Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
Creating a data-driven organization requires developing a data-driven culture. Key aspects of a data-driven culture include having a strong testing culture that encourages hypothesis generation and experimentation, an open and sharing culture without data silos, a self-service culture where business units have necessary data access and analytical skills, and broad data literacy across all decision makers. Ultimately, an organization is data-driven when it uses data to drive impact and business results by pushing data through an analytics value chain from collection to analysis to decisions and actions. Maintaining a data-driven culture requires continuous effort as well as data leadership from a chief data or analytics officer.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
Creating a data-driven organization requires developing a data-driven culture. Key aspects of a data-driven culture include having a strong testing culture that encourages hypothesis generation and experimentation, an open and sharing culture without data silos, a self-service culture where business units have necessary data access and analytical skills, and broad data literacy across all decision makers. Ultimately, an organization is data-driven when it uses data to drive impact and business results by pushing data through an analytics value chain from collection to analysis to decisions and actions. Maintaining a data-driven culture requires continuous effort as well as data leadership from a chief data or analytics officer.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
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 Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Key Elements of a Successful Data Governance ProgramDATAVERSITY
At its core, Data Governance (DG) is all about managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/Stewardship programs that manage data in support of organizational strategy. Delegates will understand why Data Governance can be tricky for organizations due to data’s confounding characteristics. This webinar will focus on four key DG elements:
- Keeping DG practically focused
- DG must exist at the same level as HR
- Gradually add ingredients (practicing and getting better)
- Data Governance in action: storytelling
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
This document discusses architecting a data lake. It begins by introducing the speaker and topic. It then defines a data lake as a repository that stores enterprise data in its raw format including structured, semi-structured, and unstructured data. The document outlines some key aspects to consider when architecting a data lake such as design, security, data movement, processing, and discovery. It provides an example design and discusses solutions from vendors like AWS, Azure, and GCP. Finally, it includes an example implementation using Azure services for an IoT project that predicts parts failures in trucks.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
The document provides guidance on designing a data and analytics strategy. It discusses why data and analytics are important for business success in the digital age. It outlines 13 approaches to a data and analytics strategy organized by core business strategy and value proposition. It emphasizes the importance of data literacy, governance, and quality. It provides examples of how organizations have used data and analytics to improve outcomes. The overall message is that a clear strategy is needed to communicate the business value of data and maximize its impact.
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.
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Introduction to Data Engineer and Data Pipeline at Credit OKKriangkrai Chaonithi
The document discusses the role of data engineers and data pipelines. It begins with an introduction to big data and why data volumes are increasing. It then covers what data engineers do, including building data architectures, working with cloud infrastructure, and programming for data ingestion, transformation, and loading. The document also explains data pipelines, describing extract, transform, load (ETL) processes and batch versus streaming data. It provides an example of Credit OK's data pipeline architecture on Google Cloud Platform that extracts raw data from various sources, cleanses and loads it into BigQuery, then distributes processed data to various applications. It emphasizes the importance of data engineers in processing and managing large, complex data sets.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
The document outlines the stages of a management consulting engagement: 1) negotiating the engagement between the consultant and client, 2) planning the engagement, 3) conducting the assignment through problem identification, data analysis, solution development, and reporting, 4) evaluating the engagement and providing post-engagement follow-up. Key aspects of each stage are discussed at a high level, including proposal letters, work plans, data collection techniques, and project management.
The document discusses key aspects of project integration management including:
1) The importance of project integration management and coordinating all project knowledge areas.
2) How the Airbus A380 project faced integration issues due to software version mismatches.
3) Key project integration processes like developing plans, directing execution, monitoring, and change control.
4) Methods for project selection like NPV, ROI, weighted scoring, and how a project charter and management plan are used.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
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 Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Key Elements of a Successful Data Governance ProgramDATAVERSITY
At its core, Data Governance (DG) is all about managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/Stewardship programs that manage data in support of organizational strategy. Delegates will understand why Data Governance can be tricky for organizations due to data’s confounding characteristics. This webinar will focus on four key DG elements:
- Keeping DG practically focused
- DG must exist at the same level as HR
- Gradually add ingredients (practicing and getting better)
- Data Governance in action: storytelling
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
This document discusses architecting a data lake. It begins by introducing the speaker and topic. It then defines a data lake as a repository that stores enterprise data in its raw format including structured, semi-structured, and unstructured data. The document outlines some key aspects to consider when architecting a data lake such as design, security, data movement, processing, and discovery. It provides an example design and discusses solutions from vendors like AWS, Azure, and GCP. Finally, it includes an example implementation using Azure services for an IoT project that predicts parts failures in trucks.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
The document provides guidance on designing a data and analytics strategy. It discusses why data and analytics are important for business success in the digital age. It outlines 13 approaches to a data and analytics strategy organized by core business strategy and value proposition. It emphasizes the importance of data literacy, governance, and quality. It provides examples of how organizations have used data and analytics to improve outcomes. The overall message is that a clear strategy is needed to communicate the business value of data and maximize its impact.
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.
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Introduction to Data Engineer and Data Pipeline at Credit OKKriangkrai Chaonithi
The document discusses the role of data engineers and data pipelines. It begins with an introduction to big data and why data volumes are increasing. It then covers what data engineers do, including building data architectures, working with cloud infrastructure, and programming for data ingestion, transformation, and loading. The document also explains data pipelines, describing extract, transform, load (ETL) processes and batch versus streaming data. It provides an example of Credit OK's data pipeline architecture on Google Cloud Platform that extracts raw data from various sources, cleanses and loads it into BigQuery, then distributes processed data to various applications. It emphasizes the importance of data engineers in processing and managing large, complex data sets.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
The document outlines the stages of a management consulting engagement: 1) negotiating the engagement between the consultant and client, 2) planning the engagement, 3) conducting the assignment through problem identification, data analysis, solution development, and reporting, 4) evaluating the engagement and providing post-engagement follow-up. Key aspects of each stage are discussed at a high level, including proposal letters, work plans, data collection techniques, and project management.
The document discusses key aspects of project integration management including:
1) The importance of project integration management and coordinating all project knowledge areas.
2) How the Airbus A380 project faced integration issues due to software version mismatches.
3) Key project integration processes like developing plans, directing execution, monitoring, and change control.
4) Methods for project selection like NPV, ROI, weighted scoring, and how a project charter and management plan are used.
The document discusses the benefits of centralized control of investment programs and projects. It argues that traditional project management metrics are not enough and a program manager needs a wider dataset to ensure activities contribute to strategic goals. It outlines the components of a centralized approach, including aligning goals to business goals, undertaking the right projects, providing consistent reporting, risk management, and knowledge sharing. It provides an example of how Friends Provident centralized control over 832 concurrent projects to bring order and deliver benefits.
PMP Training - 04 project integration managementejlp12
Project Integration Management involves identifying, defining, combining, and coordinating the various processes and activities within the five Project Management Process Groups. It includes developing the project charter and project management plan, directing and managing project execution according to the plan, monitoring and controlling the work, performing integrated change control, and closing the project or phase once complete. The project charter and management plan provide the approved scope, schedule, and cost baselines against which project performance is monitored and measured.
Online PMP Training Material for PMP Exam - Integration Management Knowledge ...GlobalSkillup
This document provides an overview of integration management processes in project management. It discusses the six key integration management processes: develop project charter, develop project management plan, direct and manage project work, monitor and control project work, perform integrated change control, and close project or phase. For each process, it describes the inputs, tools and techniques, and outputs involved. It also discusses concepts like corrective and preventive action, deliverables, work performance data, and how change control is performed. The overall purpose is to explain how integration processes pull together all aspects of a project to ensure successful delivery of project objectives and requirements.
The document discusses the key processes involved in project management. It outlines the typical project life cycle stages of initiation, planning, execution, monitoring and controlling, and closing. For each stage, it provides breakdown processes that occur within that stage. It also includes sample questions and answers related to project management processes and terminology.
Project implementation refers to transforming a proposed project into reality through putting activities, resources, and management structures into action. It involves two main phases - project activation, which makes arrangements to start the project, and project operation, which practically manages the project to transform inputs into outputs. Implementation can use top-down, bottom-up, or collaborative approaches. A project implementation plan details the schedule, staffing, finances, reporting, sustainability measures, time controls, and supervision needed. Key factors for success include strong political commitment, careful preparation and management, and stakeholder involvement, while common challenges are financial, management, and technical problems.
Project Integration Management involves five key processes:
1. Develop Project Charter - Defines the project.
2. Develop Project Management Plan - Guides how the project will be executed.
3. Direct and Manage Project Execution - Performing the work outlined in the plan.
4. Monitor and Control Project Work - Tracking progress and addressing issues.
5. Perform Integrated Change Control - Reviewing and approving/managing changes.
These processes span the project lifecycle from initiating to closing and involve balancing the project's scope, schedule, costs, quality, resources, risks, procurements and stakeholder engagement.
This document discusses various aspects of project management including:
1. It describes the different stages of a project including planning, scheduling, controlling, and closing.
2. It outlines several key project management knowledge areas such as scope, time, cost, quality, human resources, communications, risk, and procurement.
3. It provides an overview of the project management process including integration, scope, time, cost, quality, human resources, communications, risk, and procurement management.
The document discusses project implementation, including defining it as converting project inputs to outputs. It outlines key phases like project activation and operation. A project implementation plan is described as including a schedule, roles, stakeholder participation, structure, finances, reporting, and sustainability. Methods for implementation planning like Gantt charts are explained. Factors affecting success and challenges are listed. Effective management of implementation is emphasized as setting up systems and offices, recruiting staff, defining responsibilities, and establishing records and financial procedures.
1. Project management is the application of knowledge, skills, tools, and techniques to project activities to meet project objectives.
2. 66% of IT projects fail, come over budget, or run past deadlines, wasting $55 billion annually in the US.
3. Successful project management requires defining project scope, schedule, costs, quality standards, and risks as well as tracking performance against the project plan.
The document discusses project financial management. It outlines the importance of financial management in driving revenue and profitability goals. It then describes the roles and responsibilities of various parties in financial management, such as the project manager, project controller, and client service partner. Finally, it provides an overview of financial management activities and concepts used during different project phases, from pursuit to planning, managing, and closing the project. Key aspects covered include developing pricing models, reviewing budgets, updating forecasts, generating invoices, and monitoring billings, collections, and payments.
The document discusses the key aspects of project management including the project life cycle and its phases. It describes the five phases of a project life cycle as initiation, planning, execution, monitoring and control, and closeout. For each phase, it provides the key outputs and activities. For example, in the planning phase the outputs include creating a work breakdown structure, developing schedules, and determining roles and responsibilities. The document also covers other areas such as what is a project, factors for project success and failure, the role of a project manager, and common project management tools.
The document discusses project management and outlines key aspects of planning and executing projects. It defines project management as planning, scheduling, directing and controlling resources to complete goals and objectives. It describes characteristics of projects, the project management lifecycle consisting of 5 phases, and lists essential qualities of a project manager including leadership, communication skills, and time management. It also provides details on various project planning activities such as defining goals, deliverables, schedules, supporting plans like human resources and risk management.
Project management involves planning, scheduling, controlling, and closing a project to meet specified goals of scope, time, and cost. It includes identifying requirements and stakeholders, creating a work breakdown structure and schedule, estimating costs, monitoring and controlling the project, and managing risks, quality, human resources, communications, procurement, and documents. The project management process groups are initiation, planning, execution, monitoring and controlling, and closing.
The document discusses key concepts in project management including what constitutes a project, the project lifecycle, and project management processes. It defines a project as a temporary endeavor with a defined beginning and end that is undertaken to create a unique product or service. The project lifecycle involves five main processes: initiating, planning, executing, monitoring and controlling, and closing. It also outlines the ten knowledge areas that project management processes fall within according to the PMBOK framework.
A project is a temporary endeavor undertaken to produce a unique product or service. Projects are different from operations in that they have a definite beginning and end, and produce something new. A successful project satisfies customer requirements on time and budget. Projects often fail due to issues like scope creep, poor planning, lack of resources or sponsorship. Project management is the application of skills to meet stakeholder needs and expectations by managing scope, time, cost, quality and risk. Key areas of project management include scope, issue, cost, quality, communication, risk and change management.
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
This document discusses the importance of data observability for improving data quality. It begins with an introduction to data observability and how it works by continuously monitoring data to detect anomalies and issues. This is unlike traditional reactive approaches. Examples are then provided of how unexpected data values or volumes could negatively impact downstream processes but be resolved quicker with data observability alerts. The document emphasizes that data observability allows issues to be identified and addressed before they become costly problems. It promotes data observability as a way to proactively improve data integrity and ensure accurate, consistent data for confident decision making.
Empowering the Data Driven Business with Modern Business IntelligenceDATAVERSITY
By consolidating data engineering, data warehouse, and data science capabilities under a single fully-managed platform, BigQuery can accelerate computation, reduce data analysis costs, and streamline data management.
Following in-depth interviews with a security services provider and a telecommunications company, Nucleus Research found that customers moving to Google Cloud BigQuery from on-premises data warehouse solutions accelerate data processing by over 75 percent while reducing data ongoing administrative expenses by over 25 percent.
As BigQuery continues to optimize its platform architecture for compute efficiency and multicloud support, Nucleus expects the vendor to see rapid adoption and further penetrate the data warehouse market.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Including All Your Mission-Critical Data in Modern Apps and AnalyticsDATAVERSITY
To stay competitive, you need to swiftly deliver innovative web and mobile apps and analytics solutions that include all your critical data—including mainframe and IBM i. Join us to hear how forward-thinking companies are using modern cloud-based platforms to deliver solutions that drive better customer experiences and greater insight—all while extending the value of their core systems.
Assessing New Database Capabilities – Multi-ModelDATAVERSITY
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
While vendor offerings have exploded in recent years, in due time frameworks will integrate components into what amounts to, for practical purposes, a single offering for multiple workloads, perhaps even for the enterprise.
A multi-model database is a database that can store, manage, and query data in multiple models, such as relational, document-oriented, key-value, graph (triplestore), and column store.
An enterprise will find reduced overhead and other synergies from choosing a single vendor for these workloads.
This session will explore the multi-model option and some criteria that decision makers should evaluate when choosing a multi-model solution.
Likely lots of well-organized data. Periodically, it is useful to interact with your Data Governance group to reevaluate the relative value of the various collections in the warehouse. More and more organizations are using warehousing as a strategy and focusing less on the actual technology. This program will provide a refocus on data warehousing as a capability that supports BI activities, enables more effective business analyses and decision-making, and provides some contribution to innovation initiatives. What are the capabilities required and how does their operation compare to cloud-based options?
Learning objectives:
- Warehousing capabilities
- What to use these capabilities in support of
- Where they can be deployed
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
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Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)Rebecca Bilbro
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.