Actionable Requirements

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This presentation describes the process of producing rich product requirements by collaborative game play. It also discusses the phases of product innovation in both waterfall and agile environments.

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Gartner: Master Data Management Functionality

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MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.

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This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.

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Essential Reference and Master Data Management

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Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities. Learning objectives: - Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK) - Understand why these are an important component of your Data Architecture - Gain awareness of Reference and MDM Frameworks and building blocks - Know what MDM guiding principles consist of and best practices - Know how to utilize reference and MDM in support of business strategy

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Activate Data Governance Using the Data Catalog

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Enterprise Architecture vs. Data Architecture

DATAVERSITY

Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.

Capability Model_Data Governance

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The document outlines a data governance capability model that includes core data management capabilities and cross-domain support disciplines. It lists the key functions of enterprise data governance such as providing oversight of data assets, assessing compliance, managing risks, and enhancing the value of data. Some of the core capabilities include master data management, metadata management, data lifecycle management, data security and privacy, and data quality management.

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The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as different groups being unable to communicate and coordinate. It then contrasts manual versus automated approaches to data governance. The rest of the document provides details on key aspects of establishing a successful data governance program, including defining a framework, roles and responsibilities, and developing a roadmap for continuous improvement.

RWDG Slides: Building a Data Governance Roadmap

DATAVERSITY

A Data Governance roadmap is typically based on the results of a best practice assessment. The assessment defines the outcomes required to achieve Data Governance best practices while the roadmap details the “actionable streams” required to formalize a Data Governance program and achieve those outcomes. In this month’s webinar, Bob Seiner will share the process he follows to build a Data Governance roadmap of actionable streams and the steps required to complete the streams. In addition, Bob will describe the activities that are common to most organizations getting started or evaluating the success of their program. Topics to be discussed in this webinar include: • Criteria for defining best practices • Using the assessment results to build the roadmap • Examples of repeated actionable streams • The role of the program administrator in executing the roadmap • Communicating the roadmap to the stakeholders

Building a Data Strategy – Practical Steps for Aligning with Business Goals

DATAVERSITY

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Master Data Management – Aligning Data, Process, and Governance

DATAVERSITY

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.

Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJX

DATAVERSITY

Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization. Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization. In this session Bob will share: - The five levels of Data Governance roles - A proven Operating Model of Roles and Responsibilities - How to customize the model to meet your requirements - Setting appropriate role expectations - How to operationalize the roles and demonstrate value

Data quality overview

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This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.

Business Intelligence & Data Analytics– An Architected Approach

DATAVERSITY

Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.

Most Common Data Governance Challenges in the Digital Economy

Robyn Bollhorst

Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to: - Establish and scale a best in class enterprise data governance program - Identify and focus on the most critical data and information to bolster incremental wins and garner executive support - Ensure readiness for automation with SAP MDG on HANA

Requirements for a Master Data Management (MDM) Solution - Presentation

Vicki McCracken

Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.

Top 10 Artifacts Needed For Data Governance

First San Francisco Partners

This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization. Topics covered: - Which artifacts are most important to get started - Important artifacts for more mature programs - How to ensure the artifacts are used and implemented, not just written - How to integrate governance artifacts into operational processes - Who should be involved in creating the deliverables

DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...

Christopher Bradley

This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.

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On business capabilities, functions and application features

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Master Data Management – Aligning Data, Process, and Governance

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Business Intelligence & Data Analytics– An Architected Approach

Most Common Data Governance Challenges in the Digital Economy

Requirements for a Master Data Management (MDM) Solution - Presentation

Top 10 Artifacts Needed For Data Governance

DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...

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Actionable Requirements

1. Actionable Requirements by Armond Mehrabian September 21, 2010

2. Actionable Requirements  The challenges we face  Defining product requirements  Assessing opportunities  Discovering solutions  Tools for opportunity assessment  The process of solution discovery and definition  The agile product innovation process  Q&A

3. Actionable Requirements

4. Actionable Requirements

5. Actionable Requirements  Why is it so difficult?  Requires deep domain knowledge  Requires leadership

6. Actionable Requirements Agile teams are now developing software more quickly than ever before. Unfortunately, this doesn’t mean they are always aimed at building the right products. -Mike Cohn Author of Agile Estimating and Planning

7. Actionable Requirements  Levels of planning What are you trying to understand?

8. Actionable Requirements

9. Actionable Requirements  Some tools for opportunity assessment

10. Actionable Requirements  Tools for customer understanding

11. Actionable Requirements  Tools for customer understanding

12. Actionable Requirements  Tools for customer understanding

13. Actionable Requirements  Tools for customer understanding

14. Actionable Requirements  Tools for customer understanding

15. Actionable Requirements  Discover what is most painful to your customers

16. Actionable Requirements  Understand customer’s strategic direction

17. Actionable Requirements  Discover what is most important to your customers

18. Actionable Requirements  Discover what is most important to your customers

19. Actionable Requirements  Discover what is most important to your customers

20. Actionable Requirements  Discover the unusual and exciting features for your product

21. Actionable Requirements Some best practices  Have a Customer Advisory Board (CAB)  Need 2 for enterprise software  Plan 3-6 months in advance  Your team  Facilitator  Helper  Observer/Photographer  Try it on internal customers and teams first!

22. Actionable Requirements  Compile and consolidate learning  Revisit the planning onion  Write up the marketing requirements document (MRD)  Determine the minimal marketable feature-set (MMF) Let’s look at an example

23. Actionable Requirements A one-page opportunity assessment 1) Exactly what problem will this solve? (value proposition) 2) For whom do we solve the problem? (target market) 3) How will we measure success? 4) What alternatives are out there? 5) Why we’re best suited to do this? 6) Why now? 7) How will we deploy this? 8) What is the preliminary cost? 9) What factors are critical to success?

24. Actionable Requirements

25. Actionable Requirements  Why do we need product discovery?

26. Actionable Requirements Prototyping It is far more effective to sit in a chair than judge its comfort by a picture of it. -Henry Dreyfuss, Designing for People

27. Actionable Requirements Benefits of a high fidelity prototype  Provides a way to test out your ideas before spending time and money to build them for real  Forces you to think about the problem at a much greater level of detail  Gives developers a much greater detail product requirement

28. Actionable Requirements

29. Actionable Requirements

30. Actionable Requirements Conclusion  We can have a process for requirements gathering  Three phases to actionable requirements People Tools/Artifacts Opportunity Product Mkg MRD Assessment Product Mgmnt Innovation Games Architecture Solution Discovery User Experience PRD and Design Lead Engineer Prototype Product Mgr HiFi Wireframe Solution Execution Engineering/Test Fully functioning Teams product

31. Actionable Requirements Resources:  Innovation Games – Luke Hohmann  Ten Faces of Innovation – Tom Kelley  Inspired – Marty Cagan  Purple Cow – Seth Godin  Agile Estimation and Planning – Mike Cohn  Dilbert – Scott Adams

32. Actionable Requirements Presenter:  Armond Mehrabian  Email: amehrabian@portofinosolutions.com  Twitter: armond_m  LinkedIn: http://www.linkedin.com/in/amehrabian

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