Organizations are increasingly investing in data analytics to improve decision-making. Dashboards, self-service BI, data mining, predictive analytics, machine learning and cognitive technologies are being evaluated, deployed and used as organizations push to adopt data-driven decision-making. Effectively using these analytic technologies requires a disciplined focus on better decisions. Some organizations are using decision modeling, and the DMN standard, to achieve analytic excellence.
Successful digital programs extend their Digital Business Platforms with 3 critical elements: decision modeling, predictive analytics and business rules technology. Coordinating these technologies into a virtual decision hub. Decision Management automates and improves every digital interaction and delivers agile, data-driven, real-time outcomes.
Get deployed! Many Analytics Teams have experience with building what seems like a great model–valid, predictive, powerful–only to see disappointing or even no business impact. Some models are not deployed, or take so long to deploy their accuracy is lost. Even deployed models are often not used effectively.
What can you do? Learn the 5 questions to ask before deploying your model.
Get the business understanding right! Analytics Teams know that one of their biggest challenges is effective communication and collaboration with their business partners. Projects are plagued with too many iterations to get to a solution, too many detours responding to unfocused requests, and too often the final model results in a positive analytic result that can’t demonstrate business value.
What can you do? Analytics and decision modeling expert James Taylor of Decision Management Solutions outlines six questions to ask your business partner before you start modeling and shows you why decision modeling is the best approach to building this shared understanding.
DMN is a great standard and we’ve both achieve considerable successes with it: its help to improve the transparency, accuracy and agility of many business decisions and helped us to deliver better decisions and decision services to our clients. However, like any released product, DMN 1.1 can benefit from usage suggested refinements.
A decision modeling approach using DMN is the best practice for for scaling BRMS. Decision modeling address three key challenges of a existing BRMS program, improving traceability, sustaining business engagement and maximizing re-use while minimizing duplication.
Learn how decision models based on the Decision Model and Notation (DMN) standard can be more easily integrated with business rules being managed and deployed using JBoss BRMS, improving traceability and business ownership.
Decisions First Modeler Enterprise Edition Integration with JBoss BRMSDecisionsFirst Modeler is a collaborative decision modeling solution using the new Decision Model and Notation (DMN) standard. DecisionsFirst Modeler provides a diagram-based, business user friendly front-end to the business rules environment.
If you are kicking off your first BRMS project, don’t start by gathering the rules! Often teams will be advised to begin their project by gathering all the relevant rules, in a natural language or rulebook approach.
But these rules-first approaches address issues that don’t exist with modern BRMS technology, resulting in redundant and counter-productive efforts.
A decisions-first, decision modeling approach using the Decision Model Notation (DMN) standard is the best practice for business rules projects when implementing a modern BRMS.
In this recording of our live webinar, you will learn why building a decision model that is linked to the business context (metrics, processes, logical data structures) and then implementing this directly in a linked BRMS is faster and cheaper while resulting in more accurate rules, more business engagement and better value realized.
A claims handling pilot delivers data-driven claims risk, fraud and wastage decisions directly into your claims process. Using real-world examples, learn how you can maximize straight through “Jet” processing while minimizing risk and fraud using a decision-centric, continuous improvement business architecture. Our proven decisions-first approach delivers the 5 elements of a powerful claims handling platform: decision model, business rules, risk and fraud analytics, impact analysis and continuous improvement.
Successful digital programs extend their Digital Business Platforms with 3 critical elements: decision modeling, predictive analytics and business rules technology. Coordinating these technologies into a virtual decision hub. Decision Management automates and improves every digital interaction and delivers agile, data-driven, real-time outcomes.
Get deployed! Many Analytics Teams have experience with building what seems like a great model–valid, predictive, powerful–only to see disappointing or even no business impact. Some models are not deployed, or take so long to deploy their accuracy is lost. Even deployed models are often not used effectively.
What can you do? Learn the 5 questions to ask before deploying your model.
Get the business understanding right! Analytics Teams know that one of their biggest challenges is effective communication and collaboration with their business partners. Projects are plagued with too many iterations to get to a solution, too many detours responding to unfocused requests, and too often the final model results in a positive analytic result that can’t demonstrate business value.
What can you do? Analytics and decision modeling expert James Taylor of Decision Management Solutions outlines six questions to ask your business partner before you start modeling and shows you why decision modeling is the best approach to building this shared understanding.
DMN is a great standard and we’ve both achieve considerable successes with it: its help to improve the transparency, accuracy and agility of many business decisions and helped us to deliver better decisions and decision services to our clients. However, like any released product, DMN 1.1 can benefit from usage suggested refinements.
A decision modeling approach using DMN is the best practice for for scaling BRMS. Decision modeling address three key challenges of a existing BRMS program, improving traceability, sustaining business engagement and maximizing re-use while minimizing duplication.
Learn how decision models based on the Decision Model and Notation (DMN) standard can be more easily integrated with business rules being managed and deployed using JBoss BRMS, improving traceability and business ownership.
Decisions First Modeler Enterprise Edition Integration with JBoss BRMSDecisionsFirst Modeler is a collaborative decision modeling solution using the new Decision Model and Notation (DMN) standard. DecisionsFirst Modeler provides a diagram-based, business user friendly front-end to the business rules environment.
If you are kicking off your first BRMS project, don’t start by gathering the rules! Often teams will be advised to begin their project by gathering all the relevant rules, in a natural language or rulebook approach.
But these rules-first approaches address issues that don’t exist with modern BRMS technology, resulting in redundant and counter-productive efforts.
A decisions-first, decision modeling approach using the Decision Model Notation (DMN) standard is the best practice for business rules projects when implementing a modern BRMS.
In this recording of our live webinar, you will learn why building a decision model that is linked to the business context (metrics, processes, logical data structures) and then implementing this directly in a linked BRMS is faster and cheaper while resulting in more accurate rules, more business engagement and better value realized.
A claims handling pilot delivers data-driven claims risk, fraud and wastage decisions directly into your claims process. Using real-world examples, learn how you can maximize straight through “Jet” processing while minimizing risk and fraud using a decision-centric, continuous improvement business architecture. Our proven decisions-first approach delivers the 5 elements of a powerful claims handling platform: decision model, business rules, risk and fraud analytics, impact analysis and continuous improvement.
Learn how to innovate risk management and customer processes with decision and process management, from leading experts Roger Burlton and James Taylor.
To succeed, an analytics or data science team must effectively engage with business experts who are often inexperienced with advanced analytics, machine learning and data science. They need a framework for connecting business problems to possible analytics solutions and operationalizing results. Decision modeling brings clarity to analytics projects, linking analytics solutions to business problems to deliver value.
One of the prime causes of complex business processes is the inclusion of decision-making in process designs. Organizations that identify the decisions in their processes and manage them as peers – not part of the process but supporting it – find they can simplify process designs, increase agility and bring business users and IT into better alignment.
This webinar will build on real case studies to show you how keeping decisioning and process entangled creates complexity, how to find decisions in your complex processes and how Decision Management delivers simpler, more manageable processes.
Establishing a shared understanding of the business problem across business, IT and analytics teams is critical for successful predictive analytics projects. Recently decision modeling has begun to be adopted as a way to specify business requirements for predictive analytics projects. This session will introduce decision modeling and describe how it helps predictive analytics practitioners. The value of the technique will be illustrated with both experience working with real-world projects and of using the approach to teach students of analytics.
A discussion of the value of Decision Management and decision modeling to the effective management of large, complex operations - including that of a large, global, financial services organization. Presented by James Taylor of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
PASS Business Analytics 2015 - Most organizations lack an approach that lets them specify their requirements for BI or for analytics more broadly. Their ability to find opportunities for, and successfully use, more advanced analytics is limited. In this session, James Taylor will introduce decision modeling with DMN, a new standards-based approach to modeling decisions. He will introduce the core concepts of the approach and show how it can be used to drive more effective requirements for BI, dashboard and analytic projects. Attendees will learn how to begin with the decision in mind, defining their BI requirements in terms of the decision-making they need to improve.
Decision management and business rules management systems are the ideal platform for an agile and cost-effective compliance approach. In regulated industries like financial services, leading companies are building compliance into every process and system with consistency and transparency across the entire organization and with the agility to meet ever more challenging deadlines. Companies that fail to do so incur huge costs with manual checks and balances and risk significant fines.
In this webinar James Taylor, CEO of Decision Management Solutions and Jan Purchase, Director and Founder of Investment Banking Specialists Lux Magi, share know-how and best practices from their extensive experience of helping clients implement decision management and business rules management systems to conquer complexity, improve agility, lower costs and measure ongoing effectiveness in financial compliance.
The webinar includes illustrations of how the decision management approach has been applied in compliance projects and a walkthrough of real decision model from one of these.
As businesses have an increasing obligation to demonstrate compliance with regulations there is a need for a business architecture view that not only tracks regulations impact but also connects seamlessly to diverse, distributed implementations in automated systems and manual procedures. The Decision Model Notation (DMN) has been used to create a decision architecture for regulatory compliance at a leading global financial organization. This Regulatory Architecture includes business decisions impacted by a variety of global financial regulations – the Dodd Frank Act, in particular. This business architecture has been modeled in the form of decision requirement models and aligned with business process and business organization architectures. Presented by Gagan Saxena of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
The new Decision Model and Notation (DMN) standard has been used to gather requirements for and to design Enterprise IT Management dashboards at two Fortune 200 Financial Corporations. These dashboards are used to manage 100+ projects being released every 2 weeks into production across hundreds of critical applications ranging from mainframe, client-server, web and mobile applications.
Presentation from BBC2014
A presentation on Customer Decision Management and how it results in more accurate, more real-time, more consistent, more agile and more scalable customer decisions. Presented at Teradata Partners 2013
Business analysts know that modeling business processes, rather than writing about them, defines them more accurately. Business process models make it easier to validate requirements, easier to see opportunities for improvement and easier to manage the process once it is implemented. Replacing traditional specifications with logical business process models based on standard notations like BPMN improves requirements and increases the likelihood of project success.
Yet over-complex processes are common. Complex process models make it harder to engage business owners and reduce the manageability of implementations. One of the prime causes of over-complex processes is the inclusion of decision-making in process designs. Business analysts that identify the decisions in their processes and model them separately – not part of the process but supporting it – find they can simplify process designs, increase agility and bring business users and IT into better alignment. With the publication of a new standard notation - OMG's Decision Model and Notation - and the inclusion of decision modeling in the BABOK, it's time for business analysts to improve their process models by modeling decisions.
Key learning points:
Decisions are central to straight through processing, process innovation and process effectiveness.
Process models obscure decision-making and become over-complex when it is embedded
A standards-based approach to decision modeling is a key technique for process analysts
DecisionsFirst Modeler enables organizations to accurately specify their business using decision requirements models; structure and manage the supporting business rules; and streamline business process design.
The Enterprise Edition integration with IBM ODM delivers traceability from business objectives through decision requirements to the business rules running in production. This ensures that DecisionsFirst Modeler users have full access to all the rule editing, validation, simulation, deployment and management capabilities of IBM ODM.
DecisionsFirst Modeler is a collaborative decision modeling solution using the new Decision Model and Notation (DMN) standard. DecisionsFirst Modeler provides a diagram-based, business user friendly front-end to the business rules environment.
Decision Modeling is a new Technique in v3 of the BABOK(r) Guide. It has also become a key element of the Business Intelligence and Business Process Management Perspectives. At the June 2014 IIBA Bay Area Event, James Taylor presents Decision Modeling as a technique (following the new Decision Model and Notation standard), shows how modeling decisions improves business analysis and requirements specification, and discusses the role of decision modeling in business process, business rules, business intelligence and analytic projects.
The 9 secrets of successful customer feedback and action programsGenroe
Ever wondered how successful customer feedback and action programs differ from unsuccessful programs? We did. So we asked 80 organizations, operating at various levels of success, what they were doing.
Do you see a problem that is so obvious that everyone should see it, but they don’t? Do you have great data about a pain point for your customers, but don’t know where to go with it? In this session, we’ll talk about project briefs — what they are, and how they can be an invaluable tool for building consensus and getting your stakeholders and teams on board.
In this session, you will learn:
1) How to pull together various data points into a cohesive project brief; 2) How to use a project brief to effectively present the problem/issue; 3) And, most importantly, why a project brief isn’t the right platform for solutioning.
Presented November 29, 2018, at Quadrus Conference Center for Information Development World 2018.
Connecting the dots with intelligent analysisMovate
Tracing the problem to its origins is helping a global networking products provider enhance customer satisfaction (CSAT), first call resolution (FCR) rates, and in turn service experience
What began in 2003 as an engagement to provide technical support for one product line has grown today into a long-term partnership to support a wider range of products while consistently improving CSAT and FCR scores. The key factor that enabled CSS Corp to achieve this is its team’s continuous and relentless strive to provide valuable and actionable insights, recommendations and corrective action plans to the client. Proactive initiatives like that have made us their partner of choice.
Measuring the effectiveness of your digital assetsMedullan
In this webinar you’ll learn:
- how to get started with impact measurement - translating business goals into effective measures, KPIs and high level reporting
- how to find actionable insights in measurement data
the art and science of experimentation and product refinement
Leading organizations today are looking to scale their advanced analytics capabilities, especially data mining and predictive analytics, to improve business performance, reduce fraud and improve customer responsiveness. However traditional analytic project approaches are hard to scale and difficult to implement in the real-time environment required in modern enterprise architectures.
One of the most important steps in a predictive analytic effort is correctly framing the problem a way that creates a shared understanding of the business problem across business, IT and analytics teams. A decision requirements model makes it clear how to best leverage analytics. Watch the webinar recording at http://decisionmanagement.omnovia.com/archives/223762
Learn how to innovate risk management and customer processes with decision and process management, from leading experts Roger Burlton and James Taylor.
To succeed, an analytics or data science team must effectively engage with business experts who are often inexperienced with advanced analytics, machine learning and data science. They need a framework for connecting business problems to possible analytics solutions and operationalizing results. Decision modeling brings clarity to analytics projects, linking analytics solutions to business problems to deliver value.
One of the prime causes of complex business processes is the inclusion of decision-making in process designs. Organizations that identify the decisions in their processes and manage them as peers – not part of the process but supporting it – find they can simplify process designs, increase agility and bring business users and IT into better alignment.
This webinar will build on real case studies to show you how keeping decisioning and process entangled creates complexity, how to find decisions in your complex processes and how Decision Management delivers simpler, more manageable processes.
Establishing a shared understanding of the business problem across business, IT and analytics teams is critical for successful predictive analytics projects. Recently decision modeling has begun to be adopted as a way to specify business requirements for predictive analytics projects. This session will introduce decision modeling and describe how it helps predictive analytics practitioners. The value of the technique will be illustrated with both experience working with real-world projects and of using the approach to teach students of analytics.
A discussion of the value of Decision Management and decision modeling to the effective management of large, complex operations - including that of a large, global, financial services organization. Presented by James Taylor of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
PASS Business Analytics 2015 - Most organizations lack an approach that lets them specify their requirements for BI or for analytics more broadly. Their ability to find opportunities for, and successfully use, more advanced analytics is limited. In this session, James Taylor will introduce decision modeling with DMN, a new standards-based approach to modeling decisions. He will introduce the core concepts of the approach and show how it can be used to drive more effective requirements for BI, dashboard and analytic projects. Attendees will learn how to begin with the decision in mind, defining their BI requirements in terms of the decision-making they need to improve.
Decision management and business rules management systems are the ideal platform for an agile and cost-effective compliance approach. In regulated industries like financial services, leading companies are building compliance into every process and system with consistency and transparency across the entire organization and with the agility to meet ever more challenging deadlines. Companies that fail to do so incur huge costs with manual checks and balances and risk significant fines.
In this webinar James Taylor, CEO of Decision Management Solutions and Jan Purchase, Director and Founder of Investment Banking Specialists Lux Magi, share know-how and best practices from their extensive experience of helping clients implement decision management and business rules management systems to conquer complexity, improve agility, lower costs and measure ongoing effectiveness in financial compliance.
The webinar includes illustrations of how the decision management approach has been applied in compliance projects and a walkthrough of real decision model from one of these.
As businesses have an increasing obligation to demonstrate compliance with regulations there is a need for a business architecture view that not only tracks regulations impact but also connects seamlessly to diverse, distributed implementations in automated systems and manual procedures. The Decision Model Notation (DMN) has been used to create a decision architecture for regulatory compliance at a leading global financial organization. This Regulatory Architecture includes business decisions impacted by a variety of global financial regulations – the Dodd Frank Act, in particular. This business architecture has been modeled in the form of decision requirement models and aligned with business process and business organization architectures. Presented by Gagan Saxena of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
The new Decision Model and Notation (DMN) standard has been used to gather requirements for and to design Enterprise IT Management dashboards at two Fortune 200 Financial Corporations. These dashboards are used to manage 100+ projects being released every 2 weeks into production across hundreds of critical applications ranging from mainframe, client-server, web and mobile applications.
Presentation from BBC2014
A presentation on Customer Decision Management and how it results in more accurate, more real-time, more consistent, more agile and more scalable customer decisions. Presented at Teradata Partners 2013
Business analysts know that modeling business processes, rather than writing about them, defines them more accurately. Business process models make it easier to validate requirements, easier to see opportunities for improvement and easier to manage the process once it is implemented. Replacing traditional specifications with logical business process models based on standard notations like BPMN improves requirements and increases the likelihood of project success.
Yet over-complex processes are common. Complex process models make it harder to engage business owners and reduce the manageability of implementations. One of the prime causes of over-complex processes is the inclusion of decision-making in process designs. Business analysts that identify the decisions in their processes and model them separately – not part of the process but supporting it – find they can simplify process designs, increase agility and bring business users and IT into better alignment. With the publication of a new standard notation - OMG's Decision Model and Notation - and the inclusion of decision modeling in the BABOK, it's time for business analysts to improve their process models by modeling decisions.
Key learning points:
Decisions are central to straight through processing, process innovation and process effectiveness.
Process models obscure decision-making and become over-complex when it is embedded
A standards-based approach to decision modeling is a key technique for process analysts
DecisionsFirst Modeler enables organizations to accurately specify their business using decision requirements models; structure and manage the supporting business rules; and streamline business process design.
The Enterprise Edition integration with IBM ODM delivers traceability from business objectives through decision requirements to the business rules running in production. This ensures that DecisionsFirst Modeler users have full access to all the rule editing, validation, simulation, deployment and management capabilities of IBM ODM.
DecisionsFirst Modeler is a collaborative decision modeling solution using the new Decision Model and Notation (DMN) standard. DecisionsFirst Modeler provides a diagram-based, business user friendly front-end to the business rules environment.
Decision Modeling is a new Technique in v3 of the BABOK(r) Guide. It has also become a key element of the Business Intelligence and Business Process Management Perspectives. At the June 2014 IIBA Bay Area Event, James Taylor presents Decision Modeling as a technique (following the new Decision Model and Notation standard), shows how modeling decisions improves business analysis and requirements specification, and discusses the role of decision modeling in business process, business rules, business intelligence and analytic projects.
The 9 secrets of successful customer feedback and action programsGenroe
Ever wondered how successful customer feedback and action programs differ from unsuccessful programs? We did. So we asked 80 organizations, operating at various levels of success, what they were doing.
Do you see a problem that is so obvious that everyone should see it, but they don’t? Do you have great data about a pain point for your customers, but don’t know where to go with it? In this session, we’ll talk about project briefs — what they are, and how they can be an invaluable tool for building consensus and getting your stakeholders and teams on board.
In this session, you will learn:
1) How to pull together various data points into a cohesive project brief; 2) How to use a project brief to effectively present the problem/issue; 3) And, most importantly, why a project brief isn’t the right platform for solutioning.
Presented November 29, 2018, at Quadrus Conference Center for Information Development World 2018.
Connecting the dots with intelligent analysisMovate
Tracing the problem to its origins is helping a global networking products provider enhance customer satisfaction (CSAT), first call resolution (FCR) rates, and in turn service experience
What began in 2003 as an engagement to provide technical support for one product line has grown today into a long-term partnership to support a wider range of products while consistently improving CSAT and FCR scores. The key factor that enabled CSS Corp to achieve this is its team’s continuous and relentless strive to provide valuable and actionable insights, recommendations and corrective action plans to the client. Proactive initiatives like that have made us their partner of choice.
Measuring the effectiveness of your digital assetsMedullan
In this webinar you’ll learn:
- how to get started with impact measurement - translating business goals into effective measures, KPIs and high level reporting
- how to find actionable insights in measurement data
the art and science of experimentation and product refinement
Leading organizations today are looking to scale their advanced analytics capabilities, especially data mining and predictive analytics, to improve business performance, reduce fraud and improve customer responsiveness. However traditional analytic project approaches are hard to scale and difficult to implement in the real-time environment required in modern enterprise architectures.
One of the most important steps in a predictive analytic effort is correctly framing the problem a way that creates a shared understanding of the business problem across business, IT and analytics teams. A decision requirements model makes it clear how to best leverage analytics. Watch the webinar recording at http://decisionmanagement.omnovia.com/archives/223762
A rebroadcast of one of the best reviewed sessions at this year's Predictive Analytics World. Learn the critical success factors in delivering business value with advanced analytics.
Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
The Decision Management Manifesto lays out key principles of Decision Management - why decisions are central to your requirements process, why it makes sense to explicitly design decisions before applying technology. Using real world projects this webinar explains the rationale for each part of the manifesto and shows the value it can bring to your projects now and in the future.
While many Digital Transformation initiatives are focused on improving the customer experience, often too little attention is paid to the customer-facing operational decisions that impact customers every day. To get the most from your Digital Transformation efforts, your customers’ experience and the decisions that impact it cannot be ignored.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Aggregage
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Hannah Flynn
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Introduction to Decision Strategy Manager, the tool used to create Decision Strategies.
Introduction to the Decisioning Components, the building blocks of Decision Strategies
Identifying and managing the decisions within a business process are critical next steps for greater efficiency and effectiveness in organizations today.
Driving Market Impact through Operationalizing Agile MarketingCMG Partners
To really win in today's marketplace, marketing teams need a new operating system – one that allows them to maximize their impact through speed, relevance and customer focus. Use this Slideshare to learn how agile marketing can help your team realize its greatest potential; the presentation covers:
- Basics and benefits of agile marketing
- Best practices from agile marketing adopters
- What to watch out for as you implement agile
- How to operationalize agile in marketing
Does your Rules Consultant think execution matters more than management? That's “old school” thinking. Find out if your Rules Consultant is providing your business with real value by watching this webinar.
Measuring the Effectiveness of Data Analysis Projects_ Key Metrics and Strate...Soumodeep Nanee Kundu
In today's data-driven world, organizations are increasingly investing in data analysis projects to gain valuable insights, make informed decisions, and drive business success. These projects encompass a wide range of activities, from data collection and preprocessing to advanced analytics and machine learning. However, measuring the effectiveness of these projects can be challenging. Determining whether a data analysis project has achieved its objectives is essential for justifying investments, optimizing processes, and ensuring ongoing success. In this article, we will explore various strategies and key metrics for measuring the effectiveness of data analysis projects.
The change in the buyer’s journey has dramatically shifted the way marketers work. With marketers taking increasing ownership over pipeline, there is unprecedented pressure for marketers to target more effectively and to create more meaningful campaign touches.
But how do you know who to target and what kind of interaction is most meaningful for them? Most importantly, how can you maximize the impact of each interaction? With data. Join EverString and Lionbridge for a live discussion on how to uncover hidden pipeline by leveraging data and predictive modeling.
The speed, volume and complexity of decisions – as well as the impact they have on customer experience – demand automated, real-time decision making. Digital decisioning is an emerging best practice for delivering business impact from AI, machine learning, and analytics. Digital decisioning is an approach that ensures your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every customer touchpoint.
Audio on our YouTube Channel: https://youtu.be/cGxPYnE5PTM
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Modeling the desired data-driven approach to decision-making brings clarity to the analytic insight required to improve the decision.
Modeling the decision-making establishes clearly what it will take to operationalize that analytic.
Decision models allow multiple decision-making approaches to be effectively coordinated by business owners.
The structure of the decision model provides data about how and why the decision was made that has tremendous value in analyzing decision performance, linking this decision performance to business results and closing the loop for continuous improvement.
Customers use multiple channelsFor example web, partner branch, internal call center or agents with e-submission
A common service suggests actionsThis is the NBA decision hub
Using business rules and predictive analytics in combinationAnalytics determine value, propensity, segmentation while rules handle eligibility etc.
These are continuously improvedUsing data gathered from all the various systems about what worked and what did not
And managed by a decision modelA DMN based decision model logically orchestrates the rules and analytics involved
That contains channel-specific decisions that reuse common onesCommon decisions are leveraged but so are channel specific decisions where necessary
And scopes out partner decisions distinct from internal onesThe model also shows which decisions are made inside the NDA decision hub and which, like customer value, are made by partners about their own customers and passed to the hub as inputs
All described with business rulesThe logic for all this can be easily managed using rules linked to the decision model.