Generatieve AI in Business Analyse

ChatGPT / CoPilot / Gemini voor End-to-End Business Analyse

Let op: wegafsluiting A12 vanaf Veenendaal richting Utrecht!

 

De rol van Kunstmatige Intelligentie in strategische Business Analyse en Functionele Analyse

In de wereld van analyse zijn in de afgelopen twee decennia talrijke technieken voor business analyse en modelleertalen ontwikkeld en vastgesteld als standaarden. Deze methoden vormen tegenwoordig de hoeksteen in zowel private ondernemingen als overheidsinstanties. Kunstmatige Intelligentie, ongetwijfeld een van de meest baanbrekende technologieën tot nu toe, opent nieuwe deuren voor analisten met innovatieve tools en mogelijkheden. OpenAI’s ChatGPT, bijvoorbeeld, kan op diverse manieren toegepast worden in strategische, business analyse en functionele analyses.

Uw rol als analist zal structureel veranderen

Gezien de beperking van het aantal uren op een werkdag staan analisten vaak onder tijdsdruk om hun analyses grondig uit te voeren. Het gebruik van Kunstmatige Intelligentie tools zoals ChatGPT kan aanzienlijk bijdragen aan de efficiëntie van uw analytische werkzaamheden. Deze geavanceerde chatbot van OpenAI kan diverse taken aanpakken en fungeert uitstekend als een ‘analyse-assistent’.

Dankzij deze technologie kunnen analyses die voorheen dagen duurden, nu in slechts enkele uren worden afgerond zonder in te boeten aan kwaliteit. Sterker nog, de resultaten zijn vaak beter, consistenter en nauwkeuriger. Natuurlijk blijft de rol van de analist cruciaal voor het bieden van de juiste sturing en context, aangezien Kunstmatige Intelligentie en ChatGPT niet onfeilbaar zijn. Maar we kunnen u verzekeren dat zowel de basisverwerking als de analyse zelf aanzienlijk worden versneld door deze technologieën. Dit is geen toekomstvisie, maar een haalbare realiteit met AI en ChatGPT.

Wat u kunt verwachten

Tijdens de workshop ‘AI-Gedreven Business Analyse’ leidt Christian Gijsels u op een inspirerende en interactieve manier door de wereld van Kunstmatige Intelligentie, met speciale aandacht voor ChatGPT. Hij zal u laten zien hoe u ChatGPT kunt omvormen tot de meest krachtige assistent die u zich ooit had kunnen voorstellen.

Gefaciliteerd door analyse expert Christian Gijsels

De workshop wordt geleid en gefaciliteerd door Christian Gijsels, expert in analyse. Met een achtergrond die zowel enterprise architectuur, zakelijke en functionele analyse, als modellering omvat, brengt hij twintig jaar ervaring in consultancy, mentoring en opleiding in. Hij heeft zijn kennis uitgebreid via cursussen, workshops en postdoctorale programma’s.
Christian Gijsels staat bekend om zijn innovatieve benadering en diepgaande kennis van AI en ChatGPT, wat de basis vormt voor deze baanbrekende cursus. Hij heeft zijn expertise toegepast in zowel de private sector als bij overheidsorganisaties, met als doel de effectiviteit en snelheid van analytisch werk te verbeteren. In deze workshop deelt hij zijn waardevolle inzichten en ervaringen.

Wat u leert in deze workshop

In deze intrigerende workshop leert u, met de Business Case als uitgangspunt:

  • Navigeren door het landschap van toonaangevende leveranciers, producten en technologieën
  • Het optimaliseren van de configuratie van ChatGPT voor betere antwoorden, inclusief aangepaste instructies en contextbeheer
  • Het creëren van effectieve ChatGPT prompts om vragen nauwkeurig te beantwoorden
  • Het opzetten van een Business Case in ChatGPT door de analytische context te definiëren, als basis voor alle verdere analyses
  • Het definiëren en uitvoeren van capability analyses in strategie met ArchiMate in ChatGPT
  • Het ontwikkelen en analyseren van Business Architectuur met ArchiMate in ChatGPT
  • Het vormgeven en onderzoeken van Business Analysis met BPMN in ChatGPT
  • Het uitvoeren van Functionele Analyse en deze analyseren met UML in ChatGPT
  • Het opstellen en analyseren van User Story’s in ChatGPT.

 

Bestemd voor ú

Deze workshop is geschikt voor zowel de beginnende als de medior business analist of functioneel analist die op een efficiënte manier wil leren werken en analyseren. En die daarbij gebruik wil maken van de meest geavanceerde tools van dit moment. Hieronder vallen onder meer de volgende functies:

  • Strategische analisten
  • Enterprise & Business architecten
  • BPM projectgroepen
  • Business Analisten & Proces Analisten
  • Business Process Eigenaars en informatie analisten
  • Functionele & UML analisten
  • End-to-End analisten
  • Software architecten en engineers.

 

Aarzel daarom niet en schrijf u snel in!

Bovenaan de pagina bij Downloads kunt u de volledige PDF brochure downloaden.

Christian Gijsels

Christian Gijsels is momenteel werkzaam als zelfstandig consultant en trainer. Daarvoor was Christian werkzaam binnen de Cronos Holding, waar hij verantwoordelijk was voor de The Business Analysts, een groep van een 85-tal business analisten, functionele analisten en projectmanagers.

Lees meer

Van der Valk Hotel Utrecht

Van der Valk Hotel Utrecht
Winthontlaan 4-6
3526 KV Utrecht
Telefoon 030 8000 800

Van der Valk Hotel Utrecht is gelegen langs de A12 afslag 17 (Utrecht / Jaarbeurs / Kanaleneiland). U kunt parkeren in de parkeergarage van het hotel en ontvangt van ons na afloop een uitrijkaart. Ook met openbaar vervoer is het hotel zeer goed te bereiken. Vanaf Centraal Station Utrecht vertrekt er tijdens de spits iedere 7 minuten een bus (buslijn 63, 65, 66, 74 en 77) of sneltram (lijn 20 en 21) en arriveert u binnen 10 minuten bij het hotel. Voor vertrekpunten en -tijden van treinen en bussen kunt u kijken op www.9292.nl of bellen met 0900-9292.

Alhoewel het hotel beschikt over een ruime parkeergarage kunnen wij geen plaatsen garanderen. Wij adviseren daarom om met openbaar vervoer te reizen.

Van der Valk Hotel Utrecht biedt mogelijkheden voor overnachting. Echter, het hotel geeft geen korting op verblijf aan evenementbezoekers. Indien u er wilt overnachten zult u daarom via de reguliere wijze bij Van der Valk moeten reserveren.

Meer informatie over het hotel en de locatie vindt u op www.vandervalkhotelutrecht.nl.

 

The course starts at 09.30 am and ends at 5 pm. Registration commences at 08.30 am.

[IF ONLINE FORMAT] We will send the course materials and meeting instructions well in advance as well as the invitation with hyperlink to join us online. The workshop will start at 13:30 and lasts until 17:00 CET. The online meeting will be available at least one half hour earlier so please log in timely in order to check your sound and video settings beforehand.

 

Course Topics

Essentials of Data Modelling

  • What really is a data model or concept model?
  • Essential components – entities, relationships, attributes, and rules
  • Hands-on case study – how data modelling resolved business issues, and supported other business analysis techniques
  • Guidelines for comprehension – how to lay out Entity-Relationship Diagrams (“ERDs”)
  • The narrative parts of a data model – definitions and assertions
  • Group exercise – getting started on a data model, then refining it
  • Common misconceptions about data models and data modelling
  • The real purpose of a data model
  • Contextual, Conceptual, and Logical Data Models – purpose, audience, definition, and examples
  • Overview of a three-phase methodology for developing a data model

Establishing the initial conceptual data model

  • Top down vs. bottom up approaches to beginning a data model – when is each appropriate?
  • A bottom-up approach focusing on collecting and analyzing terminology
  • A structure for sorting terms and discovering entities
  • Exercise – developing an initial conceptual data model
  • Entities – what they are and are not
  • Guidelines for naming and defining entities
  • Three questions to help you quickly develop clear, useful entity definitions
  • Exercise – identifying flawed entities
  • Six criteria that entities must satisfy, and four common errors in identifying entities
  • Identifying relationships
  • Fundamental vs. irrelevant or transitive relationships
  • Good and bad relationship names
  • Multiplicity or cardinality – 1:1, 1:M, and M:M relationships, and useful facts about each
  • Common errors and special cases – recursive, multiple, and supertype-subtype relationships
  • Attributes – guidelines and types
  • Attributes in conceptual models vs. logical models

Developing the initial logical data model by adding rigor, structure, and detail

  • Transition to the logical model – shifting the focus from entities to attributes
  • Multi-valued, redundant, and constrained attributes, with simple patterns for dealing with each
  • An understandable guide to normalisation – first, second, and third normal forms
  • Higher order (fourth and fifth) and Boyce-Codd normal forms
  • Exercise – developing the initial logical data model
  • Four types of entities – kernel, characteristic, associative, and reference
  • Guidelines and patterns for dealing with each type of entity
  • How to draw your E-R Diagram for maximum readability and correctness
  • Optional and mandatory relationships
  • Considering time and history when looking at relationships
  • Typical attribute documentation
  • A common source of confusion and disagreement – primary keys
  • What primary keys are, what they’re really for, and three essential criteria
  • The four Ds of data modelling – definition, dependency, detail, and demonstration
  • E-R Diagramming – symbol sets and their problems, rules for readability and comprehension

Correctly handling attributes

  • Granularity – dealing with non-atomic and semantically overloaded attributes
  • Dealing with reference data and the “types vs. instances” problem
  • Three attributes that always need a qualifier
  • Vector modelling – entity or attribute?

Interesting structures – generalisation, recursion, and the two together

  • Generalisation (subtyping) – when to use it, and when not to
  • Generalisation with and without specification
  • Guidelines for using recursive relationships
  • Generalisation and recursion working hand-in-hand as a cure for literalism
  • Recognizing lists, trees, and networks, and modelling them with recursive relationships
  • Modelling difficult rules by combining generalisation (subtyping) and recursion
  • Staying clear on generalisation vs. roles, states, and aggregation

Modelling time, history, and time-dependent business rules

  • Historical vs. audit data, and when to show them on a data model
  • Thanks, Sarbanes-Oxley! Why we need “as-of reporting” and how to model data corrections
  • “Do you need history?” – how to tell when your client is misleading you
  • Modelling time – special considerations for recording past, present, and future values
  • Four variations on capturing history in a data model
  • Seven questions you should always ask when a date range appears

Modelling rules on relationships and associations

  • Using multi-way associations to handle complex rules
  • “Use your words” – how assertions, scenarios, and other techniques will improve your modelling
  • Associative entities – circular relationships, shared parentage, and other issues
  • Alternatives for modelling constraints across relationships
  • Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues
  • A simpler view – why the five normal forms could be reduced to three

Preparing and delivering a data model review presentation

  • Context – your audience, and why the model matters to them
  • It’s a story, not a data model! Building a storyboard
  • Five key techniques for presenting data models or other technical subjects
  • The mechanics of the data model review presentation
  • A demonstration

Bridging the “E-R vs. Dimensional” divide – the world’s shortest course on dimensional modelling

  • The perils of dimensional modelling without understanding the underlying E-R model
  • Spotting facts and dimensions – the relationship between dimensional models and E-R models
  • Saving time – building a first-cut dimensional model from an ER model

The course starts at 09.30 am and ends at 5 pm. Registration commences at 08.30 am.

[IF ONLINE FORMAT] We will send the course materials and meeting instructions well in advance as well as the invitation with hyperlink to join us online. The workshop will start at 13:30 and lasts until 17:00 CET. The online meeting will be available at least one half hour earlier so please log in timely in order to check your sound and video settings beforehand.

 

Course Topics

Essentials of Data Modelling

  • What really is a data model or concept model?
  • Essential components – entities, relationships, attributes, and rules
  • Hands-on case study – how data modelling resolved business issues, and supported other business analysis techniques
  • Guidelines for comprehension – how to lay out Entity-Relationship Diagrams (“ERDs”)
  • The narrative parts of a data model – definitions and assertions
  • Group exercise – getting started on a data model, then refining it
  • Common misconceptions about data models and data modelling
  • The real purpose of a data model
  • Contextual, Conceptual, and Logical Data Models – purpose, audience, definition, and examples
  • Overview of a three-phase methodology for developing a data model

Establishing the initial conceptual data model

  • Top down vs. bottom up approaches to beginning a data model – when is each appropriate?
  • A bottom-up approach focusing on collecting and analyzing terminology
  • A structure for sorting terms and discovering entities
  • Exercise – developing an initial conceptual data model
  • Entities – what they are and are not
  • Guidelines for naming and defining entities
  • Three questions to help you quickly develop clear, useful entity definitions
  • Exercise – identifying flawed entities
  • Six criteria that entities must satisfy, and four common errors in identifying entities
  • Identifying relationships
  • Fundamental vs. irrelevant or transitive relationships
  • Good and bad relationship names
  • Multiplicity or cardinality – 1:1, 1:M, and M:M relationships, and useful facts about each
  • Common errors and special cases – recursive, multiple, and supertype-subtype relationships
  • Attributes – guidelines and types
  • Attributes in conceptual models vs. logical models

Developing the initial logical data model by adding rigor, structure, and detail

  • Transition to the logical model – shifting the focus from entities to attributes
  • Multi-valued, redundant, and constrained attributes, with simple patterns for dealing with each
  • An understandable guide to normalisation – first, second, and third normal forms
  • Higher order (fourth and fifth) and Boyce-Codd normal forms
  • Exercise – developing the initial logical data model
  • Four types of entities – kernel, characteristic, associative, and reference
  • Guidelines and patterns for dealing with each type of entity
  • How to draw your E-R Diagram for maximum readability and correctness
  • Optional and mandatory relationships
  • Considering time and history when looking at relationships
  • Typical attribute documentation
  • A common source of confusion and disagreement – primary keys
  • What primary keys are, what they’re really for, and three essential criteria
  • The four Ds of data modelling – definition, dependency, detail, and demonstration
  • E-R Diagramming – symbol sets and their problems, rules for readability and comprehension

Correctly handling attributes

  • Granularity – dealing with non-atomic and semantically overloaded attributes
  • Dealing with reference data and the “types vs. instances” problem
  • Three attributes that always need a qualifier
  • Vector modelling – entity or attribute?

Interesting structures – generalisation, recursion, and the two together

  • Generalisation (subtyping) – when to use it, and when not to
  • Generalisation with and without specification
  • Guidelines for using recursive relationships
  • Generalisation and recursion working hand-in-hand as a cure for literalism
  • Recognizing lists, trees, and networks, and modelling them with recursive relationships
  • Modelling difficult rules by combining generalisation (subtyping) and recursion
  • Staying clear on generalisation vs. roles, states, and aggregation

Modelling time, history, and time-dependent business rules

  • Historical vs. audit data, and when to show them on a data model
  • Thanks, Sarbanes-Oxley! Why we need “as-of reporting” and how to model data corrections
  • “Do you need history?” – how to tell when your client is misleading you
  • Modelling time – special considerations for recording past, present, and future values
  • Four variations on capturing history in a data model
  • Seven questions you should always ask when a date range appears

Modelling rules on relationships and associations

  • Using multi-way associations to handle complex rules
  • “Use your words” – how assertions, scenarios, and other techniques will improve your modelling
  • Associative entities – circular relationships, shared parentage, and other issues
  • Alternatives for modelling constraints across relationships
  • Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues
  • A simpler view – why the five normal forms could be reduced to three

Preparing and delivering a data model review presentation

  • Context – your audience, and why the model matters to them
  • It’s a story, not a data model! Building a storyboard
  • Five key techniques for presenting data models or other technical subjects
  • The mechanics of the data model review presentation
  • A demonstration

Bridging the “E-R vs. Dimensional” divide – the world’s shortest course on dimensional modelling

  • The perils of dimensional modelling without understanding the underlying E-R model
  • Spotting facts and dimensions – the relationship between dimensional models and E-R models
  • Saving time – building a first-cut dimensional model from an ER model

testimonials

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

Jan Withagels Project & Program Manager, Cronos

“Top workshop. Perfect location, relevant and practical content with an immediate and high value. I recommend this course to anyone who wants to improve his efficiency & productivity in this age of AI.”

In-house Info

Wilt u deze sessie exclusief binnen uw eigen organisatie aanbieden voor een groep medewerkers? Een zeer aantrekkelijke optie met hoog rendement! Nu ook online of op locatie met deelnemers in de zaal plus online deelnemers.

MEER INFO