7 steps for guides how to build a successful data strategy

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

RWDG Slides: Achieving Data Quality with Data Governance

To improve Data Quality, organizations must focus on improving three data-related activities – the definition, production, and usage of the data. Formalizing accountability for these activities strengthens the stewards’ ability to influence improvements in the quality of the data. In this RWDG webinar, Bob Seiner and his guest, Anthony J. Algmin, will share examples of how organizations have focused their Data Governance programs on achieving improvements in Data Quality. The delivery of the program must advocate and enhance the delivery of standards, validation, reporting, and data value improvement. You may be surprised by how that delivery can be simplified. In this webinar, Bob will talk about: • The relationship between Data Governance and Data Quality • The activities of defining, producing, and using data • Stewards influencing improvements in Data Quality • Standardization and validation of data through Data Governance • Simplifying Data Governance’s purpose toward Data Quality

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.

Data strategy - The Business Game Changer

Amit Pishe

RWDG Slides: Applying Governance to Business Processes

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.

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?

DMBOK and Data Governance

Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.

Real-World Data Governance: Setting Appropriate Business Expectations

This document announces a webinar on setting appropriate business expectations for data governance. The webinar will discuss level-setting expectations with business stakeholders and sponsors to define what success means for governing data at their organization. It will also cover considerations for setting expectations, such as existing governance capabilities and maintaining a non-invasive approach. Common mistakes to avoid include lack of executive support and proper planning.

Slides: Powering a Sustainable Data Governance Program – Learnings & Best Pra...

This document summarizes a presentation about E.ON Energy's data governance program. E.ON implemented a metadata management platform and data governance practices to address issues like limited data access, lack of a data catalog, and inefficient data usage. Initial results included defining data domains, connecting systems, training users, and standardizing reporting. The program aims to accelerate value from data, ensure compliance, demand trusted insights, and foster collaboration across the organization. Senior leaders must engage to support such initiatives, and building a data-driven culture is key to success.

Real-World Data Governance: Metadata to Empower Data Stewards - Introducing t...

Metadata is the most valuable tool of the Data Steward. Where the stewards get their metadata and how they participate in the process of delivering core metadata is an issue organizations have been struggling with for years. The Operational Metadata Store or OMS may be the answer. The traditional Operational Data Store or ODS is a database designed to integrate data from numerous sources that supports business operations and then feeds that data back into the operational systems. This Real-World Data Governance webinar with Bob Seiner and a panel of industry pundits will hold a lively discussion on the practicality of creating the ODS using metadata as the data, utilizing the metadata from a variety of existing sources to operationalize your data stewards. The session will focus on: Identifying the most significant metadata for your organization Identifying existing sources of metadata – known and hidden Identifying when that metadata will be most useful to your data stewards Defining a lifecycle that encourages data steward participation Delivering a model that incorporates all of the above

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RWDG Slides: Operationalize Data Governance for Business Outcomes

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

Measuring Data Quality Return on Investment

Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.

What You Should & Shouldn't Tell Your Management about Data Governance

The document provides guidance on what to communicate to management about data governance. It recommends emphasizing that data governance formalizes existing informal practices, can improve areas like risk management and data quality, and does not require large investments. It advises against positioning data governance as a huge challenge, technical solution, or revolution. Business value examples show how data governance could address issues like undocumented workarounds.

Aug 2017 damaga-peter-vennel

The document introduces Peter Vennel, who is the Head of Data Management at Safe-Guard Products International. It provides an overview of Peter's credentials and roles in data governance organizations. The document then covers key topics in data management and governance, including definitions, operating models, roles and responsibilities, and maturity assessments. It concludes with an overview of Peter's data governance journey at Safe-Guard Products International so far.

Ashish dwivedi

Ashish Dwivedi

Real-World Data Governance: Metadata & Data Governance

This document describes an upcoming webinar on real-world data governance. The webinar will discuss the relationship between metadata and data governance, with metadata seen as both a natural byproduct of an effective data governance program and something that must itself be governed. The webinar will also cover how to leverage this relationship to improve data management and view metadata and data governance as complementary. Additional webinars on related topics are scheduled for August through December.

Necessary Prerequisites to Data Success

Far more organizations attempt to do more with data than succeed. Understanding common prerequisites to unrestricted data practices will help you determine the extent of these challenges in your organization and increase your chances of success. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that, there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers — aka the “Seven Deadly Data Sins” — and in the process will also - Elaborate upon the three critical factors that lead to strategy failure - Demonstrate a two-stage Data Strategy implementation process - Explore the sources and rationales behind the “Seven Deadly Data Sins” and recommend solutions

Real-World Data Governance: Comparing World Class Solutions in Data Governanc...

This document outlines the agenda for a webinar on comparing world class data governance solutions. The webinar will feature a panel of practitioners discussing their approaches to data stewardship, metadata, and master data governance. The panelists include professionals from PNC Bank, the Church of Latter Day Saints, and the Data Governance Institute. The webinar will be moderated by Robert Seiner and cover identifying data stewards, handling metadata, governing master data, and taking questions from the audience.

The Value of Metadata

North Texas Chapter of the ISSA

Metadata has the potential to impact nearly every part of your enterprise. From helping you connect data across business processes to holding the key to your most valuable assets, this underdog data is finally getting the attention it deserves. But, according to a Dataversity report on Metadata, nearly a third of organizations have only begun to address managing this valuable data and a quarter have no metadata strategy at all. Part of what has held organizations back is that metadata is notoriously sneaky data to manage, and even more difficult to put into action using traditional relational database technology. This webinar will look at the critical importance of metadata and highlight mission critical metadata apps that have taken a new approach with enterprise NoSQL technology and semantic data models. Organizations including commercial entities, intelligence agencies, and some of your favorite entertainment companies using this approach have made good on the promise of metadata, and this webinar will cover how you can make metadata the hero in your organization.

NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...

This document discusses how enterprise information management is key to effective governance, risk management, and compliance (GRC). It defines GRC and explains that traditional GRC strategies often fail because information is siloed across unstructured files and structured data systems. Effective GRC requires synchronizing information and activities across governance, risk, and compliance to operate efficiently, enable information sharing, report activities, and avoid duplication. The document proposes that an information management system like M-Files can bridge the gap by structuring unstructured content and building relationships between structured and unstructured data. This allows information to be more easily found, visualized, and analyzed to support GRC.

DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...

<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>   <p>This program's learning objectives include:</p>   <ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>

RWDG Slides: Building a Data Governance Roadmap

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

ADV Slides: 2021 Trends in Enterprise Analytics

The document discusses trends in enterprise advanced analytics for 2021 and beyond. Some key trends include remote work continuing, strong tech spending rebound led by cloud capabilities, leading organizations increasing focus on AI/ML with model deployment taking center stage, more edge AI, rise of data lakes, new technology stacks focusing on data fabrics and AI pipelines, increased automation, open source becoming more prevalent, Kubernetes becoming the standard analytics stack, and general AI beginning to emerge. Winning approaches for 2021 include cloud, AI, data lakes, data warehousing, MDM, agile development, Kubernetes, automation, data quality, and DevOps/MLOps.

How to Strengthen Enterprise Data Governance with Data Quality

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

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