1) The document discusses adopting an effective decision making framework using common methods and processes. It emphasizes capturing analytic insight as an asset and improving collaboration.
2) Decision Model and Notation (DMN) is presented as a common language that can be used to model decisions. DMN provides constructs to define decisions, their requirements, and relationships in diagrams.
3) Examples of applying DMN to decisions around retail conversion rate are shown. Components such as decisions, data sources, and knowledge bases are modeled visually.
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
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
Learn how to innovate risk management and customer processes with decision and process management, from leading experts Roger Burlton and James Taylor.
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.
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.
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.
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.
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.
Your Challenge
Internal stakeholders usually have different – and often conflicting – needs and expectations that require careful facilitation and management.
Vendors have well-honed negotiating strategies. Without understanding your own position and leverage points, it’s difficult to withstand their persuasive – and sometimes pushy – tactics.
Software – and software licensing – is constantly changing, making it difficult to acquire and retain subject matter expertise.
Our Advice
Critical Insight
Conservatively, it’s possible to save 5% of the overall IT budget through comprehensive software contract review.
Focus on the terms and conditions, not just the price.
Learning to negotiate is crucial.
Impact and Result
Look at your contract holistically to find cost savings.
Guide communication between vendors and your organization for the duration of contract negotiations.
Redline the terms and conditions of your software contract.
Prioritize crucial terms and conditions to negotiate.
Innovative Data Leveraging for Procurement AnalyticsTejari
This webinar will explore the types of problems and questions faced by procurement executives that can benefit most through the application of analytical solutions (e.g. innovation, strategic cost management, risk mitigation, etc.). In addition, we will cover the different forms of cognitive solutions that are emerging to drive real-time decision-making and predictive sourcing capabilities.
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.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Our business partner and insurance operations expert, Rob Berg, will show you how he helped a major insurance company reducing costs and cycle time using Trisotech Digital Enterprise Suite through process simulation.
In this webinar, analysts, architects and other subject matter experts will learn how to:
- Generate defensible data to make clear and objective decisions
- Accurately model the way things are and the way you would like them to be
- Apply real-life data to process models
- Bring a static model to life by simulating the impact of process changes
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
Bubble columns are widely used in the chemical and biochemical process industries. In order to develop design tools for engineering purposes, a large amount of research has been carried out in the area of CFD of gas-liquid flows. In this paper a transient Euler-Lagrange solver developed using the open source Caelus library is used to simulate the gas-liquid flow in a 3D square cross-sectioned bubble column. The turbulence is modelled using large eddy simulation (LES). The results of the simulations are compared to published PIV measurements. It is found that, good quantitative agreement with experimental data is obtained when drag, lift and virtual mass forces are used.
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.
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
Learn how to innovate risk management and customer processes with decision and process management, from leading experts Roger Burlton and James Taylor.
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.
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.
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.
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.
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.
Your Challenge
Internal stakeholders usually have different – and often conflicting – needs and expectations that require careful facilitation and management.
Vendors have well-honed negotiating strategies. Without understanding your own position and leverage points, it’s difficult to withstand their persuasive – and sometimes pushy – tactics.
Software – and software licensing – is constantly changing, making it difficult to acquire and retain subject matter expertise.
Our Advice
Critical Insight
Conservatively, it’s possible to save 5% of the overall IT budget through comprehensive software contract review.
Focus on the terms and conditions, not just the price.
Learning to negotiate is crucial.
Impact and Result
Look at your contract holistically to find cost savings.
Guide communication between vendors and your organization for the duration of contract negotiations.
Redline the terms and conditions of your software contract.
Prioritize crucial terms and conditions to negotiate.
Innovative Data Leveraging for Procurement AnalyticsTejari
This webinar will explore the types of problems and questions faced by procurement executives that can benefit most through the application of analytical solutions (e.g. innovation, strategic cost management, risk mitigation, etc.). In addition, we will cover the different forms of cognitive solutions that are emerging to drive real-time decision-making and predictive sourcing capabilities.
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.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Our business partner and insurance operations expert, Rob Berg, will show you how he helped a major insurance company reducing costs and cycle time using Trisotech Digital Enterprise Suite through process simulation.
In this webinar, analysts, architects and other subject matter experts will learn how to:
- Generate defensible data to make clear and objective decisions
- Accurately model the way things are and the way you would like them to be
- Apply real-life data to process models
- Bring a static model to life by simulating the impact of process changes
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
Bubble columns are widely used in the chemical and biochemical process industries. In order to develop design tools for engineering purposes, a large amount of research has been carried out in the area of CFD of gas-liquid flows. In this paper a transient Euler-Lagrange solver developed using the open source Caelus library is used to simulate the gas-liquid flow in a 3D square cross-sectioned bubble column. The turbulence is modelled using large eddy simulation (LES). The results of the simulations are compared to published PIV measurements. It is found that, good quantitative agreement with experimental data is obtained when drag, lift and virtual mass forces are used.
How to Evaluate Solutions and Build your Evaluation CommitteeBlytheco
In the fourth installment of the series "Are You Ready for Replatforming?", we take a look at a formalized process for creating criteria and steps for making an ERP or CRM solution transition, including who should be involved in the process and how they should participate.
What are the 4 types of business analytics Services.pdfGeorge Anisa
Unlock the power of data with our comprehensive business analytics services. Our team of experts will analyze your data to provide valuable insights and strategic recommendations for your business. Maximize your performance, optimize decision-making, and drive growth with our tailored analytics solutions. Contact us today to get started.
https://www.impressico.com/uk/en/services/offerings/data-engineering-analytics-bi/
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Data analytics is a rapidly growing field that involves the extraction, analysis, and interpretation of data to provide meaningful insights and inform decision-making processes. With the increase in the amount of data generated every day, the demand for skilled data analysts is expected to continue to rise. In this article, we'll explore the future scope of data analytics and the importance of data analytics courses in Faridabad to help you understand why it's a promising career choice.
An introduction to BRIDGEi2i - Analytics Solutions company focused on solving complex based problems based on data mining and advanced analytics on big data. Visit http://www.bridgei2i.com
Data-driven decision-making is an incredible process that helps data science professionals boost their businesses. Explore DDDM in detail and learn how you can master it in 2024
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This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
Running head BUSIENSS POLICY DEVLEOPMENT AND IMPLEMENTATION 1B.docxsusanschei
Running head: BUSIENSS POLICY DEVLEOPMENT AND IMPLEMENTATION 1
BUSIENSS POLICY DEVLEOPMENT AND IMPLEMENTATION 2
Business Policy Development and Implementation
Student’s Name
Institutional Affiliation
Business Policy Development and Implementation
Introduction
In a constantly changing organizational and business environment, companies are increasingly facing stiff competition. As such, they are compelled by the need to improve their performance and do things in the most desired ways in order to meet the needs of consumers (Gomes & Romão, 2013). In light of these patterns, organizations can no longer be dependent upon the traditional analytical tools. Instead, they have seen the need to use models and frameworks such as the balanced scorecard to examine the degree to which companies work to attain their mission and vision. In addition, strategic decision-making must also be done through observance of the constantly changing trends and patterns that may affect the status of an organization. In this report, I provide ways in which our team members made logical decisions and how we utilized the balanced scorecard framework.
Decision Logic and What Supported the Decisions
The decisions that we embraced in the Capsim project was driven by the desire to generate a competitive advantage over other rivals. Therefore, we saw the need to take a closer look at the use of decision-making strategies within our business and operational environment. The main decisions that we made were associated with issues such as product development, research and development, innovation, marketing, as well as human resources and finance. There are various factors that made it possible for the team to make strategic decisions. First, having ready access to information, ranging from the details of the potential markets for the organization’s products and services, as well as estimates of next year’s labor requirements, is important. The more accurate and complete the information is, the more effective the strategic decisions we made. For instance, we relied on technological tools such as information systems to provide the team with accurate information about business intelligence issues (Spetzler, Winter & Meyer, 2016). Decision-making on production and product development functions heavily relied on technological tools such as spreadsheets and databases to calculate the efficacy of different machines, and to introduce new products in the production process. In order to support marketing decisions, we utilized Big Data technologies and analytics to determine the effects of different pricing strategies and to keep the records of consumer profiles. These tools were also utilized to plan the launching of new products and services. Human resources decisions were made with the purpose of planning for the next round and determining the pay awards. Planning decisions also sought to determine employee records and rewards. Finance decisions were also made to draw up p ...
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.
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The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
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25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
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.
Similar to BuildingEffectiveDecisionMakingFramework_v1.05 (20)
2. Introduction
Jim Parnitzke
Big Data Analytics, Enterprise Architecture
Advisor, Expert, Trusted Partner, and Publisher
Hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized
information management and architecture thought leader. Over his career, Jim has served in executive,
technical, publisher (commercial software), and practice management roles across a wide range of industries.
Using analytic insight to solve the toughest problems
Contact:
(c) 904.607.6299
Linked In: http://www.linkedin.com/in/jimparnitzke
Twitter: http://twitter.com/jparnitzke
j.parnitzke@comcast.net
jim.parnitzke@gmail.com
5. Analytics is important…
Making better decisions is more important.
Faster, better outcomes
Precise answers for hard-to-solve problems
Uncovering new growth opportunities
Increasing competitive capability
Improving business results
6. How important?
• Fact:
– over 94% of all Companies have Big Data and Analytics in their top 10
priorities to enable better decision making
– Organizations competing on analytics outperform their peers
8. Decision (Meriam-Webster Dictionary)
1 a : the act or process of deciding
b : a determination arrived at after consideration : conclusion <make a
decision>
2 : a report of a conclusion <a 5-page decision>
3 : promptness and firmness in deciding : determination <acting with decision>
4 a : win; specifically : a victory in boxing decided on points <a unanimous decision>
b : a win or loss officially credited to a pitcher in baseball <has five wins in eight decisions>
9. Organizations make decisions every day…
• Strategic: Few in number, large impact
– Should we acquire this company or exit this market segment?
• Tactical: Management and control, moderate impact
– Re-organize the supply chain
– Change risk management approach
• Operational: Day-to-day decisions
– Improve conversion rates
– Select next best offer for a customer
– Select the terms for a loan
– Which supplier to use
– How to handle this claim; which need to be fast-tracked
11. Marketing analytics drive business decisions daily
Customer Profiling and Segmentation
Up Sell Opportunity Analysis
Real-time Product and Service Recommendations
Try and Buy Usage Analytics
Next Best Offer
Retention Analytics
Digital Marketing and Path to Purchase Analysis
Sell through and Sales Channel Analysis
Lead to Cash
Path to Purchase
Multi-Channel and Attribution Analysis
Customer Lifetime Value
Forecasting (Time Series Analysis)
14. Where is analytic insight captured in your organization?
Is the Intellectual Property managed like any other asset?
Are standard tools and methods used to model, capture,
manage, and share this significant property?
Have you even tested the decisions modeled or executed
through peer review or common diagnostics for defects?
Has your modeling approach evolved at the same speed as
the new technologies and tools used to accelerate the
decision making process?
Is proven practice shared with others?
15. If you answered yes to all five questions
congratulations…
you are exceptional
17. We have used a variety of techniques to accurately describe
the requirements for legacy information systems.
They work pretty well.
Many now realize that current approaches
do not solve the decision-making need that is important to
capturing and managing analytic insight to drive better
outcomes.
For the rest of us…
18. Actionable
Readily understandable by business users
Used by business analysts to create decision requirements
and models
Implemented by technical developers responsible for
automating the decisions in processes
Managed and monitored by stakeholders who own the
results and outcomes of the decisions
The ideal decision framework should be:
19. Better decisions share common values
Actionable. - analysis for analysis sake is ridiculous.
They begin with the right questions. Value is demonstrated in
defining the decision in advance. Learn what data and metrics
are important and make a difference.
Non-trivial. Enough said…
Measurable. Know which measures matter and which don’t.
21. An effective decision making framework
Uses common methods and processes with clear goals and
objectives to manage and measure outcomes
Adopts a common language across business, IT and analytic
communities improving meaningful communication
Captures and manages analytic insight like any other asset
Improves collaboration, increases reuse, shares proven
practice to solve complex problems once
Eases implementation and deployment of decision services
24. Use common methods and processes
Cross Industry Standard Process for Data Mining (CRISP-DM) is a process model that
describes commonly used analytic approaches. It is the leading methodology with 3-4
times as many people using this model as Sample, Explore, Modify, Model and Assess
(SEMMA) developed by the SAS Institute Inc.
25. Design and build independent
decision services using business
rules and advanced analytics
Decision Services
Legacy Systems Websites Business Process Event Correlation Enterprise Applications Mobile
Decision
Service
Business Rules Predictive Analytics
Data Warehouse, Operational Data Stores, Big Data
Create a closed loop between
operations and analytics to
measure results and drive
improvement
Decision Analysis
Identify and model the decisions
that are most important to
operational processes
Decision Discovery
Decision Model Notation
ActionDecision
Actionable goals and objectives
26. Adopt a common language
Decision Model Notation
The OMG Decision Model and Notation standard provides a common
notation; a standardized bridge for the gap between the business decision
design and decision implementation.
The purpose of DMN is to provide the constructs that are needed to model
decisions, so that organizational decision-making can be readily depicted in
diagrams, accurately defined by business analysts. Addresses two different
perspectives by existing modeling standards:
Business process models (e.g. BPMN) can describe the coordination of
decision-making within business processes by defining specific tasks or
activities within which the decision-making takes place.
Decision logic (e.g. PRR, PMML) can define the specific logic used to
make individual decisions, for example as business rules, decision
tables, or executable analytic model
27. Capture Analytic Insight
Where to start
DMN provides a third perspective – the Decision Requirements Diagram
Business process models define tasks within business processes where
decision-making is required to occur. Decision Requirements Diagrams
will define the decisions to be made in those tasks, their
interrelationships, and their requirements for decision logic
Decision logic (business rules) will define the required decisions in
sufficient detail to allow validation and automation.
Taken together, Decision Requirements Diagrams and decision logic can
provide a complete decision model which complements a business process
model by specifying in detail the decision-making carried out in process tasks.
35. Retail Conversion Rate
Retail Conversion Rate
Retail Conversion
Rate Data Flows DRAFT
SIZ
E
FSCM NO DWG NO
RE
V
SCALE 1 : 1 SHEET 1 OF 11
Assemble
Demographic
Profile
Filtered and Validated
Close (Conversion) Rate
Data Set
Assemble
Conversion Rate
Reporting in
context
Assembly Database
ETL
Transformation
Data Warehouse
Actual Sales and Payroll
FPA (acronym)
Store level weather.
Actuals and two-week forecast
Transformed
Weather Forecast
Enterprise
Reporting Platform
Retail Selling Channels
Point of Sales
Order Fulfillment
Installers
Designers
Retail Sales
Transactions
Location, Camera, Count Type,
Date, Time Interval, Count
Report Scheduler
Video Imaging
Video Management System
Video Business Intelligence
Analytic Publisher
Report Generator
Retail Traffic Database
Retail Traffic Counts
Traffic Reporting
Time and Attendance
Kronos
Time and Attendance
Scheduling
Accuweather Pro
WSI Weather Service
Employee actual hours reported at store i on day t
Assemble
Employee
Values
Assemble
Retail Traffic
Values
Customers who entered per period
store , camera, day
Assemble
Sales and
Transaction Counts
for Store for period
Retail Sales
Transactions
Per capita income for store location,
number of like-kind stores within x miles
where store is located,
Intra-day traffic variability for store locationCustomer Profile
Data Warehouse
Conversion Rate
Data Sets
Formatted
Conversion Rate
Reporting
Assemble
Weather Related
Values
Detailed weather forecast information.
Assemble Raw
Video Formats
Raw Video Feeds captured at store i on day t
HDFS
Transactions
Employees
Video
Retail Traffic Weather
Close (Conversion) Rate Data
Captured in Hadoop
Apply Business
Rules and
Assemble
Source Data
Total number of customers who entered store i on day t
Average number of customers who entered per hour store i on day t
Sales volume for store i on day t
Average sales volume per period for store i on day t
Number of customer transactions per period at store i on day t
Average number of transactions per period for store i on day t
Proportion of customers who made a transaction at store i on day t
Value in U.S. dollars of customers' shopping basket at store i on day t
Total number of employee hours reported at store i on day t
Average no. of employee hours per hour reported at store i on day t
Total number of like-kind stores within x miles where store i is located
Daily temperature for store location i
Per capita income for store location i
Average inter-day traffic variability for store location i
Intra-day traffic variability for store location i on day t
Growth in average traffic for store location i in period p
Average conversion rate for store location i in period p-l
Filtered Conversion
Rate Data Set
Per capita income for store location,
number of like-kind stores within x miles
where store is located,
Intra-day traffic variability for store location
Employee forecasted hours reported at store i on day t
Reporter Client
Reporting Results
Conversion Rate Processing
Results Captured in Assembly
Platform
Analytics Repository
Analytic Data Sets
Check for Additional Values:
Seasonality Vectors
Macro-economic Sets
Other data values
Check for Additional Values:
Seasonality Vectors
Macro-economic Sets
Other data values
37. Components
1) Decision Customer Traffic
2) Decision Items per purchase
3) Decision Gross margin
4) Decision Transaction Count
5) Decision Total number of customers who entered store i on day t
6) Decision Average number of customers who entered per hour store i on day t
7) Decision Number of customer transactions at store i on day t
8) Decision Average number of transactions per hour for store i on day t
9) Decision Sales volume for store i on day t
10) Decision Average sales volume per hour for store i on day t
11) Decision Value in U.S. dollars of customers' shopping basket at store i on day t
12) Decision Average inter-day traffic variability for store location i
13) Decision Intra-day traffic variability for store location i on day t
14) Decision Growth in average traffic for store location i in period p
15) Decision Average conversion rate for store location i in period p-l
16) Decision Proportion of customers who made a transaction at store i on day t
17) Decision Daily weather for store location i
18) Decision Total number of employee hours reported at store i on day t
19) Decision Average no. of employee hours per hour reported at store i on day t
20) Decision Conversion Rate
21) Decision Average purchase value
1) Data Source Retail Traffic Count
2) Data Source Point of Sale
3) Data Source Weather Information
4) Data Source Location Time and Attendence
1) Know How Retail Conversion Knowledge
2) Know How Time Series Analysis
3) Know How Time Series Forecast
Decisions
Data Sources
Know How (Knowledge Base)
40. The framework you have seen:
Uses common methods and processes with clear goals and
objectives to manage and measure outcomes
Adopts a common language across business, IT and analytic
communities improving meaningful communication
Captures and manages analytic insight as any other asset
Improves collaboration, increases reuse, and shares proven
practice to solve complex problems once
Eases implementation and deployment of decision services
41. Now you can capture and manage analytic
insight to make better decisions…
Faster, better outcomes
More precise answers
Solve hard problems once
Uncover new growth opportunities
Increase competitive capability
Improve business results
42. Thank You…
Jim Parnitzke
Big Data Analytics, Enterprise Architecture
Advisor, Expert, Trusted Partner, and Publisher
Hands-on technology executive, trusted partner, advisor, software publisher, and widely recognized
information management and architecture thought leader. Over his career, Jim has served in
executive, technical, publisher (commercial software), and practice management roles across a wide
range of industries.
Contact:
(c) 904.607.6299
Linked In: http://www.linkedin.com/in/jimparnitzke
Twitter: http://twitter.com/jparnitzke
j.parnitzke@comcast.net
jim.parnitzke@gmail.com
james.parnitzke@nascentblue.com
If you interested in using analytic insight to solve your toughest problems contact me
below and learn how powerful easy-to-use tools can help across a wide variety of pre-
defined subject areas. Information management, Big Data, Analytics, Master Data
Management, Program and Project Management libraries and tools are available today
to begin delivering fast, quick, and distinctive results.
To get started contact me when ready, look forward to helping us succeed together.
46. Business rules can be expressed using modeling approaches to include:
• Unified Modeling Language
• Business Process Execution Language
• Business Process Modeling Notation
• Decision Model and Notation
• Semantics of Business Vocabulary and Business Rules (SBVR)
47. A simple example…
Gather platform
characteristics profiles
Determine the analytics
operating model in use (can
be more than one)
Gather user profiles and
population counts for each
form of use
Develop platform and tool
signatures
Gather current state
portfolio, interview
stakeholders, and document
findings Refine Critical Analytic
Capabilities as defined
to meet site specific
needs
Weight Critical
Analytic Capability
according to each
operating model in use
Gather data points and
align with the findings
Assemble findings and
complete decision
models for platform
and tooling
optimization
1
2
3
4
5
6
7
8
9
CRISP-DM Method
Nine Step Method
Sample Platform and Tooling Optimization Decision Model
Editor's Notes
Organizations are significantly more likely to outperform their peers.
Organizations achieving competitive advantage with analytics are 220% more likely to be substantially outperform their industry peers
Source:
Survey Chimp, 2015; Big Data Analytics Guide: 2012 http://fm.sap.com/data/UPLOAD/files/SAP_ANALYTICS2012_WEB_ALL_PGS.pdf
IBM IBV/MIT Sloan Management Review Study 2011
Copyright Massachusetts Institute of Technology 2011
Product Recommendation Engine
Recommend a product item or category based on historical or recent purchases and interactions
Recommend (at the appropriate time) the products that customers would repurchase (air filters, light bulbs, etc.)
Recommend list of products for a customer to purchase or add to their account
Recommend local store-level items that are on clearance to drive trips
Product Affinity Targeting - Determine the best group of customers to include in product-focused email or direct mail campaigns
Simple Product Recommendation
Recommend a product item or category based on historical or recent purchases and interactions
Recommend (at the appropriate time) the products that customers would repurchase (air filters, light bulbs, etc.)
Recommend list of products for a customer to purchase or add to their account
Recommend local store-level items that are on clearance to drive trips
Product Affinity Targeting - Determine the best group of customers to include in product-focused email or direct mail campaigns