Indien er een keuze is voor online of live deelname, kunt u dat bij inschrijving op het aanmeldformulier aangeven. Van die keuze (online of live) kunt u niet zonder voorafgaand overleg afwijken vanwege anderhalve meter beperkingen met gereserveerde zitplaatsen en het prijsverschil in deelnamekosten.

De kosten voor deelname aan de workshop in onze cursuszaal op locatie bedragen € 1.935,- per persoon indien u zich uiterlijk 30 dagen voor aanvang registreert en daarna € 2.150,- (excl. BTW)*. Documentatie, lunch, koffie én het standaardwerk Agile Data Warehouse Design door Lawrence Corr zijn inbegrepen.

Als u zich aanmeldt voor het online volgen van deze workshop, bedragen de kosten € 1.755,- per persoon indien u zich uiterlijk 30 dagen voor aanvang registreert en daarna € 1.950,- (excl. BTW)*.

Bent u werkzaam bij een gemeente of provincie? Dan kunt u de ook nog BTW terugvorderen via het BTW compensatiefonds, waardoor u uiteindelijk 21% voordeliger uit bent. Informeer bij uw administratie of u hiervoor in aanmerking komt.
Bij aanmelding verklaart u zich akkoord met onze inschrijvingsvoorwaarden.

*)Bij afwijking in prijs tussen de website en de PDF Brochure prevaleert altijd de informatie op deze pagina van de website. 

Aantrekkelijke kortingen
Meldt u tegelijkertijd meerdere personen van één bedrijf aan voor hetzelfde evenement, dan geldt al voor de tweede en derde deelnemer een korting van 10%. Vanaf vier deelnemers ontvangen álle deelnemers 15% korting (de deelnemers dienen op dezelfde factuur te staan).

Het programma start om 09.30 uur en duurt tot 17.00 uur. Registratie is mogelijk vanaf 08.30 uur.

Modelstorming – Agile BI Analysis and Design

• BI/DW design requirements, challenges and opportunities: the need for agility and collaboration
• The 7 Vs of Data. The 3 Vs of Big Data + 4 more that technology alone cannot solve. Facts and opinions. Configuration, calibration and collaboration. The need for collaborative data design
• Reactive data warehouse design vs. proactive design
• The 6Ws Game – Breaking the ice, introducing the Ws and basic visual collaboration techniques. Adding a 7th W for analytical data modeling
• Waterfall BI vs. Agile BI: balancing ‘just enough design up front’ (JEDUF) and ‘just in time’ (JIT) data modeling
• Traditional BI requirements gathering limitations: data-driven vs. report-driven. Supply vs. demand. Avoiding the ‘field of dreams’ build-it-and-they-will-come approach. Becoming value-driven
• Modelstorming with BI stakeholders: the case for collaborative data modeling

Agile Dimensional Modeling Fundamentals

• Modeling for measurement: the case for dimensional modeling, star schemas, facts & dimensions
• Business Event Analysis and Modeling (BEAM): an agile approach to dimensional modeling
• Thinking dimensionally with 7Ws (who, what, when, where, how many, why & how). Supporting a measurement mindset

Collaborative Data Requirements Gathering

• Introduction to BEAM: “Who does what?” a simple question for discovering business events
• Describing business event details using the 7Ws. Modeling a single business event in 7 simple questions: using the BEAM script
• Documenting business events with the BEAM Canvas (and lots of Post-it notes)
• Modeling multiple business events in just enough detail. Avoiding technical data modeling jargon. Modelstorming data as verbs, nouns and adjectives
• Modeling related business processes (multiple business events) using the BEAM Storyboard

Visual Dimensional Modelstorming Tools

• NoERD: Not only Entity Relationship Diagrams – using more appropriate diagrams for collaborative modeling with subject matter experts
• Business Model Canvas: aligning business intelligence and data analytics with business model definition and innovation. Value-driven BI job one: measuring the business model
• BEAM Canvas: a detailed BI model canvas for systematic data set & database schema design
• Data Stories: Using narrative to engage stakeholders and help them describe data-rich business events
• Data Story Types: Discrete, Evolving and Recurring business events
• BEAM Tables and Data Story Themes: modeling data requirements by (structured) example
• Data Timelines: Sorting multiple business events chronologically. Modeling time and process measurement
• BEAM Storyboard: modeling processes, shared data definitions and development priorities using a collaboration-friendly, non-technical version of the dimensional matrix
• Recording priorities and estimates for agile BI data development within the design
• Hierarchy Charts: modeling BI drill-downs, rollups and critical measurement levels for cross process comparison. Using the Ws to identify ‘hot’ levels
• Change Stories: capturing historical data requirements and reporting defaults (slowly changing data rules)

Agile Star Schema Design

• Converting analytical data requirements into logical data models. Turning data story nouns, verbs, and adjectives into entities, relations, attributes.
• Converting a BEAM canvas into star schemas and other ER diagrams/models. Turning business events into facts and dimensions or hubs, links and satellites
• Test-driven design: agile/lean data profiling for validating and improving requirements models
• Data warehouse reuse: identifying, defining and developing conformed dimensions and facts. Developing an agile data warehouse bus architecture. Coping with conformance failure
• Designing flexible, high performance star schemas: maximising the benefits of surrogate keys
• How Much/How Many: Designing facts, measures and KPIs (Key Performance Indicators)
• Fact types: transactions, periodic snapshots, accumulating snapshots
• Fact additivity: fully additive, semi-additive and non-additive data. Full documenting and correctly aggregating semi-additive measures
• Fact performance and usability: indexing, partitioning, aggregating and consolidating facts
• Capturing complex slowly changing data rules: mixing type 1 and 2 attributes. Differentiating between minor and major changes and corrections via multi-column dependencies
• Designing facts, measures and KPIs (Key Performance Indicators)
• Fact table types: transactions, periodic snapshots, accumulating snapshots. Choosing the appropriate types
• Refactoring star schemas: responding to change, dealing with data debt
• Agile process measurement: designing and developing derived fact tables for multi-verb/event analysis
• Recording a BEAM Storyboard as a Dimensional Matrix – bus architecture.
• Enhance Star Schema: drawing better database diagrams. Taking advance of position and visually consistent layouts to encourage reuse and encode common data model patterns. Using BEAM short codes to fully document dimensional design decisions
• Developing Data Warehouse Matrix dashboards. Documenting the design without complex global schemas

Dimensional Design Patterns

• Using the 7W data types to identify common analytical data challenges ant the dimensional design patterns that address them.
• Adapting design pattern and understanding their limitations – when to uses them, when not to.

Who & What dimension patterns: customers, employees, products and services

• Modeling large volatile populations with rapidly changing attributes: mini-dimensions & customer facts
• Customer segmentation: business to business (B2B), business to consumer (B2C) dimensions: Swappable population subsets for performance, usability and compliance
• Multi-level dimensions: supporting business events with varying levels of descriptive information and variable granularity measurements
• Recursive customer relationships and organisation structures: variable-depth hierarchy maps for analysing 1:M and M:M recursive relationships
• Current and historical reporting perspectives: hybrid slowly changing dimensions. Using highly efficient swappable views to support multiple historic perspectives with agility. Avoiding unnecessary type 6 and 7 slowly changing dimensions
• Mixed business models: heterogeneous products/services, diverse attribution, ragged hierarchies: swappable attribute subset dimensions
• Product and service decomposition: component (bill of materials) and product unbundling analysis

When & Where dimension patterns: dates, times and locations

• Flexible date handling, ad-hoc date ranges and year-to-date analysis
• Describing and measuring time: modeling when details as dimensions and facts. Coping with specialist clocks and varying schedules
• Multinational BI: national languages reporting, multiple currencies, time zones & national calendars
• Understanding journeys and trajectories: modeling events with multiple geographies – the importance of first and last locations

Why & How dimension patterns: cause and effect

• Causal factors: trigger events, referrals, promotions, weather and exception reason dimensions
• Fact specific dimensions: transaction and event status descriptions
• Multi-valued dimensions: bridge tables, weighting factors, impact and ‘correctly weighted’ analysis. Avoiding unnecessary model complexity – efficiently handling exceptional data which is only ‘slightly’ multi-valued
• Behaviour Tagging: modeling causation and outcome with dimensional overloading. Using step dimensions to understand sequential behaviour

Review

• Further reading/viewing for encouraging and supporting modelstorming within your organisation
• Downloadable BEAM templates and other online modelstorming resources
• Documented and Distributed Modelstorming: photography, visual collaboration tools and platforms
• The immutable laws of data warehousing – anticipating agile BI.