DataOps, GitOps, and Docker containers are changing the role of Data Modeling, now at the center of end-to-end metadata management.
Success in the world of self-services analytics, data meshes, as well as micro-services and event-driven architectures can be challenged by the need to maintain interoperability of data catalogs/dictionaries with the constant evolution of schemas for databases and data exchanges.
In other words, the business side of human-readable metadata management must be up-to-date and in-sync with the technical side of machine-readable schemas. This process can only work at scale if it is automated.
It is hard enough for IT departments to keep in-sync schemas across the various technologies involved in data pipelines. For data to be useful, the business users must have an up-to-date view of the structures, complete with context and meaning.

In this session, we will review the options available to create the foundations for a data management framework providing architectural lineage and curation of metadata management.

The integration of Generative AI into marketing data modeling marks a transformative step in understanding and engaging customers through data-driven strategies. This presentation will explore the definition and capabilities of Generative AI, such as predictive analytics and synthetic data generation. We will highlight specific applications in marketing, including personalized customer interactions, automated content creation, and enhanced real-time decision-making in digital advertising.

Technical discussions will delve into the methodologies for integrating Generative AI with existing marketing systems, emphasizing neural networks and Transformer models. These technologies enable sophisticated behavioral predictions and necessitate robust data infrastructures like cloud computing. Additionally, we will address the ethical implications of Generative AI, focusing on the importance of mitigating biases and maintaining transparency to uphold consumer trust and regulatory compliance.

Looking forward, we will explore future trends in Generative AI that are poised to redefine marketing strategies further, such as AI-driven dynamic pricing and emotional AI for deeper consumer insights. The speech will conclude with strategic recommendations for marketers on how to leverage Generative AI effectively, ensuring they remain at the cutting edge of technological innovation and competitive differentiation.

 You will:

• Understand the Role of Generative AI in Marketing Data Modeling in Personalization and Efficiency: Learn how Generative AI can be utilized to analyze consumer data, enhance targeting accuracy, automate content creation, and improve overall marketing efficiency through personalized customer interactions.
• Recognize the Importance of Ethical Practices in AI Implementation of Data Modeling and Execution: Identify the ethical considerations necessary when integrating AI into marketing strategies, including how to address data privacy, avoid algorithmic bias, and maintain transparency to ensure responsible use of AI technologies.
• Anticipate and Adapt to Future AI Trends in Marketing: Acquire insights into emerging AI trends such as dynamic pricing and emotional AI, and develop strategies to incorporate these advancements into marketing practices to stay ahead in a rapidly evolving digital landscape.

Dr. Kyle Allison, renowned as The Doctor of Digital Strategy, brings a wealth of expertise from both the industry and academia in the areas of e-commerce, business strategy, operations, digital analytics, and digital marketing. With a remarkable track record spanning more than two decades, during which he ascended to C-level positions, his professional journey has encompassed pivotal roles at distinguished retail and brand organizations such as Best Buy, Dick’s Sporting Goods, Dickies, and the Army and Air Force Exchange Service.

Throughout his career, Dr. Allison has consistently led the way in developing and implementing innovative strategies in the realms of digital marketing and e-commerce. These strategies are firmly rooted in his unwavering dedication to data-driven insights and a commitment to achieving strategic excellence. His extensive professional background encompasses a wide array of business sectors, including the private, public, and government sectors, where he has expertly guided digital teams in executing these strategies. Dr. Allison’s deep understanding of diverse business models, coupled with his proficiency in navigating B2B, B2C, and DTC digital channels, and his mastery of both the technical and creative aspects of digital marketing, all highlight his comprehensive approach to digital strategy.

In the academic arena, Dr. Allison has been a pivotal figure in shaping the future generation of professionals as a respected professor & mentor, imparting knowledge in fields ranging from digital marketing, analytics, and e-commerce to general marketing and business strategies at prestigious institutions nationwide, spanning both public and private universities and colleges. Beyond teaching, he has played a pivotal role in curriculum development, course creation, and mentorship of doctoral candidates as a DBA doctoral chair.

As an author, Dr. Allison has significantly enriched the literature in business strategy, analytics, digital marketing, and e-commerce through his published works in Quick Study Guides, textbooks, journal articles, and professional trade books. He skillfully combines academic theory with practical field knowledge, with a strong emphasis on real-world applicability and the attainment of educational objectives.

Dr. Allison’s educational background is as extensive as his professional accomplishments, holding a Doctor of Business Administration, an MBA, a Master of Science in Project Management, and a Bachelor’s degree in Communication Studies.

For the latest updates on Dr. Allison’s work and portfolio, please visit DoctorofDigitalStrategy.com.

Do you want to communicate better with consumers and creators of enterprise data?  Do you want to effectively communicate within IT application teams about the meaning of data?  Do you want to break down data silos and promote data understanding?  Conceptual data models are the answer!

Many companies skip over the creation of conceptual data models and go right to logical or physical modeling.  They miss reaping the benefits that conceptual models provide:

• Facilitate effective communication about data between everyone who needs to understand basic data domains and definitions.
• Break down data silos to provide enterprise-wide collaboration and consensus.
• Present a high-level, application neutral overview of your data landscape.
• Identify and mitigate data complexities.

You will learn what a conceptual model is, how to create one on the back of a napkin in 10 minutes or less, and how to use that to drive communication at many levels.

Kasi Anderson has been in the data world for close to 25 years serving in multiple roles including data architect, data modeler, data warehouse design and implementation, business intelligence evangelist, data governance specialist, and DBA.  She is passionate about bridging the gap between business and IT and working closely with business partners to achieve corporate goals through the effective use of data. She loves to examine data ecosystems and figure out how to extend architectures to meet new requirements and solve challenges. She has worked in many industries including manufacturing and distribution, banking, healthcare, and retail.

 In her free time, Kasi loves to read, travel, cook, and spend time with her family.  She enjoys hiking the beaches and mountains in the Pacific Northwest and loves to find new restaurants and wineries to enjoy.       

Laurel Sturges, a seasoned data professional, has been an integral part of the tech community helping businesses better understand and utilize data for over 40 years. She refers to problem solving as an adventure where she really finds passion in the process of discussing and defining data, getting into all the details including metadata, definitions, business rules, and everything that goes along with it.

Laurel is an expert in creating and delivering quality business data models and increasing communication between key business stakeholders and IT groups. She provides guidance for clients to make informed decisions so her partners can build a quality foundation for success.

She has a diverse background serving in a multitude of roles educating individuals as a peer and as an external advisor. She has served in many industries including manufacturing, aviation, and healthcare. Her specialization is relational data theory and usage of multiple modeling tools.

Outside of the data world, Laurel is learning to garden and loves to can jams and fresh fruits and veggies. Laurel is an active supporter of Special Olympics of Washington and has led her company’s Polar Plunge for Special Olympics team for 10 years, joyfully running into Puget Sound in February!

This presentation is aimed at anyone looking to make their data warehouse initiatives and products more agile and improve collaboration between teams and departments.

Flight Levels will be introduced—a lightweight and pragmatic approach to business agility. It helps organizations visualize and manage their work items across different operational levels to ensure effective collaboration and alignment with strategic goals.

The presentation explains the three Flight Levels:

• Strategic Level: Focuses on executing the long-term goals and strategic initiatives to set the direction for the entire organization.
• Coordination Level: Coordinates work between various teams and departments to manage interactions and dependencies.
• Operational Level: Concentrates on daily work and project execution by operational teams.

By visualizing work, improving communication, and continuously optimizing collaboration, Flight Levels provide a hands-on approach to enhancing agility in any organization. Practical examples from Deutsche Telekom will illustrate how Flight Levels are successfully used to increase transparency, efficiency, and value creation.

KERSTIN LERNER is a Flight Levels Guide at Deutsche Telekom and an independent agile coach. She has more than 15 years of international experience in a variety of agile roles. Through her coaching and training and as a knowledge catalyst, she helps leaders, organizations and teams to be more successful in their business.

Creating measurable and lasting business value from data involves much more than just curating a data set or creating a data product, and then feeding it into a BI, ML, or AI tool. This talk will explain 11 core organizational capabilities required to create business value from data and information and explain how companies have successfully leveraged these capabilities in their BI, ML, and AI initiatives. Examples will also be given of companies that have failed in these initiatives by ignoring these foundational principles.

Larry Burns has worked in IT for more than 40 years as a data architect, database developer, DBA, data modeler, application developer, consultant, and teacher. He holds a B.S. in Mathematics from the University of Washington, and a Master’s degree in Software Engineering from Seattle University. He most recently worked for a global Fortune 200 company as a Data and BI Architect and Data Engineer. He contributed material on Database Development and Database Operations Management to the first edition of DAMA International’s Data Management Body of Knowledge (DAMA-DMBOK) and is a former instructor and advisor in the certificate program for Data Resource Management at the University of Washington in Seattle. He has written numerous articles for TDAN.com and DMReview.com and is the author of Building the Agile Database (Technics Publications LLC, 2011), Growing Business Intelligence (Technics Publications LLC, 2016), and Data Model Storytelling (Technics Publications LLC, 2021). His forthcoming book is titled, “A Vision for Value”.

Data modeling is on life support. Some say it’s dead. The traditional practices are increasingly ignored, ridiculed, and forgotten by modern data practitioners. Diehard data modeling practitioners are wandering in the forest. To put it politely, the world of data modeling is in an existential crisis.

At the same time, data modeling is more critical than ever. With AI’s rising popularity, many organizations rush to incorporate it into their infrastructure. Without consideration of the underlying data framework, the result will be unpleasant for many organizations.

In this talk, Joe argues that data modeling is a key enabler for success with AI and other data initiatives. For data modeling to survive, we must return to basics and revamp data modeling to work with modern business workflows and technologies. Long live data modeling!

Joe Reis is a “recovering data scientist” and co-author of the best-selling O’Reilly book, Fundamentals of Data Engineering. He’s a business-minded data nerd who’s worked in the data industry for 20 years, with responsibilities ranging from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else. 

Joe hosts the popular data podcasts The Monday Morning Data Chat and The Joe Reis Show, keynotes major data conferences worldwide, and advises and invests in next-generation data product companies.

Data is a cornerstone of contemporary organisational success, a notion exemplified by financial entities such as Nedbank, which navigate both market and regulatory landscapes. This talk will chart Nedbank’s voyage in data modelling, centering on a significant data migration initiative. The narrative will be framed by the PPP paradigm-People, Process, and Technology.We will share the human element challenges in team development and retention, our internal and external process collaborations, and the pivotal role these play in our modelling endeavours. The technology segment will cover our adaptation and utilisation of the IBM banking and financial markets data model, detailing the implementation, customisation, and the technological infrastructure supporting our data and processes. Insights into our successes, learnings, and areas for enhancement will be shared, culminating in a contemplation of our project’s triumphs and future prospects.

Thembeka Snethemba Jiyane is a distinguished data modeler at Nedbank Group, renowned for her expertise in data analysis, warehousing, and business intelligence. With a robust background in SQL and data modeling tools, she excels in delivering top-tier data solutions. Her career includes pivotal roles at the South African Reserve Bank, BCX, and Telkom, which have all contributed to her profound knowledge and experience. Thembeka holds a BSc in Computer Science and Mathematics and an Honours BSc in IT, reflecting her commitment to continuous learning. Her analytical prowess and problem-solving skills are matched by a passion for leveraging data to drive organizational transformation, making her an invaluable contributor to any enterprise.

How can we use the niche technologies and power of LLMs and Gen AI to automate data modeling? Can we feed business requirements with correct prompt engineering to enable architects to use LLMs to create domain, logical and physical models? Can it help recommend entities, attributes and their definitions? How can Gen AI be used to integrate company specific guidelines into these models? Can it prompt on potential attributes that may contain PII data? Can this further extent into testing the performance of query patterns on these tables and suggest improvements before this over to development team use? Please join us to see how we can unlock the potential of LLMs and Gen AI as data architects.

Eve Danoff is an Engineering Director in Next Generation Data Management at American Express. With over 30 years of experience in data architecture and database design, she collaborates closely with critical platforms across Technology organizations, inspiring her team to apply a consistent and scalable approach to building and managing innovative domain and database design solutions. 

Tinto Kurian is an Sr. Engineering Manager in Next Generation Data Management at American Express. He has a Master’s in Data Science and over 18 years of experience designing, building and supporting mission critical distributed, scalable solutions using niche technology solutions while collaborating with business leaders in implementing transformative initiatives for corporate systems.

The Health industry costs modern economies close to 10% of GDP.  This means it is a very large, complex and political environment.  Every data person wants to create a data standard (if only the world spoke one language and it was mine!).  We have been building common data models that map to everyone else’s standards.  This means that we support multiple standards, integrate multiple different data sources, provide common outputs.  This session contains both political and technical techniques we used to produce a “standard” data model from multiple sources, stakeholders and for multiple purposes.

This session will work from the top to the bottom on how we achieved this outcome (which is ongoing).

Lloyd is a global authority on the practical development of data management implementation. He is a published author, presenter and trainer and actively contributes to advancing the data management profession. Lloyd has overseen and executed many enterprise-scale data management projects: across four continents, over 38 years, and spanning the financial services, utilities, government and education industries. He has implemented well over $1Bn of change.  He is a strategic shaper of large-scale data management change initiatives, in the role of strategist, architect or governance facilitator.

James Nettle is a data management professional with a background in plant science and ecology. He holds a Bachelor of Science in Plant Science/Ecology with honours from the University of Sydney, a Master of Science Innovation from Macquarie University, and a Data Vault Mastery Level Accreditation from Genesee Academy.

James has worked as a Data Ecologist at Dendra Systems, managing large datasets on plant distributions to support ecological research. He has also held roles as an educator at the Royal Botanic Gardens Sydney and as a Science Demonstrator at the University of Sydney.

A skilled data professional, James earned his CDMP Associate certification in just six months. He has worked on data transformation projects for several large firms and made significant contributions to the Fast Healthcare Interoperability Resources (FHIR) Technical and Design Working Groups.

Dimensional data models have become pivotal in structuring data to facilitate efficient, intuitive analysis and reporting. This brief explores why dimensional models serve as the optimal foundation for both industry-specific and cross-industry data models. Using the development and success of Business Performance Analytics (BPA) for Microsoft Dynamics 365 ERP as an example, this discussion emphasizes the extensibility and adaptability of dimensional models, showcasing their capability to meet varied business requirements while maintaining scalability and performance.

Jinnie Wong is a seasoned data modeling and business intelligence expert with over 22 years of experience as a finance leader, including roles as CFO and Controller. Combining financial acumen with technical expertise, Jinnie has successfully designed scalable, high-performance data models, notably leading the development of Business Performance Analytics (BPA) for Microsoft Dynamics 365 ERP. Launched in October 2024 and swiftly adopted by Dynamics 365 clients, BPA exemplifies her ability to create adaptable solutions for both industry-specific and cross-industry applications. Jinnie’s work emphasizes data governance, compliance, and innovative analytics, empowering organizations to achieve strategic, data-driven decision-making.