big Data,
Reports, Predictive Analytics,
Spreadsheets

Decision Intelligence view
What data, analytics, reports,
human expertise, and other
assets are relevant?
What
outcomes
do we
need or
want to
reach ?
What
decisions
will get us
there?

Source: SAP Leo
“Decision Intelligence is on the
SAP roadmap.”

Data-led Solutions for Managing Disaster Risk
Capacity, Emergency, and Post-Emergency
Management and Preparedness (CEP MAP)

“What is critical in today’s
complex world is the ability to
see over the horizon and around
corners to understand the impact
of today’s decisions on all of the
desired outcomes.”
KarlWhitelock
Senior Analyst
Stratecast Partners
(a division of Frost & Sullivan)

0% 20% 40% 60% 80% 100%
Predictive analytics
Documents (e.g. Word)
Human expertise for cause-and-effect links
Influence diagrams
Behavioral economics
Bayesian networks
Agent-based models
Natural Language Processing (NLP)
Productivity improvement in project
Non-ML Artificial IntelligenceSimulation
What of the following Decision Intelligence (/ AI in decision making)
technologies do you use or offer? (check all that apply)
Vendors
N=23

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Quantitative decision analysis
Structuring and mapping decisions
Qualitative decision analysis
Forecasting
Designing fully automated decisions
Overcoming cognitive biases
Human decision making / cognitive science
Unintended consequence analysis
Sensemaking
Human decision making / neuroscience
Cognitive Function Analysis
What of the following Decision Intelligence best practices do you use or
offer?
Vendors
Users
N=23

Weitere ähnliche Inhalte

Was ist angesagt?

This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset. Below topics are explained in this Support Vector Machine presentation: 1. What is Machine Learning? 2. Why support vector machine? 3. What is support vector machine? 4. Understanding support vector machine 5. Advantages of support vector machine 6. Use case in Python - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

Simplilearn

The-DALL·E-2-prompt-book-v1.02.pdf

YoDaddie

Responsible Generative AI

CMassociates

[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221] Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling. As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks. In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.

Explainable AI in Industry (KDD 2019 Tutorial)

Krishnaram Kenthapadi

Abstract – Although industries have started to adopt AI and Machine Learning in almost every sector to solve complex business problems, but are these models always trustworthy? Machine Learning models are not any oracle but rather are scientific methods and mathematical models which best describes the data. But science is all about explaining complex natural phenomena in the simplest way possible! So, can we make ML and DL models more interpretable, so that any business user can understand these models and trust the results of these models? In order to find out the answer, please join me in this session, in which I will take about concepts of Explainable AI and discuss its necessity and principles which help us demystify black-box AI models. I will be discussing about popular approaches like Feature Importance, Key Influencers, Decomposition trees used in classical Machine Learning interpretable. We will discuss about various techniques used for Deep Learning model interpretations like Saliency Maps, Grad-CAMs, Visual Attention Maps and finally go through more details about frameworks like LIME, SHAP, ELI5, SKATER, TCAV which helps us to make Machine Learning and Deep Learning models more interpretable, trustworthy and useful!

Explainable AI - making ML and DL models more interpretable

Aditya Bhattacharya

Generative AI

Carlos J. Costa

Artificial Intelligence Algorithms

Laguna State Polytechnic University

Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.

Lecture1 introduction to machine learning

UmmeSalmaM1

Intepretability / Explainable AI for Deep Neural Networks

Universitat Politècnica de Catalunya

Leveraging Generative AI & Best practices

DianaGray10

Overview and Importance of Data Quality for Machine Learning Tasks

Hima Patel

Machine Learning

Rahul Kumar

Abstract: Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory. Bio: Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.

Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...

Sri Ambati

The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.

The Future of AI is Generative not Discriminative 5/26/2021

Steve Omohundro

Accenture Technology Vision - How the trends apply to higher education

accenture

In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.

Generative AI Use-cases for Enterprise - First Session

Gene Leybzon

Introduction to artificial intelligence

School of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University

Join us for an eye-opening webinar where we will demonstrate the incredible power and productivity of OpenAI GPT-3 in FME data integration scenarios. From natural language processing to automated workflow generation and predictive modeling, we will show how GPT-3 can tackle even the most complex and daunting data integration challenges without a single line of code. This is a must-attend event for anyone looking to unlock the full potential of their data and streamline their integration workflows. Be prepared to be amazed and astounded at the capabilities of GPT-3 and FME!

Unleashing the Power of OpenAI GPT-3 in FME Data Integration Workflows

Safe Software

Future of AI - 2023 07 25.pptx

Greg Makowski

Discuss the impact and opportunity of using Generative AI to support your development and creative teams * Explore business challenges in content creation * Cost-per-unit of different types of content * Use AI to reduce cost-per-unit * New partnerships being formed that will have a material impact on the way we search and engage with content Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com

GENERATIVE AI, THE FUTURE OF PRODUCTIVITY

Andre Muscat

Was ist angesagt? (20)

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

The-DALL·E-2-prompt-book-v1.02.pdf

Responsible Generative AI

Explainable AI in Industry (KDD 2019 Tutorial)

Explainable AI - making ML and DL models more interpretable

Generative AI

Artificial Intelligence Algorithms

Lecture1 introduction to machine learning

Intepretability / Explainable AI for Deep Neural Networks

Leveraging Generative AI & Best practices

Overview and Importance of Data Quality for Machine Learning Tasks

Machine Learning

Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...

The Future of AI is Generative not Discriminative 5/26/2021

Accenture Technology Vision - How the trends apply to higher education

Generative AI Use-cases for Enterprise - First Session

Introduction to artificial intelligence

Unleashing the Power of OpenAI GPT-3 in FME Data Integration Workflows

Future of AI - 2023 07 25.pptx

GENERATIVE AI, THE FUTURE OF PRODUCTIVITY

Ähnlich wie Decision Intelligence: a new discipline emerges

Waste to Wealth #COMIT2019

Comit Projects Ltd

Software Analytics = Sharing Information

Thomas Zimmermann

AI in Production.pdf

nourhanrezk

Mohammed AL Madhani

Mohammad Al Madhani

Intro to ai application emeritus uob-final

Luis Fernando Gonzalez Sanchez

Regression and correlation

VrushaliSolanke

'Machine learning’ is one of those cringy phrases, almost (if not already) taboo in the world of high-tech SaaS. Applying true machine learning to an organization’s product(s), however, can have real benefit for the business, its clients, and the industry as a whole. From credit card fraud investigations to the way that a car is built, machine learning has permeated our everyday life without a common understanding of what it is and how to implement it.

Machine Learning: Addressing the Disillusionment to Bring Actual Business Ben...

Jon Mead

About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai

AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?

Skyl.ai

Opportunities with data science

Ashiq Rahman

The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere. The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data. Join Cloudera and Forrester to learn: - What we mean by pervasive analytics, how it impacts your organization, and how to get started - How leading organizations are using pervasive analytics for competitive advantage - How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster

The Value of Pervasive Analytics

Cloudera, Inc.

Deep learning, a new class of AI (Artificial Intelligence) algorithms is making big promises to unlock an unprecedented level of intelligence from voluminous forms of structured and unstructured data produced from online data factories and internet-enabled smart devices. But despite the big hype about big data, deep learning and AI in general, less than half of the projects undertaking by companies looking to push the boundaries of analytics through data science fail to deliver the expected results according to a recent Gartner’s study. From our experience, a major factor in this failure is the myopic view of technology coupled with lack of understanding of what’s needed to build an ecosystem of analytics technology architecture, talent resources and systems of governance. We present a national e-health analytics transformation case study where we describe the recipe for how we envision analytics to be able to create the spin-off factor to reshape and revolutionize the industry landscape through our tested and proven framework of “Transform and Digitize”, Inform and Contextualize”, Embed and Institutionalize, “Innovate and Evangelize”. For organizations, large and small, to deepen their learning and win with analytics a holistic approach has to address all the underlying components across the full analytics value chain…. it’s a never-ending journey!

BDW16 London - Amjad Zaim, Cognitro Analytics: How Deep is Your Learning

Big Data Week

Introduction to AI with Business Use Cases

Jack C Crawford

Analytics, data science, & artificial intelligencesy

honey725342

SSE 2017 10-09

Galina Shubina

Dr. dzaharudin mansor microsoft

Soo Chin Hock

Odsc east 2022 slides

gmoseley3

“How is Watson Changing the Future of the Automotive Industry?” presented in Livonia, MI. Event participants were introduced to the age of cognitive computing, where cognitive analytics evaluate complex data in new ways to help solve the industry's most challenging problems. Cognitive computing has arrived, and its potential to transform the industry is momentous. Learn how cognitive solutions are being applied in the automotive industry and how industry leaders are embracing this ground breaking technology to spark the digital future.

How is Watson Changing the Future of the Automative Industry?

IBM Watson

AI Developments and Trends (OECD)

AnandSRao1962

[WSO2Con USA 2018] Unravelling Todays Disruptive Tech Landscape

WSO2

Agile Chennai 2021 - Keynote | AI in Agility by Mukesh Jain

AgileNetwork

Ähnlich wie Decision Intelligence: a new discipline emerges (20)

Waste to Wealth #COMIT2019

Software Analytics = Sharing Information

AI in Production.pdf

Mohammed AL Madhani

Intro to ai application emeritus uob-final

Regression and correlation

Machine Learning: Addressing the Disillusionment to Bring Actual Business Ben...

AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?

Opportunities with data science

The Value of Pervasive Analytics

BDW16 London - Amjad Zaim, Cognitro Analytics: How Deep is Your Learning

Introduction to AI with Business Use Cases

Analytics, data science, & artificial intelligencesy

SSE 2017 10-09

Dr. dzaharudin mansor microsoft

Odsc east 2022 slides

How is Watson Changing the Future of the Automative Industry?

AI Developments and Trends (OECD)

[WSO2Con USA 2018] Unravelling Todays Disruptive Tech Landscape

Agile Chennai 2021 - Keynote | AI in Agility by Mukesh Jain

Kürzlich hochgeladen

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Discover the innovative features and strategic vision that keep WSO2 an industry leader. Explore the exciting 2024 roadmap of WSO2 API management, showcasing innovations, unified APIM/APK control plane, natural language API interaction, and cloud native agility. Discover how open source solutions, microservices architecture, and cloud native technologies unlock seamless API management in today's dynamic landscapes. Leave with a clear blueprint to revolutionize your API journey and achieve industry success!

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2

Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Orbitshub

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

DBX First Quarter 2024 Investor Presentation

Dropbox

[BuildWithAI] Introduction to Gemini.pdf

Sandro Moreira

Understanding the FAA Part 107 License ..

Christopher Logan Kennedy

In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.

Platformless Horizons for Digital Adaptability

WSO2

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

MadyBayot

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

Kürzlich hochgeladen (20)

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Strategies for Landing an Oracle DBA Job as a Fresher

WSO2's API Vision: Unifying Control, Empowering Developers

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

FWD Group - Insurer Innovation Award 2024

DBX First Quarter 2024 Investor Presentation

[BuildWithAI] Introduction to Gemini.pdf

Understanding the FAA Part 107 License ..

Platformless Horizons for Digital Adaptability

Boost Fertility New Invention Ups Success Rates.pdf

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

MS Copilot expands with MS Graph connectors

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Why Teams call analytics are critical to your entire business

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

AWS Community Day CPH - Three problems of Terraform

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Decision Intelligence: a new discipline emerges

1. AI System Dynamics Operations Research Big Data Predictive Analytics Complexity Computational Neuroscience Statistical Analysis Agent based models Simulation Game Throey Design Thinking Copyright © 2018 Quantallia LLC 1) Explosion of tech… 3) This is why we are here today: to crystallize technology into solutions …we are (6) just at the beginning of the journey Lots of complex business problems Decision Intelligence … (5) and decisions are the unifying principle Human Computer Interaction *Sustainable Development Goals are from the UN 2030 Agenda

2. In 2009, we asked: “If technology could solve just one problem for you that it has never solved before, what would it be?”

3. THE DECISION ARCHETYPE Decision Lever Outcome Outcome Outcome External Factors Decision Lever Intermediates External Factors Externals Intermediates Intermediates Goal Goal Goal Each dependency may require various kinds of tech

8. How much should we invest in clean, energy-efficient manufacturing equipment, given that we have to pay a carbon tax?

9. “WHY?” Decision Lever Outcome Outcome Outcome External Factors Decision Lever Intermediates External Factors Externals Intermediates Intermediates Goal Goal Goal

10. “HOW?” Decision Lever Outcome Outcome Outcome External Factors Decision Lever Intermediates External Factors Externals Intermediates Intermediates Goal Goal Goal

11. “IDEOLOGY” Decision Lever Decision Lever

12. Traditional View What will be the outcome? What decisions can we make? Data, Analytics, Big Data, Reports, Predictive Analytics, Spreadsheets

13. Decision Intelligence view What data, analytics, reports, human expertise, and other assets are relevant? What outcomes do we need or want to reach ? What decisions will get us there?

14. Source: SAP Leon

15.

16. Source: Raytheon

17. Source: The Millenium Project

18. Source: The Millenium Project

19.

20.

21.

22.

23. Source: SAP Leo “Decision Intelligence is on the SAP roadmap.”

24.

25.

26. Data-led Solutions for Managing Disaster Risk Capacity, Emergency, and Post-Emergency Management and Preparedness (CEP MAP)

27.

28.

29.

30. “What is critical in today’s complex world is the ability to see over the horizon and around corners to understand the impact of today’s decisions on all of the desired outcomes.” KarlWhitelock Senior Analyst Stratecast Partners (a division of Frost & Sullivan)

31.

32. 32

33. 0% 20% 40% 60% 80% 100% Predictive analytics Documents (e.g. Word) Human expertise for cause-and-effect links Influence diagrams Behavioral economics Bayesian networks Agent-based models Natural Language Processing (NLP) Productivity improvement in project Non-ML Artificial IntelligenceSimulation What of the following Decision Intelligence (/ AI in decision making) technologies do you use or offer? (check all that apply) Vendors N=23

34. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Quantitative decision analysis Structuring and mapping decisions Qualitative decision analysis Forecasting Designing fully automated decisions Overcoming cognitive biases Human decision making / cognitive science Unintended consequence analysis Sensemaking Human decision making / neuroscience Cognitive Function Analysis What of the following Decision Intelligence best practices do you use or offer? Vendors Users N=23

35.

36.

37. AI System Dynamics Operations Research Big Data Predictive Analytics Complexity Computational Neuroscience Statistical Analysis Agent based models Simulation Game Throey Design Thinking *Sustainable Development Goals are from the UN 2030 Agenda 1) Explosion of tech… …we are (6) just at the beginning of the journey Lots of complex business problems Decision Intelligence … (5) and decisions are the unifying principle Human Computer Interaction 3) This is why we are here today: to crystallize technology into solutions Lorien Pratt Lorien.pratt@quantellia.com www.lorienpratt.com Copyright © 2018 Quantellia LLC. All rights reserved.

Hinweis der Redaktion

You may not realize it, but today is an important moment in history . My goal for the next few minutes is to explain why, and to help you to understand how our hosts at prowler, as well as the other speakers you’ll hear from today, are crystallizing a movement that will have massive impact on humanity for decades to come. No pressure, right? Let’s do this. Western society has, to date, been about analysis: breaking problems down into discrete components. But this approach is reaching diminishing returns in a complex world. Our technologies, like our problems, must be interlinked. For most important problems, though, there’s a gap between these technologies and each other, and also between the technologies and the hardest problems that humanity faces. This is our challenge today, and it is a historic moment, because we are beginning on the path towards unification.
This is the decision intelligence mental model map Homework: go home and ask a friend to describe a tough decision they had to face. As they talk, listen for levers, outcomes, externals, and chains of cause-and-effect “If I make this decision today, how will it affect my objectives in the future?”Every decision made by a human is a mental simulation: if I take this action, what will happen? This framework integrates human decision makers with machine intelligence – each star is a location where technology can play a part.
Over 8,000 followers
Computers can also help to run these models backwards.
Ideology is doing the same thing we’ve always done, without awareness of the “why”
How to get there? Platform ecosystems integrated into systems of systems and mediated by a technology framework. The DI framework I showed you above is just one of several that will ultimately be used. All serve slightly different purposes. This is what drove amazon, itunes, the app store, facebook, and much more. A platform ecosystem is the most important business model today, and dominates many industries. We need a platform ecosystem to unify these technologies into a common framework. Decision Intelligence Mental Model Map (used to determine which is the best decision) DMN: the Decision Modeling Notation (used to record conditions under which to make which decisions) Prowler’s ontology… Several more, which need to be unified.
This shape is exploding everywhere. Here, the World Economic Forum shows how we must integrate technology with society to find solutions
An image from Raytheon
Every decision is a mental simulation. And actions taken in one realm impact others. It is understanding these connections that we solve the great problems. And statistics, machine learning, agent-based modeling, simulation, complexity science, system dynamics, cognitive science, and more, are all needed to understand things.
And this one is probably familiar too
How to get there? Platform ecosystems integrated into systems of systems and mediated by a technology framework. The DI framework I showed you above is just one of several that will ultimately be used. All serve slightly different purposes. This is what drove amazon, itunes, the app store, facebook, and much more. A platform ecosystem is the most important business model today, and dominates many industries. We need a platform ecosystem to unify these technologies into a common framework. Note that this picture is from SAP, for which Decision Intelligence is on the road map. Decision Intelligence Mental Model Map (used to determine which is the best decision) DMN: the Decision Modeling Notation (used to record conditions under which to make which decisions)
Gongos
You can build an automated “decision dashboard” to help everyone to understand the decision blueprint. Different organizations and situations usually require a different look-and-feel
So no pressure. But let’s do this. Thank you.

Decision Intelligence: a new discipline emerges

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie Decision Intelligence: a new discipline emerges

Ähnlich wie Decision Intelligence: a new discipline emerges (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Decision Intelligence: a new discipline emerges

Hinweis der Redaktion