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
In this webinar, IIA Faculty Member James Taylor, CEO of Decision Management Solutions, will show how to improve analytic results with decision modeling. Decision modeling focuses analytic efforts, clarifies the business goals of analytic projects, and improves collaboration between analytic, business and IT organizations. James will introduce decision modeling, show how it can be used in a wide range of analytic projects and share experiences from using decision modeling in various industries.
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.
Delivering the promise of data mining and predictive analytics requires an operational platform that is agile, business-friendly and decision-centric - decision modeling with DMN and business rules.
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
In this webinar, IIA Faculty Member James Taylor, CEO of Decision Management Solutions, will show how to improve analytic results with decision modeling. Decision modeling focuses analytic efforts, clarifies the business goals of analytic projects, and improves collaboration between analytic, business and IT organizations. James will introduce decision modeling, show how it can be used in a wide range of analytic projects and share experiences from using decision modeling in various industries.
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.
Delivering the promise of data mining and predictive analytics requires an operational platform that is agile, business-friendly and decision-centric - decision modeling with DMN and business rules.
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are:
• What is Business Intelligence?
• Data / information / knowledge
• What is Data Analytics?
• What is Business Analytics?
• What is Big Data?
• Types of Data
• Types of Analytics
• What is Business Intelligence?
For more information, click here: https://www.mindsmapped.com/courses/business-analysis/advanced-business-analyst-training/
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
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.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are:
• What is Business Intelligence?
• Data / information / knowledge
• What is Data Analytics?
• What is Business Analytics?
• What is Big Data?
• Types of Data
• Types of Analytics
• What is Business Intelligence?
For more information, click here: https://www.mindsmapped.com/courses/business-analysis/advanced-business-analyst-training/
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
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 path to a better bottom line is paved by large numbers of operational decisions made by people, by processes and by software applications. Systematically improving each operational decision – at scale – is at the core of Decision Management. Business Architects and Analysts identify, describe and model operational decisions in Decision Discovery.
In this webinar, James Taylor, CEO of Decision Management Solutions, and Dr. Juergen Pitschke, Founder and Managing Director at BCS, will show you how to get started with Decision Management on your next application development or business process improvement project with Decision Discovery. Learn how to:
Identify decisions, sub-decisions and information and knowledge resources (including rules and analytics)
Describe decisions in detail (Decision Tables and other Metaphors)
Model decisions in a DMN-conformant decision modeling tool for communication and documentation
Link to execution environments
Decision CAMP 2014 - James Taylor - Decision Management 101Decision CAMP
Decision Management is both an approach and a technology stack.
In this opening day workshop, Decision Management consultant and author James Taylor will introduce both.
We'll begin with the discovery and modeling of suitable decisions, move into the construction of decision services and wrap up with the importance of decision analysis for continuous improvement. The critical technology capabilities - managing decision logic, embedding analytics, monitoring decision performance, and optimizing results - will all be introduced and presented in a coherent architecture for building Decision Management Systems.
Different adoption paths and some best practices will conclude the session, putting you on a path to Decision Management success.
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.
Impact 2012 1640 - BPM Design considerations when optimizing business process...Brian Petrini
Whilst it is not always possible to remove and automate human tasks in a process, if it can be done, it often leads to the most dramatic optimization, leading to fully straight through processing. The challenge is that if straight through processing is the goal, we may need to design the process differently from the beginning, with automation in mind. This lecture uses tried and tested techniques for assessing processes to establish whether they are likely to be able to evolve to full automation, and recommends design patterns to be used to simplify the progression from manual to decision supported to completely automated.
#MITXData "How the Data Revolution is Turning the Marketing World Upside Down...MITX
-Michele Goetz, Senior Analyst, Forrester
-Beatriz Santin, Senior Director of Marketing and Product, Experian QAS
Ever wished the data revolution never came and threw your world into chaos? Know that you can't turn back but don't know where to start or how to get there fast? Excited to finally have a seat at the table but anxious about how to deliver against rising expectations?
This session presented by Experian QAS, a part of Experian Marketing Services, and Forrester will explore the sentiments of marketers as we change our day-to-day and look for new avenues to propel business growth. From B2B to B2C, relevance is more important than ever – but how can we leverage data to make our brands stand out amongst all the others? Join to hear case studies and practical advice to guide you in a world where data is there not only for you but also for your customers and your competitors – to analyze and to consume.
Millennial Credit: The Insights You've Been MissingExperian
Much has been written about the Millennial generation over the past few months. Businesses, the media and lenders all want to know more about Gen Y. Move beyond the bold headlines and gain an inside look into the actual data with insights from Experian.
Implementing a Successful, Scalable, Governed BI ProgramPyramid Analytics
Explore elements, challenges, and tips in orchestrating a successful BI program. See related highlights from the BARC BI Survey 14 and from Gartner research. This slide presentation accompanies a webinar given in March 2015. For more contextual information related to these slides, see the list of content on the “Additional resources” slide of this presentation.
Turn provides a monthly customer newsletter. Within the newsletter, is a Partner Spotlight section. In September 2013's edition, Data Partner, Experian, presents the overview of ConsumerView.
ConsumerView is Experian’s marketing database used in targeting, segmenting and enriching the views of consumers around the world with demographic, self-reported, life event, aggregated auto, aggregated credit, direct response, property and mortgage information
The Art and Science of Implementing Faster DecisioningExperian
Living in a digital world means consumers expect rapid responses - in all facets of their lives. How are credit unions living up to this challenge? Are they utilizing technology to auto-decision more loans? This presentation reviews the current state and provides insights into how more financial companies can speed up their decisioning process to better service customers and become more efficient.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
What opportunities are you looking for to improve your business performance? In this webinar you will learn six opportunities that are readily available when you adopt a decision management approach to business rules and predictive analytics.
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
Similar to Framing Analytic Requirements with Decision Modeling (20)
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
Hear insurance industry expert Craig Bedell and Decision Management expert James Taylor discuss the importance of digital decisioning to improving insurance productivity.
See slides with audio here: https://youtu.be/YgCOkc23s8k
Join Decision Management Solutions, Velocity Business Services and Datarobot as we discuss the importance of operational decisions, industrialized predictive analytics and business learning in creating a predictive enterprise.
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.
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.
Learn how to innovate risk management and customer processes with decision and process management, from leading experts Roger Burlton and James Taylor.
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
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
A DMN-based full-fledged implementation of the “UServ Product Derby” decision model showing a DMN Decision Requirements Model and a set of DMN-based Decision Tables that implement it. The derby, recently renamed The Decision Management Challenge, deals with vehicle insurance problems including eligibility and premium calculation policies for a hypothetical insurance company.
Presented by James Taylor of Decision Management Solutions and Dr. Jacob Feldman of OpenRules at the Building Business Capability Conference (BBCCon) 2015.
In this webinar recording, James Taylor, CEO of Decision Management Solutions and Claye Greene, Managing Director of Government Solutions Provider TechBlue share learnings and best practices from their extensive experience helping clients modernize their legacy systems with the targeted decision management approach. You will learn why you don’t need to modernize the whole application, why focusing on business rules is not enough; decision management is the essential ingredient and how to use decision modeling to identify and scope targeted legacy modernization efforts.
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.
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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
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).