Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Você sabe o que é ou o que faz um Banco de Dados? Veja essa apresentação e sua continuação (vou postar em breve) e conheça um pouquinho mais sobre tecnologia.
Aula 4 - Diagrama Entidade Relacionamento (com exercício no final)Janynne Gomes
O que é um Diagrama Entidade Relacionamento (DER)?
• Elementos do DER
– Entidades
– Atributos
• Tipos de atributos
– Relacionamentos
• Auto-relacionamento
• Grau de relacionamento
• Atributos
• Cardinalidade
Você sabe o que é ou o que faz um Banco de Dados? Veja essa apresentação e sua continuação (vou postar em breve) e conheça um pouquinho mais sobre tecnologia.
Aula 4 - Diagrama Entidade Relacionamento (com exercício no final)Janynne Gomes
O que é um Diagrama Entidade Relacionamento (DER)?
• Elementos do DER
– Entidades
– Atributos
• Tipos de atributos
– Relacionamentos
• Auto-relacionamento
• Grau de relacionamento
• Atributos
• Cardinalidade
Banco de Dados Não Relacionais vs Banco de Dados Relacionaisalexculpado
Uma breve abordagem sobre o conceito de banco de dados não relacionais, tendo como ponto de origem os bancos relacionais atuais. Apresento de forma sucinta as vantagens e desvantagens dos dois.
Foi apresentado no Campus Universitário da UAN do Camama.
Devido ao aumento da quantidade de dados, começaram a surgir demandas de escalabilidade e a necessidade de se trabalhar com dados de forma mais flexível do que as regras do modelo relacional. Em 2009, surgiu o termo NoSQL. Este novo modelo, faz referência a várias soluções desenvolvidas que caracterizam-se por ter esquema flexível, executar de forma distribuída e geralmente possuir o código aberto.
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Banco de Dados Não Relacionais vs Banco de Dados Relacionaisalexculpado
Uma breve abordagem sobre o conceito de banco de dados não relacionais, tendo como ponto de origem os bancos relacionais atuais. Apresento de forma sucinta as vantagens e desvantagens dos dois.
Foi apresentado no Campus Universitário da UAN do Camama.
Devido ao aumento da quantidade de dados, começaram a surgir demandas de escalabilidade e a necessidade de se trabalhar com dados de forma mais flexível do que as regras do modelo relacional. Em 2009, surgiu o termo NoSQL. Este novo modelo, faz referência a várias soluções desenvolvidas que caracterizam-se por ter esquema flexível, executar de forma distribuída e geralmente possuir o código aberto.
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Talks about best practices and patterns on how to design an efficient cube in Kylin. Covers concepts like mandatory dimension, hierarchy dimension, derived dimension, incremental build, aggregation group etc.
This presentation elaborates on design decisions and design options when it comes to designing the master data architecture.
The presentation was given at the 16th Americas Conference on Information Systems (AMCIS 2010) in Lima, Peru.
AWS re:Invent 2016: Best Practices for Data Warehousing with Amazon Redshift ...Amazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Structured Approach to Solution ArchitectureAlan McSweeney
The role of solution architecture is to identify answer to a business problem and set of solution options and their components. There will be many potential solutions to a problem with varying degrees of suitability to the underlying business need. Solution options are derived from a combination of Solution Architecture Dimensions/Views which describe characteristics, features, qualities, requirements and Solution Design Factors, Limitations And Boundaries which delineate limitations. Use of structured approach can assist with solution design to create consistency. The TOGAF approach to enterprise architecture can be adapted to perform some of the analysis and design for elements of Solution Architecture Dimensions/Views.
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data-Ed Slides: Data Architecture Strategies - Constructing Your Data GardenDATAVERSITY
Data architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong data architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright data architect, but rather to enable you to envision a number of uses for data architectures that will maximize your organization’s competitive advantage.
With that being said, we will:
- Discuss data architecture’s guiding principles and best practices
- Demonstrate how to utilize data architecture to address a broad variety of organizational challenges and support your overall business strategy
- Illustrate how best to understand foundational data architecture concepts based on the DAMA International Guide to Data Management Body of Knowledge (DAMA DMBOK)
Data-Ed Online Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is data architecture that organizes data assets so they can be used in your business strategy to create real business value. Even though this is important, data architectures are still being used ineffectively. The various uses of data architecture are referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecture to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will maximize your organization’s competitive advantage.
Takeaways:
•How to utilize data architecture to address a broad variety of organizational challenges and how to utilize data architectures in support of business strategy
•Understanding foundational data architecture concepts based on the DAMA DMBOK
•Data architecture guiding principles & best practices
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Every organization produces and consumes data. Because data is so important to day to day operations, data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, NoSQL, data scientist, etc., to seek solutions for their fundamental issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data effort. It is a vital activity that supports the solutions driving your business.
This webinar will address fundamental data modeling methodologies, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Learning Objectives:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data”, “NoSQL”, “data scientist”, and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of data modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
- Address fundamental data modeling methodologies, their differences and various practical applications, and trends around the practice of data modeling itself
- Discuss abstract models and entity frameworks, as well as some basic tenets for application development
- Examine the general shift from segmented data modeling to more business-integrated practices
DataEd Slides: Data Architecture versus Data ModelingDATAVERSITY
Data Modeling is how we do Data Architecture. Many are confused when it comes to data. Architecture, models, data – it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture components. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has, and what it needs to accomplish to employ Data Modeling and Data Architecture to achieve its mission.
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
DataEd Slides: Data Modeling is FundamentalDATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities of your organization. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.
Many are confused when it comes to data. Architecture, models, data - it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has and what it needs to accomplish to employ Data Modeling and Architecture to achieve its mission.
Data-Ed Webinar: Design & Manage Data Structures DATAVERSITY
Data structures enable you to store and organize data so that it can be used efficiently. But how do you know to apply the correct one? There is a difference between structuring master data, reference data and analytics data. This webinar will discuss the various data structures available and when to use each one. We will show how data structures should support your organizational data strategy and how each method can contribute to business value.
Takeaways:
Application of correct data structures to fit business needs
How different structures create different business value
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Takeaways:
Metadata value proposition: How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Similar to Data-Ed Online: Data Architecture Requirements (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
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.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Data-Ed Online: Data Architecture Requirements
1. Data architecture is foundational to an information-
based operational environment. It is your data
architecture that organizes your data assets so they
can be leveraged in your business strategy to create
real business value. Even though this is important, not
all data architectures are used effectively. This webinar
describes the use of data architecture as a basic
analysis method. Various uses of data architecture to
inform, clarify, understand, and resolve aspects of a
variety of business problems will be demonstrated. As
opposed to showing how to architect data, your
presenter Dr. Peter Aiken, will show how to use data
architecting to solve business problems. The goal is for
you to be able to envision a number of uses for data
architectures that will raise the perceived utility of this
analysis method in the eyes of the business.
Copyright 2014 by Data Blueprint
1
Welcome: Data Architecture Requirements
Date: May 13, 2014
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD
2. Copyright 2014 by Data Blueprint
Two Most Commonly Asked Questions
1. Will I get copies of the slides after the
event?
2. Is this being recorded so I can view it
afterwards?
2
3. Copyright 2014 by Data Blueprint
3
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Ask questions and submit
your comments: #dataed
4. Copyright 2014 by Data Blueprint
Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized data
management thought-leader
– 30 years of experience
– Recipient of multiple international awards
– Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) Pres. DAMA International (dama.org)
• 9 books and dozens of articles
• Multi-year immersions with
organizations as diverse as the
US DoD, Deutsche Bank, Nokia, Wells
Fargo, the Commonwealth of Virginia
and Walmart
4
6. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
6
8. You can accomplish
Advanced Data Practices
without becoming proficient
in the Basic Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
Copyright 2014 by Data Blueprint
Data Management Practices Hierarchy
Basic Data Management Practices
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
8
Data Program Management
Data Stewardship Data Development
Data Support Operations
Organizational Data Integration
9. Data Program
Coordination
Feedback
Data
Development
Copyright 2014 by Data Blueprint
Standard
Data
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
9
Organizational
Data Integration
Data
Stewardship
Data Support
Operations
Data
Asset Use
Integrated
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area
Organizational DM Practices
10. Copyright 2014 by Data Blueprint
10
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations
Five Integrated DM Practices
11. Copyright 2014 by Data Blueprint
11
Data Management Functions
DAMA DM BoK & CDMP
• Published by DAMA International
– The professional association for Data
Managers (40 chapters worldwide)
– DMBoK organized around
– Primary data management functions focused
around data delivery to the organization (more
at dama.org)
– Organized around several environmental
elements
• CDMP
– Certified Data Management Professional
– DAMA International and ICCP
– Membership in a distinct group made up of
your fellow professionals
– Recognition for your specialized knowledge in
a choice of 17 specialty areas
– Series of 3 exams
– For more information, please visit:
• http://www.dama.org/i4a/pages/index.cfm?
pageid=3399
• http://iccp.org/certification/designations/cdmp
12. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
12
13. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
13
16. Copyright 2014 by Data Blueprint
16
Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2
Data Modeling for Business Value
• Goal must be shared IT/business understanding
– No disagreements = insufficient communication
• Data sharing/exchange is largely and highly automated and
thus dependent on successful engineering
– It is critical to engineer a sound foundation of data modeling basics
(the essence) on which to build advantageous data technologies
• Modeling characteristics change over the course of analysis
– Different model instances may be useful to different analytical problems
• Incorporate motivation (purpose statements) in all modeling
– Modeling is a problem defining as well as a problem solving activity - both are inherent to
architecture
• Use of modeling is much more important than selection of a specific modeling
method
• Models are often living documents
– The more easily it adapts to change, the resource utilization
• Models must have modern access/interface/search technologies
– Models need to be available in an easily searchable manner
• Utility is paramount
– Adding color and diagramming objects customizes models and allows for a more engaging and
enjoyable user review process
17. Copyright 2014 by Data Blueprint
17
Levels of Abstraction, Completeness and Utility
• Models more downward facing - detail
• Architecture is higher level of abstraction - integration
• In the past architecture attempted to gain complete (perfect)
understanding
– Not timely
– Not feasible
• Focus instead on
architectural components
– Governed by a framework
– More immediate utility
• http://www.architecturalcomponentsinc.com
19. Copyright 2014 by Data Blueprint
19
Architecture
Architecture is both the process and product
of planning, designing and constructing
space that reflects functional, social, and
aesthetic considerations.
A wider definition may comprise all design
activity from the macro-level (urban design,
landscape architecture) to the micro-level
(construction details and furniture).
In fact, architecture today may refer to the
activity of designing any kind of system and
is often used in the IT world.
20. Copyright 2014 by Data Blueprint
20
Architecture Representation
• Architectures are the symbolic
representation of the structure,
use and reuse of resources
• Common components are represented
using standardized notation
• Are sufficiently detailed to permit both business analysts and
technical personnel to separately read the same model, and
come away with a common understanding and yet they are
developed effectively
21. Copyright 2014 by Data Blueprint
21
Understanding
• A specific definition
– 'Understanding an architecture'
– Documented and articulated as a digital blueprint
illustrating the
commonalities and
interconnections
among the
architectural
components
– Ideally the understanding
is shared by systems and humans
23. Copyright 2013 by Data Blueprint
healthcare.gov
23
• 55 Contractors!
• "Anyone who has written a
line of code or built a system
from the ground-up cannot
be surprised or even
mildly concerned that
Healthcare.gov did not work
out of the gate,"
Standish Group International
Chairman Jim Johnson said in a
recent podcast.
• "The real news would have
been if it actually did work.
The very fact that most of it
did work at all is a success
in itself."
• Software programmed to
access data using traditional
data management
technologies
• Data components
incorporated "big data
technologies"
http://www.slate.com/articles/technology/bitwise/2013/10/
problems_with_healthcare_gov_cronyism_bad_manage
ment_and_too_many_cooks.html
24. Copyright 2014 by Data Blueprint
24
• Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products,
procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• Security Architecture
– Arrangement of security controls relation to IT Architecture
• Technical Architecture/Tarchitecture
– Relation of software capabilities/technology stack
– Structure of the technology infrastructure of an enterprise, solution or system
– Typical elements: Networks, hardware, software platforms, standards/protocols
• Data/Information Architecture
– Arrangement of data assets supporting organizational strategy
– Typical elements: specifications expressed as entities, relationships, attributes,
definitions, values, vocabularies
Typically Managed Architectures
25. Copyright 2014 by Data Blueprint
Information Architectures
• The underlying (information) design principals upon
which construction is based
– Source: http://architecturepractitioner.blogspot.com/
• … are plans, guiding the transformation of strategic
organizational information needs into specific
information systems development projects
– Source: Internet
• A framework providing a structured description of an
enterprise’s information assets — including structured
data and unstructured or semistructured content —
and the relationship of those assets to business
processes, business management, and IT systems.
– Source: Gene Leganza, Forrester 2009
• "Information architecture is a foundation discipline
describing the theory, principles, guidelines,
standards, conventions, and factors for managing
information as a resource. It produces drawings,
charts, plans, documents, designs, blueprints, and
templates, helping everyone make efficient, effective,
productive and innovative use of all types of
information."
– Source: Information First by Roger & Elaine Evernden, 2003 ISBN 0
7506 5858 4 p.1.
• Defining the data needs of the enterprise and
designing the master blueprints to meet those needs
– Source: DM BoK
25
26. Copyright 2014 by Data Blueprint
26
Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/
What do you use an information architecture for?
27. Copyright 2014 by Data Blueprint
Data Architecture – Better Definition
27
• All organizations have information
architectures
– Some are better understood and
documented (and therefore more
useful to the organization) than
others.
• Common vocabulary expressing
integrated requirements ensuring
that data assets are stored,
arranged, managed, and used in
systems in support of
organizational strategy [Aiken 2010]
28. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
28
29. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
29
30. Copyright 2014 by Data Blueprint
Vocabulary is Important-Tank, Tanks, Tankers, Tanked
30
31. Copyright 2014 by Data Blueprint
How one inventory item proliferates data throughout the chain
31
555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1:
18,214 Total items
75 Attributes/ item
1,366,050 Total attributes
System 2
47 Total items
15+ Attributes/item
720 Total attributes
System 3
16,594 Total items
73 Attributes/item
1,211,362 Total attributes
System 4
8,535 Total items
16 Attributes/item
136,560 Total attributes
System 5
15,959 Total items
22 Attributes/item
351,098 Total attributes
Total for the five systems show above:
59,350 Items
179 Unique attributes
3,065,790 values
32. Copyright 2014 by Data Blueprint
32
• Generates unnecessary costs & negative impacts on operations, including:
– Resources are focused on non-value added tasks of maintaining obsolete inventory,
which creates distractions to the agency’s main mission
• Storage
– Physical/real estate needed to house items
• Handling
– Includes transportation and human resources
dedicated to moving, maintaining, counting
and securing outdated inventory
• Opportunity
– Inventory could be returned to manufacturer or
sold to free up financial assets for more needed
and critical supplies
• Systemic
– Cost of inventorying information and maintaing
paper or electronic records which should be used to
support mission-critical acquisitions and distribution
• Maintenance
– Repairing of expired items
Business Value: Agency units are carrying $1.5 billion worth of expired inventory
33. Copyright 2014 by Data Blueprint
33
Would you build a house without an
architecture sketch?
Model is the sketch of the system to be
built in a project.
Would you like to have an estimate how
much your new house is going to cost?
Your model gives you a very good idea of
how demanding the implementation work
is going to be!
If you hired a set of constructors from all
over the world to build your house, would
you like them to have a common
language?
Model is the common language for the
project team.
Would you like to verify the proposals of
the construction team before the work
gets started?
Models can be reviewed before thousands
of hours of implementation work will be
done.
If it was a great house, would you like to
build something rather similar again, in
another place?
It is possible to implement the system to
various platforms using the same model.
Would you drill into a wall of your house
without a map of the plumbing and electric
lines?
Models document the system built in a
project. This makes life easier for the
support and maintenance!
Why Architectural Models?
39. Copyright 2014 by Data Blueprint
39
Web Developers Understand IA
http://www.jeffkerndesign.com
40. Copyright 2014 by Data Blueprint
40
Web Developers Understand IA
http://www.jeffkerndesign.com
41. Copyright 2014 by Data Blueprint
41
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
Database Architecture Focus
42. database
architecture
engineering
effort
DataData
DataData
Data
Data
Data
Focus of a
software
architecture
engineering
effort Program A
Program B
Program C
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 1
Application
domain 2Application
domain 3
Data
Focus of a
Data
Data
Copyright 2014 by Data Blueprint
42
Data Architecture Focus has Greater Potential Business Value
• Broader focus
than either
software
architecture or
database
architecture
• Analysis scope is
on the system
wide use of data
• Problems caused
by data
exchange or
interface
problems
• Architectural
goals more
strategic than
operational
43. Copyright 2013 by Data Blueprint
Data
Data
Data
Information
Fact Meaning
Request
Strategic Information Use: Prerequisites
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one
MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
Wisdom & knowledge are
often used synonymously
Data
Data
Data Data
43
44. Copyright 2014 by Data Blueprint
44
A B
C D
A B
C D
A
D
C
B
How are data structures expressed as architectures?
• Details are
organized into
larger
components
• Larger
components
are organized
into models
• Models are
organized into
architectures
45. Copyright 2014 by Data Blueprint
45
How are Data Models Expressed as Architectures?
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose information is
managed in support of strategy
– Examples
• Entities/objects are organized into models
– Combinations of attributes and entities are
structured to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Examples
• Models are organized into architectures
– When building new systems, architectures are
used to plan development
– More often, data managers do not know what
existing architectures are and - therefore - cannot
make use of them in support of strategy
implementation
– Why no examples?
More Granular
More Abstract
46. Copyright 2014 by Data Blueprint
46
Architectures Comprise a Network of Networks
47. Copyright 2014 by Data Blueprint
47
How do data structures support organizational strategy?
• Consider the opposite question?
– Were your systems explicitly designed to be
integrated or otherwise work together?
– If not then what is the likelihood that they will
work well together?
– In all likelihood your organization is spending
between 20-40% of its IT budget
compensating for poor data structure
integration
– They cannot be helpful as long as their
structure is unknown
• Two answers
– Achieving efficiency and
effectiveness goals
– Providing organizational dexterity for rapid
implementation
48. Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
Copyright 2014 by Data Blueprint
48
What Questions Can Architectures Address?
• How and why do the
components interact?
• Where do they go?
• When are they needed?
• Why and how will the
changes be
implemented?
• What should be
managed organization-
wide and what should be
managed locally?
• What standards should
be adopted?
• What vendors should be
chosen?
• What rules should
govern the decisions?
• What policies should
guide the process?
49. !
!
!
!
Copyright 2014 by Data Blueprint
49
Organizational Needs
become instantiated
and integrated into an Data/Information
Architecture
Informa(on)System)
Requirements
authorizes and
articulates
satisfyspecificorganizationalneeds
Data Architectures produce and are made up of information models that
are developed in response to organizational needs
50. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
50
51. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
51
52. Copyright 2014 by Data Blueprint
52
Less ROT
Technologies
Process
People
Data Leverage
• Permits organizations to better manage their sole non-depleteable, non-
degrading, durable, strategic asset - data
– within the organization, and
– with organizational data exchange partners
• Leverage
– Obtained by implementation of data-centric technologies, processes, and human skill
sets
– Increased by elimination of data ROT (redundant, obsolete, or trivial)
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
1. lowers organizational IT costs and
2. increases organizational knowledge worker productivity
53. Copyright 2014 by Data Blueprint
53
Conceptual Logical Physical
Validated
Not Validated
Architecture Evolution Framework
Every change can
be mapped to a
transformation in
this framework!
54. Copyright 2013 by Data Blueprint
Application-Centric Development
Original articulation from Doug Bagley @ Walmart
54
Data/
Information
Network/
Infrastructure
Systems/
Applications
Goals/
Objectives
Strategy
• In support of strategy, organizations
develop specific goals/objectives
• The goals/objectives drive the development
of specific systems/applications
• Development of systems/applications leads
to network/infrastructure requirements
• Data/information are typically considered
after the systems/applications and network/
infrastructure have been articulated
• Problems with this approach:
– Ensures data is formed to the applications and
not around the organizational-wide information
requirements
– Process are narrowly formed around applications
– Very little data reuse is possible
55. Copyright 2014 by Data Blueprint
Data-Centric Development
Original articulation from Doug Bagley @ Walmart
55
Systems/
Applications
Network/
Infrastructure
Data/
Information
Goals/
Objectives
Strategy
• In support of strategy, the organization
develops specific goals/objectives
• The goals/objectives drive the development of
specific data/information assets with an eye to
organization-wide usage
• Network/infrastructure components are
developed supporting organizational data use
• Development of systems/applications is
derived from the data/network architecture
• Advantages of this approach:
– Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs
and compliment organizational process flows
– Maximum data/information reuse
56. Copyright 2014 by Data Blueprint
Why is Data Architecture Important?
• Poorly understood
– Data architecture asset value is not well
understood
• Inarticulately explained
– Little opportunity to obtain learning and
experience
• Indirectly experienced
– Cost organizations millions each year in
productivity, redundant and siloed efforts
– Example: Poorly thought out software
purchases
56
57. Copyright 2014 by Data Blueprint
57
Architectural Work Product
Components may be defined as:
• The intersection of common business functionality and the
subsets of the organizational technology and data
architectures used to implement that functionality
• Component definition is an important activity because CM2 component
engineering is focused on an entire component as an analysis unit. A
concrete example of a component might be
– The business processes, the technology and the data supporting
organizational human resource benefits operations. This same
component could be described simply as the "PeopleSoft™ version
7.5 benefits module implemented on Windows 95." illustrates the
integration of the three primary PeopleSoft metadata structures
describing the: business processes used to organization the work
flow, menu navigation required to access system functionality, and
data which when combined with meanings provided by the panels
provided information to the knowledge workers.
59. Copyright 2014 by Data Blueprint
System
Process
Process
2
Process
1
Process
3
Subprocess
1.1
Subprocess
1.2
Subprocess
1.3
59
Hierarchical System Functional Decomposition
60. Copyright 2014 by Data Blueprint
Level 1 Level 2 Level 3
Pay Employment Recruitment
and Selection
personnel Personnel Employee relations
administration Employee compensation changes
Salary planning
Classification and pay
Job evaluation
Benefits administration
Health insurance plans
F lexible spending accounts
Group life insurance
Retirement plans
Payroll Payroll administration
Payroll processing
Payroll interfaces
Development N/A
Training
administration
Career planning and skills
inventory
Work group activities
Health and
safety
Accidents and workers
compensation
Health and safety programs
A three-level
decomposition of
the model views
from the
governmental pay
and personnel
scenario
60
61. Copyright 2014 by Data Blueprint
H ealth car e system
1 Patient administration
1.1 R egistration
1.2 Admission
1.3 Disposition
1.4 Transfer
1.5 M edical record
1.6 Administration
1.7 Patient billing
1.8 Patient affairs
1.9 Patient management
2 Patient appointments
and scheduling
2.1 Create or maintain
schedules
2.2 Appoint patients
2.3 R ecord patient encounter
2.4 I dentify patient
2.5 I dentify health care
provider
3 Nursing
3.1 Patient care
3.2 Unit management
4 Laboratory
4.1 R esults reporting
4.2 Specimen processing
4.3 R esult entry processing
4.4 Laboratory management
4.5 Workload support
5 Pharmacy
5.1 Unit dose dispensing
5.2 Controlled Drug
I nventory
5.3 Outpatient
6 R adiology
6.1 Scheduling
6.2 E xam processing
6.3 E xam reporting
6.4 Special interest and
teaching
6.5 R adiology workload
reporting
7 Clinical dietetics
7.1 E stablish parameters
7.2 R eceive diet orders
8 Order entry and results
8.1 R eporting
8.2 E nter and maintain
orders
8.3 Obtain results
8.4 R eview patient
information
8.5 Clinical desktop
9 System management
9.1 Logon and security
management
9.2 Archive run
M anagement
9.3 Communication software
9.4 M anagement
9.5 Site management
10 Facility quality assurance
10.1 Provider credentialing
10.2 M onitor and evaluation
A relatively
complex model
view
decomposition
61
62. Copyright 2014 by Data Blueprint
DSS
"Governors"
Taxpayers Clients
Vendors Program Deliver
62
Data model is comprised of model views
DSS Strategic Data Model
Taxpayer view
Client view
Governance view
Program Delivery view
Vendor view
63. Copyright 2014 by Data Blueprint
Taxpayer view
Payments Taxpayers
Social
Service
Programs
Taxpayer
Benefits
63
64. Copyright 2014 by Data Blueprint
Client view
Payments
Clients Client
Benefits
Local
Wellfare
Agencies
64
65. Copyright 2014 by Data Blueprint
Governance view
Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
65
66. Copyright 2014 by Data Blueprint
Social
Service
Programs
Clients
Service
Delivery
Partners
Local
Wellfare
Agencies
66
Program Delivery view
67. Copyright 2014 by Data Blueprint
Payments
Social
Service
Programs
Clients
Local
Wellfare
Agencies
Goods
and
Services
Vendors
67
Vendor view
68. Copyright 2014 by Data Blueprint
Governmental
Resources
Governance Governments Payments Taxpayers
State Board
of Social
Services
Social
Service
Programs
Clients Client
Benefits
Taxpayer
Benefits
Policy
Approval
Service
Delivery
Partners
Local
Wellfare
Agencies
Goods
and
Services
Vendors
68
DSS Strategic Level Data Model
69. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
69
70. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
70
71. Copyright 2014 by Data Blueprint
71
Challenge
Package Implementation Example
• "Green screen" legacy system to be replaced with Windows Icons
Mice Pointers (WIMP) interface; and
• Major changes to operational processes
– 1 screen to 23 screens
• Management didn't think workforce could adjust to simultaneous
changes
– Question: "How big a change will it be to replace all instances of
person_identifier with social_security_number?"
• Answer:
– (from "big" consultants) "Not a very big change."
72. Copyright 2014 by Data Blueprint
Home Page
Business Process
Name
Business Process
Component
Business Process
Component Step
72
PeopleSoft Process Metadata
Home Page Name
(relates to one or more)
Business Process Name
(relates to one or more)
Business Process Component Name
(relates to one or more)
Business Process Component Step Name
74. Home Page Name
Business Process Name
Business Process Component Name
Business Process Component Step Name
Peoplesoft Metadata Structure
Copyright 2014 by Data Blueprint
processes
(39)
homepages
(7)
menugroups
(8)
components
(180)
stepnames
(822)
menunames
(86)
panels
(1421)
menuitems
(1149)
menubars
(31)
fields
(7073)
records
(2706)
parents
(264)
reports
(347)
children
(647)
(41) (8)
(182)
(847)
(949)
(86)
(281)
(1259)(1916)
(5873)
(264)
(647)(708)
(647)
(25906)
(347)
74
PeoplesoftMetadataStructure
75. Quantity
System
Component
Time to make
change Labor Hours
1,400 Panels 15 minutes 350
1,500 Tables 15 minutes 375
984
Business process
component steps
15 minutes 246
Total 971
X $200/hour $194,200
X 5 upgrades $1,000,000
Copyright 2014 by Data Blueprint
75
Business Value - Better Decisions
76. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
76
77. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
77
78. Copyright 2014 by Data Blueprint
78
A National Cancer Institute
• This Virginia cancer center is a
leader in shaping the fight
against cancer
• Over 500 researchers and staff
tend to over 12,000 patients
annually
• This requires robust
information management and
analytical services
• The problem: It takes 1 month
to run a report on an incident,
i.e. a patient’s hospital visit that
shows all touch points
79. Copyright 2014 by Data Blueprint
Other Departments
SQL
SQLSAS
Cancer
Registry
Claims
Database
File
Export
Physician
Invoices
Patient
(Hospital)
Patient
(Physician)
Patient
(Registry)
Billing Data
(Hospital)
Billing Data
(Physician)
Diagnoses
(Hospital)
Diagnoses
(Physician)
Diagnoses
(Registry)
Physicians
(Hospital)
Physicians
(Physician)
Access
SQL
SQL
SAS
SQL
Excel
Excel
Hospital
Claims
Text
Files FTP FTP
Text
Files
FTP or
Email
Word
Word
Word
Current State Assessment
80. Copyright 2014 by Data Blueprint
Other Departments
SSI
S
Cancer
Registry
Hospital Claims
Staging
SSI
S
Physician
Invoices
Patient
Demographics
Billing Data
(Hospital)
Billing Data
(Physician)
Diagnoses
(Hospital)
Diagnoses
(Physician)
Diagnoses
(Registry)
Physicians
(Hospital)
Physicians
(Physician)
SSI
S
SSI
S
Consolidated/
Sandbox
SSIS SSA
S
Patient
(Consolidated)
RP
T
Physicians
(Consolidated)
Diagnoses
(Consolidated)
SSR
S
SharePoint
Excel
Email
One-off reports
Reusable reports
Conceptual Target Architecture
81. 0
25
50
75
100
Current Improved
Copyright 2013 by Data Blueprint
Reversing The Measures
• Currently:
– Analysts spend 80% of their time manipulating data and 20% of their time
analyzing data
– Hidden productivity bottlenecks
• After rearchitecting:
– Analysts spend less time manipulating data and more of their time analyzing data
– Significant improvements in knowledge worker productivity
81
Manipulation Analysis
A 20% improvement results in a doubling of productivity!
82. Copyright 2013 by Data Blueprint
Results: It is not always about money
• Solution:
– Integrate multiple databases into one
to create holistic view of data
– Automation of manual process
• Results:
– Data is passed safely and effectively
– Eliminate inconsistencies,
redundancies, and corruption
– Ability to cross-analyze
– Significantly reduced turnaround time
for matching patients with potential
donor -> increased potential to make
life-saving connection in a manner
that is faster, safer and more reliable
– Increased safe matches from 3 out of
10 to 6 out of 10
82
83. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
83
84. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
84
85. Copyright 2014 by Data Blueprint
Engineering
Architecture
85
Engineering/Architecting Relationship
• Architecting is used to
create and build systems
too complex to be treated
by engineering analysis
alone
• Architects require
technical details as the
exception
• Engineers develop the
technical designs
• Craftsman deliver
components supervised
by:
– Building Contractor
– Manufacturer
86. USS Midway
& Pancakes
Copyright 2014 by Data Blueprint
86
What is this?
• It is tall
• It has a clutch
• It was built in 1942
• It is still in regular use!
87. Copyright 2014 by Data Blueprint
Improving Data Quality during System Migration
87
• Challenge
– Millions of NSN/SKUs
maintained in a catalog
– Key and other data stored in
clear text/comment fields
– Original suggestion was manual
approach to text extraction
– Left the data structuring problem unsolved
• Solution
– Proprietary, improvable text extraction process
– Converted non-tabular data into tabular data
– Saved a minimum of $5 million
– Literally person centuries of work
89. Time needed to review all NSNs once over the life of the project:Time needed to review all NSNs once over the life of the project:
NSNs 2,000,000
Average time to review & cleanse (in minutes) 5
Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:Time available per resource over a one year period of time:
Work weeks in a year 48
Work days in a week 5
Work hours in a day 7.5
Work minutes in a day 450
Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Person years required to cleanse each NSN once prior to migration:
Minutes needed 10,000,000
Minutes available person/year 108,000
Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead) $60,000.00
Projected Years Required to Cleanse/Total DLA Person Year Saved 93
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
Copyright 2014 by Data Blueprint
89
Quantitative Benefits
90. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
90
91. • Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• Example: Software Package Implementation
• Example: Donation Center Processing
• Example: Text Mining/Analytics
• Take Aways, References & Q&A
Copyright 2013 by Data Blueprint
Data Architecture Requirements
91
92. Copyright 2014 by Data Blueprint
92
Take Aways
• What is an information architecture?
– A structure of data-based information assets
supporting implementation of organizational strategy
– Most organizations have data assets that are not supportive of strategies -
i.e., information architectures that are not helpful
– The really important question is: how can organizations more effectively use their
information architectures to support strategy implementation?
• What is meant by use of an information architecture?
– Application of data assets towards organizational strategic objectives
– Assessed by the maturity of organizational data management practices
– Results in increased capabilities, dexterity, and self awareness
– Accomplished through use of data-centric development practices (including
taxonomies, stewardship, and repository use)
• How does an organization achieve better use of its information
architecture?
– Continuous re-development; the starting point isn't the beginning
– Information architecture components must typically be reengineered
– Using an iterative, incremental approach, typically focusing on one component at a
time and applying formal transformations
93. June Webinar:
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June 10, 2014 @ 2:00 PM ET/11:00 AM PT
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