Data Modeling Masterclass
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    Data Modeling Masterclass

    Steve Hoberman - the world's leading data modelling instructor - presents a Best Practices Approach to Developing a Competency in Data Modeling

    Date: 14-16 October 2019 (10-18h)
    Location: Parker Hotel (Diegem)
    Price: 2550 EUR (excl. 21% VAT)

    Presented in English by Steve Hoberman


    Check out the LEARNING OBJECTIVES, the FULL PROGRAMME and WHO PRESENTS this.


    Full Programme:
    9.30h - 10.00h
    Registration and welcome with coffee/tea and refreshments

    The timing of this workshop is from 10h till 18h00 at the latest. There is a coffee/tea/refreshments break in the morning and in the afternoon (timing varies slightly), and there is a lunch buffet provided from 13h till 14h00.

    10.00h
    1. Modeling Basics

    Assuming no prior knowledge of data modeling, we introduce our first case study which illustrates four important gaps filled by data models. Next, we will explain data modeling concepts and terminology, and provide you with a set of questions you can ask to quickly and precisely build a data model. We will also explore each component on a data model and practice reading business rules. We will complete several exercises, including one on creating a data model based upon an existing set of data. You will be able to answer the following questions by the end of this section:

    2. Overview to the Data Model Scorecard®

    The Scorecard is a set of ten categories for validating a data model. We will explore best practices from the perspectives of both the modeler and reviewer, and you will be provided with a template to use on your current projects. Each of these following ten categories heavily impacts the usefulness and longevity of the model:

    3. Ensuring the model captures the requirements

    There is no one way to elicit requirements – rather it requires knowing when to use certain elicitation techniques such as interviewing and prototyping. We will focus on techniques to ensure the data model meets the business requirements. You will be able to answer the following questions by the end of this section:

    4. Validating model scope

    We will focus on techniques for validating that the scope of the requirements matches the scope of the model. If the scope of the model is greater than the requirements, we have a situation known as “scope creep.” If the model scope is less than the requirements, we will be leaving information out of the resulting application. You will be able to answer the following questions by the end of this section:

    DAY 2
    5. Understanding conceptual, logical, and physical data models

    The conceptual data model captures a business need within a well-defined scope, the logical data model captures the business solution, and the physical data model captures the technical solution. Relational, dimensional, and NoSQL techniques will be described at each of these three levels. We will also practice building several data models and you will be able to answer the following questions by the end of this section:

    6. Following acceptable modeling principles

    We will cover Consistency, Integrity, and Core modeling principles. You will be able to answer the following questions by the end of this section:

    7. Determining the optimal use of generic concepts

    Abstraction is a technique for redefining business terms into more generic concepts such as Party and Event. This module will explain abstraction and cover where it is most useful. You will be able to answer the following questions by the end of this section:

    8. Applying consistent naming standards

    Consistent naming standards will get your organization one step closer to a successful enterprise architecture. We will focus on techniques for applying naming standards and you will be able to answer the following questions by the end of this section:

    DAY 3
    9. Arranging the model for maximum understanding

    A data model is a communication tool and if the model is difficult to read it can hamper communication. We will focus on techniques for arranging the entities, attributes, and relationships to maximize readability. You will be able to answer the following questions by the end of this section:

    10. Writing clear, complete, and correct definitions

    Although definitions may not appear on the data model diagram itself, the definitions are integral to data model precision. We will focus on techniques for writing useable definitions. You will be able to answer the following questions by the end of this section:

    11. Fitting the model within an enterprise architecture

    A data modeler is not only responsible to the project for capturing the application requirements, but also responsible to the organization to ensure all terms and relationships are consistent within the larger framework of the enterprise data model. We will focus on techniques for ensuring the data model fits within a “big picture”. You will be able to answer the following questions by the end of this section:

    12. Comparing the metadata with the data

    A logical or physical data model should not be considered complete until at least some data analysis has been done on the data that will be loaded into the resulting data structures. We will focus on techniques for confirming that the attributes and their rules match reality. Does the attribute Customer Last Name really contain the customer’s last name, for example? You will be able to answer the following questions by the end of this section:

    Summary and Conclusions
    18.00h
    End of three-day workshop

    Quotes on the Data Modeling Master Class

    Thanks again for such an excellent opportunity to participate in this master class, it was extraordinary! The coverage of data modeling, the pace, the content, the interaction with you and the class, all of it was just awesome! (S. Johnson, U.S. Dept. of Energy)

    This was the most comprehensive, informative, energetic, interesting and just plain FUN class I have ever taken on the subject of Data Modeling. (G. Werner, Long Island Railroad)

    In my long professional career, I have participated in many training seminars but I have never encountered a class in which the subject had been so thoroughly considered and presented in such a clear and engaging manor. As a fairly new, but full time data modeler, I expect to use the things that I learned in this class every day. (G. Schmid, Travelers Insurance)

    An excellent course with focus on the basics. I can only imagine the effort that went into developing this course. Lucky that I happened to find it. (R. Sampath, Deloitte)

    Having never done data modeling before, I can now say, I am excited about implementing the skills I’ve learned. (L. Felder, Johns Hopkins HealthCare)

    Truly enjoyed this class even though I have been modeling databases for 24 years. (M. Austin, Wells Fargo)

    Every participant was at a different level of knowledge and everyone learned from the class. (S. Slivova, Senior Business Analyst, Gen Re - A Berkshire Hathaway Company)




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