A 3-day course exploring the Business & Human aspects of Data Modelling. Also it addresses Data Modelling for Big Data together with the techniques and uses for data models beyond simply Relational DBMS development.
For Business focused Data models, a different set of skills and techniques are needed to those we see for creating “Database” focused models. The “Business data modelling” component of this course describes the techniques for Top down, Bottom Up & Middle out capture of requirements and how to elicit these from the business community. The human centric aspects of business data model development will be explored together with examining “how far is enough”? The course leverages experiences gained in over 40 years of Data modelling from the tutor, who authored “Data Modelling for the Business”, “A handbook for aligning business with IT using high level data models”.
Additionally, the courses addresses the thorny issue of “do we still need to data model in the big data era”? As the volume, variety, velocity and veracity of data keeps growing, the types of data generated by applications become richer than before. As a result, traditional relational databases are challenged to capture, store, search, share, analyse, and visualize data. Many companies attempt to manage big data challenges using a NoSQL (“Not only SQL”) database and may employ a distributed computing system such as Hadoop. NoSQL databases are typically key-value stores that are non-relational, distributed, horizontally scalable, and schema-free.
Many organisations ask, “do we still need data modelling today?” Traditional data modelling focuses on resolving the complexity of relationships among schema-enabled data. However, these considerations do not apply to non-relational, schema-less databases. As a result, old ways of data modelling no longer apply.
This course will show Data modelling approaches that apply to not only Relational, but also to Big Data, NoSQL, XML, and other formats. In addition, the uses of data models beyond simply development of databases will be explored.
What you will learn
At the end of the course, delegates would have gained the following:
Level set understanding & terminology:
o Understand the differences in Data model levels.
o Discover techniques for eliciting business data requirements to produce meaningful business data models.
o Learn about the need for and application of Data Models in Big Data and NoSQL environments
o See the areas where Data modelling adds value to Data Management activities beyond Relational Database design
o Understand the critical role of Data models in other Data Management disciplines particularly Master Data Management and Data Governance.
o Understand how to create data models that can be easily read by humans
o Recognise the difference between Enterprise, Conceptual, Logical, Physical and Dimensional Data models
o Through practical examples, learn how to apply different Data modelling techniques
o Learn the best practices for developing Data models forBig Data and NoSQL environment
Practitioners who will need to read, consume or create data models. Users who wish to gain a better understanding of data during Information Management initiatives including:
Christopher Bradley has spent 38 years in the forefront of the Information Management field, working for International organisations in Information Management Strategy, Data Governance, Data Quality, Information Assurance, Master Data Management, Metadata Management, Data Warehouse and Business Intelligence.
He is VP of Professional Development for DAMA-International, the inaugural Fellow of DAMA CDMP, past president of DAMA UK. He is an author of the DMBOK 2 and author & examiner for professional certifications.