Enterprise Data World 2016 and CDO Vision Mural Summary

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The document lists the topics covered at a conference on April 19th, 2016 facilitated by Hannah Sanford. The topics included an opening and closing keynote, sessions on linking data strategy to mission, technology innovation and leadership, enterprise analytics strategy, scaling data culture, the chief data officer role, information asset management, data quality issue management, data modeling for NoSQL databases, and an advanced seminar on data governance.

19Apr2016 Graphic Facilitation by Hannah Sanford
Graphic Mural Summary
for

19Apr2016 Graphic Facilitation by Hannah Sanford
Opening Keynote

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – Linking Data to Strategy and Mission

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – Technology Innovation and Leadership

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – Enterprise Analytics Strategy

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – Scaling and Sustaining the Data Culture

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – The Top Data Job: Changing Minds and Maintaining Commitment

19Apr2016 Graphic Facilitation by Hannah Sanford
CDOVision – Information Asset Management on Both Sides of the Balance Sheet

19Apr2016 Graphic Facilitation by Hannah Sanford
Closing Keynote

19Apr2016 Graphic Facilitation by Hannah Sanford
Panel Discussion: Enterprise Data Quality Issue Management and Data Stewardship

19Apr2016 Graphic Facilitation by Hannah Sanford
Panel Discussion: Data Modeling for NoSQL Databases

19Apr2016 Graphic Facilitation by Hannah Sanford
Closing Keynote: Town Hall

19Apr2016 Graphic Facilitation by Hannah Sanford
Advanced Seminar: Advanced Data Governance

In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this installment, we will hear from Rafael Richards, Physician Informatician, Office of Informatics and Analytics in the Veterans Health Administration (VHA), about “Transformations for Integrating VA data with FHIR in RDF.” The VistA EHR has its own data model and vocabularies for representing healthcare data. This webinar describes how SPARQL Inference Notation (SPIN) can be used to translate VistA data to the data represented used by FHIR, an emerging interchange standard.

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In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.

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Our speaker, Joshua Mandel, will provide a lightning tour of Fast Healthcare Interoperability Resources (FHIR), an emerging clinical data standard, with a focus on its resource-oriented approach, and a discussion of how FHIR intersects with the Semantic Web. We'll look at how FHIR represents links between entities; how FHIR represents concepts from standards-based vocabularies; and how a set of FHIR instance data can be represented in RDF.

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Enterprise Data World: Data Governance - The Four Critical Success Factors

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Let’s face it, developing and implementing an Enterprise Data Governance program can be very frustrating. Issues can pop up quite unexpectedly. Support ebbs and flows for seemingly illogical reasons. And, acceptance and adoption seem to be hit or miss. So, how can practitioners ensure their program will be as successful as possible? This webinar is designed to help practitioners understand the Four Critical Success Factors necessary for developing and sustaining an effective Data Governance program, as identified by Joy Medved based on her 20+ years as an international data consultant. Joy will provide an overview of the Four Critical Success Factors, as well as share Common Program Barriers she has experienced that lead to success breakdown. Joy will also help practitioners learn how to identify if one or more of these critical success factors is plaguing your program, and which barriers might be at fault. Rounding out the topic, Joy will share her Key Program Components, designed to help ensure successful Data Governance development and implementation. Some of the topics discussed in this webinar include: The Four Critical Success Factors for developing and implementing a DG program Common Program Barriers that may be hampering your ability to drive a successful program How to identify which barriers might be plaguing your program efforts Key Program Components to help ensure a successful DG program

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Enterprise Data World Webinar: Mastering & Referencing Data for the Enterprise

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Developing and implementing a master record and reference data through a best practice approach and lessons learned for your organization’s data business needs. The ability to leverage the immense data available across an organization has become paramount for continued growth and success. Companies need accessible and reliable information to make more informed and effective business decisions across channels while continuing to provide quality service to their customers. Many companies recognize the need to develop a Master Data Program, as part of an overall Data Strategy initiative, to consistently manage the quality, data interoperability, accessibility, and availability of core data (e.g., customer, product). Determining where to start, finding business opportunities, defining what master data management means in your organization, and addressing the cultural and political realities are all critical to make your master data program successful and valuable. This seminar will present real world approaches used for successfully implementing a Master Data Domain and program from several organization cases including Walgreens.

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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.

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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

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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.

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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.

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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?

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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

Data Management Best Practices

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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.

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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.

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Enterprise Data World 2016 and CDO Vision Mural Summary

1. 19Apr2016 Graphic Facilitation by Hannah Sanford Graphic Mural Summary for

2. 19Apr2016 Graphic Facilitation by Hannah Sanford Opening Keynote

3. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – Linking Data to Strategy and Mission

4. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – Technology Innovation and Leadership

5. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – Enterprise Analytics Strategy

6. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – Scaling and Sustaining the Data Culture

7. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – The Top Data Job: Changing Minds and Maintaining Commitment

8. 19Apr2016 Graphic Facilitation by Hannah Sanford CDOVision – Information Asset Management on Both Sides of the Balance Sheet

9. 19Apr2016 Graphic Facilitation by Hannah Sanford Closing Keynote

10. 19Apr2016 Graphic Facilitation by Hannah Sanford Panel Discussion: Enterprise Data Quality Issue Management and Data Stewardship

11. 19Apr2016 Graphic Facilitation by Hannah Sanford Panel Discussion: Data Modeling for NoSQL Databases

12. 19Apr2016 Graphic Facilitation by Hannah Sanford Closing Keynote: Town Hall

13. 19Apr2016 Graphic Facilitation by Hannah Sanford Advanced Seminar: Advanced Data Governance

Enterprise Data World 2016 and CDO Vision Mural Summary

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