Speaker: Dan Linstedt

I will be walking you through an introduction to Data Vault.  We will cover topics that include: Architecture, Methodology, Modeling, and Implementation.  The focus of this presentation is to offer anyone with an interest, information about Data Vault itself.

Some of the topics I will cover include:

• What is Data Vault in a nutshell?
• Why is it important?
• How does it help business?
• What is it's role in the Data Lake / Data Hub?
• How does Data Vault work with Big Data?

And a few other topics.  Anyone is welcome to come and check out this early session.

Speaker: Heli Helskyaho

Machine learning is here and it will stay. Everybody should know what it is about and where it could be used. So what is machine learning and where can it be used?

What is unsupervised learning, supervised learning, or reinforcement learning? What is deep learning? In this presentation we will talk about clustering, regression, classification, and so much more. We will talk about measuring, improving the model, feature selection, feature transformation, principal component analysis, hyper-parameter tuning and much more.

At the end of the session we discuss what else there is to learn and how to get started with machine learning.

In this presentation you learn what machine learning is all about and hopefully get so excited about the whole thing you want to learn more! After this presentation it will be easier to start learning.  This presentation is an improved version of a presentation awarded as the best presentation at KScope19 conference on Emerging technologies track.

Please note: Heli is a Certified Data Vault 2.0 Practitioner, and has been working with Data Vault for over 5 years.  This presentation is NOT to be missed!  She will tie machine learning to Data Vault across both of her presentations.

Speakers: Keith Ellis, Jas Phul, Dan Linstedt

IIBA is the International Institute of Business Analysis is a non-traditional presenter for DataVaultAlliance. Dan Linstedt will kick off the session with an introduction about IIBA and why he sees business analysis as essential and where failures are happening in implementation. Keith Ellis, IIBA’s Chief Engagement and Growth Officer, and Jas Phul, Director Product Development will talk to how to better connect projects to the business to improve business outcome.

The IIBA team will focus on the profession of business analysis and what to look for in competent analysts, core methods in elicitation and management as well as standards in business analysis. Finally, the team is going to look at using business analysis to improve outcomes specifically in data initiatives and as a foundation to data warehouse implementations.

Agenda:

• How is the business analysis profession changing?
• Business analysis competencies - what are you looking for in your analysis capability?
• How do you improve business outcomes in data project?
  

Speaker: Heli Helskyaho

The biggest problem with machine learning is data. There is not enough data, the data is not correct, etc. Data Vault is an excellent source for machine learning since it has good quality data that has been modeled, understood, and checked with the Data Vault methodology processes.

How to do machine learning in a Data Vault database? What tools are available and how would it be best to get started.
In this presentation we will talk about these questions and see some examples of machine learning in real life.

Topics Covered:

• why Data Vault is a great source for Machine Learning
• how to use your Data Vault data for Machine Learning

Speaker: Scott Ambler

We live in a VUCA (volatile, uncertain, complex, and ambiguous) world. If COVID-19 has taught us anything, it is the need for our organizations to be resilient and flexible in the face of change. We've seen many organizations struggle, and we’ve seen many organizations thrive? What are the successful organizations doing that you’re not, and what do you need to do to become more effective?

This session explores how you can learn to work smarter at the individual, team, and organizational levels based on the situation that you face, even as that situation evolves around you. The Disciplined Agile (DA) tool kit provides contextualized and easy-to-navigate guidance for evolving your way of working (WoW) to improve your overall effectiveness. DA describes a robust, pragmatic, and comprehensive approach to business agility, teaching you how to respond swiftly and effectively to changing conditions. The focus of this session will be on data-oriented aspects of an organization, and how they can evolve with the times.

• Learn how to enable to teams to work smarter via a fit-for-purpose WoW
• Discover how to lead and govern teams that working in disparate manners
• Understand that agile frameworks are only a start, your real goal is to become a learning organization

https://www.pmi.org/

Speaker: Bruce McCartney

The Business Vault is increasingly a critical component of the Data Vault 2.0 architecture. The purpose of this presentation is to discuss some of the Artificial Intelligence advances in automating 'soft' business rules.

The audience will learn various possible implementation patterns for automating business rules using Artificial Intelligence. This includes an overview of rules engines, machine learning, deep learning and causal inference taxonomies and techniques to augment the raw data into actionable insights. Additional introduction to some modelling terminology and techniques will give audience a high-level of understanding of the use of data science techniques to automate business rule development as well as some alternatives for auditing and debugging AI pipelines.

The presentation will outline various implementation patterns for using Artificial Intelligence in your information pipelines, including exposure to software tools fro development and management of these new paradigms. Finally a practical discussion of AI for augmenting data catalogs (identifying business keys) and how AI is used to forecast weather.

Topics Covered:

• Business Vault Overview
• Rules Engine Approach to Business Rules
• Machine and deep learning Overview
• Causal Inference

Speaker: Lynn Noel

Digital business requires transformation. Digital transformation depends on data. Data requires good management. Data management depends on a warehouse to store data and analytics to make use of it. Data Vault is the best choice for scalable EDW and BI, so it’s commonly found at the core of transformations for digital business. 

WWDVC is an opportunity to step back out of the weeds and zoom to the Big Picture of what we do. This presentation puts DV in context at a high level by layering two frameworks to generate insights: the industry-standard Data Management Book of Knowledge (DAMA-DMBOK) and the futurist Machine, Platform, Crowd exploring machine intelligence, big data and the sharing economy.

Data managers must innovate faster than ever to organize and govern human and AI networks of people and things that produce bigger and less structured data. What do we need to know to deliver data management differently at digital speed and scale?

• Who are the new executive sponsors for data management?
• What does a governance roadmap need to do in the first 3-6 months?
• Why and how must data governance guide technical platform governance?
• Where do you start when your data environment seems too big to know?
• How does advanced analytics drive and disrupt data management strategy?

Topics Covered:

• A Matter of Scale: Where Does Data Vault Fit With Digital Business?
• DV, DMBOK, & Machine, Platform, Crowd: Layering Frameworks to Generate Insights
• Digital Business as a Megatrend: Why Are We Building Data Vaults?
• Machine, Platform, Crowd: Core Concepts in Digital Business
• DMBoK 2.0 and Beyond: Digital, Disruption, & Transformation in the DMBoK
• From Disruption to Transformation: Data Strategy Innovations
• Call to Action: A Manifesto for Digital Business

Speaker: Richard Strange

Can Data Vault help with data-driven academic research? Academic research is following the trends of the private sector, with the dual problems of growing volume and complexity of data. As reproducibility of data-driven research comes under greater scrutiny, the need to share data sets is more important than ever, but how do we bridge the gap between current practice and the necessary capabilities in research?

Topics Covered:

• Current state and challenges of data-driven research
• Case studies
• Model approaches for applying Data Vault methods

Speaker: Dan Linstedt

We will be offering the announcement of our brand new early adopter program:

We will be introducing our brand new Vendor Tool Certification Program, discussing the ins and outs of getting a tool certified.  We welcome all participants and all vendors to this session.  Come hear what is necessary to getting a tool certified.  We are happy to launch this initiative and share what our upcoming requirements will be.

The three areas of engagement are:

• Consulting
  work for hire, simple statement of work.  You can hire DVA or one of our resources to meet with your team for a white-board or working session.  Resulting in verbal feedback for enhancements, suggestions for improvements for your tool OR your consulting company.  Remember, we can help your consulting company position, sell, and market Data Vault properly as well.  There are restrictions around leveraging this engagement type which will be discussed during this presentation.
• Tool Certification
  This is the alpha program strictly for tool / software vendors.  We will describe this, define this, and provide a hand-out that details what's in this area of engagement and how it works.  Any vendor in the market world-wide may apply to participate, and yes, there will be yearly fees and renewals to keeping the certification status active.
• Joint Marketing, and Lead Sharing
  This program will encompass multiple levels of marketing statements, as well as allow you to directly interact with community members.  The benefit here is: community members are already using data vault, as it is a paid community - you would receive warm leads.   If the API or lead-sharing components are selected, we can extend our platform to send you leads (IF they opt-in by the community members), and even connect directly to your API or your CRM or both through automated B2B integration.

Speaker: Fabio de Salles

"It is possible to commit no mistakes and still lose."

There were no other way to build traditional Data Warehouse at the beggining than using waterfall-like project management. As time passed and projects accumulated dysfunctions and waste it became clear something better was needed. Hence came the Agile Manifest rewriting the playbook to what success was and how to achieve it. But old projects die hard. The more apparently successful it is the most it resists change and stays unchallenged on duty. This talk will present one such case: The curious impact of applying Data Vault on legacy DW+Waterfall running projects.

The project was running for a couple of years, using the most expensive and advanced technologies and processes like Oracle, Dimensional Modeling and a host of other such items. Initially high successful the project delivered apriori reporting to customers of a niche ERP. As time passed the increased complexity carried by Dimensional Modeling ended up bringing the progress to a screeching halt: ETL time surged to half a day (it at all completed), reports waited in line waiting for a developer to tack them, while data errors spiked around. Customers were starting to find ways around and costs took the profits out to a walk, a long, seemingly unending walk.

Finally deciding to reign in such mess the company's CEO contracted a Data Vault consulting to rise the project to profitability again. And something totally unexpected happened: The project reached so great a success it died out!

How can DV make a heavy and busy Data Warehouse project so successful and result in dissolving it into thin air? Attend to the presentation to know the full story on how you can make no mistakes and still fail.

(Stay through for this is a happy ending story, with lots of profits reaped after all!)

What you will learn:

• How Data Vault can rescue problematic projects;
• What happens to DW projects when productivity rises continually;
• The value of teams.

Speaker: Nols Ebersohn

Have you ever considered the Laws of Nature in our physical environment? You know, little things like - what goes up must come down? Otherwise known as the law of gravity. There is the laws of thermodynamics and many others.  The interesting phenomenon of laws of nature is that it is true in both the positive and the negative. For instance: What goes up must come down. It also survives the negative of: What goes up cannot stay up indefinitely.

So what are these laws of nature for information? What are the consequences of these laws of nature on our design? How does it change the way we approach information management and in particular, how and when do these laws affect your Data Vault?

These are perplexing questions! Given the universality of the laws of nature, if you violate these laws, it tends to result in unwanted or unexpected disasters. This discussion will explore these laws and the impacts on design and the relevance to your data vault deployments.

What you will learn:

• What are the laws of nature of Information?
• What are the effects of these laws on information design?
• What are the effects at a Model level?
• What are the effects at an architectural level?
• What are the consequences of violating the laws of nature pertaining to information?

Speakers: Bill Inmon (WH Inmon), Francesco Puppini

The Unified Star Schema is a new approach for Information Marts. The data model is a single star schema, and it is centered on a table called the “Puppini Bridge”. This central table does not depend on the business requirements. The methodology to build the Puppini Bridge is repeatable, pattern-based, and ideal for automation.

The Unified Star-Schema does not affect existing Data Vault 2.0 constructs, but is a new way of looking at classic dimensional modeling on the way out of the DW.

It handles not only the classic Fact-Dimension relationship, but also the multi-fact queries, as well as the scenario of non-conformed granularities. It is ideal for self-service, because it’s extremely easy to use, also for business users.

We will start by presenting a brief history of the Data Warehouse. Then we will illustrate a problem that exists today in the traditional Dimensional Modeling: the redundancy. We will show how this approach reduces the redundancy to zero.

This solution has been tested in several client implementations, but not yet on massive data volumes. The open question is: will it scale?

Topics Covered:

Speaker: Hannu Jarvi

Machine Learning / Artificial Intelligence is largely about recognizing patterns in data.
You probably have seen, on TV news reports, stories about crime and noticed how the accused perpetrator’s or victim’s face has been blurred or pixelated.

The resolution has been lowered to hide the patterns that make that person recognizable. Something very similar happens when the granularity of data changes in traditional Data Warehousing. The data is “pixelated”. When Machine Learning is no longer able to recognize patterns in the data as a result “pixelated” data; that Information is lost forever.

Data Vault is the first Data Warehousing paradigm that seeks to preserve all information, thus it is also the first paradigm that provides a solid foundation for ML/AI.

The Machine Learning pipeline itself creates a lot of new information. In addition to the end result – for example, an evaluation of an X-ray - it provides a lot of intermediate information: Alternative explanations with varying probabilities, different results with different parameter values, etc. - information that may eventually prove valuable when analyzed against actual outcomes.

The volume and complexity of this kind of information may grow exponentially. A Data Lake, the data management workhorse of ML/AI community, cannot deal with this complexity. The information is “Lost in the Exhaust” of the ML pipeline.

This presentation explores ideas for capturing ALL value created in ML/AI pipelines. The ideas are based on challenges faced by some leading ML scientists in Healthcare and Pharma, and present the opportunities provided by Data Vault 2.0 for solving these challenges.

Topics Covered:

• Why AI / ML needs pristine data in its original grain
• Why DV is an ideal foundation for AI / ML
• Why Dimensional Models don't work for AI / ML

Speaker: Stephen C Moon

Learn how to leverage Data Vault to enable your DataOps pipeline to manufacture and distribute data and analytics products.

Enterprise Architecture
- Vision, Current State, Future State, & Roadmap Development
- Value Proposition Design
- Business and Operations Modeling
- Business Process Decomposition / Modeling
- Cost Modeling

Data Operations (DataOps)
- Acquisition, Processing (Curation and Enrichment), & Distribution
- Organizational Consulting (People, Process, & Technology)
- Data Architecture & Engineering
- Data Vault
- Data Modeling
- Data Warehouses and Data Lakes
- Batch; Stream; Extract, Transform, & Load (ETL)
- SQL and Spark
- Business Intelligence (BI), Analytics, and Machine Learning (ML) Enablement
- Security and Compliance

What you will learn:

• Business Intelligence (BI), Analytics, and Machine Learning (ML) Enablement
• Business and Operations Modeling
• Value Proposition Design

Speaker: Gabor Gollnhofer

This is a case study on a DW transition from “traditional” (a hodgepodge of 3NF with history, dimensional and flat-wide tables) to Data Vault based. This Data Warehouse (in its first “incarnation”) was built almost 20 years ago. Since then its major parts were reorganized three times. Because of changes in internal & external environment we had to change our ETL tool (again). Finally, we’ve decided to do a complete reorganization including our ETL & modelling tools, development processes and to move to Data Vault based modelling.

Of course, we had to do this in a short period with limited internal & external staff. And the old and new DW had to work together so that the major processes could be migrated one-by-one.

Topics Covered:

• Why we’ve chosen to use Data Vault
• What were the pain points, pitfalls and solutions
• What are the results & what we’ve learnt during this transition

Beware! This is not the usual "bright & shiny" presentation, I'll share some "worst practices" on the development & operations of the DW. And if the audience wants to participate, we could also exchange our "horror stories" during the Q&A ;-)

Speaker: Michael Lutz

Fox Chase has embarked on a journey to re-engineer our Data Warehouse internals with the Data Vault 2.0 Methodology. DV 2.0 strongly emphasizes metadata driven automation and we are attempting to follow the methodology in its entirety. The technologies employed by our solution include Python, Jinja templates, Airflow, and Oracle. The Jinja templates defining the nature of the output can be re-expressed to support other target database technologies.

We will examine all aspects of the solution with the goal of defining a roadmap for others to implement DV 2.0 with this open source toolset. Additional discussion points include Data Vault design decisions in the Healthcare vertical, the motivation for Fox Chase deciding to take these steps, and an engineer’s perspective on tackling the learning curve of these technologies.

What you will learn:

• How to implement DV 2.0 with open source technologies: Python, Jinja, and Airflow
• Walk through the experience of making DV 2.0 design decisions in the Healthcare vertical
• Motivations for migrating to the DV 2.0 Methodology

Speaker: Dan Linstedt

Its that time again, time to wrap up the WWDVC 2020 conference, and bring it to a close.  I will be talking about the things we saw, things we learned, and potentially next years' conference as well.  I look forward to all of you that can make it to closing remarks.

Thank you, and see you next year!