This document discusses an agile solution for enterprise data modeling and data management provided by A.I. Consultancy Limited and Pacific Rim Telecomm Datacomm Ltd. It outlines the benefits of enterprise data modeling, problems with traditional top-down approaches, and their hybrid agile solution using off-the-shelf modeling tools. Their solution aims to deliver initial data models quickly and support ongoing data governance through modular implementation and tailored training.
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
resentation of use cases of Master Data Management for Customer Data. It presents the business drivers and how Talend platform for MDM can adress them.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
resentation of use cases of Master Data Management for Customer Data. It presents the business drivers and how Talend platform for MDM can adress them.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
The Non-Invasive Data Governance FrameworkDATAVERSITY
Data Governance is already taking place in your organization. The actions of defining, producing and using data are not new. People in your organization have, at a minimum, an informal level of accountability for the data they use. The Non-Invasive Data Governance framework provides a method to formalize accountability based on people’s existing responsibilities.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series where he will provide a detailed framework for how to implement a Non-Invasive Data Governance program. This hour will be spent walking through the five most important components of a successful program described from the perspectives of the executive, strategic, tactical and operational levels of your organization.
In the webinar Bob will share:
The graphic for the Non-Invasive Data Governance Framework
A detailed description of the core program components
The importance of viewing the components from different perspectives
A detailed walk-through of each segment of the framework
How to use the framework to implement a successful program
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 such as “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 o
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
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
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.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
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.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Lessons in Data Modeling: Why a Data Model is an Important Part of Your Data ...DATAVERSITY
Data can provide tremendous value to an organization in today’s information-driven economy. New customer insights, better efficiency, and new product innovation are just some of the ways organizations are obtaining value through data. But in order to achieve this value, a strong data architecture is required to ensure that the data infrastructure runs smoothly, while at the same time aligning with business needs and corporate culture. A Data Strategy can assist in building a data architecture foundation through:
Identifying business requirements, rules & definitions via a business-centric data model
Creating a data inventory & integrating disparate data sources
Building a technical data architecture through data models & related artifacts
Coordinating the people, processes and culture necessary for success
Identifying tools & technology needed for creating & maintaining high quality data
Leading IT analyst firm Enterprise Management Associates (EMA) indicates that effective deployments of configuration management databases (CMDBs) or federated configuration management systems (CMSs) strongly correlate with success in digital and IT transformation. This fact may come as a surprise given the current industry hype to the contrary, but the reasons for having effective, dynamic and relevant service modeling systems could never be greater.
These slides, based on the webinar hosted by EMA and Micro Focus, draw on extensive EMA research and consulting to show exactly why and how this is true.
The Non-Invasive Data Governance FrameworkDATAVERSITY
Data Governance is already taking place in your organization. The actions of defining, producing and using data are not new. People in your organization have, at a minimum, an informal level of accountability for the data they use. The Non-Invasive Data Governance framework provides a method to formalize accountability based on people’s existing responsibilities.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series where he will provide a detailed framework for how to implement a Non-Invasive Data Governance program. This hour will be spent walking through the five most important components of a successful program described from the perspectives of the executive, strategic, tactical and operational levels of your organization.
In the webinar Bob will share:
The graphic for the Non-Invasive Data Governance Framework
A detailed description of the core program components
The importance of viewing the components from different perspectives
A detailed walk-through of each segment of the framework
How to use the framework to implement a successful program
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 such as “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 o
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
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
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.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
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.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Lessons in Data Modeling: Why a Data Model is an Important Part of Your Data ...DATAVERSITY
Data can provide tremendous value to an organization in today’s information-driven economy. New customer insights, better efficiency, and new product innovation are just some of the ways organizations are obtaining value through data. But in order to achieve this value, a strong data architecture is required to ensure that the data infrastructure runs smoothly, while at the same time aligning with business needs and corporate culture. A Data Strategy can assist in building a data architecture foundation through:
Identifying business requirements, rules & definitions via a business-centric data model
Creating a data inventory & integrating disparate data sources
Building a technical data architecture through data models & related artifacts
Coordinating the people, processes and culture necessary for success
Identifying tools & technology needed for creating & maintaining high quality data
Leading IT analyst firm Enterprise Management Associates (EMA) indicates that effective deployments of configuration management databases (CMDBs) or federated configuration management systems (CMSs) strongly correlate with success in digital and IT transformation. This fact may come as a surprise given the current industry hype to the contrary, but the reasons for having effective, dynamic and relevant service modeling systems could never be greater.
These slides, based on the webinar hosted by EMA and Micro Focus, draw on extensive EMA research and consulting to show exactly why and how this is true.
Enterprise applications and databases do not just help in running the business - they are your business. And every year, they grow in size and complexity, making them harder to manage. Uncontrolled data growth threatens application performance and service level agreements, increases maintenance costs, and exposes enterprises to legal liability for data privacy and security.
Noble-D, a cloud focused analytics consulting firmnoble-d
Noble-D is a cloud focused analytics consulting firm that offers strategy, architecture, design, engineer, support and talent acquisition services to their clients. Formed by senior analytics executives from the industry, Noble-D is a firm driven by a mission that every company, regardless of their size must have access to cloud enabled computing resources to compete fairly in global marketplace.
IT Governance – The missing compass in a technology changing worldPECB
The webinar covers:
• Overview of IT Governance
• Benefits of IT Governance
• IT Governance implementation : Approach and Methodology
• Key critical success factors
Presenter:
This webinar was presented by Mr. Oladapo Ogundeji, from Digital Jewels and PECB partner.
Link of the recorded session published on YouTube: https://youtu.be/Ux_Yk4JLy0M
http://www.csm-corp.com/it-consulting-services-company-it-consultants/
For over 30 years, our IT Consultants have helped some of the world’s most demanding organizations solve their most difficult technology challenges. Our IT Consulting Services Professionals are 100% committed to delivering Relentless IT Support. Every one of our employees is dedicated to delivering an outstanding customer experience.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges can often 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 reoccurring.
Learning objectives:
-Help you understand foundational Data Quality concepts for improving Data Quality at your organization
-Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
-Share case studies illustrating the hallmarks and benefits of Data Quality success
Amidst an industry cloud of confusion about what “AIOps” is and what it can do, these slides--based on the webinar from EMA research--delineates a clear path to victory for business and IT stakeholders seeking to use machine learning to optimize the performance of critical business services.
Best Practices for Managing Customizations for Quote-to-CashApttus
A business doesn’t stand still – it constantly evolves to remain innovative and competitive, with technical requirements changing accordingly. Quote-to-Cash systems underpin the middle office, automating a range of critical, complex business processes, involving a variety of integrations. Hear from the experts on how to efficiently identify, assess, prioritize and upgrade customizations needed for enabling business objectives over the lifetime of a Quote-to-Cash solution.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Agile Enterprise Data Model & Data Management Solution
1. Enterprise Data Model &
Data Management
Agile Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
2. EDM & Data Management Agile Solution
1. What is Enterprise Data Model (EDM)
2. Why EDM
3. Problems of Traditional Top-down Data Management
Approach
4. Our Agile EDM & Data Governance Solution
5. Why A.I. Consultancy Limited
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
3. 1. What is Enterprise Data Model (EDM)
A holistic, enterprise-level,
implementation independent conceptual or
logical data model providing a common
consistent view of data across the
enterprise.
Source: DAMA-BOK V2 https://dama.org/
DAMA-BOK V2
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
4. 2. Why EDM
Issues of over-looking EDM and data governance practice:
➢ Data from different systems/sources cannot be integrated to support end-to-end
business processes and management information requirements due to bottom up point
solutions.
➢ Inconsistent business jargons and information/data definition restricting BPM (business
process management) and BI (business intelligence) initiatives.
➢ Inconsistent data definition and duplicated data modeling effort result in project delay
and cost over-run.
➢ Rigid application and data architecture cannot address fast changing business
requirements.
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
5. 2. Why EDM
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Lease calculation
rules and formula. Address
Asset
Building
Building FloorBuilding Space
Building Zone
Charge Type
Demand Note
Engineer Skills
External Engineer
Facility Asset
Facility Parts
HVAC Equipment
Incident
Internal Engineer
Lease Charge
Maintenance Contract
Maintenance Engineer
Other Equipment
Parts
Payment Recipt
Property Asset
Room
Skill to Fix Parts
Skills
Supplier
Tenancy Agreement
Turnover Rent Scheme
Unit
Unit Type
Works Order
Works Order Parts
have
have
have
Have
have
fix
serve /
assign to
require
require
Facility Management
Asset Management
Lease Management
• The same entity
appears in different
Subject Areas or
Systems.
• Different entities in
different Subject
Areas are related.
• Their definitions,
formats and rules
have to be
standardized in
order to support
integration and
derive meaningful
analytics for valid
business decisions.
6. 2. Why EDM
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Rigid data structure and data disparity will restrict IT’s agility, so not only
your hardware and software infrastructure, your data base design must
be scalable too, i.e. flexible enough to address the next 5 to 10 years
requirements!
7. 2. Why EDM
Benefits of EDM and data governance:
➢ Enterprise wide data inter-operability
➢ IT projects are on-time and within budget
➢ Faster and more efficient BPM and BI results
➢ IT agility enable business agility
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
8. 3. Problems of Traditional Top-down Data Management
Approach
• It takes long time, even years, to harvest early results.
• It demands enormous effort to develop a full EDM internally!
• It requires scarce data management and architectural talent which cannot be
developed internally in short time-frame.
• It is difficult for internal IT to keep abreast of all latest technology advance (e.g.
IoT and big data) and understand their impact on business strategies, data
infrastructure, standard and data model.
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
9. 4. Our Agile EDM & Data Governance Solution
• Use Agile Method and a hybrid top-down & bottom-up approach to achieve data
management and governance quick-win.
• Off-the-shelf industry specific EDM and world No. 1 data governance solution
(erwin™) allow delivering 1st draft of your own EDM design in weeks.
• Tailored training program to get your staff up and running in short time-frame.
• Low start-up cost (can be rolled out module by module according to customer’s
specific situations, i.e. by individual Subject Area in the EDM)
• Minimal impact on existing IT plan (start from current high-value business critical
projects, no need to re-prioritize your project portfolio).
• Provide Mapping Report for IT to link data model design with business strategies
such that IT is able to engage business users in critical IT decisions, rather than IT
bears the business risk alone.
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
10. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Can your data structure design represents customers both as
individual and group?
11. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Can your data structure design support the
management of sales pipeline (i.e. customer
life cycle)?
12. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Can your data structure design
support the seamless integration with
BIM Cobie Spreadsheet?
13. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Can your data structure
design support future FM
outsourcing business
strategy?
Outsourcing FM function means much more than just able to issue a work-order to external service provider!!
14. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
15. 4. Our Agile EDM & Data Governance Solution
Use case 1: Pilot data management from a specific subject area or system
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
1. Use erwin DM
Reverse Engineer
to capture a model
of existing system
2. Compare with
Industry EDM
3. Work with users to
resolve the difference
and decide the to-be
data model
4. Plan for
implementation
16. 4. Our Agile EDM & Data Governance Solution
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
Use case 2: New IT Project
1. Extract
corresponding
Subject Area Model
from the Industry
EDM as the draft of
to be model
2. Discuss with users
to refine and
confirm the to-be
system data model
3. Include the confirmed
to-be data model as
requirements in RFP
4. Or as requirement
specification for
inhouse or
external developer
17. 5. Why A.I. Consultancy Limited
• We have extensive business experience and knowledge in gaming,
real estate, retail, and hospitality industry.
• We have in-depth knowledge in data modeling, data architecture
and governance, also in Agile approach, IT projects and portfolio
management.
• We have practiced traditional top-down architecture approach for
many years and learnt the lesson.
• It’s not easy for you to find partners who have both industry
knowledge and technical expertise at the same time.
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd
18. Want to know more?
A.I. Consultancy Limited (Macau)
www.architectedinfo.com
itmcimn@netvigator.com
853-63192039
Pacific Rim Telecomm Datacomm Limited (Hong Kong)
www.pr-data.com
rico.Chiang@pr-data.com
852-90823965
A.I. Consultancy Limited
Pacific Rim Telecomm Datacomm Ltd