Programma

In today’s data-driven landscape, the concept of a knowledge graph has emerged as a pivotal framework for managing and utilizing interconnected data and information. Stemming from Google’s proclamation that shifted the focus from searching for strings to understanding entities and relationships, the term encapsulates a network of interconnected entities and concepts, facilitating data integration, sharing, and utilization within organizations.

Amid the widespread adoption of knowledge graphs across diverse domains, ensuring the accuracy, reliability, and consensus of semantic information becomes an imperative. The construction and utilization of these graphs present multifaceted challenges, ranging from ensuring data quality to scaling and adapting to evolving contexts.

Implementing a successful Knowledge Graph initiative within an organization demands strategic decisions before and during its execution. Often overlooked are critical considerations such as managing trade-offs between knowledge quality and other factors, prioritizing knowledge evolution, and allocating resources effectively. Neglecting these facets can lead to friction and suboptimal outcomes.

This half-day seminar delves into the technical, business, and organizational dimensions essential for data practitioners and executives embarking on a Knowledge Graph initiative. Offering insights gleaned from real-world case studies, the seminar provides a comprehensive framework that combines cutting-edge techniques with pragmatic advice. It equips participants to navigate the complexities of executing a knowledge graph project successfully.

Moreover, the session addresses pivotal strategic dilemmas encountered during the design and execution phases of knowledge graph projects, and outlines potential approaches to tackle these challenges, empowering attendees with actionable strategies to optimize their initiatives.

Learning Objectives

• Understand the key factors determining the feasibility and viability of implementing a knowledge graph in an organization.
• Identify and articulate the fundamental questions crucial for preparing and launching a successful knowledge graph initiative.
• Learn techniques to determine and prioritize the content requirements of a knowledge graph.
• Grasp best practices in schema design for knowledge graphs, addressing real-world challenges of uncertainty and vagueness.
• Explore strategies and guidelines for populating a knowledge graph, evaluating available knowledge extraction systems.
• Gain insights into assessing and prioritizing quality dimensions within a knowledge graph.
• Explore practical applications of knowledge graphs, such as entity disambiguation and semantic search, optimizing performance through design principles.
• Gain insights into methodologies for ongoing maintenance and evolution of knowledge graphs, ensuring their sustained relevance and adaptability across time.

Who is it for?

• Data practitioners: Data scientists, data engineers, data analysts, and database administrators seeking to deepen their understanding of knowledge graphs, their implementation, and the technical intricacies involved.
• Technology Leaders: Architects, CTOs , and IT professionals exploring or leading initiatives involving data integration, semantic technologies, and knowledge management systems.
• Business Executives and Managers: Leaders and decision-makers responsible for overseeing data strategies, innovation, and organizational transformation, aiming to comprehend the strategic implications and business value derived from knowledge graph initiatives.

Course Outline

The seminar will walk participants through 8 key stages of introducing, developing, delivering and evolving Knowledge Graphs in an organization. These are:

Stage 1 – “Knowing where you are getting into”

• Clarification of the knowledge graph concept
• Key factors influencing the ease or difficulty of building a knowledge graph
• Evaluating feasibility and viability of implementing a knowledge graph in a specific organization and for a particular business problem

Stage 2 – ”Setting up the stage”

• Exploring 5 key questions essential before initiating knowledge graph development
• Defining what, why, how, who, and the stakeholders involved in the project
• Outlining actions required to seek and discover answers to these questions

Stage 3 – “Deciding what to build”:

• Delving into knowledge graph specification
• Use of competency questions for gap analysis between organizational knowledge capabilities and needs
• Scoping and prioritizing knowledge graph content

Stage 4 – “Giving it a shape”

• Schema design using Ontology Representation and Engineering
• Identification of conceptual modeling best practices, dilemmas, and pitfalls
• Addressing uncertainty and vagueness

Stage 5 – “Giving it substance”

• Exploring the challenging task of knowledge graph population
• Description of population tasks and associated difficulties
• Designing optimal population pipelines

Stage 6 – “Ensuring it’s good”:

• Assessing knowledge graph quality, defining dimensions, and metrics
• Insights into quality trade-offs and prioritization of dimensions
• Measuring quality and effective prioritization of focus areas

Stage 7 – “Making it useful”:

• Typical knowledge graph applications
• Guidelines and best practices for optimizing knowledge graph usefulness and value

Stage 8 – “Making it last”:

• Addressing the challenge of knowledge graph maintenance and evolution
• Detecting, measuring, and monitoring concept drift
• Best practices for enabling continuous improvement and preventing knowledge graph obsolescence over time.

Most companies today are storing data and running applications in a hybrid multi-cloud environment. Analytical systems tend to be centralised and siloed like data warehouses and data marts for BI, cloud storage data lakes for data science and stand-alone streaming analytical systems for real-time analysis. These centralised systems rely on data engineers and data scientists working within each silo to ingest data from many different sources and engineer it for use in a specific analytical system or machine learning models. There are many issues with this centralised, siloed approach including multiple tools to prepare and integrate data, reinvention of data integration pipelines in each silo and centralised data engineering with poor understanding of source data unable to keep pace with business demands for new data.

To address these issues, a new approach called Data Mesh emerged in late 2019 attempting to accelerate creation of data for use in multiple analytical workloads. Data Mesh is a decentralised business domain-oriented approach to data ownership and data engineering to create a mesh of reusable data products that can be created once and shared across multiple analytical systems and workloads.

This half-day workshop looks at the development of data products in detail and also, how can you use a data marketplace to share and govern the sharing of data products across the enterprise to shorten time to value.

Learning Objectives:

• Strengths and weaknesses of centralised data architectures used in analytics
• The problems caused in existing analytical systems by a hybrid, multi-cloud data landscape
• The emergence of data mesh and data products
• What exactly a data product is and the types of data products that you can create
• The benefits that data products offer and what are the implementation options?
• How to organise to create data products in a decentralised environment so you avoid chaos?
• How business glossaries can help ensure data products are formally defined, understood by business users and semantically linked
• The critical importance of a data catalog in understanding what data is available
• What software is required to build, operate and govern a data mesh of data products for use in a data lake, a data lakehouse or data warehouse?
• What is data fabric software, how does it integrate with data catalogs and connect to data in your data estate
• An Implementation methodology to produce ready-made, trusted, reusable data products
• Collaborative domain-oriented development of modular and distributed DataOps pipelines to create data products
• How a data catalog and automation software can be used to generate DataOps pipelines
• Managing data quality, privacy, access security, versioning, and the lifecycle of data products
• Publishing semantically linked data products in a data marketplace for others to consume and use
• Governing the sharing and use of data products in a data marketplace
• Consuming data products in an MDM system
• Consuming and assembling data products in multiple analytical systems like data warehouses, lakehouses and graph databases to shorten time to value.

Who is it for?
This seminar is intended for business data analysts, data architects, chief data officers, master data management professionals, data scientists, IT ETL developers, and data governance professionals. It assumes you understand basic data management principles and data architecture plus a reasonable understanding of data cleansing, data integration, data catalogs, data lakes and data governance.

Detailed course outline
Most companies today are storing data and running applications in a hybrid multi-cloud environment. Analytical systems tend to be centralised and siloed like data warehouses and data marts for BI, cloud storage data lakes or Hadoop for data science and stand-alone streaming analytical systems for real-time analysis. These centralised systems rely on data engineers and data scientists working within each silo to ingest data from many different sources, clean and integrate it for use in a specific analytical system or machine learning models. There are many issues with this centralised, siloed approach including multiple tools to prepare and integrate data, reinvention of data integration pipelines in each silo and centralised data engineering with poor understanding of source data unable to keep pace with business demands for new data. Also, master data is not well managed.

To address these issues, a new approach emerged in late 2019 attempting to accelerate creation of data for use in multiple analytical workloads. That approach is Data Mesh. Data Mesh is a decentralised business domain-oriented approach to data ownership and data engineering to create a mesh of reusable data products that can be created once and shared across multiple analytical systems and workloads. A Data Mesh can be implemented in a number of ways. These include using one or more cloud storage accounts on cloud storage, on an organised data lake, on a Lakehouse, on a data cloud, using Kafka or using data virtualisation. Data products can then be consumed in other pipelines for use in streaming analytics, Data Warehouses or Lakehouse Gold Tables, for use in business intelligence, feature stores for use data science, graph databases for use in graph analysis and other analytical workloads.

This half-day workshop looks at the development of data products in detail. It also looks at the strengths and weaknesses of data mesh implementation options for data product development. Which architecture is best to implement this? How do you co-ordinate multiple domain-oriented teams and use common data infrastructure software like Data Fabric to create high-quality, compliant, reusable, data products in a Data Mesh. Is there a methodology for creating data products? Also, how can you use a data marketplace to share and govern the sharing of data products? The objective is to shorten time to value while also ensuring that data is correctly governed and engineered in a decentralised environment. It also looks at the organisational implications of Data Mesh and how to create sharable data products for use as master data, in a data warehouse, in data science, in graph analysis and in real-time streaming analytics to drive business value? Technologies discussed includes data catalogs, data fabric for collaborative development of data integration pipelines to create data products, DataOps to speed up the process, data orchestration automation, data observability and data marketplaces.

• What are data products?
• What makes creating data products different from other approaches to creating data for use analytical workloads?
• A best practice methodology for creating data products
• How to design semantically linked data products to enable rapid consumption and use of data to produce new insights
• Quick start mechanisms to speed up data product design
• Defining common business data names for data products in a business glossary
• Data modelling techniques for data products
• Discovering data needed to build data products using a data catalog
• Developing DataOps pipelines to engineer the data needed using data fabric
• Publishing data products – the role of the data marketplace
• Governing access to and use of data products across the enterprise
• Consuming and assembling data products for use in multiple analytical workloads
• Technologies and skills needed.

Whether you call it a conceptual data model, a domain model, a business object model, or even a “thing model,” the concept model is seeing a worldwide resurgence of interest. Why? Because a concept model is a fundamental technique for improving communication among stakeholders in any sort of initiative. Sadly, that communication often gets lost – in the clouds, in the weeds, or in chasing the latest bright and shiny object. Having experienced this, Business Analysts everywhere are realizing Concept Modelling is a powerful addition to their BA toolkit. This session will even show how a concept model can be used to easily identify use cases, user stories, services, and other functional requirements. 

Realizing the value of concept modelling is also, surprisingly, taking hold in the data community. “Surprisingly” because many data practitioners had seen concept modelling as an “old school” technique. Not anymore! In the past few years, data professionals who have seen their big data, data science/AI, data lake, data mesh, data fabric, data lakehouse, etc. efforts fail to deliver expected benefits realise it is because they are not based on a shared view of the enterprise and the things it cares about. That’s where concept modelling helps. Data management/governance teams are (or should be!) taking advantage of the current support for Concept Modelling. After all, we can’t manage what hasn’t been modelled!

The Agile community is especially seeing the need for concept modelling. Because Agile is now the default approach, even on enterprise-scale initiatives, Agile teams need more than some user stories on Post-its in their backlog. Concept modelling is being embraced as an essential foundation on which to envision and develop solutions. In all these cases, the key is to see a concept model as a description of a business, not a technical description of a database schema. 

This workshop introduces concept modelling from a non-technical perspective, provides tips and guidelines for the analyst, and explores entity-relationship modelling at conceptual and logical levels using techniques that maximise client engagement and understanding. We’ll also look at techniques for facilitating concept modelling sessions (virtually and in-person), applying concept modelling within other disciplines (e.g., process change or business analysis,) and moving into more complex modelling situations. 

Drawing on over forty years of successful consulting and modelling, on projects of every size and type, this session provides proven techniques backed up with current, real-life examples.

Topics include:

• The essence of concept modelling and essential guidelines for avoiding common pitfalls
• Methods for engaging our business clients in conceptual modelling without them realizing it
• Applying an easy, language-oriented approach to initiating development of a concept model
• Why bottom-up techniques often work best
• “Use your words!” – how definitions and assertions improve concept models
• How to quickly develop useful entity definitions while avoiding conflict
• Why a data model needs a sense of direction
• The four most common patterns in data modelling, and the four most common errors in specifying entities
• Making the transition from conceptual to logical using the world’s simplest guide to normalisation
• Understand “the four Ds of data modelling” – definition, dependency, demonstration, and detail
• Tips for conducting a concept model/data model review presentation
• Critical distinctions among conceptual, logical, and physical models
• Using concept models to discover use cases, business events, and other requirements
• Interesting techniques to discover and meet additional requirements
• How concept models help in package implementations, process change, and Agile development

Learning Objectives:

• Understand the essential components of a concept model – things (entities) facts about things (relationships and attributes) and rules
• Use entity-relationship modelling to depict facts and rules about business entities at different levels of detail and perspectives, specifically conceptual (overview) and logical (detailed) models
• Apply a variety of techniques that support the active participation and engagement of business professionals and subject matter experts
• Develop conceptual and logical models quickly using repeatable and Agile methods
• Draw an Entity-Relationship Diagram (ERD) for maximum readability
• Read a concept model/data model, and communicate with specialists using the appropriate terminology.