Data Governance Assessment - Jan Rutger Merkus MSc
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Data Governance Assessment - Data Governance Maturity Model English version - summary of research on Data Governance Maturity Model
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Data Governance Maturity Model Thesis
This document presents a thesis on designing a Data Governance Maturity Model (DGMM) to assess organizational maturity of data governance. It begins with an introduction that establishes the background and relevance of the research. The objective is to define a framework for assessing data governance maturity and giving recommendations for organizational growth. A literature review is conducted to answer contextual and content questions. Based on the literature, a DGMM is designed with dimensions, levels, and criteria. Empirical research is then conducted by interviewing experts at a research organization to validate the DGMM. The results show that the DGMM is found to be relevant and valid for assessing data governance maturity. Some additions and adjustments to the model are also identified. In conclusion
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The document provides an introduction and background on Christopher Bradley, an expert in data governance. It then discusses data governance, defining it as the design and execution of standards and policies covering the design and operation of a management system to assure that data delivers value and is not a cost, as well as who can do what to the organization. The document lists Bradley's recent presentations and publications on topics related to data governance, data modeling, master data management and information management.
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Information Management training developed by Chris Bradley.
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Advanced Data Modelling course 3 day synopsis
This is a 3 day advanced course for students with existing data modelling experience to enable them to build quality data models that meet business needs. The course will enable students to:
* Understand and practice different requirements gathering approaches.
* Recognise the relationship between process and data models and practice capturing requirements for both.
* Learn how and when to exploit standard constructs and reference models.
*Understand further dimensional modelling approaches and normalisation techniques.
* Apply advanced patterns including "Bill of Materials" and "Party, Role, Relationship, Role-Relationship"
* Understand and practice the human centric design skills required for effective conceptual model development
* Recognise the different ways of developing models to represent ranges of hierarchies
data management Wb
This document discusses big data challenges for data management at an NHS Trust in London. It begins with an introduction explaining why data has become a valuable asset for organizations. It then summarizes three articles on big data management. The first article describes using cloud computing for big data storage and processing. The second provides an overview of big data sources and management research. The third discusses opportunities for IT professionals in big data. It concludes by analyzing solutions the articles propose for the NHS Trust's big data challenges, such as cloud computing and improved network architecture, and discusses implementing changes to data management policies.
Data Systems Integration & Business Value Pt. 1: Metadata
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.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope 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.
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This document presents a thesis on designing a Data Governance Maturity Model (DGMM) to assess organizational maturity of data governance. It begins with an introduction that establishes the background and relevance of the research. The objective is to define a framework for assessing data governance maturity and giving recommendations for organizational growth. A literature review is conducted to answer contextual and content questions. Based on the literature, a DGMM is designed with dimensions, levels, and criteria. Empirical research is then conducted by interviewing experts at a research organization to validate the DGMM. The results show that the DGMM is found to be relevant and valid for assessing data governance maturity. Some additions and adjustments to the model are also identified. In conclusion
Selecting Data Management Tools - A practical approach
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Information Management Fundamentals DAMA DMBoK training course synopsis
The fundamentals of Information Management covering the Information Functions and disciplines as outlined in the DAMA DMBoK . This course provides an overview of all of the Information Management disciplines and is also a useful start point for candidates preparing to take DAMA CDMP professional certification.
Taught by CDMP(Master) examiner and author of components of the DMBoK 2.0
chris.bradley@dmadvisors.co.uk
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Information Management training developed by Chris Bradley.
Education options include an overview of Information Management, DMBoK Overview, Data Governance, Master & Reference Data Management, Data Quality, Data Modelling, Data Integration, Data Management Fundamentals and DAMA CDMP certification.
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Advanced Data Modelling course 3 day synopsis
This is a 3 day advanced course for students with existing data modelling experience to enable them to build quality data models that meet business needs. The course will enable students to:
* Understand and practice different requirements gathering approaches.
* Recognise the relationship between process and data models and practice capturing requirements for both.
* Learn how and when to exploit standard constructs and reference models.
*Understand further dimensional modelling approaches and normalisation techniques.
* Apply advanced patterns including "Bill of Materials" and "Party, Role, Relationship, Role-Relationship"
* Understand and practice the human centric design skills required for effective conceptual model development
* Recognise the different ways of developing models to represent ranges of hierarchies
data management Wb
This document discusses big data challenges for data management at an NHS Trust in London. It begins with an introduction explaining why data has become a valuable asset for organizations. It then summarizes three articles on big data management. The first article describes using cloud computing for big data storage and processing. The second provides an overview of big data sources and management research. The third discusses opportunities for IT professionals in big data. It concludes by analyzing solutions the articles propose for the NHS Trust's big data challenges, such as cloud computing and improved network architecture, and discusses implementing changes to data management policies.
Data Systems Integration & Business Value Pt. 1: Metadata
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.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope 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.
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Data Quality Management,
Master & Reference Data Management, and
Data Governance.
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This document summarizes a presentation on clinical information governance at GlaxoSmithKline (GSK). GSK is combining data modelling, master data management, enterprise service bus, data stewardship, and enterprise architecture to simplify managing clinical study information. They have established different levels of data stewardship accountability and are implementing a clinical data stewardship framework. Their goal is to transform how clinical trial data is collected, reported, archived and retrieved to make trials more efficient and enhance patient safety.
Data Modelling Fundamentals course 3 day synopsis
This is a 3 day introductory course introducing students to data modelling, its purpose, the different types of models and how to construct and read a data model. Students attending this course will be able to:
Explain the fundamental data modelling building blocks. Understand the differences between relational and dimensional models.
Describe the purpose of Enterprise, conceptual, logical, and physical data models
Create a conceptual data model and a logical data model.
Understand different approaches for fact finding.
Apply normalisation techniques.
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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.
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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
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Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
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How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
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Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve a wide adoption and confluence of Data Trust between business and IT communities in the organization.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of data pain as a company grows, and we map their situation on this spectrum of semantics.
Next, we introduce the principles and framework for business semantics management to support data governance and stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with stories from the field.
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This document summarizes a presentation on clinical information governance at GlaxoSmithKline (GSK). GSK is combining data modelling, master data management, enterprise service bus, data stewardship, and enterprise architecture to simplify managing clinical study information. They have established different levels of data stewardship accountability and are implementing a clinical data stewardship framework. Their goal is to transform how clinical trial data is collected, reported, archived and retrieved to make trials more efficient and enhance patient safety.
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This is a 3 day introductory course introducing students to data modelling, its purpose, the different types of models and how to construct and read a data model. Students attending this course will be able to:
Explain the fundamental data modelling building blocks. Understand the differences between relational and dimensional models.
Describe the purpose of Enterprise, conceptual, logical, and physical data models
Create a conceptual data model and a logical data model.
Understand different approaches for fact finding.
Apply normalisation techniques.
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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
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A white paper by Chris Bradley outlining why Information is the "blood" of an organisation.
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The presentation provides an overview of data warehousing, business intelligence, analytics, and meta-integration technologies, explaining their definitions and importance for enabling analysis of previously unintegrated information to support better business decision making. It also discusses common data warehouse failures and outlines best practices for implementing these technologies, including the use of meta-models and a focus on data quality. The presentation concludes by emphasizing the takeaways and providing references and an opportunity for questions.
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Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
DAMA BCS Chris Bradley Information is at the Heart of ALL architectures 18_06...
Information is at the heart of ALL architectures and the business.
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Business Semantics for Data Governance and Stewardship
Data quality and regulations are perpetual drivers for Data Governance and Stewardship solutions that systematically monitor the execution of data policy. And yet, there is a long road ahead to achieve Trust in Data. It is still a relatively unknown topic or comes with trauma from past failed attempts; there is no political framework with executive champions, leading to reactive rather than proactive behavior, and software support is marginal.
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve a wide adoption and confluence of Data Trust between business and IT communities in the organization.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of data pain as a company grows, and we map their situation on this spectrum of semantics.
Next, we introduce the principles and framework for business semantics management to support data governance and stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with stories from the field.
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Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition,❤b ⚡bis for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program.
BDA 2012 Big data why the big fuss?
Big Data, why the Big fuss.
Volume, Variety, Velocity ... we know the 3 V's of Big Data. But Big Data if it yields little Information is useless, so focus on the 4th V = Value.
If you haven't sorted quality & data governance for your "little data" then seriously consider if you want to venture into the world of Big Data
Big Data Readiness Assessment
The document discusses an enterprise information management (EIM) framework and big data readiness assessment. It provides an overview of key components of an EIM framework, including data governance, data integration, data lifecycle management, and maturity assessments of EIM disciplines and enablers. It then describes a big data readiness assessment that helps organizations address questions around their need for and ability to exploit big data by determining which foundational EIM capabilities must be established and what aspects need improvement before embarking on a big data initiative.
CDMP preparation workshop EDW2016
DAMA CDMP preparation workshop form Enterprise Data World 2016 in San Diego, CA
Conducted by Christopher Bradley & Katherine O'Keefe
Data Profiling: The First Step to Big Data Quality
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
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DAMA BCS Chris Bradley Information is at the Heart of ALL architectures 18_06...
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This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify learning patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, properly using learner data for SRL requires skills like data literacy, critical thinking, and ensuring ethical and responsible use of student information. Educators need competencies in areas such as data protection, privacy, and using social context to help learners apply analytics insights.
Learning Analytics for Self-Regulated Learning (2019)
This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, effectively using learner data for SRL requires competencies like data literacy, critical thinking, and ensuring ethical and responsible use of student data.
Creating a Data Management Plan for your Grant Application
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
Creating a Data Management Plan for your Grant Application
The document provides information on creating a data management plan (DMP) for grant applications. It discusses what a DMP is, why they are important, and what funders require in a DMP. A DMP outlines how research data will be collected, documented, stored, shared, and preserved. The document recommends addressing six key themes in a DMP: data types and standards; ethics and intellectual property; data access, sharing and reuse; short-term storage and management; long-term preservation; and resourcing. Developing a strong DMP helps researchers manage data effectively and makes data available and reusable by others.
PPT1-Buss Intel Analytics.pptx
This document provides an overview of a session on business intelligence, data science, and data mining. The goals of the class are to understand how to solve business problems using data analytics, various tools and methods for implementing solutions, and how to store and access large amounts of data. The focus areas include data warehousing, data mining, simulation, and deriving profitable business actions from databases. Popular tools mentioned include RapidMiner, R, Excel, SQL, Python, Weka, KNIME, Hadoop, SAS, and Microsoft SQL Server. Benefits of business intelligence include increased profitability, decreased costs and risks, and improved customer relationship management.
Research in to Practice: Building and implementing learning analytics at Tribal
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...
Top tips for topics and content - presented at the Accelerate your Data training event in Perth 17 May 2018.
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
Speaking engagement at LACE SoLAR Flare hosted by the Open University. Turning Learning Analytics Research into Practice at Tribal. A video of my talk can be found at http://stadium.open.ac.uk/stadia/preview.php?whichevent=2606&s=1&schedule=3411&option=&record=0#
L1-Introduction to Cloud Computing.ppt
This document provides an introduction and overview of a course on cloud computing. The course objectives are to understand concepts and infrastructure of cloud computing, opportunities and challenges of information management in complex environments, current techniques and tools for cloud applications, and ethical, legal and social issues related to cloud computing. The syllabus is divided into 5 modules that cover introduction to cloud computing, using cloud computing for various uses, governance in the cloud, working with cloud services, and external cloud storage and sharing. Assessment includes exams, assignments, and self-work/professional skills development activities.
Facing the Data Challenge: Institutions, Disciplines, Services and Risks
This document summarizes a presentation on managing research data challenges. It discusses gathering requirements from researchers, assessing existing data support services, conducting a skills audit, and developing a strategic plan. Key points include analyzing gaps in current services, prioritizing resources, developing skills through training, clarifying roles and responsibilities, and creating short and long-term action plans to optimize research data management support. The goal is to understand researcher needs, strengthen collaboration between support units, and engage proactively to help address data challenges.
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Learning Analytics for Self-Regulated Learning (2019)
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Research in to Practice: Building and implementing learning analytics at Tribal
Research in to Practice: Building and implementing learning analytics at Tribal
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Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...
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SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
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一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
CBU毕业证offer【微信95270640】《卡普顿大学毕业证书》《QQ微信95270640》学位证书电子版：在线制作卡普顿大学毕业证成绩单GPA修改（制作CBU毕业证成绩单CBU文凭证书样本）、卡普顿大学毕业证书与成绩单样本图片、《CBU学历证书学位证书》、卡普顿大学毕业证案例毕业证书制作軟體、在线制作加拿大硕士学历证书真实可查.
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡普顿大学卡普顿大学毕业证成绩单；
3、不清楚流程以及材料该如何准备卡普顿大学卡普顿大学毕业证成绩单；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡普顿大学卡普顿大学毕业证成绩单毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡普顿大学卡普顿大学毕业证成绩单】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡普顿大学同学朋友
你做代理，可以拯救卡普顿大学失足青年
你做代理，可以挽救卡普顿大学一个个人才
你做代理，你将是别人人生卡普顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会道银边山娃摸索着扯了扯灯绳小屋顿时一片刺眼的亮瞅瞅床头的诺基亚山娃苦笑着摇了摇头连他自己都感到奇怪居然又睡到上午点半掐指算算随父亲进城已一个多星期了山娃几乎天天起得这么迟在乡下老家暑假五点多山娃就醒来在爷爷奶奶嘁嘁喳喳的忙碌声中一骨碌爬起把牛驱到后龙山再从莲塘里采回一蛇皮袋湿漉漉的莲蓬也才点多点半早就吃过早餐玩耍去了山娃的家在闽西山区依山傍水山清水秀门前潺潺流淌的蜿蜒小溪一直都是山娃和小伙伴们盛试
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Intelligence supported media monitoring in veterinary medicine
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A market analysys on Spotify's parent Podcast company Gimlet.
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
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原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Deakin毕业证书)迪肯大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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Data Governance Assessment - Jan Rutger Merkus MSc
- 1. Data Governance assessed Jan Rutger Merkus MSc Open University of the Netherlands
- 2. Relevance - Data is everywhere - Data hype: big data - Data is passive - Data has value, when used - Data becomes a valuable asset - Data has to be managed Open University of the Netherlands
- 3. Data Management - data management technologies > data quality management > (master) data management > data security management - data management organisation > business processes, functions, authorization = Data Governance Open University of the Netherlands
- 4. Data Governance ? What is Data Governance ? How to do Data Governance ? How to measure Data Governance ? Open University of the Netherlands
- 5. Literature research 1 - What is Data Governance > definition 2 – How to measure Data Governance > organisation maturity model Open University of the Netherlands
- 6. Body of Knowledge - Germany : Otto & Weber 2011, Wende 2007 - UK : Gregory 2011, 2012, Begg 2009 - India : Kahtri & Brown 2010 - Finland : Korhonen 2013 - USA : alleen praktische ervaringen - Best practices : DMBOK, COBIT - Companies : IBM, SAP, Oracle, etc Open University of the Netherlands
- 7. Definition Data Governance (DG) - From Corporate GRC: Governance, Risk Management & Compliance - set strategies for processes, people and technology - to maximise the value of data assets - by setup of organisation, responsibilities and authorization - for the domains of data principles, data quality, metadata, data access and data lifecycle Merkus, 2015 Open University of the Netherlands
- 8. 2. Maturity Model (MM) - Maturity Model method is commonly used since maturity model for IT alignment (Luftman 2003 ) - Huner 2009, Becker 2009 , Poeppelbuss 2011 > method for designing Maturity Models Open University of the Netherlands
- 9. 2. Components MM method 1. Basic model: maturity levels, domains, qualifications 2. Descriptive: assessment method and criteria 3. Prescriptive: recommendations for grow, organisation grow paths Open University of the Netherlands
- 10. 2. Exploring Research No other and earlier scientific Maturity Model present ! > Constructed out of maturity models from domains in the definition of Data Governance > Data Governance Maturity Model= DGMM© Open University of the Netherlands
- 11. 3. Take the Self-Assessment Link: Data Governance Self-Assessment or Questionnaire Open University of the Netherlands
- 12. Data Governance Maturity Model © Links: Report or University thesis library https://www.linkedin.com/in/janmerkus DataGovernanceMM@gmail.com Open University of the Netherlands