Big Challenges in Data Modeling: Data Modeling is Dead! Long Live Data Modeling!

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Your Moderator
Karen Lopez
Sr. Project Manager / Architect
Infoadvisors
@datachick
Your Panelists

Thomas LaRock Gabriel Tanase
SQL Server MCM and Data Architect
Technical Evangelist
@sqlrockstar

#BCDModeling

Big Challenges in Data Modeling: Data Modeling is Dead! Long Live Data Modeling!

Big Challenges in Data Modeling: Data Modeling is Dead! Long Live Data Modeling!

Big Challenges in Data Modeling: Data Modeling is Dead! Long Live Data Modeling!

Big Challenges in Data Modeling: Data Modeling is Dead! Long Live Data Modeling!

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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

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Data Modeling Fundamentals

Data Modeling Fundamentals

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.

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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.

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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.

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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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Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

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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

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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

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It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

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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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With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered. Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence. Topics you will hear addressed in this webinar: Data observability – what is it and how it can complement your data quality strategy Why now is the time to incorporate data observability into your DataOps strategy How data observability helps prevent data issues from impacting downstream analytics Examples of how data observability can be used to prevent real-world issues

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By consolidating data engineering, data warehouse, and data science capabilities under a single fully-managed platform, BigQuery can accelerate computation, reduce data analysis costs, and streamline data management. Following in-depth interviews with a security services provider and a telecommunications company, Nucleus Research found that customers moving to Google Cloud BigQuery from on-premises data warehouse solutions accelerate data processing by over 75 percent while reducing data ongoing administrative expenses by over 25 percent. As BigQuery continues to optimize its platform architecture for compute efficiency and multicloud support, Nucleus expects the vendor to see rapid adoption and further penetrate the data warehouse market.

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Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.

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When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved. Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment. In this webinar, Bob will focus on: - Criteria to select the appropriate best practices for your organization - How to define the best practices for ultimate impact - Assessing against selected best practices - Focusing the recommendations on program success - Delivering a roadmap for your Data Governance program

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Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.  Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs. Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.  Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs. While vendor offerings have exploded in recent years, in due time frameworks will integrate components into what amounts to, for practical purposes, a single offering for multiple workloads, perhaps even for the enterprise. A multi-model database is a database that can store, manage, and query data in multiple models, such as relational, document-oriented, key-value, graph (triplestore), and column store. An enterprise will find reduced overhead and other synergies from choosing a single vendor for these workloads. This session will explore the multi-model option and some criteria that decision makers should evaluate when choosing a multi-model solution.

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