RE Conf 2014 Requirements Engineering in Agile Analytics · 2016. 3. 7. · RE Conf 2016...

RE Conf 2016Requirements Engineering in Agile Analytics

02. März 2016Dr. Claudia Schindler, Felix Löw

Agenda

Munich Re1

Setting the context: Challenges for RE in Analytics2

Capturing the business context: The Reporting Information System3

Providing the right data: Data Sets and Data Flows4

Experiences and lessons learnt5

REConf 2016 - Requirements Engineering in Agile Analytics / 02.03.2016

Unser integriertes Geschäftsmodell Starke Marken im Verbund

Munich Re (Gruppe)*

Assetmanagement

ErstversicherungRückversicherung Munich Health

BelgiumCorporate Insurance PartnerGreat Lakes Reinsurance (UK) PLCKA Köln.Assekuranz Agentur GmbHMSF Pritchard Syndicate 318Temple Insurance CompanyWatkins Syndicate

* Die Darstellung erhebt keinen Anspruch auf Vollständigkeit und gibt nicht die genauen Beteiligungsverhältnisse wieder.REConf 2016 - Requirements Engineering in Agile Analytics / 02.03.2016

Kompetenzfelder von Munich ReWas uns von anderen unterscheidet

Wir übernehmen veränderte und komplexe Risiken

erweitern die Grenzen der Versicherbarkeit,

entwickeln innovative Deckungskonzepte.

Wir bieten erstklassige Modellierung und

maßgeschneiderte Deckungen, effiziente Rückversicherung von

Standardrisiken, hohe Kapazität pro Risiko-Exponierung.

Wir ermöglichen attraktives Kapitalmanagement.

Transfer von Risiken an den Kapitalmarkt,

Risikomanagement für Kapitalanlagen (ALM),

Capital Relief, Solvency II Consulting.

Wir bieten risikoorientierte Services wie

Underwriting-Tools, z.B. Nathan und MIRA,

Wissenstransfer in Kundenseminaren, effiziente Kooperation über

connect.munichre.com.

Wir unterstützen Produktentwicklungen.

Service und Knowhow

Maßgeschneiderte Lösungen und Effizienz

Sicherheit und Verlässlichkeit


In allen Märkten präsent

ChicagoColumbus

Montreal

Philadelphia

Princeton

San Francisco

Toronto

Bogotá Caracas Mexico Santiago de Chile São Paulo

Auckland Melbourne Sydney

Beijing Calcutta

Hong Kong Kuala Lumpur Mumbai Seoul Shanghai Singapore

Munich London Madrid Malta

MoscowParis

Accra Cape Town Nairobi Port Louis

Milan

Zurich

Amelia

New York

Vancouver

Buenos AiresJohannesburg

TaipeiTokyo

Atlanta

Hartford Dubai


Setting the context: Challenges for RE in Analytics

2

Setting the context - It‘s all about data!

System A System B System C

How is this achieved?How is this consistently documented?

?

E

T

L


In RE we have to tackle several challenges…

What is the businesscontext ?

What data isrequired?

How is dataconsumed?

Business Process

Roles and goals

Usage requirements and contextual environment

Frequency/ Criticality

Domain Models

Data Sources

Logics

Key Figures

Device

Personas

UX

ReportDashboardInterfaceUse Cases

Iterative

Frequent user feedback

Incremental Business Value

Risk Driven


Traceability structure in Business Intelligence

Trac

eabi

lity

User StoriesUse Cases

Reports

Domain ModelData Sets

Business Processes

Data Sets

Slic

es


Capturing the business context –The Reporting Information System

3

A structured evaluation of all existing reporting content hasbeen achieved

Who is using which report forwhat purpose in whichlocation?

What is the businesscontext ?

Business Process

Business ProcessStep

Reports

Actor

Business OwnerLocation

Reporting Information

System


We are able to analyse our reporting portfolio from different perspectives…


A structured evaluation of all existing reporting content hasbeen achieved

Which report is containing whichdata?


Key Figures Characteristics Logics

Query Information

System


Providing Data -Data Flows and Data Sets

4

Existing specification is complex and confusing

• No clear responsibilityfor Excel documents

• No documentation for business

Domain Model

Domain Model



New form of specification is data centric

• Data flow per data set• Requirements

Engineers responsible• Readable by business

Domain ModelData flow

Domain ModelNFR Spec+ per Report


Data sets are the basic entities of the reporting domainmodel providing business value

Data SetData SetData Set

Data Set: A collection of data belonging together from a business perspective, e.g. a class in a domain model


Data must be transformed from the source model to thetarget model to provide data sets

SourceReport


Presenter

Presentation Notes

TPS GrossPricing benötigt um…

Every extraction process can be detailed by a drill down

ReinsuranceCoverageMgmt::Coverage

+ broker: Broker [0..1] = NULL+ brokerage: Percent = 0%+ cessionLimit: Amount+ comment: Comment+ coverageType: CoverageType+ eventLimit: Amount+ id: OverallUniqueID {readOnly}+ internalId: int = NULL+ isUnlimited: bool = TRUE+ largeLossLimit: Amount+ leadingBroker: Broker [0..1]+ limit: Amount [0..1] = NULL+ name: BoundedString+ ordinalNumberWithinContractPeriod: int

«reporting»+ isSpecialTreaty: bool = FALSE+ protectedShare: Percent = NULL+ renewalStatus: RenewalStatus = NotActive+ underwritingStatus: UnderwritingStatus = NotActive

ReinsuranceCov erageMgmt::NonProportionalReinsuranceCov erage

+ annualAggregateDeductible: Amount+ annualAggregateDeductibleType: AnnualAggregateDeductibleType+ annualAggregateLimit: Amount+ attachmentPoint: Amount

Extract relev ant fields from TPS Cov erage ::TPSCov erage

- attachmentPoint: Amount- broker: Broker- coverageId: OverallUniqueID- coverageType: CoverageType- isSpecial: bool- l imit: Amount- name: String- protectedShare: Percent- renewalStatus: RenewalStatus- underwritingStatus: UnderwritingStatus

Reused sourcesystem domain model

class

Logical reportingdata set


Every transformation process can be detailed by a drill down


But additional documents are needed

Parameter Screen Selection

Report Initial Layout and drilldown attributes

Report Overview Document: Report SpecificationLinks to all relevant documentsand specification of other topics(e.g. report specific NFR, authorization, …)

How is dataconsumed?


Experience and lessons learnt

5

Our Experiences

Project Team

Test and Developer

Business Feedback

Our Feedback

Data flows per data set help to slice requirements for iterative development Data sets used to structure test cases Unambiguous specification by using data flows instead of word documents If no source domain model exists, you can also start from the technical model

Easy to read and understand, drill down to required level of detail helps

Quality check of source system domain models Shows other projects which data are available and their structure The Business Reporting Repository supports a business value driven agile planning

Think they will like to review this kind of documentation The Business Reporting Repository enables business to manage Report portfolio


Presenter

Presentation Notes

Readable specification of existing reports will become more important Data Analyst in CU creates reports himself UWPF Reporting Data are handed over to SAS, OnSet,… Helps insight for Combined Reporting (more transformation might be required)

Thank you for listening

Ken Collier: Agile Analytics, Addison Wesley 2012

Ralph Hughes: Agile Data Warehousing for the Enterprise: A Guide for Solution Architects and Project Leaders 2015

Dr. Claudia [email protected]

Felix Lö[email protected]


http://www.amazon.de/Agile-Analytics-Value-Driven-Intelligence-Warehousing/dp/032150481X/ref=sr_1_1?ie=UTF8&qid=1455270169&sr=8-1&keywords=agile+analytics

http://www.amazon.de/Agile-Analytics-Value-Driven-Intelligence-Warehousing/dp/032150481X/ref=sr_1_1?ie=UTF8&qid=1455270169&sr=8-1&keywords=agile+analytics

http://www.amazon.de/Agile-Data-Warehousing-Enterprise-Architects/dp/0123964644/ref=sr_1_1?ie=UTF8&qid=1455270057&sr=8-1&keywords=agile+warehousing+enterprise

http://www.amazon.de/Agile-Data-Warehousing-Enterprise-Architects/dp/0123964644/ref=sr_1_1?ie=UTF8&qid=1455270057&sr=8-1&keywords=agile+warehousing+enterprise

RE Conf 2014 Requirements Engineering in Agile Analytics · 2016. 3. 7. · RE Conf 2016...

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