This document discusses governing master data. It defines key terms like data governance and data stewardship. It explains the connection between master data and data governance, and why master data needs to be governed. It discusses applying governance roles and responsibilities to master data processes. Finally, it concludes that master data governance is focusing a data governance program on improving an organization's master data.
RWDG: Data Governance and Three Levels of Metadata DATAVERSITY
There are three levels of metadata that every organization must focus on. The three levels are the semantic level, the business level and the technical level. All three levels are important components of data governance and must be stewarded to focus on the goals and scope of your data governance program.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will present a three-tiered approach to defining, producing and using all levels of metadata to further the cause of data governance. Governing the processes associated with this metadata tends to be a central focus of successful data governance programs. Join Bob to learn how to simplify the metadata focus.
In this webinar Bob will discuss:
- The three levels of metadata and how they differ
- Sources of the metadata at each level
- Metadata linkage between the levels
- Processes to govern the all levels of metadata
- Institutionalizing policy to assure quality metadata at all levels
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Real-World Data Governance Webinar: Using Data Governance to Achieve Data Qua...DATAVERSITY
Data Governance programs can focus on improving the quality of data. Improvements in quality require that people are held formally accountable for following defined processes for defining, producing and using data across the organization. These processes become the focal point of institutionalizing data quality.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will speak about how to focus your data governance program on improving the quality of data across the organization. Bob will talk about the data governance roles and processes required change organizational behavior associated with defining, producing and using quality data.
In the webinar Bob will discuss:
Defining data governance in terms of data quality
Delivering roles appropriate for improving data quality
Selecting appropriate data quality processes to govern
Using working groups to focus on data quality projects
Measuring quality to demonstrate governance performance
RWDG Webinar: Using Data Governance to Improve Data UnderstandingDATAVERSITY
For many data-focused initiatives to be considered successful, they require improved documented understanding of the organization’s data. Improvements in data understanding require accountability for the actions of putting clear definition behind your organization’s most valuable data. It makes sense that this process and associated metadata are governed.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will speak about how to focus your data governance program on improving the understanding of your organization’s data. Bob will talk about the data governance roles and processes required to improve the understanding of data and maintain the documented definitions.
In the webinar Bob will discuss:
Metadata associated with improving the understanding of data
How to select the appropriate metadata to improve understanding
Selecting processes to govern associated with improving data understanding
How improved understanding leads to improvements in project ROI
Measuring data understanding to demonstrate governance performance
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Improving Data Analytics with Data GovernanceDATAVERSITY
Organizations are dedicating tremendous resources to improve their analytical capabilities. The focus for many is to improve the quality, understanding, availability and thus the value of the data for data scientists and analysts. These people are focused on providing descriptive, predictive and prescriptive analytics for the betterment of their organization. It all starts with governed data.
Join Bob Seiner and a special guest for this month’s installment of the Real-World Data Governance webinar series where they will discuss the importance of using Data Governance to improve Data Analytics. Bob will challenge the guest with questions about why and how data governance has a positive impact on getting the most out of your data.
In this webinar, Bob and his guest will discuss:
The relationship between Data Governance and Data Analytics
Getting management to understand why Data Governance is necessary
How to focus your Data Governance program on analytics
Using the focus on analytics to bolster your Data Governance program
Final words on the symbiotic relationship between Data Governance and Data Analytics
Business Value Metrics for Data GovernanceDATAVERSITY
This document discusses how to quantify and communicate the business value of data governance initiatives. It begins with background on information capability and data maturity levels. It then discusses frameworks for understanding business value, such as key performance indicators and how initiatives can generate revenue, cost savings or avoidance. The document provides examples of how to calculate return on investment, net present value and payback period to quantify benefits. It also discusses how to effectively communicate a business case by aligning it with organizational objectives and knowing your audience.
Successful Data Governance Models and FrameworksDATAVERSITY
There are three models that any organization can follow when implementing a Data Governance program. Programs can be developed to “command-and-control” the data. Programs can be developed to focus on a specific discipline such as protecting the data. And programs can focus on formalizing accountability for data across the board. Picking the model for your organization is the trick.
The treat is what will be discussed in this Real World Data Governance webinar with Bob Seiner. Bob will present a detailed assessment of each of the three models mentioned above. Many of the components of a successful program depend on the model selected. This webinar will outline and discuss these components.
In this webinar Bob will talk about:
• The three Data Governance models and frameworks
• Comparison of the models
• The up-side and downside of each model
• How to select the appropriate model for your organization
• Detailing the tricks while providing the treats
RWDG: Data Governance and Three Levels of Metadata DATAVERSITY
There are three levels of metadata that every organization must focus on. The three levels are the semantic level, the business level and the technical level. All three levels are important components of data governance and must be stewarded to focus on the goals and scope of your data governance program.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will present a three-tiered approach to defining, producing and using all levels of metadata to further the cause of data governance. Governing the processes associated with this metadata tends to be a central focus of successful data governance programs. Join Bob to learn how to simplify the metadata focus.
In this webinar Bob will discuss:
- The three levels of metadata and how they differ
- Sources of the metadata at each level
- Metadata linkage between the levels
- Processes to govern the all levels of metadata
- Institutionalizing policy to assure quality metadata at all levels
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Real-World Data Governance Webinar: Using Data Governance to Achieve Data Qua...DATAVERSITY
Data Governance programs can focus on improving the quality of data. Improvements in quality require that people are held formally accountable for following defined processes for defining, producing and using data across the organization. These processes become the focal point of institutionalizing data quality.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will speak about how to focus your data governance program on improving the quality of data across the organization. Bob will talk about the data governance roles and processes required change organizational behavior associated with defining, producing and using quality data.
In the webinar Bob will discuss:
Defining data governance in terms of data quality
Delivering roles appropriate for improving data quality
Selecting appropriate data quality processes to govern
Using working groups to focus on data quality projects
Measuring quality to demonstrate governance performance
RWDG Webinar: Using Data Governance to Improve Data UnderstandingDATAVERSITY
For many data-focused initiatives to be considered successful, they require improved documented understanding of the organization’s data. Improvements in data understanding require accountability for the actions of putting clear definition behind your organization’s most valuable data. It makes sense that this process and associated metadata are governed.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will speak about how to focus your data governance program on improving the understanding of your organization’s data. Bob will talk about the data governance roles and processes required to improve the understanding of data and maintain the documented definitions.
In the webinar Bob will discuss:
Metadata associated with improving the understanding of data
How to select the appropriate metadata to improve understanding
Selecting processes to govern associated with improving data understanding
How improved understanding leads to improvements in project ROI
Measuring data understanding to demonstrate governance performance
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Improving Data Analytics with Data GovernanceDATAVERSITY
Organizations are dedicating tremendous resources to improve their analytical capabilities. The focus for many is to improve the quality, understanding, availability and thus the value of the data for data scientists and analysts. These people are focused on providing descriptive, predictive and prescriptive analytics for the betterment of their organization. It all starts with governed data.
Join Bob Seiner and a special guest for this month’s installment of the Real-World Data Governance webinar series where they will discuss the importance of using Data Governance to improve Data Analytics. Bob will challenge the guest with questions about why and how data governance has a positive impact on getting the most out of your data.
In this webinar, Bob and his guest will discuss:
The relationship between Data Governance and Data Analytics
Getting management to understand why Data Governance is necessary
How to focus your Data Governance program on analytics
Using the focus on analytics to bolster your Data Governance program
Final words on the symbiotic relationship between Data Governance and Data Analytics
Business Value Metrics for Data GovernanceDATAVERSITY
This document discusses how to quantify and communicate the business value of data governance initiatives. It begins with background on information capability and data maturity levels. It then discusses frameworks for understanding business value, such as key performance indicators and how initiatives can generate revenue, cost savings or avoidance. The document provides examples of how to calculate return on investment, net present value and payback period to quantify benefits. It also discusses how to effectively communicate a business case by aligning it with organizational objectives and knowing your audience.
Successful Data Governance Models and FrameworksDATAVERSITY
There are three models that any organization can follow when implementing a Data Governance program. Programs can be developed to “command-and-control” the data. Programs can be developed to focus on a specific discipline such as protecting the data. And programs can focus on formalizing accountability for data across the board. Picking the model for your organization is the trick.
The treat is what will be discussed in this Real World Data Governance webinar with Bob Seiner. Bob will present a detailed assessment of each of the three models mentioned above. Many of the components of a successful program depend on the model selected. This webinar will outline and discuss these components.
In this webinar Bob will talk about:
• The three Data Governance models and frameworks
• Comparison of the models
• The up-side and downside of each model
• How to select the appropriate model for your organization
• Detailing the tricks while providing the treats
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
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)
Webinar: Maximizing Your Potential with Data LeadershipDATAVERSITY
Data is everywhere in today’s businesses, and there are countless things for the data professional to do! It can be overwhelming to figure out what we should be doing now, tomorrow, and further down the road. Data Leadership helps us simplify, prioritize, and ultimately find the direction we need.
The value that comes from data can impact an organization in three fundamental ways: increasing revenues, decreasing costs, and managing risk. Data professionals are tasked to optimize data’s impact on these. But knowing our goals—versus how to best achieve them—are two very different things.
The Data Leadership Framework guides us in sorting out the dozens of choices to determine the best actions to take, no matter where we are in our data journey. Attend this DATAVERSITY webinar to start maximizing data value with Data Leadership!
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
The Missed Promise of Hadoop and New and Emerging TechnologiesDATAVERSITY
Hadoop, which entered the scene with great fanfare, seems to be on its way out. Or is it? Are the pundits just being pontificating, or is there something to their end-of-life proclamations?
Investment in Hadoop has been substantial, so it's not going away. But "what comes next" holds real promise.
Join John and Kelle as they dig into the current state of Hadoop and address:
Hadoop’s relevancy today
The case for and against Hadoop
Alternatives to Hadoop
Other technologies on the upswing
Balancing Data and Processes to Achieve Organizational MaturityDATAVERSITY
Data maturity and process maturity are two sides of the same coin - each of these must be balanced against the other to achieve overall organizational maturity. A focus on continuous improvement is vital to achieving breakthrough results and to balance data and process alignment. IDERA Senior Product Manager Ron Huizenga will discuss the importance of data and process modeling to drive your enterprise architecture toward a more mature state using the Data and Process Maturity Models as a benchmark and measure of performance.
RWDG Slides: Stay Non-Invasive in Your Data Governance ApproachDATAVERSITY
There are three distinct approaches to implement Data Governance. The Command-and-Control Approach, the Traditional (if you build it they will come) Approach and the Non-Invasive Data Governance Approach. Some organizations select a single approach for their program while others select to follow a hybrid method.
Bob Seiner will provide information about each approach and indicate how the Non-Invasive Approach can follow the path of least resistance with the greatest success. You may be surprised to learn that many of your present activities can be leveraged to address Stewardship, Metadata, and governed processes – all directed at staying as non-invasive as possible.
In this webinar, Bob will discuss:
- A Data Governance framework completed in a Non-Invasive way
- How the three approaches differ and when to use each
- Sticking to a single approach versus implementing a hybrid model
- How to sell Data Governance as something you are already doing
- Using the Non-Invasive Approach to win friends and influence people.
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
RWDG Slides: Corporate Data Governance - The CDO is the Data Governance ChiefDATAVERSITY
The CDO is a relatively new and evolving role. Many CDO job descriptions detail specific Data Governance responsibilities. Some CDO job descriptions read all-data-governance and all-the-time. It has become obvious. The CDO is the new chief of Data Governance.
In this Real-World Data Governance webinar, Bob Seiner and special guest Anthony Algmin will focus on the evolution of the Chief Data Officer role and associated responsibilities. Someone must lead Data Governance and the CDO is the obvious choice. Attend this webinar to learn why.
In this webinar, Bob will present:
• A Detailed CDO Job Description
• Why the CDO is the Data Governance Chief
• The Makeup of the Chief’s Tribe
• Lessons Learned from the CDO’s Office
• Suggestions for new and existing CDOs
To gain insights from Business Intelligence, you need to easily see and understand what the data tells you by using data visualizations. While these charts and graphs can be eye candy, without proper context they are nothing more than pretty pictures. Data analysts and business analysts may use a variety of techniques to create the reports that they must generate for the business, and can benefit from a database tool that helps to simplify the task and accelerate the process. Join IDERA's Stan Geiger as he explains how to convey the meaning of data effectively and quickly create useful data visualizations for various audiences within your organization.
RWDG: Measuring Data Governance PerformanceDATAVERSITY
This document discusses ways to measure the performance of a data governance program. It describes measuring the acceptability of the program within the organization, such as the number of groups participating and customer satisfaction. It also describes measuring the business value of the program, like improvements in data documentation, understanding, quality and protection. The document provides examples of specific metrics that can be used, such as the number of critical data elements standardized or dollars saved/earned due to governance. It also discusses reporting metrics at different levels of a data governance framework.
RWDG Slides: Operationalize Data Governance for Business OutcomesDATAVERSITY
Data Governance adds value to the organization when it becomes operationalized and focused on providing improved business outcomes. People in the organization acknowledge Data Governance success when they see results based on how the formalized program operates.
Join Bob Seiner for this month’s webinar, where he will focus on how to operationalize Data Governance based on your program’s purpose and demonstrate value through the communications of business outcomes. New ways to operationalize Data Governance and engage data stewards will be highlighted.
Bob will discuss :
• What it means to operationalize Data Governance
• How to link Data Governance to business outcomes – both good and bad
• Program operations designed to provide business outcomes
• Using the program purpose to demonstrate value
• Ways to engage your stewards through their job function
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
RWDG Slides: Building a Data Governance RoadmapDATAVERSITY
A Data Governance roadmap is typically based on the results of a best practice assessment. The assessment defines the outcomes required to achieve Data Governance best practices while the roadmap details the “actionable streams” required to formalize a Data Governance program and achieve those outcomes.
In this month’s webinar, Bob Seiner will share the process he follows to build a Data Governance roadmap of actionable streams and the steps required to complete the streams. In addition, Bob will describe the activities that are common to most organizations getting started or evaluating the success of their program.
Topics to be discussed in this webinar include:
• Criteria for defining best practices
• Using the assessment results to build the roadmap
• Examples of repeated actionable streams
• The role of the program administrator in executing the roadmap
• Communicating the roadmap to the stakeholders
Advanced Databases and Knowledge ManagementDATAVERSITY
These days, there are other database technologies at play besides Hadoop. As more raw data is converted to action and knowledge, finding and understanding data requires other kinds of database technology. The days of the single-vendor database environment are over.
Join Kelle and John as they talk about new database management system (DBMS) technology, including some of the unique applications of graph databases, covering:
What is graph?
How is it used?
What are some other promising new database technologies?
Examples of Big Data, analytics and graphs at work
Accelerate Your Move to the Cloud with Data Catalogs and GovernanceDATAVERSITY
As more data is migrating to the cloud, whether to increase efficiencies or take advantage of new capabilities like AI and machine learning tools, organizations are challenged on how to do so in a consumable, trusted fashion. Join us for this webcast and hear how enterprises are using data catalogs to unify approaches across their cloud and on-premises worlds, and prioritize which data assets should be moved to cloud, resulting in a more consumable and trusted data lake and ecosystem.
Data-Centric Analytics and Understanding the Full Data Supply ChainDATAVERSITY
While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed.
The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies.
This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers:
Data-centric considerations that must be taken into account in the development of analytic models
Features of a modern data supply chain
Major components in the data supply chain, with a focus on Data Lakes
Major roles and responsibilities in the data supply chain
How analytics must interact with the data supply chain
Seiner dataversity-rwdg2017-05-operating modelofdatagovernanceroles-20170518f...DATAVERSITY
Roles and responsibilities are the foundation of a successful Data Governance program. An operating model of roles focuses on all levels of the organization including the executive, strategic, tactical and operational responsibilities. A complete model also includes roles that support the program.
In this month’s RWDG webinar, Bob Seiner will present a proven Operating Model of Data Governance Roles & Responsibilities that can be applied to the existing culture of any organization. This webinar may be the most important webinar of the year because of its impact on the rest of your data governance program.
In this webinar Bob will share information about:
The Operating Model as a pyramid diagram
Three different approaches to stewardship
Five distinct levels of responsibilities
Who is expected to participate at each level?
What will be “the ask” of these people?
Formalize Data Governance with Policies and ProceduresDATAVERSITY
Policies and procedures lie at the heart of institutionalizing data governance. Data Governance is defined as the act of “executing and enforcing authority” to follow the procedures and enforce the policies. You can formalize Data Governance by clearly defining and following policies and procedures.
Join Bob Seiner for this month’s installment of the Real-World Data Governance webinar series where he will discuss how data governance can be formalized in parallel to the delivery of data policy and detailed procedures. Challenges associated with the changing the behavior of the data stewards will be identified, discussed and resolved during this session.
In this webinar Bob will discuss:
The relationship between Data Governance and Data Policy
Core guidelines to embrace through policy
DG Roles and their importance to following Policies and Procedures
Using RACIs and similar constructs to formalize Data Governance
Measuring the results of formalizing policies and procedures
RWDG Webinar: Mastering and Master Data GovernanceDATAVERSITY
Master Data and Data Governance are connected at the hip. Master Data implies that the data in the MDM resource is well defined, quality produced and effectively used. Data Governance for MDM is put in place to assure that these three things are handled properly. We can learn important lessons from Master Data Governance that will help us in Mastering Data Governance.
In this month’s RWDG webinar, Bob Seiner will focus on using the governance of Master Data initiatives to put effective Data Governance practices in place across the entire organization. Master Data requires all of the core components of a Data Governance program that can be leveraged in ways that will interest MDM and DG practitioners alike.
This webinar will cover:
• The connection between MDM and Data Governance
• Components of MDM that Require Data Governance
• Leveraging Master Data Governance for the Greater Good
• Mastering the Master Data Governance Roles
• The Role of MDM in Enterprise Data Governance
RWDG Slides: Applying Governance to Business ProcessesDATAVERSITY
This document discusses applying governance to business processes. It begins by defining key terms like data governance, data stewardship, and non-invasive data governance. It then discusses how data governance is not a single process, but the application of governance to various business processes using the components of the data governance framework, including roles, processes, communications, metrics, and tools. The document provides examples of processes that can be governed and emphasizes that the goal is to involve the right roles in processes to achieve the right results.
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
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)
Webinar: Maximizing Your Potential with Data LeadershipDATAVERSITY
Data is everywhere in today’s businesses, and there are countless things for the data professional to do! It can be overwhelming to figure out what we should be doing now, tomorrow, and further down the road. Data Leadership helps us simplify, prioritize, and ultimately find the direction we need.
The value that comes from data can impact an organization in three fundamental ways: increasing revenues, decreasing costs, and managing risk. Data professionals are tasked to optimize data’s impact on these. But knowing our goals—versus how to best achieve them—are two very different things.
The Data Leadership Framework guides us in sorting out the dozens of choices to determine the best actions to take, no matter where we are in our data journey. Attend this DATAVERSITY webinar to start maximizing data value with Data Leadership!
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
The Missed Promise of Hadoop and New and Emerging TechnologiesDATAVERSITY
Hadoop, which entered the scene with great fanfare, seems to be on its way out. Or is it? Are the pundits just being pontificating, or is there something to their end-of-life proclamations?
Investment in Hadoop has been substantial, so it's not going away. But "what comes next" holds real promise.
Join John and Kelle as they dig into the current state of Hadoop and address:
Hadoop’s relevancy today
The case for and against Hadoop
Alternatives to Hadoop
Other technologies on the upswing
Balancing Data and Processes to Achieve Organizational MaturityDATAVERSITY
Data maturity and process maturity are two sides of the same coin - each of these must be balanced against the other to achieve overall organizational maturity. A focus on continuous improvement is vital to achieving breakthrough results and to balance data and process alignment. IDERA Senior Product Manager Ron Huizenga will discuss the importance of data and process modeling to drive your enterprise architecture toward a more mature state using the Data and Process Maturity Models as a benchmark and measure of performance.
RWDG Slides: Stay Non-Invasive in Your Data Governance ApproachDATAVERSITY
There are three distinct approaches to implement Data Governance. The Command-and-Control Approach, the Traditional (if you build it they will come) Approach and the Non-Invasive Data Governance Approach. Some organizations select a single approach for their program while others select to follow a hybrid method.
Bob Seiner will provide information about each approach and indicate how the Non-Invasive Approach can follow the path of least resistance with the greatest success. You may be surprised to learn that many of your present activities can be leveraged to address Stewardship, Metadata, and governed processes – all directed at staying as non-invasive as possible.
In this webinar, Bob will discuss:
- A Data Governance framework completed in a Non-Invasive way
- How the three approaches differ and when to use each
- Sticking to a single approach versus implementing a hybrid model
- How to sell Data Governance as something you are already doing
- Using the Non-Invasive Approach to win friends and influence people.
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
RWDG Slides: Corporate Data Governance - The CDO is the Data Governance ChiefDATAVERSITY
The CDO is a relatively new and evolving role. Many CDO job descriptions detail specific Data Governance responsibilities. Some CDO job descriptions read all-data-governance and all-the-time. It has become obvious. The CDO is the new chief of Data Governance.
In this Real-World Data Governance webinar, Bob Seiner and special guest Anthony Algmin will focus on the evolution of the Chief Data Officer role and associated responsibilities. Someone must lead Data Governance and the CDO is the obvious choice. Attend this webinar to learn why.
In this webinar, Bob will present:
• A Detailed CDO Job Description
• Why the CDO is the Data Governance Chief
• The Makeup of the Chief’s Tribe
• Lessons Learned from the CDO’s Office
• Suggestions for new and existing CDOs
To gain insights from Business Intelligence, you need to easily see and understand what the data tells you by using data visualizations. While these charts and graphs can be eye candy, without proper context they are nothing more than pretty pictures. Data analysts and business analysts may use a variety of techniques to create the reports that they must generate for the business, and can benefit from a database tool that helps to simplify the task and accelerate the process. Join IDERA's Stan Geiger as he explains how to convey the meaning of data effectively and quickly create useful data visualizations for various audiences within your organization.
RWDG: Measuring Data Governance PerformanceDATAVERSITY
This document discusses ways to measure the performance of a data governance program. It describes measuring the acceptability of the program within the organization, such as the number of groups participating and customer satisfaction. It also describes measuring the business value of the program, like improvements in data documentation, understanding, quality and protection. The document provides examples of specific metrics that can be used, such as the number of critical data elements standardized or dollars saved/earned due to governance. It also discusses reporting metrics at different levels of a data governance framework.
RWDG Slides: Operationalize Data Governance for Business OutcomesDATAVERSITY
Data Governance adds value to the organization when it becomes operationalized and focused on providing improved business outcomes. People in the organization acknowledge Data Governance success when they see results based on how the formalized program operates.
Join Bob Seiner for this month’s webinar, where he will focus on how to operationalize Data Governance based on your program’s purpose and demonstrate value through the communications of business outcomes. New ways to operationalize Data Governance and engage data stewards will be highlighted.
Bob will discuss :
• What it means to operationalize Data Governance
• How to link Data Governance to business outcomes – both good and bad
• Program operations designed to provide business outcomes
• Using the program purpose to demonstrate value
• Ways to engage your stewards through their job function
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
RWDG Slides: Building a Data Governance RoadmapDATAVERSITY
A Data Governance roadmap is typically based on the results of a best practice assessment. The assessment defines the outcomes required to achieve Data Governance best practices while the roadmap details the “actionable streams” required to formalize a Data Governance program and achieve those outcomes.
In this month’s webinar, Bob Seiner will share the process he follows to build a Data Governance roadmap of actionable streams and the steps required to complete the streams. In addition, Bob will describe the activities that are common to most organizations getting started or evaluating the success of their program.
Topics to be discussed in this webinar include:
• Criteria for defining best practices
• Using the assessment results to build the roadmap
• Examples of repeated actionable streams
• The role of the program administrator in executing the roadmap
• Communicating the roadmap to the stakeholders
Advanced Databases and Knowledge ManagementDATAVERSITY
These days, there are other database technologies at play besides Hadoop. As more raw data is converted to action and knowledge, finding and understanding data requires other kinds of database technology. The days of the single-vendor database environment are over.
Join Kelle and John as they talk about new database management system (DBMS) technology, including some of the unique applications of graph databases, covering:
What is graph?
How is it used?
What are some other promising new database technologies?
Examples of Big Data, analytics and graphs at work
Accelerate Your Move to the Cloud with Data Catalogs and GovernanceDATAVERSITY
As more data is migrating to the cloud, whether to increase efficiencies or take advantage of new capabilities like AI and machine learning tools, organizations are challenged on how to do so in a consumable, trusted fashion. Join us for this webcast and hear how enterprises are using data catalogs to unify approaches across their cloud and on-premises worlds, and prioritize which data assets should be moved to cloud, resulting in a more consumable and trusted data lake and ecosystem.
Data-Centric Analytics and Understanding the Full Data Supply ChainDATAVERSITY
While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed.
The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies.
This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers:
Data-centric considerations that must be taken into account in the development of analytic models
Features of a modern data supply chain
Major components in the data supply chain, with a focus on Data Lakes
Major roles and responsibilities in the data supply chain
How analytics must interact with the data supply chain
Seiner dataversity-rwdg2017-05-operating modelofdatagovernanceroles-20170518f...DATAVERSITY
Roles and responsibilities are the foundation of a successful Data Governance program. An operating model of roles focuses on all levels of the organization including the executive, strategic, tactical and operational responsibilities. A complete model also includes roles that support the program.
In this month’s RWDG webinar, Bob Seiner will present a proven Operating Model of Data Governance Roles & Responsibilities that can be applied to the existing culture of any organization. This webinar may be the most important webinar of the year because of its impact on the rest of your data governance program.
In this webinar Bob will share information about:
The Operating Model as a pyramid diagram
Three different approaches to stewardship
Five distinct levels of responsibilities
Who is expected to participate at each level?
What will be “the ask” of these people?
Formalize Data Governance with Policies and ProceduresDATAVERSITY
Policies and procedures lie at the heart of institutionalizing data governance. Data Governance is defined as the act of “executing and enforcing authority” to follow the procedures and enforce the policies. You can formalize Data Governance by clearly defining and following policies and procedures.
Join Bob Seiner for this month’s installment of the Real-World Data Governance webinar series where he will discuss how data governance can be formalized in parallel to the delivery of data policy and detailed procedures. Challenges associated with the changing the behavior of the data stewards will be identified, discussed and resolved during this session.
In this webinar Bob will discuss:
The relationship between Data Governance and Data Policy
Core guidelines to embrace through policy
DG Roles and their importance to following Policies and Procedures
Using RACIs and similar constructs to formalize Data Governance
Measuring the results of formalizing policies and procedures
RWDG Webinar: Mastering and Master Data GovernanceDATAVERSITY
Master Data and Data Governance are connected at the hip. Master Data implies that the data in the MDM resource is well defined, quality produced and effectively used. Data Governance for MDM is put in place to assure that these three things are handled properly. We can learn important lessons from Master Data Governance that will help us in Mastering Data Governance.
In this month’s RWDG webinar, Bob Seiner will focus on using the governance of Master Data initiatives to put effective Data Governance practices in place across the entire organization. Master Data requires all of the core components of a Data Governance program that can be leveraged in ways that will interest MDM and DG practitioners alike.
This webinar will cover:
• The connection between MDM and Data Governance
• Components of MDM that Require Data Governance
• Leveraging Master Data Governance for the Greater Good
• Mastering the Master Data Governance Roles
• The Role of MDM in Enterprise Data Governance
RWDG Slides: Applying Governance to Business ProcessesDATAVERSITY
This document discusses applying governance to business processes. It begins by defining key terms like data governance, data stewardship, and non-invasive data governance. It then discusses how data governance is not a single process, but the application of governance to various business processes using the components of the data governance framework, including roles, processes, communications, metrics, and tools. The document provides examples of processes that can be governed and emphasizes that the goal is to involve the right roles in processes to achieve the right results.
RWDG Slides: Master Data Governance in ActionDATAVERSITY
Master data is data essential to operations in a specific subject area. Information treated as master data varies from one subject to another and even from one company to another. However defined, one thing for certain is that it does not become master data unless it is governed.
Join Bob Seiner for this RWDG webinar where he outlines a repeatable way to activate your Data Governance program by focusing on your master data initiatives. Get people to trust your data as the “master” by implementing a formal certification process.
In this webinar, Bob will discuss:
• What makes it Master Data Governance
• Aligning roles and responsibilities with Master Data Management (MDM)
• Qualities of “governed data”
• Governing to a “master” version of the truth
• Implementing Data Governance domain by domain
RWDG Webinar: Metadata to Support Data GovernanceDATAVERSITY
This document describes a webinar on using metadata to support data governance. It provides definitions of key terms like data governance, metadata, and non-invasive data governance. It explains that metadata is a byproduct of good governance practices like formalizing accountability and standards. The webinar will cover selecting important initial metadata, using metadata to support the governance program, and incorporating governance into processes to manage metadata. It promotes integrating governance roles and responsibilities into existing methodologies.
RWDG Slides: Utilize Governance Working Teams to Improve Data QualityDATAVERSITY
Data Governance working teams are typically formed with a specific purpose or function in mind. Teams are deployed to address enterprise-wide data issues, business function issues and operational issues. These teams are made up of the “right” people to solve the “right” problem at the “right” time. It is that easy. Or is it?
In this month’s RWDG webinar, Bob Seiner will share his experiences building working teams to improve how data is governed. Bob will talk about setting up the teams, ways to get resources to commit their time, and how to leverage their participation in a non-invasive manner.
In this webinar, Bob will discuss:
- When to make use of working teams
- How to construct a working team for a specific purpose
- Differences between working teams and communities of interest
- Monitoring and reporting on working team status
- How to deliver successful and repeatable problem-solving teams
RWDG Slides: The Stewardship Approach to Data GovernanceDATAVERSITY
This document discusses the stewardship approach to data governance. It describes how everybody who defines, produces, or uses data is a data steward. Rather than assigning data steward roles, the stewardship approach recognizes the existing responsibilities that people have. This reduces the invasiveness of data governance initiatives. The document provides guidance on engaging different types of data stewards based on their relationships to data and leveraging their existing responsibilities. It also addresses how the large number of stewards impacts the complexity of data governance programs and how best to deal with accountability.
RWDG Webinar: Big Data & BI Analytics Require Data GovernanceDATAVERSITY
Business Intelligence (BI) used to be equated to Data Warehousing. In this day of Big Data and improved analytical technologies and capabilities, BI now means a lot more. Where governing data in the data warehouse was a challenge – governing the volume of Big Data in variable formats coming at us from all directions at a high velocity to maximize its analytical value has become paramount to differentiating an organization from its competition.
Join Bob Seiner for a Real-World Data Governance webinar focused on strengthening the relationship between Data Governance and corporate Big Data & Business Intelligence initiatives. This session will focus on expanding existing programs to address the expanding needs of the organization and building new programs to address the broadened definition of BI.
This webinar will cover:
Existing Governance Applications for BI
Future of Big Data & BI Data
Relationship between Big Data, BI and Governance
Articulating Governance Value in Terms of BI
True Intelligence Derived from Governed Data
RWDG Slides: Three Ways to Manage Your Data StewardsDATAVERSITY
There are three ways to manage the data stewards in your organization. You can assign people to be data stewards, identify people as data stewards and you can recognize people as data stewards. The approach you select to associating people with their stewardship role may dictate how your data governance program is perceived by your organization.
Join Bob Seiner for this month’s installment of the Real-World Data Governance webinar series where he will be sharing three unique approaches to managing data stewards in your organization. Each approach brings with it benefits as well as challenges that must be addressed while planning a data governance program. Join us to learn how the approaches differ.
In this webinar Bob will discuss:
Details of the three ways to manage data stewards
How to select the appropriate way to manage data stewards
The benefits and challenges associated with each method
Preparing for how the organization will respond to each method
Staying true to the method you choose or altering your approach
RWDG Webinar Everybody is a Data StewardDATAVERSITY
This document discusses the concept that everybody in an organization can be considered a data steward. It begins by defining data governance and data stewardship, and introducing the concept of "Non-Invasive Data Governance". It then discusses how leadership is beginning to recognize that everyone with a relationship to organizational data should be held accountable for that relationship. The document considers how to expand the traditional view of data stewardship to include everybody, and potential benefits and challenges to this approach. It also outlines different types of data stewards and their typical responsibilities.
RWDG Webinar: Align Data Modeling with Data GovernanceDATAVERSITY
Data Modeling can be described as the discipline of data definition and database design. Data Governance must be applied to the definition, production, and usage of data in order to be effective. So therefore data modeling is an effective way to initiate a program to govern your data.
Join Bob Seiner for this month’s installment of the RWDG webinar series that will focus on how to align data modeling as a core competency of an effective data governance program. Data modeling that results in solid business definition and database design lays the groundwork for improved business understanding of the organization’s most important data.
In this webinar Bob will discuss:
Data modeling as a data governance discipline
Using data modeling to improve the business understanding of data
Why the data model is a key data governance artifact
How to use the data model as an effective communications tool
Including modeling as a core service associated with data governance
RWDG Slides: Using Agile to Justify Data GovernanceDATAVERSITY
The Agile development methodology is here to stay. Data Governance is not going away any time soon. These two discipline share some common ground but often compete when it comes to the “right” thing to do when it comes to managing the data. The disciplines need to learn to play well together. The old mantra of “do unto others” applies here in a big way.
In this month’s Real-World Data Governance webinar, Bob Seiner will share tips and techniques to take advantage of the Agile methodology to justify the need for, and practice of, Data Governance. The two disciplines are the core of delivering on-time quality data through timely applications. You will walk away from this session inspired to try ideas on your own organization.
This webinar will cover:
• The governance aspects of Agile
• Why Data Governance Practitioners Should Embrace Agile
• Agile considerations for Data Governance
• The audience of both Agile and Data Governance
• How to Use Agile to Justify Data Governance
Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJXDATAVERSITY
Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization.
Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization.
In this session Bob will share:
- The five levels of Data Governance roles
- A proven Operating Model of Roles and Responsibilities
- How to customize the model to meet your requirements
- Setting appropriate role expectations
- How to operationalize the roles and demonstrate value
Real-World Data Governance Webinar: Data Governance and Metadata Best PracticeDATAVERSITY
Best practices are defined as a method or technique that has consistently shown results superior to those achieved with other means, and that is used as a benchmark. In addition the definition goes on to say that a "best" practice can evolve to become better as improvements are discovered. A best practice can also be considered a target behavior to which you can compare your organization to deliver the actionable steps you can follow to achieve best practice.
In this Real-World Data Governance webinar, Bob Seiner focuses on defining, assessing and deploying Data Governance and metadata best practice that will move your organization in the best possible direction of success. Participants can expect to leave the webinar with a working list that can be used for self or contracted assessment.
This session will cover:
Criteria to Determine if Something is Best Practice
Development of Data Governance Best Practice
The Process to Complete the Best Practice Assessment
The Delivery of the Assessment to Management
How to Use the Assessment to Deliver Action
Using Data Governance to Protect Sensitive DataDATAVERSITY
Many Data Governance programs start out by focusing on the protection of sensitive data. Improvements in protection of data require that people are held formally accountable for following the rules associated with appropriate handling of sensitive data. Communications and awareness of data classification and data handling processes become the focus of keeping data private.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will speak about how to focus your data governance program on protecting sensitive data. Bob will talk about the data governance roles and processes required to classify data and enforce the rules associated with protecting sensitive data. It may be less complicated than you think.
In the webinar Bob will discuss:
Tips and techniques for classifying data and defining data handling rules
Delivering roles appropriate for protecting sensitive data
Selecting appropriate data sharing processes to govern
Incremental implementation to protect the entire organization
Measuring protection to demonstrate governance performance
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Everybody is a Data Steward – Get Over It!DATAVERSITY
When Data Stewardship is based on people’s relationships to data, the program is assured to cover the entire organization. People that define, produce, and use data must be held formally accountable for their actions. That may include every person in your organization. Is this a good thing? Of course, it is.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series, where he will share how formalizing accountability, based on the actions people take with data, requires heightened awareness and enforcement of data rules. These rules focus on improving Data Quality, protecting sensitive data, and increasing people’s knowledge of the data that adds value for their business.
In this webinar, Bob will discuss:
Why the “Everybody is a Data Steward” approach is different (and better)
How to recognize the Data Stewards
Formalizing accountability based on data relationships
Coverage of the entire organization
Leveraging the technique to sell stewardship
RWDG Slides: Activate Your Data Governance PolicyDATAVERSITY
What does it mean to activate a Data Governance policy? Can an inactive policy be effective? Data Governance policies can address different things depending on the organization. Some policies are very general and introduce the awareness of formal Data Governance to the organization. Other policies address specific needs like Data Quality, data documentation, and data protection.
Join Bob Seiner and a special guest for this RWDG webinar where they will tackle of the subject of how to develop and deploy an active Data Governance policy. Bob and his guest will provide specific examples of policy components and examples of how organizations use policies to govern their data.
In this webinar, Bob and his guest will discuss:
- When a Data Governance policy is necessary (and when it isn’t)
- The difference between an active and inactive policy
- Tips for activating a Data Governance policy
- Using the policy to drive Data Governance
- Getting people to follow a Data Governance policy
RWDG Slides: Apply Data Governance to Agile EffortsDATAVERSITY
Data Governance Programs and Agile Data Projects are known to conflict when it comes to how the information and data is managed. Senior leadership has come to expect both the formal governance of data and data projects to be delivered quickly and effectively. These two requirements continue to cause problems.
Bob Seiner will discuss how to govern data during Agile projects during this month’s installment of the RWDG webinar series. It is inevitable that governance and Agile need to work together and complement each discipline’s intended results. Bob will share several considerations for bringing the two together.
During this webinar Bob will discuss:
- Looking for common ground to stand on
- The data goals of an Agile effort
- The Agile goals of a Data Governance program
- Bridging the gap and building understanding
- Steps to apply governance to Agile efforts
RWDG Webinar: Data Steward Definition and Other Data Governance RolesDATAVERSITY
1. The document discusses defining data steward roles and responsibilities in a data governance program. It describes different approaches to defining data stewards and levels of data stewards, from operational to tactical.
2. The webinar will cover selecting the right approach to data stewardship for an organization and discussing an operating model of data governance roles at different levels, from executive to operational.
3. The role of the data steward is critical to data governance success and there are various ways to identify and recognize data stewards based on their existing responsibilities and relationships to the data they define, produce and use.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
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.
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.
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
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.
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
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
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.
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 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.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!