Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy.
The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.
Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape.
Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization.
As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
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
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
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
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
The document summarizes key topics from the book "Data Governance for the Executive" by Jim Orr. It discusses how data governance has traditionally been viewed narrowly but should be seen as information asset management that drives business performance. The document also outlines how data governance can demonstrate value to executives by reducing costs, improving revenues, and mitigating risks across industries. Companies estimate losing millions annually due to data quality issues.
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.
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
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.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Master Data Management (MDM) is a systematic approach to cleaning up customer data so businesses can manage it efficiently and grow effectively. MDM helps businesses achieve a single version of truth about customers. It deals with strategies, architectures, and technologies for managing customer data, known as Customer Data Integration (CDI). Implementing MDM requires gaining commitment from senior management, understanding business drivers and resource requirements, and providing estimates of benefits like reduced costs and increased sales. A pilot project should be proposed before a full implementation to demonstrate value and gather feedback.
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.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
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.
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 .
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Data Mesh at CMC Markets: Past, Present and FutureLorenzo Nicora
This document discusses CMC Markets' implementation of a data mesh to improve data management and sharing. It provides an overview of CMC Markets, the challenges of their existing decentralized data landscape, and their goals in adopting a data mesh. The key sections describe what data is included in the data mesh, how they are using cloud infrastructure and tools to enable self-service, their implementation of a data discovery tool to make data findable, and how they are making on-premise data natively accessible in the cloud. Adopting the data mesh framework requires organizational changes, but enables autonomy, innovation and using data to power new products.
7 Essential Practices for Data Governance in HealthcareHealth Catalyst
This document outlines 7 essential practices for data governance in healthcare. It discusses the growing value of healthcare data and importance of data governance. Effective data governance requires balancing broad vision with limited application and expanding only as needed. The key functions of data governance include enhancing data quality, increasing data content, encouraging data access, promoting data literacy, establishing standards for master reference data, prioritizing analytics, and managing master data. Maintaining high data quality, access, and literacy are crucial.
What is the best Healthcare Data Warehouse Model for Your Organization?Health Catalyst
Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare:
1. Enterprise Data Model
2. Independent Data Marts
3. Late-binding Solutions
The document summarizes key topics from the book "Data Governance for the Executive" by Jim Orr. It discusses how data governance has traditionally been viewed narrowly but should be seen as information asset management that drives business performance. The document also outlines how data governance can demonstrate value to executives by reducing costs, improving revenues, and mitigating risks across industries. Companies estimate losing millions annually due to data quality issues.
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.
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
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.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Master Data Management (MDM) is a systematic approach to cleaning up customer data so businesses can manage it efficiently and grow effectively. MDM helps businesses achieve a single version of truth about customers. It deals with strategies, architectures, and technologies for managing customer data, known as Customer Data Integration (CDI). Implementing MDM requires gaining commitment from senior management, understanding business drivers and resource requirements, and providing estimates of benefits like reduced costs and increased sales. A pilot project should be proposed before a full implementation to demonstrate value and gather feedback.
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.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
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.
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 .
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Data Mesh at CMC Markets: Past, Present and FutureLorenzo Nicora
This document discusses CMC Markets' implementation of a data mesh to improve data management and sharing. It provides an overview of CMC Markets, the challenges of their existing decentralized data landscape, and their goals in adopting a data mesh. The key sections describe what data is included in the data mesh, how they are using cloud infrastructure and tools to enable self-service, their implementation of a data discovery tool to make data findable, and how they are making on-premise data natively accessible in the cloud. Adopting the data mesh framework requires organizational changes, but enables autonomy, innovation and using data to power new products.
7 Essential Practices for Data Governance in HealthcareHealth Catalyst
This document outlines 7 essential practices for data governance in healthcare. It discusses the growing value of healthcare data and importance of data governance. Effective data governance requires balancing broad vision with limited application and expanding only as needed. The key functions of data governance include enhancing data quality, increasing data content, encouraging data access, promoting data literacy, establishing standards for master reference data, prioritizing analytics, and managing master data. Maintaining high data quality, access, and literacy are crucial.
What is the best Healthcare Data Warehouse Model for Your Organization?Health Catalyst
Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare:
1. Enterprise Data Model
2. Independent Data Marts
3. Late-binding Solutions
As access to healthcare data grows, healthcare leaders are using more data to make decisions. Executives and front-line clinicians need a decision-support tool that meets the demands of today’s increasingly data-rich environment. Healthcare dashboards once filled this niche, but no longer keep up with ever-growing data demands. Fortunately, an innovative visual reporting system, Leading Wisely™, picks up where dashboards fall short—enabling faster reporting and customized, self-service capability for comprehensive data-driven support. Leading Wisely’s key next-level features include:
Customization, allowing the individual user to personally tailor measures.
Proactive alerts, prompted by personalized notification settings.
User friendly layout, with easy-to-read highlights that indicate if a measure if moving off course.
Rising Healthcare Costs: Why We Have to ChangeHealth Catalyst
With rising healthcare costs, we hear so often about rate pressures on hospitals and the risk these pressures pose for their future. With healthcare reform, the burden of rising healthcare costs is shifting from payers to providers. Hospitals need to move toward value-based reimbursement models or they will face a -15.8 operating margin by 2021.Over the last 15 years premiums and employee contributions for an average family with health insurance sponsored by an employer have risen 167%. Along with these facts, government payers are reimbursing at lower levels becoming a negative margin for hospitals. These changes are not necessarily easy and can seem overwhelming. The question is whether your hospital will be a pioneer on the trail or will delay until it’s too late. The best way to get started is to understand exactly where you are today—your current cost structure and how each area of your organization is performing in terms of quality and cost, using an EDW.
Is That Data Valid? Getting Accurate Financial Data in HealthcareHealth Catalyst
A consolidated EDW is not a replacement or threat to the individual financial systems and reporting tools employed for general ledger, billing, payroll, or supply management. On the contrary, each of those systems is designed with sophisticated functionality that drives organizational efficiency. But alone, these systems realize only a portion of their true return on investment for the enterprise. As a consolidated data resource, these systems provide untold potential to address the underlying challenges to efficient, cost-effective health care.
Three Approaches to Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.
6 Steps for Implementing Successful Performance Improvement Initiatives in He...Health Catalyst
This document outlines six steps for implementing successful performance improvement initiatives in healthcare. It discusses establishing an analytics system to identify opportunities, prioritizing programs, defining permanent improvement teams, using a content system to define outcomes and interventions, and estimating return on investment. The goal is to provide a framework for sustainable performance gains through integrating improvement into strategy, using data analytics, deploying changes, and establishing ongoing monitoring teams.
Improving Patient Safety and Quality Through Culture, Clinical Analytics, Evi...Health Catalyst
According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents. Learn key best practices in patient safety and quality including: patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.
Surviving Value-Based Purchasing in Healthcare: Connecting Your Clinical and ...Health Catalyst
The document discusses the challenges healthcare organizations face with the transition to value-based purchasing. It emphasizes the need to connect clinical and financial data to track the new metrics tied to reimbursement. Specifically, it recommends forming multidisciplinary teams using aggregated data to drive sustainable quality improvements from the ground up. By educating both clinicians and financial executives on each other's perspectives, organizations can effectively collaborate to succeed under value-based payment models.
The Key to Transitioning from Fee-for-Service to Value-Based ReimbursementsHealth Catalyst
The shift from fee-for-service to value-based reimbursements has good and bad consequences for healthcare. While the shift will ultimately help health systems provide higher quality lower cost care, the transition may be financially disastrous for some. In addition, the shifting revenue mix from commercial payers to Medicare and Medicaid is creating its own set of challenges. There are, however, three keys to surviving the transition: 1) Effectively manage shared savings programs to maximize reimbursement. 2) Improve operating costs. 3) Increase patient volumes. With an analytics foundation, health systems will be able to meet and survive today’s healthcare challenges.
Database vs Data Warehouse: A Comparative ReviewHealth Catalyst
What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.
3 Phases of Healthcare Data Governance in AnalyticsHealth Catalyst
Healthcare data governance is a broad topic and covers more than data stewardship, storage, and technical roles and responsibilities. And it’s not easy to implement. It’s necessary, though, for health systems that are entering the world of analytics because the governance structure will enable the organizations to drive higher-quality, low cost care. In order for healthcare data governance to be most effective however, it needs to be adaptive because real healthcare data governance is much more fluid than any plan laid out on paper. Typically there are three phases that characterize successful analytics implementations: the early stage, the mid-term stage, and the steady state. As health systems begin to determine the effectiveness of their data governance strategy, it’s important to look at key metrics from their analytics implementations that will either trend up, remain solid, or trend down.
The Power of Geo Analytics (and maps) to Improve Predictive Analytics in Heal...Health Catalyst
As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.
The document provides tips for marketing a self-published book. It begins by emphasizing the importance of knowing your audience and book, as well as having a clear marketing plan. It then discusses tools like Helix Review that provide data on a book's strengths and comparable titles. Key recommendations are spending several hours per week on marketing through avenues like blogs, social media, and selling platforms. Resources from the publisher Lulu are highlighted.
Bridging The Gap Between Online and OfflineAgilOne
In this webinar, you’ll learn methods for merging the physical and digital identities of your customers for an accurate view of behavior across channels, what information is critical to enabling omni-channel success, and how to align marketing with consumer expectations throughout their lifecycle.
The market is moving toward an As-a-Service delivery model that provides plug-in, scalable, consumption-based business services that deliver the business outcomes that every organization demands—increased revenue or decreased costs. Early movers are at a tremendous advantage, while large incumbents may be at risk if they do not aggressively pursue As-a-Service capabilities.
The Stackies Awards, hosted by the MarTech conference and judged by chiefmartec.com, invites marketers to submit a single 16:9 slide that visualizes how they conceive of their marketing technology stack — the different products and technologies that they have harnessed into their marketing capabilities. This year, 41 companies entered their stacks and agreed to openly share them in this deck.
Scaling DevOps - delivering on the promise of business velocity and qualityXebiaLabs
This document summarizes a webinar presented by Robert Stroud and Tim Buntel on scaling DevOps practices. The webinar discussed how only 23% of enterprises deploy code monthly or faster, highlighting a need to improve release velocity. It also showed that organizations with more frequent deployments and faster lead times had significantly better outcomes. The presenters advocated for destroying silos through automation across the entire software development pipeline. They recommended transitioning from functional to product teams and packaging everything together to deploy. Metrics for success including achieving business goals and optimizing the software delivery value stream were also covered.
As the Age of Analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes the value of data to the mission of their organizations.
Adding to that challenge, the competitive nature of the data warehouse and analytics market place has resulted in significant noise from vendors and consultants alike who promise to help health systems develop their data governance strategy. Having gone on his own turbulent data governance ride as a CIO in the US Air Force and healthcare, Dale Sanders, Senior Vice President at Health Catalyst will cut through the market noise to cover the following topics:
General concepts of data governance, regardless of industry
Unique aspects of data governance in healthcare
Data governance in a “Late Binding” data warehouse
The layers and roles in data governance
The four “Closed Loops” of healthcare analytics and data governance
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.
Growing amounts of data can be overwhelming for healthcare entities to organize, manage, and distribute effectively, sometimes making data more of a burden than a benefit. However, if organizations adopt the right data mentality, they can gain insight into performance, track an intervention’s success, and improve outcomes. According to data experts, Bryan Hinton, our Chief Technology officer, and TJ Elbert, our SVP and General Manager of Data, organizations can apply five mindset changes to avoid data overload and achieve data-driven improvement:
1. Focus on data orchestration, not data computing.
2. Leverage real-time data, especially in a pandemic.
3. Prioritize data democratization over data control.
4. Use AI, if you’re not already.
5. Change current care models to fit the data.
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealth Catalyst
Author Douglas Laney is now tackling the topic of Infonomics: the practice of information economics. In his 2017 book, Infonomics: How to Monetize, Manage, and Measure Information as an asset for competitive advantage, Laney provides detailed rationale as well as a thoughtful framework for treating information as a modern-day organization’s most valuable asset.
This article walks through how healthcare organizations can leverage data to its full potential using this framework and the three principles of infonomics:
Measure - How much data does the organization have? What is it worth?
Manage - What data does the organization have? Where is it stored?
Monetize - How does the organization use data?
6 Essential Data Analyst Skills for Your Healthcare OrganizationHealth Catalyst
Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.
The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.
Why the Data Steward’s Role is Critical to Sustained Outcomes Improvement in ...Health Catalyst
The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:
Reinforces the global data governance principles.
Helps develop and refine details of local data governance practices.
Is the eyes and ears of the organization with respect to data governance and the governance committee.
Provides direction to peers regarding appropriate data definitions, usage, and access.
Anticipates local consequences of global changes
For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Health Catalyst
The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.
Why Your Healthcare Business Intelligence Strategy Can't WinHealth Catalyst
Business intelligence may hold tremendous promise but it can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.
Healthcare Information Systems - Past, Present, and FutureHealth Catalyst
The document discusses the evolution of healthcare information systems from the 1960s to present day. It begins with early systems focused on hospital accounting and administrative functions. Over time, systems expanded to support clinical departments, integrated hospital operations, and began incorporating patient data. The proliferation of these transaction systems created large amounts of siloed healthcare data. Today, chief information officers are under pressure to implement enterprise analytics solutions to merge this data and drive performance improvement through analysis of comprehensive clinical and operational information.
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseHealth Catalyst
In an industry known for its complex challenges that can take years to overcome, health systems can leverage healthcare data warehouses to generate seven quick wins—reporting and analytics efficiencies that empower healthcare organizations to thrive in a value-based world:
Provides significantly faster access to data.
Improves data-driven decision making.
Enables a data-driven culture.
Provides world class report automation.
Significantly improves data quality and accuracy.
Provides significantly faster product implementation.
Improves data categorization and organization.
Health systems that leverage healthcare data warehouses position themselves to do more than just survive the transition to value-based care; they empower themselves to achieve and sustain long-term outcomes improvement by enabling data-driven decision making based on high quality data.
The Four Pillars of Successful Self-Service Analytics in HealthcareHealth Catalyst
To prepare for successful self-service analytics, healthcare organizations must lay a strong foundation to ensure team members feel comfortable and confident with data. Many health systems are so eager to reap the benefits of self-service analytics that they rush its implementation before their team members are ready. These hurried approaches often lead to unsuccessful self-service analytics implementation that lacks the agility to support systems in a rapidly changing industry. To ensure self-service analytics success and avoid common pitfalls, healthcare organizations can focus on four pillars that build a strong self-service analytics foundation:
1. Develop a data-centric culture.
2. Promote data literacy.
3. Garner leadership support and ensure governance.
4. Define a business goal.
How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data ...Health Catalyst
Healthcare organizations increasingly understand the value of data quality, but many lack a systematic process for establishing and maintaining that quality. However, as COVID-19 response and recovery further underscores the need for timely, actionable data, organizations must take a more proactive approach to data quality.
A structured process engages technical and subject matter expertise to define, evaluate, and monitor data quality throughout the pipeline. Health systems can follow a simple, four-level framework to measure and monitor data quality, ensuring that data is fit to drive quality data-informed decisions:
Think of data as a product.
Address structural data quality first.
Define content level data quality with subject matter experts.
Create a coalition for multidisciplinary support.
4 Best Practices for Analyzing Healthcare DataHealth Catalyst
Meaningful healthcare analytics today generally need data from multiple source systems to help address the triple aim cost, quality, and patient satisfaction. Once appropriate data has been captured, pulled into a single place, and tied together, then data analysis can begin. In this article I share 4 ways to enable your analyst including providing them with
1) a data warehouse
2) a sandbox
3) a set of discovery tools
4) the right kind of direction.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Health Information Analytics: Data Governance, Data Quality and Data StandardsFrank Wang
The document discusses key concepts related to health data governance, including data governance, data quality, data standards, and master data management. It provides definitions and explanations of these topics, as well as their importance in enabling effective health information analytics. It also discusses different roles and responsibilities in data governance committees and outlines approaches to master data management.
Eight Reasons Why Chief Data Officers Will Help Healthcare Organizations Thri...Health Catalyst
The state of healthcare information technology and analytics has evolved to the point where a revised executive structure is advisable in the C-suite. This new structure calls for a Chief Data Officer (CDO) to focus on extracting data from systems and on mining value from that data, rather than getting data into systems, which is the responsibility of the CIO.
This article makes the case for the CDO, explains how the need for this emerging role evolved, outlines its responsibilities, advises on how to recruit and budget for this position, and details its domain in eight critical business areas:
Governance and standards
Managing risk
Reducing costs
Driving innovation
Data architecture and technology
Data analytics
Meeting regulatory demand
Creating business value
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
The Case for Healthcare Data Literacy: It's Not About Big DataHealth Catalyst
While many people are looking to Big Data to solve a lot of healthcare’s data problems, Big Data won’t offer a lot of solutions for a while to come. For one, healthcare doesn’t have “Big” data; there just isn’t the volume, velocity, or variety seen in other industries such as banking where Big Data has been used successfully. For another, Big Data seems to be the answer to almost every question from cancer to Alzheimer’s, and that’s blinding us to the reality of healthcare analytics. A big way toward answering healthcare’s problems would be to improve data literacy among not only consumers, but physicians and administrators as well. Learning to ask the right questions about the data and learning how to read data correctly will get us further down the road to improvement than the latest buzzword (in this case, “Big Data”) ever will.
Similar to Demystifying Healthcare Data Governance (20)
Empowering ACOs: Leveraging Quality Management Tools for MIPS and BeyondHealth Catalyst
Join us as we delve into the crucial realm of quality reporting for MSSP (Medicare Shared Savings Program) Accountable Care Organizations (ACOs).
In this session, we will explore how a robust quality management solution can empower your organization to meet regulatory requirements and improve processes for MIPS reporting and internal quality programs. Learn how our MeasureAble application enables compliance and fosters continuous improvement.
Unlock the Secrets to Optimizing Ambulatory Operations Efficiency and Change ...Health Catalyst
Today’s healthcare leaders are seeking technology solutions to optimize efficiencies and improve patient care. However, without effective change management and strategies in place, healthcare leaders struggle to strategically improve patient flow, space, to strategically improve patient flow, space, and schedule management, and implement daily huddles. The role of technology in supporting operational efficiency and change management initiatives is inevitable.
During this webinar, attendees will learn how to optimize Ambulatory Operational Efficiencies and Change Management. Attendees will also learn about the importance of visual management boards in enhancing clinic performance and insights into effective change management approaches.
Patient expectations are rising, and organizations are continuously being asked to do more with less.
Additionally, the convergence of several significant emerging market and policy trends, economic uncertainty, labor force shortages, and the end of the COVID-19 public health emergency has created a unique set of challenges for healthcare organizations.
Attend this timely webinar to learn about new trends and their impact on key healthcare issues, such as patient engagement, migration to value-based care, analytics adoption, the use of alternative care sites, and data governance and management challenges.
During this webinar, we will discuss the complexities of AI, trends, and platforms in the industry. Dive deep into understanding the true essence of AI, exploring its potential, real-world use cases, and common misconceptions. Gain valuable insights into the latest technology trends impacting healthcare and discover strategies for maximizing ROI in your technology investments.
Explore the profound impact of data literacy on healthcare organizations and how it shapes the utilization of data and technology for transformative outcomes. Understand the top technology priorities for healthcare organizations and learn how to navigate the digital landscape effectively. Furthermore, simplify industry jargon by defining common data elements, fostering clearer communication and collaboration across stakeholders.
Finally, uncover the transformative potentials of platforms in healthcare and how they can revolutionize scalability, interoperability, and innovation within your organization. Don't miss this opportunity to gain invaluable insights from industry experts and stay ahead in the ever-evolving healthcare landscape. Reserve your spot now for an enlightening journey into the future of healthcare technology!
Three Keys to a Successful Margin: Charges, Costs, and LaborHealth Catalyst
How can cost management and complete charge capture protect and enhance the margin?
In this webinar, we will look at 2024 margin pressures likely to impact your organization’s financial resiliency. This presentation will also share how organizations can move from Fee-for-Service to Value; bringing Cost to the forefront.
2024 CPT® Updates (Professional Services Focused) - Part 3Health Catalyst
Each year the CPT code set undergoes significant changes. Physicians and their office staff need to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This presentation will focus on the changes to the CPT dataset and the associated work RVU value changes that impact professional service reporting.
During this complimentary webinar, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. You will leave with an understanding of the financial implications of the changes on your practice.
2024 CPT® Code Updates (HIM Focused) - Part 2Health Catalyst
Each year the CPT code set and the HCPCS code set undergo significant changes, and your coding staff needs to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This is part two in a three-part series.
During these complimentary webinars, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. This presentation will be geared towards hospital staff with a focus on the surgical section of the CPT book in addition to surgical Category III codes.
2024 CPT® Code Updates (CDM Focused) - Part 1Health Catalyst
The document provides an overview of changes to CPT codes that will take effect in 2024, with a focus on changes relevant to clinical documentation. Key points include:
- There are 145 total codes added, 34 deleted, and 55 revised across various sections.
- Changes are provided for the Radiology, Laboratory/Pathology, and Category III sections. New codes are added for things like non-invasive coronary FFR estimation using AI and various intraoperative ultrasound exams.
- Guidelines are established for new genomic sequencing procedures codes focusing on solid organ and hematolymphoid neoplasms. Definitions are also provided for various genomic analysis techniques.
- Several Tier I and Tier II molecular
What’s Next for Hospital Price Transparency in 2024 and BeyondHealth Catalyst
The Centers for Medicare & Medicaid Services (CMS) published updates to the hospital price transparency requirements in the CY 2024 Outpatient Prospective Payment System (OPPS) Final Rule. The updates will be phased in over the next 14 months and include several significant changes including the use of a CMS-mandated template, a requirement for an affirmation statement from the hospital, and several new data elements. Join us to discover what changes are scheduled for implementation in 2024 and 2025 and how they’ll impact your facility.
During this complimentary 60-minute webinar, we’ll analyze the key provisions of the Price Transparency regulations and provide insights to help you prepare for the upcoming changes.
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementHealth Catalyst
What was once voluntary reporting will soon be made mandatory with penalties.
On July 1, 2024, all health systems will be required to collect Patient Reported Outcome Measures (PROM) as part of the Centers for Medicare & Medicaid Services (CMS) regulation for the following measures:
Hospital-Level, Risk Standardized Patient-Reported Outcomes Performance Measure (PRO-PM) Following Elective Primary Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA)
Hospital-Level Risk-Standardized Complication Rate (RSCR) Following Elective Primary THA/TKA
Are you equipped to handle these new requirements?
Mandatory data collection begins April 1, 2024, and failure to submit timely data can result in a 25 percent reduction in payments by Medicare.
Attend this webinar to learn how mobile engagement can empower your organization to meet this requirement.
2024 Medicare Physician Fee Schedule (MPFS) Final Rule UpdatesHealth Catalyst
According to the Centers for Medicare & Medicaid Services (CMS), the calendar year (CY) 2024 MPFS final rule was created to advance health equity and improve access to affordable healthcare. This webinar will cover the major policy updates of the MPFS final rule including updates to the telehealth services policy and remote monitoring services and enrollment of MFTs and MHCs as Medicare providers. The conversation will also cover policy changes on split (or shared) evaluation and management (E/M) visits, and the Appropriate Use Criteria (AUC) for Advanced Diagnostic Imaging.
What's Next for OPPS: A Look at the 2024 Final RuleHealth Catalyst
During this webinar, we’ll analyze the key provisions of the OPPS final rule and identify the significant changes for the coming year to help prepare your staff for compliance with the 2024 Medicare outpatient billing guidelines.
Insight into the 2024 ICD-10 PCS Updates - Part 2Health Catalyst
Three new codes were added to describe procedures involving a short-term external heart assist system inserted into the descending thoracic aorta. Codes were also added for fluorescence guided procedures of the female reproductive system and trunk region using pafolacianine. Additionally, new technology codes were introduced for insertion of intraluminal devices such as venous valves, leadless pacemakers, and artery bypass procedures.
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfHealth Catalyst
This document provides an overview of upcoming changes to ICD-10-CM codes for fiscal year 2024. It notes that there will be 395 new codes, 13 revisions, and 25 deletions. Specific changes include 18 new major complication or comorbidity (MCC) codes, 3 deleted MCC codes, 79 new CC codes, and 8 deleted CC codes. The presentation reviews code additions, deletions, and revisions for various body systems and disease chapters. It also outlines changes to the MCC and CC lists as well as Medicare Severity Diagnosis Related Groups (MS-DRG) updates.
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsHealth Catalyst
Many hospitals today face a perfect storm of operational and financial challenges. With increasing competition from outpatient facilities and rising care costs negatively impacting budgets, now is the time to boost your clinical registry’s value. However, collecting and analyzing data can be time-consuming and costly without the right tools. During this webinar, we will share insights and best practices for increasing the value of registry participation and how it’s possible to reduce costs while improving outcomes using the ARMUS Product Suite.
Tech-Enabled Managed Services: Not Your Average OutsourcingHealth Catalyst
The document discusses tech-enabled managed services (TEMS) as an alternative to traditional outsourcing. TEMS aims to reduce costs for health systems while maintaining performance, employees, and culture. It achieves this through specialized partnering, alleviating financial pressures, and ensuring dependable performance using a combination of people, processes, technology, and data. TEMS rebadges existing employees and takes on open positions to prevent workforce reductions. It also maintains existing processes while implementing new technology. This model is said to create wins for Health Catalyst through new employees, the health system through reduced costs and governed performance, and employees through continued work and an improved experience.
This webinar will provide an in-depth review of the CPT/HCPCS code set changes that will be effective on July 1, 2023. The review will include additions and deletions to the CPT/HCPCS code set, revisions of code descriptors, payment changes, and rationale behind the changes.
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHealth Catalyst
Chronic conditions across the United States are prevalent and continue to rise. Managing one or more chronic diseases can be very challenging for patients who may be overwhelmed or confused about their care plan and may not have access to the resources they need. At the same time, care teams are overburdened, making it difficult to provide the support these patients require to stay as healthy as possible. A new approach to chronic condition management leverages technology to enable organizations to scale high-quality care, identify gaps in care, provide personalized support, and monitor patients on an ongoing basis. Such streamlined management will result in better outcomes, reduced costs, and more satisfied patients.
COVID-19: After the Public Health Emergency EndsHealth Catalyst
In this fast-paced webinar, we will discuss the impact of the end of the public health emergency (PHE), including upcoming changes to the different flexibilities allowed during the PHE and the timeline for when these flexibilities will end. We’ll also cover coding changes and reimbursement updates.
Automated Medication Compliance Tools for the Provider and PatientHealth Catalyst
When it comes to sustaining patient health outcomes, compliance and adherence to medication regimens are critically important, especially as providers manage patients with complex care needs and multiple medications. But, with provider burnout and staffing shortages at an all-time high, an efficient solution is critical. The use of automated medication management workflows to decrease provider burnout, while improving both medication compliance and patient engagement, is the way forward.
Exosome Therapy’s Regenerative Effects on Skin and Hair RejuvenationAdvancexo
Explore the transformative effects of exosome therapy on skin and hair rejuvenation. Learn how these tiny vesicles deliver essential growth factors and stimulate cellular repair, offering natural solutions for aging skin and hair loss. Discover the science behind exosomes and their benefits in aesthetic dermatology.
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