Maximizing Salesforce Lightning Experience and Lightning Component PerformanceSalesforce Developers
We all want the Salesforce Lightning Experience to be fast- but how do we define fast, and how do we make it even faster? When you’re building a UI, everything you add to the page affects performance, and to make load times faster and perform the way users expect, we need to treat speed as an essential design feature. In this presentation, you’ll learn how to measure performance, learn a few tips on how to maximize performance, and take responsibility for your feature’s performance from design to production.
In this slidecast, Alex Gorbachev from Pythian presents a Practical Introduction to Hadoop. This is a great primer for viewers who want to get the big picture on how Hadoop works with Big Data and how this approach differs from relational databases.
Watch the presentation: http://inside-bigdata.com/slidecast-a-practical-introduction-to-hadoop/
Download the audio:
Master Data Management - Practical Strategies for Integrating into Your Data ...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 & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
How 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.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
360 Degree View Of Customer Powerpoint Presentation SlidesSlideTeam
Increase consumer satisfaction and drive sales using these 360 Degree View Of Customer PowerPoint Presentation Slides. Take the assistance of our attention-grabbing customer mapping PowerPoint slideshow to drive ROI through various means like acquiring leads, increasing customer retention, etc. By utilizing these customer relationships PowerPoint templates you can elucidate the process of customer architecture that consists of consumer clustering, CRM analysis, real-time analytics, etc. Take advantage of these consumer lifecycle PPT visuals to reveal the components of 360 frameworks like CRM systems, DBMS, support ticketing, etc. Unveil the constituents of the customer lifecycle such as data visualization, data streaming, data integration, data science, etc. using our content-ready consumer engagement PowerPoint slide deck. Adopt a holistic customer view and interact with them through face-to-face communication, email, phone amongst others. Hence, without any further delay download this customer support PPT slide designs to engage effectively with your current as well as prospective clients. https://bit.ly/2OixhJT
Maximizing Salesforce Lightning Experience and Lightning Component PerformanceSalesforce Developers
We all want the Salesforce Lightning Experience to be fast- but how do we define fast, and how do we make it even faster? When you’re building a UI, everything you add to the page affects performance, and to make load times faster and perform the way users expect, we need to treat speed as an essential design feature. In this presentation, you’ll learn how to measure performance, learn a few tips on how to maximize performance, and take responsibility for your feature’s performance from design to production.
In this slidecast, Alex Gorbachev from Pythian presents a Practical Introduction to Hadoop. This is a great primer for viewers who want to get the big picture on how Hadoop works with Big Data and how this approach differs from relational databases.
Watch the presentation: http://inside-bigdata.com/slidecast-a-practical-introduction-to-hadoop/
Download the audio:
Master Data Management - Practical Strategies for Integrating into Your Data ...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 & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
How 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.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
360 Degree View Of Customer Powerpoint Presentation SlidesSlideTeam
Increase consumer satisfaction and drive sales using these 360 Degree View Of Customer PowerPoint Presentation Slides. Take the assistance of our attention-grabbing customer mapping PowerPoint slideshow to drive ROI through various means like acquiring leads, increasing customer retention, etc. By utilizing these customer relationships PowerPoint templates you can elucidate the process of customer architecture that consists of consumer clustering, CRM analysis, real-time analytics, etc. Take advantage of these consumer lifecycle PPT visuals to reveal the components of 360 frameworks like CRM systems, DBMS, support ticketing, etc. Unveil the constituents of the customer lifecycle such as data visualization, data streaming, data integration, data science, etc. using our content-ready consumer engagement PowerPoint slide deck. Adopt a holistic customer view and interact with them through face-to-face communication, email, phone amongst others. Hence, without any further delay download this customer support PPT slide designs to engage effectively with your current as well as prospective clients. https://bit.ly/2OixhJT
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
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, customer centricity, 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.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Join us as we provide an overview of how to integrate to Salesforce using the built-in tools, and look at integration on the different layers of Salesforce (User Interface, Data Logic, and Database). We'll be providing tips, best practices, and real-life examples.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Operational Risk Management Data Validation ArchitectureAlan McSweeney
This describes a structured approach to validating data used to construct and use an operational risk model. It details an integrated approach to operational risk data involving three components:
1. Using the Open Group FAIR (Factor Analysis of Information Risk) risk taxonomy to create a risk data model that reflects the required data needed to assess operational risk
2. Using the DMBOK model to define a risk data capability framework to assess the quality and accuracy of risk data
3. Applying standard fault analysis approaches - Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) - to the risk data capability framework to understand the possible causes of risk data failures within the risk model definition, operation and use
Building an Effective BI Governance ProgramDATAVERSITY
“Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.” – Gartner
If you are in the process of building a governance initiative or responsible for governance initiatives today, you can’t afford to be in the 80%. This webinar will ensure you deliver a successful program, by providing you tools and recommendations and will run you through a practical example from start to finish.
The following will be covered:
- Define clear objectives & gain buy-in
- Involve the right stakeholders
- Define Scope
- Set clear roles and responsibilities
- Create an effective workflow
- Monitor impact
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
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, customer centricity, 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.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Join us as we provide an overview of how to integrate to Salesforce using the built-in tools, and look at integration on the different layers of Salesforce (User Interface, Data Logic, and Database). We'll be providing tips, best practices, and real-life examples.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Operational Risk Management Data Validation ArchitectureAlan McSweeney
This describes a structured approach to validating data used to construct and use an operational risk model. It details an integrated approach to operational risk data involving three components:
1. Using the Open Group FAIR (Factor Analysis of Information Risk) risk taxonomy to create a risk data model that reflects the required data needed to assess operational risk
2. Using the DMBOK model to define a risk data capability framework to assess the quality and accuracy of risk data
3. Applying standard fault analysis approaches - Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) - to the risk data capability framework to understand the possible causes of risk data failures within the risk model definition, operation and use
Building an Effective BI Governance ProgramDATAVERSITY
“Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.” – Gartner
If you are in the process of building a governance initiative or responsible for governance initiatives today, you can’t afford to be in the 80%. This webinar will ensure you deliver a successful program, by providing you tools and recommendations and will run you through a practical example from start to finish.
The following will be covered:
- Define clear objectives & gain buy-in
- Involve the right stakeholders
- Define Scope
- Set clear roles and responsibilities
- Create an effective workflow
- Monitor impact
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Big Data Taiwan 2014 Track1-3: Big Data, Big Challenge — Splunk 幫你解決 Big Data...Etu Solution
講者:SYSTEX 數據加值應用發展部產品經理 | 陶靖霖
議題簡介:認清現實吧! Big Data 是個熱門詞彙、熱門議題,但是問題的核心仍然圍繞在資料處理的流程、架構與技術,要踏入 Big Data 的領域,使用者會遭遇哪些挑戰? Splunk 被譽為「全球最佳的 Big Data Company」,究竟在資料處理的流程中擁有什麼獨特的技術優勢,能夠幫助使用者克服這些挑戰?又有哪些成功幫助使用者從資料中萃取出價值的應用案例?歡迎來認識 Splunk 以及全球 Big Data 成功案例。
從電商到媒體,從商品推薦到客戶行為分析 - Udn 的 big data 價值實踐之路Etu Solution
亞洲 Hadoop 產品與解決方案引領者 Etu,於年度 Etu Solution Day (ESD) 活動中發表「2014 年台灣 Big Data 市場 5 大趨勢預測」。Etu 也首度發表兩岸的 10 大行業、21 種 Hadoop Big Data 已經被驗證的應用,如電信業的經營分析與客服查詢、電子商務的精準推薦、數位媒體的內容推薦、零售行業的使用者行為分析、高科技製造的資料倉儲工作分流卸載與製程良率分析、政府與地產的輿情分析、電力的能源管理、保險的巨量小圖檔管理等。預期 2014 年的台灣 Big Data 市場將更為成熟,經過驗證階段後,進入最後導入階段的企業也可望有倍數的成長。
Etu 負責人蔣居裕表示:「UDN 的採用,說明了台灣企業導入 Big Data 應用的需求在特定產業力道明顯上揚,『2014 年台灣 Big Data 市場的 5 大趨勢預測』也呼應了這樣的看法。」蔣居裕說:「一、首先過河的人,要開始挑戰資料價值的海洋,越早期投入者,越用越深,越深越廣;二、Total Data BI 帶動企業採用多結構化資料倉儲。客戶行為分析、精準行銷、客戶體驗是應用目標;三、從新舊系統整合到 End-to-End 解決方案,大部分企業期待廠商能夠完整交付 Big Data 應用與專業技術顧問。『容易』(Ease) 是 Big Data 產品進入企業的關鍵字;四、資料探索工具當道,力助 Business User 比 IT 人員更能挖掘 Big Data 的價值。『探索』(Discovery) 是 Big Data 分析的神髓所在 —— 探索關聯、探索意圖、探索缺少什麼;五、Big Data 教育訓練課程,從以處理技術為主者,快速擴展到資料分析。但均會被含括在『資料科學』大傘下。資料科學家萬中選一,強調專業分工的資料科學團隊,才是實踐資料價值希望之所在。」
ESD 2013 另外還展現了藉由 Etu Appliance 所架構起來的 Etu Ecosystem,展示了由 Etu 以及 ISV 夥伴們所開發的 End-to-End 解決方案:Etu Recommender,除了原有的個人化精準推薦,現在還可與第三方工具整合,進行資料視覺化探索,建置使用者行為分析資料倉儲;合作夥伴堂朝數位整合的雲端電子刊物加值平台、PilotTV 前線媒體的收視量測系統、樺鼎商業資訊的視覺化分析工具、以及衛信科技的 SDN 網路管理完整解決方案,則分別透過 Etu Appliance 來做巨量、可擴展的檔案格式轉換運算、臉部辨識資料及時處理與分析、多結構化資料倉儲、網路資料封包預處理等工作。這些方案的共同點,就是它們都是基於不斷獲得各種產品創新獎項的 Etu Appliance 所開發或整合的應用。
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7. “In a resource- constrained environment, a user-friendly,
collaborative and comprehensive CRM system with strong
data analysis and reporting capabilities is essential.”
Ishbel Sterrick, Chief Financial Officer, The Smith Family
20. Microsoft CRM 2013 APP for Tablet
Sales Dashboard
IPad https://itunes.apple.com/us/app/microsoft-dynamics-crm/id678800460?mt=8
Windows8,8.1 Tablet http://apps.microsoft.com/windows/en-us/app/microsoft-dynamics-crm/93772212-7b72-
4aee-bc4e-b1adb7712ebe
21. Microsoft CRM 2013 APP for Mobile
CRM 2013 mobile solution for phones is now available for download on Windows Phone store, iTunes and Google Play.
Windows Phone Store - http://www.windowsphone.com/s?appid=bdf6ad14-8ff3-4db1-a9d5-336c50ef13ee
iTunes - https://itunes.apple.com/us/app/microsoft-dynamics-crm-for/id723891307?mt=8
Google Play Store - https://play.google.com/store/apps/details?id=com.microsoft.crm.crmhost&hl=en