A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
Building a data warehouse of call data recordsDavid Walker
This document discusses considerations for building a data warehouse to archive call detail records (CDRs) for a mobile virtual network operator (MVNO). The MVNO needed to improve compliance with data retention laws and enable more flexible analysis of CDR data. Key factors examined were whether to use Hadoop/NoSQL solutions and relational databases. While Hadoop can handle unstructured data, the CDRs have a defined structure and the IT team lacked NoSQL skills, so a relational database was deemed more suitable.
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
The document discusses spatial data and analysis. It defines spatial data as information that can be analyzed based on geographic context, such as locations, distances and boundaries. It then describes the three common types of spatial data - points, lines and polygons - and how they are used to answer questions about proximity and relationships between objects. Finally, it outlines some of the key sources for spatial data, challenges in working with spatial data, and provides a model for how to deliver spatial data and analysis.
A presentation to the ETIS Business Intelligence & Data Warehousing Working Group in Brussels 22-Mar-13 discussing what Saas & Cloud means and how they will affect BI in Telcos
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
Building a data warehouse of call data recordsDavid Walker
This document discusses considerations for building a data warehouse to archive call detail records (CDRs) for a mobile virtual network operator (MVNO). The MVNO needed to improve compliance with data retention laws and enable more flexible analysis of CDR data. Key factors examined were whether to use Hadoop/NoSQL solutions and relational databases. While Hadoop can handle unstructured data, the CDRs have a defined structure and the IT team lacked NoSQL skills, so a relational database was deemed more suitable.
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
The document discusses spatial data and analysis. It defines spatial data as information that can be analyzed based on geographic context, such as locations, distances and boundaries. It then describes the three common types of spatial data - points, lines and polygons - and how they are used to answer questions about proximity and relationships between objects. Finally, it outlines some of the key sources for spatial data, challenges in working with spatial data, and provides a model for how to deliver spatial data and analysis.
A presentation to the ETIS Business Intelligence & Data Warehousing Working Group in Brussels 22-Mar-13 discussing what Saas & Cloud means and how they will affect BI in Telcos
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
Presentation from 2014 International Data Quality Summit (www.idqsummit.org, Twitter hashtag #IDQS14). Techniques for business analysts and data scientists to facilitate better requirements gathering in data and analytic projects.
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
In this new paper, I explore the organisational and cultural challenges of implementing information governance and data quality. I identify potential problems with the traditional centralised methods of data quality management, and offer alternative organistional models which can enable a more distributed and democratised approach to improving your organisations data. I also propose a simple four-step approach to delivering immediate business value from your data.
A template for capturing the overall high-level business requirements and expectations for business solutions with a significant impact on or requirement for data. (cf. the “Project Mandate” document in PRINCE2).
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
A template defining an outline structure for the clear and unambiguous definition of the discreet data elements (tables, columns, fields) within the physical data management layers of the required data solution.
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
Moving From Scorecards To Strategic ManagementWynyard Group
In their recent book “Strategy Maps”, Robert Kaplan and David Norton stated that “73 percent of companies achieving outstanding performance clearly communicate their strategy and strategic measures, whereas only 28 percent of the underperformers take such an action.”
What is becoming more evident is that there far more to performance management than just technology or implementing scorecards. This session will take you through the various steps you can take to transform your organisation and achieve outstanding performance. This session will show how you can implement a performance management system that is aligned to your organisation’s strategic direction
• Why implementing scorecards is not “strategic management”
• What are the steps involved in moving from a scorecard application to an enterprise strategic performance management system.
• Help take your performance management system to the next level
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
This document discusses data mart approaches to architecture. It defines a data mart as a subset of a data warehouse that supports the requirements of a particular department. It notes that data marts are often built and controlled by a single department. The document outlines the key differences between data warehouses and data marts such as scope, subjects covered, data sources, size and implementation time. It also discusses the types of data marts and why organizations implement them to improve response times, decision making and match user views. Dimensional modeling concepts are introduced along with examples from healthcare and banking organizations.
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
Using the right data model in a data martDavid Walker
A presentation describing how to choose the right data model design for your data mart. Discusses the pros and benefits of different data models with different rdbms technologies and tools
Capturing Business Requirements For Scorecards, Dashboards And ReportsJulian Rains
This white paper discusses capturing business requirements for scorecards, dashboards, and reports. It defines the scope of information needed, including the report purpose, measures, dimensions, hierarchies, time periods, and other functional requirements. It also covers non-functional requirements like volume and capacity, performance, availability, and security. Further analysis is then needed to check data availability, prioritize requirements, define validation rules, and design supporting processes.
This document provides sample requirements for a data warehousing project at a telecommunications company. It includes examples of business, data, query, and interface requirements. The business requirements sample outlines requirements for collecting and analyzing customer, organization, and individual data. The data requirements sample defines dimensions for party (customer) data and hierarchies. The performance measures sample defines a measure for vanilla rated call revenue amount.
Gathering Business Requirements for Data WarehousesDavid Walker
This document provides an overview of the process for gathering business requirements for a data management and warehousing project. It discusses why requirements are gathered, the types of requirements needed, how business processes create data in the form of dimensions and measures, and how the gathered requirements will be used to design reports to meet business needs. A straw-man proposal is presented as a starting point for further discussion.
Big Data Week 2016 - Worldpay - Deploying Secure ClustersDavid Walker
A presentation from the Big Data Week conference in 2016 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements
Data Works Berlin 2018 - Worldpay - PCI ComplianceDavid Walker
A presentation from the Data Works conference in 2018 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements and in the process became on e of the few fully certified PCI compliance clusters in the world
Data Works Summit Munich 2017 - Worldpay - Multi Tenancy ClustersDavid Walker
A presentation from the Data Works Summit conference in 2017 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster to support multiple business cases in a multi-tenancy cluster.
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
Presentation from 2014 International Data Quality Summit (www.idqsummit.org, Twitter hashtag #IDQS14). Techniques for business analysts and data scientists to facilitate better requirements gathering in data and analytic projects.
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
In this new paper, I explore the organisational and cultural challenges of implementing information governance and data quality. I identify potential problems with the traditional centralised methods of data quality management, and offer alternative organistional models which can enable a more distributed and democratised approach to improving your organisations data. I also propose a simple four-step approach to delivering immediate business value from your data.
A template for capturing the overall high-level business requirements and expectations for business solutions with a significant impact on or requirement for data. (cf. the “Project Mandate” document in PRINCE2).
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
A template defining an outline structure for the clear and unambiguous definition of the discreet data elements (tables, columns, fields) within the physical data management layers of the required data solution.
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
Moving From Scorecards To Strategic ManagementWynyard Group
In their recent book “Strategy Maps”, Robert Kaplan and David Norton stated that “73 percent of companies achieving outstanding performance clearly communicate their strategy and strategic measures, whereas only 28 percent of the underperformers take such an action.”
What is becoming more evident is that there far more to performance management than just technology or implementing scorecards. This session will take you through the various steps you can take to transform your organisation and achieve outstanding performance. This session will show how you can implement a performance management system that is aligned to your organisation’s strategic direction
• Why implementing scorecards is not “strategic management”
• What are the steps involved in moving from a scorecard application to an enterprise strategic performance management system.
• Help take your performance management system to the next level
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
This document discusses data mart approaches to architecture. It defines a data mart as a subset of a data warehouse that supports the requirements of a particular department. It notes that data marts are often built and controlled by a single department. The document outlines the key differences between data warehouses and data marts such as scope, subjects covered, data sources, size and implementation time. It also discusses the types of data marts and why organizations implement them to improve response times, decision making and match user views. Dimensional modeling concepts are introduced along with examples from healthcare and banking organizations.
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
Using the right data model in a data martDavid Walker
A presentation describing how to choose the right data model design for your data mart. Discusses the pros and benefits of different data models with different rdbms technologies and tools
Capturing Business Requirements For Scorecards, Dashboards And ReportsJulian Rains
This white paper discusses capturing business requirements for scorecards, dashboards, and reports. It defines the scope of information needed, including the report purpose, measures, dimensions, hierarchies, time periods, and other functional requirements. It also covers non-functional requirements like volume and capacity, performance, availability, and security. Further analysis is then needed to check data availability, prioritize requirements, define validation rules, and design supporting processes.
This document provides sample requirements for a data warehousing project at a telecommunications company. It includes examples of business, data, query, and interface requirements. The business requirements sample outlines requirements for collecting and analyzing customer, organization, and individual data. The data requirements sample defines dimensions for party (customer) data and hierarchies. The performance measures sample defines a measure for vanilla rated call revenue amount.
Gathering Business Requirements for Data WarehousesDavid Walker
This document provides an overview of the process for gathering business requirements for a data management and warehousing project. It discusses why requirements are gathered, the types of requirements needed, how business processes create data in the form of dimensions and measures, and how the gathered requirements will be used to design reports to meet business needs. A straw-man proposal is presented as a starting point for further discussion.
Big Data Week 2016 - Worldpay - Deploying Secure ClustersDavid Walker
A presentation from the Big Data Week conference in 2016 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements
Data Works Berlin 2018 - Worldpay - PCI ComplianceDavid Walker
A presentation from the Data Works conference in 2018 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements and in the process became on e of the few fully certified PCI compliance clusters in the world
Data Works Summit Munich 2017 - Worldpay - Multi Tenancy ClustersDavid Walker
A presentation from the Data Works Summit conference in 2017 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster to support multiple business cases in a multi-tenancy cluster.
Big Data Analytics 2017 - Worldpay - Empowering PaymentsDavid Walker
A presentation from the Big Data Analytics conference in 2017 that looks how Worldpay, a major payments provider, uses data science and big data analytics to influence successful card payments.
An introduction to data virtualization in business intelligenceDavid Walker
A brief description of what Data Virtualisation is and how it can be used to support business intelligence applications and development. Originally presented to the ETIS Conference in Riga, Latvia in October 2013
Those responsible for data management often struggle due to the many responsibilities involved. While organizations recognize data as a key asset, they are often unable to properly manage it. Creating a "Literal Staging Area" or LSA platform can help take a holistic view of improving overall data management. An LSA makes a copy of business systems that is refreshed daily and can be used for tasks like data quality monitoring, analysis, and operational reporting to help address data management challenges in a cost effective way for approximately $120,000.
A linux mac os x command line interfaceDavid Walker
This document describes a Linux/Mac OS X command line interface for interacting with the AffiliateWindow API. It provides scripts that allow sending API requests via cURL or Wget from the command line. The scripts read an XML request file, send it to the AffiliateWindow API server, and write the response to an XML file. This provides an alternative to PHP for accessing the API from the command line for testing, auditing, or using other development tools.
Connections a life in the day of - david walkerDavid Walker
David Walker is a Principal Consultant who leads large data warehousing projects with staff sizes between 1 to 20 people. He enjoys rugby and spends time with his family in Dorset when not traveling for work. The document provides biographical details about Walker's background, responsibilities, interests, and perspectives on technology and business challenges.
Conspectus data warehousing appliances – fad or futureDavid Walker
Data warehousing appliances aim to simplify and accelerate the process of extracting, transforming, and loading data from multiple source systems into a dedicated database for analysis. Traditional data warehousing systems are complex and expensive to implement and maintain over time as data volumes increase. Data warehousing appliances use commodity hardware and specialized database engines to radically reduce data loading times, improve query performance, and simplify administration. While appliances introduce new challenges around proprietary technologies and credibility of performance claims, organizations that have implemented them report major gains in query speed and storage efficiency with reduced support costs. As more vendors enter the market, appliances are poised to become a key part of many organizations' data warehousing strategies.
Storage Characteristics Of Call Data Records In Column Store DatabasesDavid Walker
This document summarizes the storage characteristics of call data records (CDRs) in column store databases. It discusses what CDRs are, what a column store database is, and how efficient column stores are for storing CDR and similar machine-generated data. It provides details on the structure and content of sample CDR data, how the data was loaded into a Sybase IQ column store database for testing purposes, and the results in terms of storage characteristics and what would be needed for a production environment.
UKOUG06 - An Introduction To Process Neutral Data Modelling - PresentationDavid Walker
Data Management & Warehousing is a consulting firm that specializes in enterprise data warehousing. The document discusses process neutral data modeling, which is a technique for designing data warehouse models that are less impacted by changes in source systems or business processes. It does this by incorporating metadata into the data model similar to how XML includes metadata in data files. The approach defines major entities, their types and properties, relationships between entities, and occurrences to model interactions between entities in a consistent way that supports managing changes.
Oracle BI06 From Volume To Value - PresentationDavid Walker
The document discusses challenges with a European mobile telco's data warehouse that contains over 150 billion call detail records. It takes too long to get answers from the data warehouse and it is underutilized. The document recommends establishing quick service teams, performing data profiling and cleansing, integrating the data warehouse into business processes, using business information portals, and RSS feeds to address engagement, user, and technical issues. This will help users get timely, accurate information and increase adoption of the data warehouse.
Openworld04 - Information Delivery - The Change In Data Management At Network...David Walker
Network Rail implemented a new information delivery strategy using Oracle technologies like the Balanced Scorecard, Discoverer, and Portal. They developed executive scorecards quickly for mandated KPIs and then additional scorecards. Data comes from various sources into staging areas and warehouses accessible with Discoverer. A portal provides integrated access. Applications replace Excel/Access and improve data quality. The approach involves a small agile team and spreading solutions across the business.
IRM09 - What Can IT Really Deliver For BI and DW - PresentationDavid Walker
This document summarizes a discussion between the Data Management and Carehousing business about delivering Business Intelligence. Some of the key points covered include:
1. The business has substantial front-loaded costs to pay for Business Intelligence and Carehousing. There are also ongoing costs for system changes and maintenance.
2. The business must understand that Business Intelligence is an ongoing, long-term development and not a one-off project.
3. It is important for the business and IT to agree on what a successful Business Intelligence solution would look like.
IOUG93 - Technical Architecture for the Data Warehouse - PresentationDavid Walker
The document outlines a technical architecture for implementing a data warehouse. It discusses business analysis, database schema design, project management, data acquisition, building a transaction repository, data aggregation, data marts, metadata and security, middleware and presentation layers. The goal is to help users find the information they need from the data warehouse. Contact information is provided at the end.
ETIS11 - Agile Business Intelligence - PresentationDavid Walker
The document discusses techniques for becoming more agile in business intelligence projects. It advocates for establishing small, skilled teams with strong user relationships and delegated authority. True agile organizations allow teams to operate outside standard corporate procedures and regularly deliver incremental improvements. Large organizations tend to prioritize processes and risk avoidance over agility, creativity, and benefits. Successful examples demonstrate recognizing the need to overcome bureaucracy through practices like Lockheed Martin's SkunkWorks model.
ETIS10 - BI Governance Models & Strategies - PresentationDavid Walker
The document discusses business intelligence (BI) governance models and strategies. It defines BI governance and outlines key components of a BI governance framework, including the executive steering committee, programme management, user forums, certification committees, project management, implementation teams, and exploitation teams. It also discusses the importance of data modeling, data quality, data warehousing development, and data security and lifecycle management processes to a well-governed BI program.
ETIS10 - BI Business Requirements - PresentationDavid Walker
The document discusses what makes business requirements useful for BI projects. It states that only 30% of documented requirements are valuable as many are never referred to, become outdated, or cover the wrong topics. To be useful, requirements need to be understandable, easily accessible and revisable by business users, and testable against delivered solutions. The document then provides details on a three-step process for creating achievable requirements through business, data, and query requirements. It stresses that requirements are an essential part of the overall methodology that should be used throughout the project lifecycle.
2. Wat gebeurt er wanneer u een kleine
zwarte doos toevoegt aan een auto?
• Deze kleine doos kan in
ongeveer een uur in een
auto worden geplaatst
• Een basismodel verzamelt
de volgende informatie:
– Lengtegraad, breedtegraad
en hoogte
– X-, Y- & Z-acceleratie
– Snelheid
– Rijrichting
– Afgelegde afstand sinds het
laatste rapport
– Doos-ID
– Datum en tijd
26 november 2013
http://datamgmt.com
2
3. Over het verzamelen van gegevens in
een Round Robin databank
• Een Round Robin Databank of circulaire buffer registreert
de gegevens regelmatig (bv. milliseconden)
• Na een interval of een afgelegde afstand wordt een
gebundeld rapport verzonden naar de server
(gebruiksrapporten)
– Gebruiksrapporten kunnen afzonderlijk worden gebufferd
indien er geen gegevenstransmissiesignaal is
• Als er zich een ongeval voordoet wordt de gehele inhoud
van de buffer naar de server verzonden
(ongevalsrapporten)
26 november 2013
http://datamgmt.com
3
4. Gegevens verzenden
1.
De
zwarte
doos
verzendt
gegevens
naar
de
centrale
verzamelserver(s)
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mobiele
gegevensnetwerken
2.
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gegevensbestanden
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de
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Informa>
e
Acceptan
t
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CRM
26 november 2013
3.
Aanvulling
van
de
gegevens
met
gegevens
uit
de
opera>onele
systemen
en
externe
gegevensbronnen
http://datamgmt.com
4
5. Gebruiksrapporten
• Rijden in de bebouwde kom genereert
meestal rapporten tijdens een kortere
periode
– Korte afstanden in langzaam rijdend verkeer
– Gemiddeld elke 15 seconden
• Rijden op de snelweg genereert doorgaans
rapporten over een kortere afstand
– Lange afstanden rijden aan hoge snelheid
– Doorgaans elke 1 km
• Ritten worden gedefinieerd van motor
aanzetten tot uitzetten
• Gebruiksrapporten beschrijven het
rijgedrag
26 november 2013
http://datamgmt.com
5
6. Ongevalsrapporten
• Hoe reed de bestuurder
direct voorafgaand aan het ongeval?
• Waar kwam de impact vandaan?
– X-, Y- & Z-acceleratie bepalen impactpunt
• Negatieve versnelling aan de voorzijde - u botst tegen
hen
• Positieve acceleratie aan de achterkant - zij botsen
tegen u
• Ongevalsrapporten worden gebruikt om de
schuld te bepalen
26 november 2013
http://datamgmt.com
6
7. Basisgegevensmodel
Sociale
Media
Verzekeringsnemer
s
Bestuurders
Polissen
Verzekeringnemer
Acceptant
Gegevens
Bestuurder
Acceptant
Gegevens
Voertuigen
Geografisch
Laag
Informa>e
Vorderingen
Gegevens
Punten
Ongevallen
26 november 2013
Reizen
http://datamgmt.com
7
8. Gegevensvolumes
• 88 - 290 bytes per gegevenspunt
– Afhankelijk van het type zwarte doos
• Gemiddeld 124 gegevenspunten per reis
• 81 ritten per maand per voertuig
• Bewaard gedurende 5 jaar
– ~165 Mb per klant
– ~ 1 Tb per 7000 klanten
– ~ 15 Tb voor 100.000 klanten
• Al de rest is onbelangrijk
– Polissen, vorderingen, referentiedata, enz. zijn
onbeduidend in vergelijking met ritgegevens
26 november 2013
http://datamgmt.com
8
9. Gegevens verzameld op het moment
van de offerte
• Voertuig
– Merk, model en grootte van de
motor, alarm, aanpassingen, #
stoelen, waar geparkeerd (dag &
nacht), gebruik (sociaal, gezin,
pendelen, enz.), aantal kilometers
per jaar
• Verzekeringnemer en andere bestuurders
– Adres, leeftijd, Geslacht§, burgerlijke staat, # kinderen, andere
voertuigen, werksituatie, beroep, sector, woning, eerdere
vorderingen & veroordelingen, licentietype & aanvullende
kwalificaties, medische aandoeningen
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verboden
als
factor
door
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Hof
van
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21
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26 november 2013
http://datamgmt.com
9
10. Verzamelde gegevens ten tijde van de
vordering
•
•
•
•
•
•
•
•
•
•
•
Type ongeval
Locatie van het ongeval
Weersomstandigheden
Andere betrokken partijen
Types van de betrokken voertuigen
Letsels
Schade aan voertuigen
Schade aan derden en eigendommen
Betrokkenheid politie
Beschrijving van het ongeval
Foto's en Schetsen
26 november 2013
http://datamgmt.com
10
11. Gegevens verzameld over geografie
• Bedrijfs- & overheidsbronnen
– Wegaanduiding, wegtype,
snelheidsbeperkingen
– Gemiddelde snelheid op de weg, per dag, per
dag van de week en tijdstip
– Nuttige punten
• Supermarkten, benzinestations, parkeerplaatsen,
pretparken, stadions, enz.
– Meteorologische informatie
• Neerslag, temperatuur, tijdstip zonsopgang/
zonsondergang
• Open bronnen
– Wikipedia
– Google en Bing/Apple Maps
26 november 2013
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11
12. Verzamelde gegevens uit sociale media
• Klant vindt de verzekeringsmaatschappij 'Leuk' op
Facebook
– "Wauw - net een
goede deal voor mijn autoverzekering"
• Klant praat met zijn/haar vrienden
– "Net een deuk in mijn auto gereden, zal proberen hen
ook voor whiplash binnen te doen!"
• Ja, mensen zijn echt
zo dom!
26 november 2013
http://datamgmt.com
12
13. Verzamelen geavanceerde gegevens
• Meer sensoren in de kleine zwarte doos
• Voertuig Interface Modules (VIM's)
– Biedt een interface tussen de on-board diagnoselink (bv.
OBD-II) en de zwarte doos van een voertuig
– Afhankelijk van het voertuig heeft men toegang tot
gegevens zoals olie/water/bandenspanning, laatste
onderhoud, dashboard waarschuwingslampjes
die oplichten, ABS-gebruik,
airbag, was Bluetooth actief,
verlichting aan/uit
ruitenwissers aan/uit, enz.
26 november 2013
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14. Een opmerking inzake
gegevensbescherming
• Bescherming van persoonsgegevens &
privacywetgeving
– Deze verschillen per land, dus hoeveel u van
de verzamelde gegevens kunt gebruiken zal
ook variëren
• U kunt sowieso niet alle gegevens gebruiken
– Het Europese Hof van Justitie verbiedt
verzekeringsmaatschappijen om geslacht als
factor te gebruiken na 21 December 2012
• Opt-in/Opt-out gegevensgebruik
– Het is ook mogelijk, met toestemming, om
individuele en geaggregeerde gegevens aan
derden door te verkopen
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15. Opstellen van een verzekeringsofferte
• Aanbiedingen zijn meestal opgesteld als 'Pay &
Go' mobiele telefooncontracten
– Vast element - dekt de apparaatkosten,
administratieve aspecten, enz.
– Gebruiks(risico)element - prijs per gereden km
met verschillende tarieven voor verschillende
serviceniveaus
• Bijvoorbeeld het rijden in de spits of het donker draagt
een hogere prijs dan het rijden bij daglicht buiten de
piekuren
– Gebruiksbundels - eerste 500 km inbegrepen
per maand
• Een tegemoetkoming is vereist als ze allemaal zijn
opgebruikt anders rijd je niet verzekerd - normaal
gesproken wordt dit automatisch
afgeschreven van de creditcard
26 november 2013
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16. Onverwachte Gevolgen
• Gedrag van de bestuurder is gewijzigd, maar dit kan niet de
verwachte resultaten tot gevolg hebben
HTp://www.dailymail.co.uk/news/ar>cle-‐2359150/teenage-‐driver-‐passenger-‐died-‐broke-‐limit-‐beat-‐11pm-‐insurance-‐curfew.html
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17. Nieuwe onderneming &
vernieuwingsoffertes
• 1ste jaat acceptant
– Nieuwe polistarieven zijn gebaseerd op de
traditionele (niet-telematica) acceptanttarieven
– Geen verlengingen
• 2e jaar + acceptant
– Nieuwe polisprijzen zijn gebaseerd op gegevens
over bestaande klanten en auto's met
vergelijkbare profielen
– Verlengingen op basis van het individuele
risicoprofiel
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18. (De-)Stimuleren
• Wortelen
– Bonuskilometers voor het rijden binnen de
snelheidsbeperkingen, bij daglicht, buiten
piekuren, goed weer, niet parkeren op de
openbare weg, enz.
• Stokken
– Hogere kosten per km voor persistente
snelheidsovertredingen, regelmatig hard remmen
(opgespoord via snelheidsmeter), enz.
– Opmerking: Te veel duperen van klanten zal hen
dwingen om weg te gaan en zal invloed hebben
op de reputatie
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19. Wat tonen de gegevens?
•
•
•
•
•
•
•
Gedrag tijdens het rijden
Beleidsnaleving
Claimbeoordeling
First Responder
Diefstal en fraude
Risicoprofiel
Klantgedrag
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20. Gedrag tijdens het rijden
• Rijdt iemand volgens de snelheidsbeperkingen?
– Gemiddelde snelheid als percentage van de
maximumsnelheid per wegtype, gebruiker en tussen
data
• Remt de persoon regelmatig hard?
– # negatieve X-acceraties per 1000 gereden
kilometers per wegtype, gebruiker en tussen data
• Rijdt de persoon onnodig lange uren?
– Aantal ritten langer dan X uur
– Aantal minuten pauze tussen ritten
26 november 2013
http://datamgmt.com
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21. Beleidsnaleving
• Totaal aantal gereden kilometers
• Wordt een voertuig dat geregistreerd staat voor sociaal,
gezin & recreatief gebruik ook gebruikt voor woon/
werkverkeer of het bedrijf
– Regelmatig rijden tussen A en B tijdens de ochtend en tussen
B en A tijdens de avond
• Plaats waar de auto geparkeerd is gedurende de nacht
– Meestal op een punt nabij het adres van de verzekeringnemer
of compleet ergens anders
• Taxichauffeurs & bezorgers
– Kopen geen commerciële polis, maar kan opgemerkt worden
door hun rijpatroon
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22. Claimbeoordeling
• Wanneer de aanvraag is ingediend kunnen
de details worden gecontroleerd
– Locatie van het ongeval - zelfs een kijkje nemen
op Google Maps
– Punt van het ongeval en wie botst tegen wie
– Mooi weer, hoeveelheid licht
– Snelheid en G-kracht op het moment van de
botsing
– Rolde het voertuig door?
26 november 2013
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23. First Responder
• Wanneer zich een ongeval voordoet:
– Probeer contact op te nemen met de klant als
het ernstig genoeg is
– Nooddiensten verwittigen indien nodig
– Zorg voor het/de gewenste herstel/reparateurs
om het incident te behandelen ter verlaging van
de kosten
– Perceptiebonus - mijn verzekeringsmaatschappij
geeft echt om mij!
26 november 2013
http://datamgmt.com
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24. Diefstal en fraude
• Diefstal
– Apparaat traceert voortdurend, dus als een voertuig
als gestolen wordt opgegeven kan het opgespoord
en gevorderd worden
• Fraude
– Fraude kan valse verkeersongevallen of
georkestreerde botsingen zijn met valse
verzekeringsvorderingen of overdreven
schuldvorderingen
– Veel van de details kunnen nu worden gevalideerd
(locatie, weer, snelheid, botsing, enz. )
26 november 2013
http://datamgmt.com
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25. Risicoprofiel
• Welke combinatie van kenmerken voor zowel
bestuurder als voertuig heeft de laagste totale
vorderingswaarde per 100.000 gereden
kilometers?
• Zijn een groot aantal kleine schadegevallen
duurder dan een klein aantal grote
vorderingen?
• Statistische cluster analysetechnieken voor het
bepalen van voorstellen met hoog en laag risico
26 november 2013
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26. Gedrag van de klant
• Voetbalsupporter
– Gaat regelmatig naar he stadion
– Gaan ze ook naar uitwedstrijden?
• Zakenreiziger
– Parkeert regelmatig het voertuig bij de luchthaven
• School
– Van thuis naar school en terug, twee keer per dag
• Verandering van functie
– Wijzigt plaats van dagelijkse parkeerplek
• Deze informatie kan (met toestemming) worden verkocht aan derden
– Marketingbedrijven, voetbalclubs, enz.
– Deze technieken zijn al in gebruik in sommige mobiele telefoonbedrijven
26 november 2013
http://datamgmt.com
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27. Beveiligingsservices
• Fact of Life
• Rechtbanken zullen toegang tot gegevens eisen
als iemand onder verdenking staat
– Antiterrorismewet , georganiseerde misdaad, enz.
• Gegevens worden aangewend na een voorval
ter tracering van
– Waar kwamen ze vandaan
– Wie hebben ze bezocht voor de feiten
– Enz.
26 november 2013
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28. De toekomst
• Pay & Go weggebruik tarief
– Regeringen die auto's laten uitrusten met telematica
en weggebruikgegevens naar hen laten opsturen
• Lagere Premies en hogere winsten
– Als alle auto's telematica hebben dan zullen lage
risicoklanten niet worden gebruikt voor de
subsidiëring van hoge risicoklanten - een deel van
dit voordeel wordt overgebracht op de consument
door middel van een lagere premie en een deel
wordt behouden door de verzekeringsmaatschappij
26 november 2013
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29. Een Waarneming
• Een aantal van de bewijzen van telematica zijn
contra-intuïtief of druisen in tegen wat de
acceptanten "kennen" als juist
• Om zakelijke gebruikers gebruik te laten maken
van de gegevens en zo de manier waarop ze
risico's bepalen wijzigen, is moeilijk
• Als u wijzigingen aanbrengt over hoe risico's
worden bepaald, moet u het effect van de
wijzigingen opvolgen
26 november 2013
http://datamgmt.com
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30. Wie doet dit in het Verenigd Koninkrijk?
26 november 2013
http://datamgmt.com
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31. Probeer het eens...
• InstaMapper GPS Tracker
– Http://www.insta-mapper.com
– iPhone en Android App
– Heeft GPS maar geen snelheidsgegevens
• Andere toepassingen zijn beschikbaar maar
dit is degene die ik gebruikt heb voor de
conceptproef
26 november 2013
http://datamgmt.com
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32. David M. Walker
Data Management & Warehousing
DANK U / THANK YOU
26 november 2013
http://datamgmt.com
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33. Neem met ons contact op
• Databeheer en opslag
– Website: http://www.datamgmt.com
– Telefoon: +44 (0) 118 321 5930
• David Walker
– E-mail: davidw@datamgmt.com
– Telefoon: +44 (0) 7990 594 372
– Skype: datamgmt
– White Papers: http://scribd.com/davidmwalker
• DeltIQ
– Website: http://detiqgroup.com
– Telefoon: +31 (0) 34 621 6709
26 november 2013
http://datamgmt.com
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34. Over Ons
Data Management & Warehousing is een consultancybureau in het
Verenigd Koninkrijk dat succesvolle business intelligence en
datawarehousing oplossingen biedt sinds 1995.
Onze consultants hebben samengewerkt met grote bedrijven over
de hele wereld, inclusief de V.S., Europa, Afrika en het Middenoosten .
Wij hebben in tal van bedrijfstakken gewerkt, zoals
telefoonmaatschappijen, productie, retail, financiële instellingen en
transport. Wij bieden bestuurlijk en projectmanagement, evenals
deskundigheid op het vlak van toonaangevende technologieën.
In Nederland werkt Data Management & Warehousing samen met
DeltIQ Group .
26 november 2013
http://datamgmt.com
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