‘DMBOK R2’ highlights the key topics discussed during a virtual meeting on the revised edition of the Certified Data Management Professional (CDMP) exams and the Data Management Body of Knowledge (DMBoK). The meeting covers updates to the DMBoK, data governance, data modelling, professional data management, data quality, metadata management, risk management, big data, and sustainability concerns. Suggestions were made to improve exam questions and align exam answers with the DMBoK’s definitions. Howard Diesel provides valuable insights and learning opportunities for professionals in the field of data management and information technology.

Erwin Bisschops will explore the Theory of Perception and how individuals can perceive the same object in different ways. To demonstrate this concept, an experiment will be conducted using images of a rabbit/duck, an old/young lady, and a nurse/frog. He will delve into Decision Intelligence, which combines quantitative and qualitative elements to enhance decision-making and actions.

The Theory of Perception and Decision-Making will be explored, focusing on applying it to business intelligence. The challenges of self-service business intelligence will be examined, along with how Gartner's magic quadrant for analytics and BI platforms considers components like security, Cloud enablement, metadata, natural language queries, data storytelling, reporting, and visualisations.

The discussion will shift to the focus on technical capabilities in BI, which has led to a lack of understanding of how decisions are made. The alternative of an engineering approach to decision-making will be proposed, as opposed to romanticising gut-feel decisions.

Discover some fascinating topics that may interest you!

These include analysing data tools in an online store, the importance of delivery dates and partnership associations in e-commerce, exploring the relationship between gardening associations and point-of-sale data models, key factors to consider when testing data warehouse automation tools, challenges in managing relationships in data vault, perspectives on data vault modelling and automation, discussing automation tools and data integration, an overview of common data management challenges and their solutions, the significance of data warehousing and related topics, feedback on Insert Statement and Presentation, tips for implementing data modelling and technical patterns, integrating database automation tools with Azure, implementing automation tools and data integration in software development, the benefits of data modelling, and selecting the right tools for creating a data warehouse.

Data centralisation and report automation have become essential for effective data management. Centralised data management is crucial for businesses as it provides several benefits, such as improved data accuracy, better security, and easier access. Faith Sithole discusses the importance of an efficient interface for data storage cannot be overstated, as it plays a key role in ensuring data centralisation. She notes that report automation is equally important as it helps businesses save time and resources while improving the accuracy and consistency of reports. Implementing a data warehouse also has several benefits, such as improved data quality, better data analysis, and streamlined reporting. At the same time, data centralisation has many advantages but is not the same as data virtualisation. Implementing data systems has limitations and challenges, including data reporting and analysis, data flexibility, and data lineage. Manual processes can also impact data quality and reporting accuracy. Lastly, a data validation and reporting control framework is emphasised to ensure data quality and accuracy.

‘How-to Store Data for Data Managers’ provides an overview of various concepts and transactions in data management, including transactional and distributed databases, consistency in data lake houses, data replication, federated database systems, CAP theorem, and lambda architecture. Howard Diesel covers the process of blockchain and data management, data lake houses and Snowflake, microservices and data mesh, cloud storage and data management, and process control networks and data security. Additionally, he offers insights on cloud cost management, naming conventions, and choosing between a hosted Azure instance and a VM for a small company. The webinar provides a glimpse into the challenges and features of data lakes and the mesh, infrastructure, edge computing, and distributed databases.

Lucien Pierce is a lawyer presenting on various topics related to the future of law firms and the challenges of data anonymisation and usage in healthcare and insurance. He will discuss the importance of proactive client communication, data destruction, and legal considerations in data profiling. He will cover the implications of insurance fraud and the balance between public interest and privacy in legislation. Additionally, he will address the challenges of database integration and responsible data usage and how they impact the future of society.

Knowledge management is critical to organisational success, and knowledge graphs are essential. ‘Document & Content for Data Executives’ covers the challenges and importance of knowledge management, including the transfer of implicit knowledge to explicit knowledge, the importance of tacit knowledge, and the role of after-action reports. Howard Diesel discusses the phases of knowledge management, the principles of knowledge management, and the dimensions of knowledge management and data quality measures. The webinar highlights the importance of training and knowledge sharing in data management and the measurement of knowledge management. Howard emphasises the significance of knowledge management in organisational learning and success.

'Data Ontology is the Future with Estie Boshoff' introduces data ontology, including notes on domains and taxonomies and an overview of ontologies and their applications in data analysis. It covers the challenges and advantages of ontology modelling and discusses the tools and skills required for data modelling and ontology. The importance of ontologies in data governance and knowledge discovery, along with the differences between property graphs and knowledge graphs, is also highlighted. Additionally, the use of Neo4j for data import and diagram building, transaction analysis, and risk assessment is discussed, along with the importance of data and knowledge graphs in data analysis.

‘Data Management Maturity Assessments for Data Citizens’ provides an overview of the importance of data management maturity assessments, readiness assessments, stakeholder involvement, and training to achieve business agility.

It emphasises the role of data citizens, semantics, data modelling, and the structure and benefits of enterprise data models. Additionally, the webinar covers data flows, lineage, and the data value chain and discusses different approaches to data storage and knowledge graphs. Finally, it highlights the challenges of implementing reference architectures and the significance of examples in understanding the processes.

'Data is not a 4-letter word with Lisel Engelbrecht' highlights the challenges, importance, and opportunities related to data organisation, consumption, analytics, and management. Challenges include disjointed concepts and data literacy, while importance lies in understanding and loving your data and leveraging it for business growth. Automation and data platforms can pose challenges; Executives must balance data management and analysis risks and rewards. Lastly, opportunities for career growth in data management abound, and those looking to monetise data skills should focus on self-service frameworks and value data.

The importance of data literacy and building data communities. Understanding the data literacy gap and how to close it. Overcoming challenges in data literacy and communication and measuring effectiveness.

Passive learning is challenging! This challenge is true for both the learner and the facilitator. Have you tried Active learning? This article provides more detail of the benefits of Active learning verse passive learning. Read more to learn about a Winning Learning Formula that drives End User Adoption.

Over the last five years, several organisations have reached out to me to assess the maturity of their Data Office. Each time I perform an assessment, I am curious as to what led to the request and the response to the results. In general, the results have been lower than I thought.