Your enterprise’s success at creating value from change is founded on three core capabilities – strategy, architecture and portfolio management. Together, they drive the organisation’s process for achieving its goals through an ever-evolving mix of stability and change. Keeping them in sync is vital, and the dynamics involved are complex. Goals have a diversity of horizons and timings. Some are defined years ahead, while others for this month or less. There are ones that are targeted to a regular rhythm, such as quarterly Revenues and Costs. Others are when-and-if-necessary, such as legal and regulatory Compliance. Few, if any, are set in stone. Many are shaped by external developments, and most we will change as often as we choose, as we continuously learn from results and events. Chris’s Fruition Trilogy lays out the essentials in close-to-real-life but fictional stories. Don’t miss this rare opportunity to hear from Chris himself, and ask him the change-related questions you’d just love him to answer.

Chris is the author of the world’s only trilogy of business novels. His books are written as first-person stories – with a different narrator each time – based on three decades of hands-on work and as a mentor and trainer, in the inter-related disciplines of Strategy, Enterprise Architecture and Portfolio Management. Chris’s storytelling draws on elements of corporate strategy, economics, behavioural psychology and film theory. Like life itself, you can either take the books on face value, or“read between the lines” and discover what else is going on.

So, you finally decided that you are going to write that book! How should you go about it? You could retreat to a mountain cabin and unplug from the world, but who can afford that? Although procrastinators over do the tools and techniques to avoid writing, it only takes a 2% investment of your time to make a plan and perform the required tracking. This session will give a quick overview of the most common project management tools used for construction, design, and IT projects. You may not think you need a Work Breakdown Structure or a Gantt chart or Requirements Analysis, but through their use you will be able to concentrate on composition and not worry about the logistical aspect of writing your book.

Daniel A. McGrath, Ph.D., is the President of Llano Estacado Management Science Co. He also is an Instructor in the Industrial, Manufacturing, and Systems Engineering Department at Texas Tech University. He has over 35 years of experience analyzing big data sets with many quantitative and statistical tools and software packages. He has a diversity of degrees: BA in Geography and Geosciences, MS in Soil Science, PhD in Systems and Engineering Management with all being from Texas Tech. He has worked extensively in environmental, project management, continuous improvement, business intelligence, and financial job functions. In addition, he has been a Project Management Professional and a Six Sigma Master Black Belt. He lives in Texas and travels widely, helping customers find their lost cities of gold!

More than half of work is accomplished by knowledge workers–usually defined as those who must “think for a living” [Davenport, 2005]. I contend that all knowledge workers work with data. Since most learn about data individually (if at all), the opportunity to gain from communal or best practices learning has not been present. Most refer to this as a lack of data literacy. Whether applied at the individual or organizational level, literacy is a binary concept and our data needs are more varied. Data proficiency and data acumen are more descriptive/useful terms and these should also be used to describe today’s organizational data knowledge requirements. This session will describe five specific data knowledge requirement levels and objective behaviors that must be demonstrated by those operating at each level.

Peter Aiken, an acknowledged Data Management (DM) authority, is an Associate Professor at Virginia Commonwealth University, past President DAMA International, and Associate Director of the MIT International Society of Chief Data Officers. For more than 35 years, Peter has learned from working with hundreds of data management practices in 30 countries including some of the world’s most important. Among his 10 books are the first on CDOs (the case for data leadership), the first describing the monetization data for profit/good, and the first on modern strategic data thinking. International recognition has resulted in an intensive schedule of events worldwide. Peter also hosts the longest running DM webinar series (hosted by dataversity.net). From 1999 (before Google, before data was big, and before data science), he founded Data Blueprint, a consulting firm that helped more than 150 organizations leverage data for profit, improvement, competitive advantage and operational efficiencies. His latest venture is Anything Awesome.

The biggest challenge in writing for multiple levels of experience, especially when describing complex scenarios, is to make the narrative easy to understand for novices yet not so boring for experienced readers. The second requirement is in my opinion best met by making the subject matter of the example interesting in its own right, especially if it’s a subject area not often included in other textbooks. Understandability (for all readers) is enhanced by paying attention to:
1. making sentences easier for the reader to parse, by breaking up complex sentences (using bracketed clauses, footnotes or numbered or bulleted lists, and typographical conventions);

2. catering for multiple levels of experience without interrupting the flow through use of footnotes;

3. ensuring technical terms are understood by the reader;

4. careful design of diagrams and tables;

5. careful choice of examples.

Graham has had 4 decades of experience in assisting businesses and government departments to acquire relevant and effective IT solutions, and manage information effectively. He has developed specialist expertise in a variety of areas, in particular data modelling and business rules. He has presented papers at conferences in Australia, the US, the UK, France and Germany, as well as meetings of DAMA (the Data Management Association) – in Australia and the US – and ACS (the Australian Computer Society). He has developed and delivered training courses in data modelling, database design and business rules, and delivered them in Australia, the US and Canada. He co-authored with Graeme Simsion the widely-used textbook Data Modeling Essentials, and Morgan Kaufmann published his book Writing Effective Business Rules in February 2012. Graham has also written articles on natural language business rule statements for the Business Rules Journal. Technics Publications has just published his new data modeling textbook, Data Modeling for Quality.

As Gil Scott-Heron put it in his classic song of the same title: “The revolution will not televised… The revolution will be live.” Data-Centric is ushering in a revolution. It requires that we change the way we think. And it requires we act on the new way we think. The Data-Centric revolution will not be televised. It will not be hyped (hopefully). It will occur quietly in the minds, priorities and practices that firms undertake. In this session we will outline what the Data-Centric Revolution is. We will contrast it to the prevailing approach to enterprise application implementation: the Application-Centric Quagmire. We will distinguish it from Data-Driven, which while laudable, is not the same thing. We will provide case studies of the few firms that already are Data-Centric and several that are on their way. We will describe the three legs of the Data-Centric stool. We will explain the central role of an enterprise ontology and the key ingredients of a Data-Centric Architecture. We will suggest how you might get started.

Dave McComb is the author of the bestsellers, Software Wasteland and The Data-Centric Revolution, and the President and co-founder of Semantic Arts, a consulting firm that helps organizations uncover the meaning of the data from their information systems. For 20 years, Semantic Arts has helped firms of all sizes in this endeavor, including Procter & Gamble, Goldman Sachs, Schneider-Electric, Lexis Nexis, Dun & Bradstreet and Morgan Stanley. Prior to Semantic Arts, Dave co-founded Velocity Healthcare, where he developed and patented the first fully model driven architecture.

Organizations need to design, hire and manage their analytical teams. All organizations want their analytics teams and function to not only exist but to perform in a highly effective manner. Analytics teams are not like development teams or typical project teams. We will discuss what makes them different and how to hire and manage for high performance and operational impact. In this session we’ll be discussing best practices relating to investing in, hiring and managing your analytics team, including the following topics:

• We will discuss how to build an analytics teams using the Artisanal or Modular model.
• Analytics team evolve and grow into Hybrid organizations. We will discuss how to manage this evolution.
• High performance analytics teams are unique. We will discuss how to best manage them.
• We will define how to build robust analytics processes that drive results in the real world
• We will examine how best to move from robust analytical processes to integrating analytical models and processes into operational production processes

John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL. John is the author of the new book – Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates. John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017.

The road to Data Management ruin is paved with good intentions, both strategic and tactical. The need for data management is everywhere across your company, yet often hidden in plain sight. Although Data Leaders know that data management and governance will help grow, improve and protect their businesses, many have trouble articulating that value in a way that resonates with Business Stakeholders to secure funding and support. To win them over, Data Leaders must create a compelling narrative to evangelize how data programs enable the strategic intentions of their enterprise. In this session you will learn practical tips to help you understand the Role of Data Management in Data Storytelling and Data Literacy Efforts, Prove Data Management is Mission Critical and Macro-Trend agnostic, and Leverage the 3Vs of Data Storytelling.

Scott Taylor, The Data Whisperer, has enlightened countless business executives to the value and power of proper data management. He focuses on business alignment and the “strategic WHY” rather than system implementation and the “technical HOW.”  As Principal Consultant for MetaMeta Consulting he helps Enterprises and Tech Brands tell their data story. An avid business evangelist and original thinker, he continually shares his passion for the strategic value and importance of data management through industry events, public speaking opportunities, blogs, videos, whitepapers, podcasts and even cartoons and puppets shows. He lives in Bridgeport, CT where he often kayaks in Black Rock harbor.  He can also juggle pins and blow a square bubble.

In natural language, individual things are typically referenced by proper names or definite descriptions. Data modeling languages differ considerably in their support for such linguistic reference schemes. This seminar provides a comparative review of reference scheme modeling within Object-Role Modeling (ORM), the Unified Modeling Language (UML), the Barker dialect of Entity Relationship (ER) modeling, relational database modeling, the Web Ontology Language (OWL), and LogiQL (an extended form of datalog). We identify which kinds of reference schemes can be captured within these languages as well as those reference schemes that cannot be captured. Our analysis covers simple reference schemes, compound reference schemes, disjunctive reference and context-dependent reference schemes.

Dr. Terry Halpin, BSc, DipEd, BA, MLitStud, PhD, is a data modeling consultant and former professor of computer science. His industrial experience includes years of employment in the USA at Asymetrix Corporation, InfoModelers Inc., Visio Corporation, Microsoft Corporation, and LogicBlox, as well as contract work as a data modelling consultant for several industrial organizations, including the European Space Agency. His prior academic experience includes employment as a senior lecturer in computer science at the University of Queensland, as well as professorships in computer science at various universities in the USA, Malaysia, and Australia. His doctoral thesis formalized Object-Role Modeling (ORM/NIAM), and his current research focuses on conceptual modeling and rule-based technology. He has authored over 200 technical publications and nine books, including Information Modeling and Relational Databases and Object-Role Modeling Fundamentals, and has co-edited nine books on information systems modeling research. He is a member of IFIP WG 8.1 (Information Systems), is an editor or reviewer for several academic journals, was a regular columnist for the Business Rules Journal, and is a recipient of the DAMA International Achievement Award for Education and the IFIP Outstanding Service Award.

Enterprise data improvements are not just about data architecture and technology. Sometimes the challenges reside among involved people. This presentation is about including, engaging, and convincing people that may be reluctant to enterprise-wide changes, explained in three separate stories: “That kind of pricing data does not work here”, “Benign neglect until a disaster happens”, and “The truth is in the data”.

Håkan is a trainer, speaker, author, and consultant in data management, mainly within data governance and business data design where he practices his diplomatic data leadership concept. He has experiences from a wide range of business areas and industries, including manufacturing industries, authorities, utilities, retail, real estate, and managed services. He is acknowledged as contributing author of the second edition of the DAMA Data Management Body of Knowledge (DMBOK2) and is a board member in the Enterprise Data and BI & Analytics Conferences Europe Advisory Board.

Learn how schema integration techniques can be applied to the design and implementation of an ODS (Operational Data Store). We will present and define schema integration and highlight the main steps, processes, and deliverables when using this approach for your data integration activities. We will examine model sources including JSON, XML, relational data feeds, and Hadoop generated key value pairs.

Certified CIMP master data & data quality professional. Angelo R Bobak is a data warehouse, data architect and published author with over 25 years of experience in Business Intelligence, Data Architecture, Data Modeling, Master Data Management, and Data Quality in Finance, Telecommunications, Automotive, ITIL Service Delivery and other industries using Microsoft BI Stack. He is owner and president of GRUMPYOLDITGUY.COM, a web based video training site on database technology.

This presentation consists of a few personal reflections on my life as a writer, with the emphasis on what I’ve tried to achieve in my writings. The presentation is serious but not solemn. Topics addressed include: Good writing including some professional advice, How I got into this business, What I’ve learned, Why I write, Reporting on research, and Countering myths and nonsense.

C. J. Date is an independent author, lecturer, researcher, and consultant, specializing in relational database technology. He is best known for his book An Introduction to Database Systems (eighth edition, Addison-Wesley, 2004), which has sold some 900,000 copies and is used by several hundred colleges and universities worldwide. Mr Date was inducted into the Computing Industry Hall of Fame in 2004. He enjoys a reputation that is second to none for his ability to communicate complex technical subjects in a clear and understandable fashion.

Data governance is an enigma. To some, it can mean everything, to others, it can mean nothing at all. No other subject has the power to incorporate the trident of people, processes and technology so effectively. Why? Because data governance has the ability to impact all areas and yet be abstract and detailed at the same! Join Harkish as he provides a high level overview into understanding the diversity of data governance and its application through the prism of people, process and technology. At the people level, Harkish will explore the impact of data governance in the workplace and the emergence of data centric roles. Shifting to process, there are a number of tools that can help implementation. For example, data governance frameworks, assessments and standards. But which approach is best or is it a combination of all? Finally as technology evolves, what does the future look like for data governance? How might Machine Learning and cloud technologies help manage data governance?

Harkish is an author and a strategic technology architect. His skills include digital transformation, emerging technologies, and cloud migration solutioning.
Experienced in data strategy, architecture, governance, policy, and sharing. Currently working in consulting as a data strategy lead in the business and finance sector. Outside of IT, Harkish also has a keen interest sports, music and movies.

If there is one lesson that IT and, in particular, data/information management should learn from the pandemic it is that process efficiency, speed, and cost minimization work only if the overall system is stable, predictable, and controllable. The same lesson applies to artificial intelligence / machine learning. Therefore, even though digital transformation is accelerating as physical distancing has become the norm, the data management and architecture insights of decades past – when the physical IT infrastructure was less dependable – are becoming more relevant than ever. It’s back to the future…

Dr. Barry Devlin is among the foremost authorities on business insight and one of the founders of data warehousing in 1988. With over 30 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer, and author of “Data Warehouse—from Architecture to Implementation” and “Business unIntelligence—Insight and Innovation beyond Analytics and Big Data”. As founder and principal of 9sight Consulting (www.9sight.com), Barry provides strategic consulting and thought-leadership to buyers and vendors of BI solutions.

The Unified Star Schema (USS) is a data mart that sits in the presentation layer of a Data Warehouse. The main advantage of the USS is that it minimizes (or completely eliminates) the need of a PHANTOM LAYER. No matter if the deliverable is a report, a dashboard, a cube, or a machine learning model: the need of transformations with the USS is drastically reduced. The USS is compatible with every existing approach of Data Warehouse (Inmon, Kimball or Data Vault), and it is compatible with every existing technology of data storage: from relational databases to CSV files on the cloud.

The Unified Star Schema is in fact a generalization of the Dimensional Modeling. It is a “multi-fact” solution, capable of working with “non-conformed dimensions”. Imagine that you have 50 fact tables and a bunch of dimensions: well, they will all fit into one single star schema!

The USS does not produce duplicates, and it has an impressively good performance. It is compatible with every type of database, and with every existing BI tool.

You will learn:

1. Weak points of the full denormalization
2. Weak points of the traditional dimensional modeling
3. Oriented Data Models
4. Definition of “Generalized Fan Trap”
5. Definition of Chasm Trap
6. How to handle the Multi-Fact queries
7. The Union
8. Union combined with Aggregation
9. Why the USS is a universal solution

Francesco Puppini is an Italian freelance consultant in Business Intelligence and Data Warehousing. He has worked on over 30 different projects across 10 Countries of Europe, for clients from several industry sectors. He is currently working as a Qlik specialist, after 18 years spent on Business Objects, SQL, Teradata and Data Modeling.

Today, many businesses are challenged in transforming their data into insights. Gartner reported that over 80% of analytics solutions do not deliver business results and McKinsey said just 20% of the analytics initiatives have achieved scale. To avoid analytics failures, organizations are looking for best analytics practices that offer prescriptive, superior, and reusable guidance. One best analytics practice is strong data governance. However, the scope of data governance is wide and varied in business enterprises. This session will explore the essential elements of data governance for business results and the best practices for starting and commissioning a data governance program.

Prashanth Southekal is the Managing Principal of DBP-Institute, a Data Analytics consulting and education firm. He has consulted for over 50 organizations including P&G, GE, Shell, Apple, SAS, and SAP and solved problems that are at the intersection of data, technology, and business productivity. Apart from his consulting assignments, he is an Analytics Advisor for SAS-Institute (Western Canada), Evalueserve (Switzerland) and Grihasoft (India). Mr. Southekal is the author of two books – Data for Business Performance and Analytics Best Practices. Apart from his consulting pursuits, he is an Adjunct faculty of Data Analytics at the University of Calgary (Calgary, Canada) and IE Business School (Madrid, Spain). He has a PhD from ESC Lille (FR) and MBA from Kellogg School of Management (US).

Learn strategies to mitigate could what pull virtual teams apart, encourage involvement and participation, become a more empathetic leader, reduce stress (yours/theirs), build trust virtually, and improve understanding of what drives workplace behaviors in other cultures.

Catherine Mercer Bing is the CEO of ITAP International, Inc., a global consulting firm that specializes in the impact that cultural differences have on business interactions. Ms. Bing has 30+ years in human resources development. She spent more than eight years working as an internal corporate HR professional, and 16 years as an outside communications, HR and cross-cultural consultant. She has travelled and worked with international partners in Argentina, Australia, Austria, Belgium, Canada, China, Denmark, France, Germany, Holland, India, Ireland, Kenya, Malaysia, Mexico, the Philippines, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, and the UK.

With AI having become a buzzword, sometimes it’s hard to know what to make of it. Yet, it can actually be of help in data science work and knowing about it can benefit you, whether you are a data scientist or someone involved in DS projects as a stakeholder. In this session, we’ll cover these topics:

• What is AI?
• What is DS?
• AI and ML
• How AI fits into the DS pipeline
• DS-related applications of AI
• AI-based Optimization Algorithms
• Robotic Process Automation (RPA) and data science
• Limitations of AI in DS work
• The ethics of it all
• How you can learn more about AI and DS

Dr. Zacharias Voulgaris was born in Athens, Greece. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology, and then to Data Science through a PhD on machine learning. He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 Web Services (WA). He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing. Zacharias has authored several books on Data Science, mentors aspiring data scientists through Thinkful, and maintains a Data Science / AI blog.

Looking for data has been the bane of existence for technical and non-technical users alike. There is simply too much data and not enough information about it to help users figure out what data they need in order to power their statistical models or inform their decision-making. This session introduces the Data Catalog: the solution that provides the back story of data so that users can know which data to use with confidence.

Bonnie O’Neil is a Principal Computer Scientist at The MITRE Corporation and is a well-known expert on all phases of data architecture including data catalogs, data quality, business metadata, and governance. She has assisted both Fortune 500 companies and government agencies in data management projects for over 30 years. She is a regular speaker and workshop/tutorial leader at many conferences, and the author of four books.

In 1890 it was easy for the shopkeeper to know his/her customer. In today’s world it is more difficult. But hearing the voice of your customer is even more important today than it was a century ago. This presentation will discuss the ways you can hear the voice of the customer in today’s world.

Bill Inmon – the “father of data warehouse” – has written 59 books published in nine languages. Bill’s latest adventure is the building of technology known as textual disambiguation (textual ETL) – technology that reads raw text in a narrative format and allows the text to be placed in a conventional data base so that it can be analyzed by standard analytical technology, thereby creating unique business value for Big Data/unstructured data. Bill was named by ComputerWorld as one of the ten most influential people in the history of the computer profession.

Let’s talk about Flow. We have all experienced it. Flow is what you feel when you are challenged in the moment but know you have the skill to meet that challenge; you are off-the-chain productive; time has no meaning; you feel great when you finish your work. However, we often arrive at a state of flow accidently. The next day, under almost identical conditions, Flow doesn’t happen. This presentation discusses my research in flow-based decision making and flow-based leadership. My goal is to help you identify ways to maximize your Flow states by consciously triggering flow. Being able to work in flow more often will increase your productivity and result in a higher sense of well-being overall.

Judith L. Glick-Smith, Ph.D., is an author, speaker, researcher, and consultant. She is the founder of MentorFactor, Inc., the home of The Center for Flow-Based Leadership®, which focuses on helping organizations prepare for the unexpected and facilitate agility by implementing flow-based work environments. Dr. Glick-Smith has been studying flow-based decision making and leadership in Fire & EMS since 2007. Her current research is an ethnography of the Georgia Smoke Diver program and its flow-based leadership model. She is the author of Flow-Based Leadership: What the Best Firefighters Can Teach You About Leadership and Making Hard Decisions, which was named the #1 Public Safety Leadership Book of 2016 by Fire Chief Magazine. She is a co-author and co-editor of two additional books: Visionary Leadership in a VUCA World: Thriving in the New VUCA Context, which made Forbes’ Recommended Creative Leadership Reading List for Summer 2017, and Exceptional Leadership by Design: How Design in Great Organizations Produces Great Leadership.