10th & 11th OCTOBER 2016ABION Spreebogen Waterside Hotel

Berlin, GermanyVersion: 2016-10-07

EUROPE2016

Monday, October 10th

7:00 – 9:00 Breakfast and Registration

Agile as a complex ecology:from manufacturing toservice

Dave SnowdenCognitive Edge[United Kingdom]

Framing Business Problemsas Data Mining Problems

Asoka DiggsIntel Corp.[United States]

Welcome and Announcements

Lunch

KEYNOTE: Humans are neither ants nor data processorsDave Snowden, Cognitive Edge [United Kingdom]

Business InformationModeling: A Methodology forData-Intensive Projects, DataScience and Big DataGovernance

Torsten PriebeSimplity AT GmbH[Austria]

Benefit from automationpossibilities of Data Vaults

Stefan Kauschheureka e-Business GmbH[Germany]

PS-3C Modeling: proposedway of agile EDW datamodeling on the Data Lake

Rogier WerschkullDIKW[Netherlands]

Customer case of Teradata Evolutionary Big DataStrategies

Michael MüllerMID GmbH[Germany]

Data Modeling for NoTables

Thomas FrisendalTF Informatik[Denmark]

9:00 – 12:00

12:00 – 13:15

13:15 – 13:30

13:30 – 14:30

14:30 – 15:30

15:30 – 16:00

16:00 – 17:00

Foundational DataModeling

Tools & NoSQL Advanced DataModeling

Communication Skills& Case Studies

Business InformationModeling using the fact-based approach

Clifford HeathInfinuendo[Australia]

Advanced Data ModelingChallenges Workshop

Steve HobermannSteve Hoberman& Associates, LLC[United States]

Modeling of ReferenceSchemes

Dr. Terry HalpinProfessor at INTI InternationalUniversity, Malaysia[Australia]

CDMPCertification

Testing

Testing

Testing

Fact-Based ERM: killing threebirds with one stone

Jan Pieter ZwartHAN University of AppliedSciences[Netherlands]

Afternoon Snacks

GR GL PA CH

GR CH PA GL

Rooms: GR|Grunewald GL|Glienicke PA|Pankow CH|Charlottenburg KÖ|Köpenick BV|Bellevue

DMZ on Twitter:#DMZone

Get the Guidebook Appand schedule your DMZ:

www.guidebook.com/getitRedeem Code: dmzEurope2016

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18:00 – 20:00 Data, Drinks & Dinner (Drinks open end)PA GL GR BVCH

KÖ

NoSQL Data ModelingOverview

Steve HobermannSteve Hoberman& Associates, LLC[United States]

Fact-Based ModelingWorkshop

Dr. Terry HalpinProfessor at INTI,Int. University, Malaysia[Australia]

How to compose a fact-basedmodel into any kind ofschema

Clifford HeathInfinuendo[Australia]

From Conceptual to PhysicalData Vault Data Model

Dirk LernerITGAIN GmbH[Germany]

Data Modeling forSustainable Systems

Graham Witt[Australia]

9:00 – 12:00

Welcome and Announcements

KEYNOTE: From BI to AI - Models, Ethics and EconomicsBarry Devlin, 9sight Consulting [South Africa]

12:00 – 13:15

13:15 – 13:30

13:30 – 14:30

Why Business Analysts andData Modelers should holdhands more

George McGeachieMetadata Matters[United Kingdom]

Data Modeling forPolyglot Persistence

Phill RadleyBT[United Kingdom]

The Journey from ER Modelerto Data Scientist

Asoka DiggsIntel Corp.[United States]

smartDATA and Industrie 4.0

Dr. Siegmund Priglingerdr.priglinger consulting GmbH[Germany]

14:30 – 15:30

15:30 – 16:00 Afternoon Snacks

Business Keys, SurrogateKeys, Hash Keys – BestPractices of BusinessIntelligence Key ManagementDr. Michael HahneHahne Consulting GmbH[Germany]

Your project is under attack!How to strike back!

Jeroen GietemaAuthor ot "The Projectsaboteur"[Netherlands]

Positioning Data Modeling inthe Big Data Area

Martijn ImrichXomnia[Netherlands]

16:00 – 17:00

Tuesday, October 11th

7:00 – 9:00 Breakfast and Registration

Foundational DataModeling

Tools & NoSQL Advanced DataModeling

Communication Skills& Case Studies

CDMPCertification

Testing

Testing

Testing

Rooms: GR|Grunewald GL|Glienicke PA|Pankow CH|Charlottenburg KÖ|Köpenick BV|Bellevue

GL GR CH PA

GR PA CH

GL CHPAGR

8:00 – 8:30MorningSession

Casetalk –Data Modeling by example

Marco WobbenBCP Software[Netherlands] GR

Lunch

Even non-relational databaseshave relationships

Pascal DesmaretsHackolade[Belgium] CH

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Growing yourself as a DataManagement Professional:Challenges we face todaySue GeuensDAMA International[United States] GL

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Marshmallow ChallengeChristian HädrichDörffler & Partner GmbH[Germany] PA

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Humans are neither antsnor data processorsDave Snowden, Cognitive Edge

Murray Gell-Mann is an American physicist whoreceived the 1969 Nobel Prize in physics fa-mously said : "The only valid model of a humansystem is the system itself”. In this address Prof.Snowden, who is Director of the Centre for App-lied Complexity at Bangor University in Wales,and Chief Scientific Officer of Cognitive Edge willoutline new approaches to the use of distributedethnography in understanding requirementscapture and knowledge-information flows in or-ganisations. Focusing on allow unarticulatedneeds to surface the approach offers a radicalnew way to model both cultural and informationaspects of an organisation. Based on complexi-ty and cognitive science the approach seeks toscale understanding of both actual and potentialneeds in a fractal manner, self similar at all le-vels of management.

Dave Snowden is the foun-der and chief scientific offi-cer of Cognitive Edge. Hiswork is international in na-ture and covers govern-ment and industry lookingat complex issues relatingto strategy, organisational

decision making and decision making. He has pionee-red a science based approach to organisations dra-wing on anthropology, neuroscience and complexadaptive systems theory. He is a popular and passio-nate keynote speaker on a range of subjects, and iswell known for his pragmatic cynicism and iconocla-stic style.

He currently holds positions as extra-ordinary Profes-sor at the Universities of Pretoria and Stellenboschand has held similar positions at Hong Kong Polytech-nic University, Canberra Univerity, the University ofWarwick and The University of Surrey. He held theposition of senior fellow at the Institute of Defenceand Strategic Studies at Nanyang University and theCivil Service College in Singapore during a sabbaticalperiod in Nanyang.

His paper with Boone on Leadership was the coverarticle for the Harvard Business Review in November2007 and also won the Academy of Managementaware for the best practitioner paper in the same ye-ar. He has previously won a special award from the

Academy for originality in his work on knowledgemanagement. He is a editorial board member of se-veral academic and practitioner journals in the field ofknowledge management and is an Editor in Chief ofE:CO. In 2006 he was Director of the EPSRC (UK) re-search programme on emergence and in 2007 wasappointed to an NSF (US) review panel on complexityscience research.

He previously worked for IBM where he was a Direc-tor of the Institution for Knowledge Management andfounded the Cynefin Centre for Organisational Com-plexity; during that period he was selected by IBM asone of six on-demand thinkers for a world wide ad-vertising campaign. Prior to that he worked in a ran-ge of strategic and management roles in the servicesector.

His company Cognitive Edge exists to integrate aca-demic thinking with practice in organisations throug-hout the world and operates on a network modelworking with Academics, Government, CommercialOrganisations, NGOs and Independent Consultants.He is also the main designer of the SenseMaker®software suite, originally developed in the field ofcounter terrorism and now being actively deployed inboth Government and Industry to handle issues ofimpact measurement, customer/employee insight,narrative based knowledge management, strategicforesight and risk management.

From BI to AI –Models, Ethics and EconomicsBarry Devlin, 9sight Consulting

The creative use of big data from social mediaand the Internet of Things is transformingbusiness models, empowering start-ups, andenhancing or destroying existing business value.Many proponents focus on marketing uses ofbig data, but most value—and disruption—willcome from its daily operational application innovel, transformative ways.

From slim beginnings thirty years ago in BI tosupport decision making, data collection andanalysis is now enabling AI systems to make fi-nancial recommendations or answer customercalls. They are diagnosing cancer and offeringtreatment options, and driving autonomoustrucks. Large swathes of decision making andaction taking are being automated, untouchedby human hand. Even complex decisions are in-

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creasingly augmented by cognitive computingsystems.

Making the right design choices is vital in desi-gning and managing such environments. Howe-ver, true business and IT leadership requiresconsidering the broader ethical and economic is-sues raised by this transformation. Concernsaround personal privacy, employment and socialdisruption must all be urgently addressed if indi-vidual businesses and society at large are tosuccessfully navigate this data driven transfor-mation of all aspects of business and technolo-gy, and even society.

Dr. Barry Devlin is amongthe foremost authorities onbusiness insight and one ofthe founders of data ware-housing, having publishedthe first architectural paperon the topic in 1988. Withover 30 years of IT experi-

ence, including 20 years with IBM as a DistinguishedEngineer, he is a widely respected analyst, consultant,lecturer and author of the seminal book, Data Ware-house—from Architecture to Implementation, and nu-merous white papers, blogs and more. His new bookBusiness unIntelligence—Insight and Innovationbeyond Analytics and Big Data was published in2013. Barry is founder and principal of 9sight Consul-ting. He specializes in the human, organizational andIT implications of deep business insight solutions inall technology environments. Barry is based in CapeTown, South Africa and operates worldwide.

NoSQL Data Modeling OverviewSteve Hoberman,Steve Hoberman & Associate

NoSQL implementations are often built with litt-le or no data modeling…or at the other extremecompletely over-architected – both ends of thespectrum producing suboptimal results. In thispresentation, understand how data modelingcontributes to the process of learning about thedata, and is therefore a required technique,even when the resulting database is not relatio-nal. We will explore what distinguishes NoSQLfrom a relational database, and what distinguis-hes a relational database from relational mode-ling. We will cover the optimal situations each

of the four types of NoSQL databases can beused. We will learn a streamlined approach todata modeling that ensures the NoSQL databasewill be built with a thorough understanding ofthe current requirements, as well as designed tohandle future needs. We will cover conceptual,logical, and physical data modeling for NoSQL,and how each stage increases our knowledge ofthe data and reduces assumptions and poor de-sign decisions. Exercises will be used to reinfor-ce techniques.

Steve Hoberman has trai-ned more than 10,000people in data modelingsince 1992. Steve is knownfor his entertaining and in-teractive teaching style(watch out for flying can-dy!), and organizationsaround the globe havebrought Steve in to teach his Data Modeling MasterClass, which is recognized as the most comprehensivedata modeling course in the industry. Steve is the au-thor of nine books on data modeling, including thebestseller Data Modeling Made Simple. One of Steve’sfrequent data modeling consulting assignments is toreview data models using his Data Model Scorecard®technique. He is the founder of the Design Challengesgroup and recipient of the 2012 Data AdministrationManagement Association (DAMA) International Pro-fessional Achievement Award.

Agile as a complex ecology: frommanufacturing to serviceDave Snowden, Cognitive Edge

The wider vision of the Agile manifesto too of-ten ends up as a series of structured methodsdesigned to produce a product based on an un-derlying manufacturing method. Indeed muchthat is Agile is drawn directly from a manu-facturing environment. Any manufacturingprocess a priori makes assumptions about thedegree to which something can be defined andmeasured. In an ecology some aspects aresusceptible to such a way of thinking but in ma-ny cases needs are not, indeed cannot be articu-lated given the speed of change of technologicalcapability. With the Internet of Things we aremoving to a more fragmented ecology in whichapplications will emerge from the three way in-

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teraction of people, software objects and thewider environment.

The underlying architecture and metaphor ofsoftware development will have to undergo ra-dical change to accommodate these changes.Understanding the difference between sequenti-al iteration in methods such as SCRUM and par-allel short cycle experimentation wheredefinition comes at the end, not the start of theprocess will be key. Techniques in which partialcopying creates rapid mutation of need to allownovel forms of capability to emerge are beingdeveloped based on metaphors drawn directlyfrom evolutionary biology, in particular exaptati-on (rapid repurposing of existing capability tomeet unanticipated needs) and epi-genetics(Culture activates or deactivates genetic capabi-lity) are starting to provide a fertile, more simple(but not simplistic) approach to scale than simp-ly extended the engineering metaphor.

This workshop will give participants early in-sight into both the theory and practice of thisapproach.

Dave Snowden is the foun-der and chief scientific offi-cer of Cognitive Edge. Hiswork is international in na-ture and covers governmentand industry looking atcomplex issues relating tostrategy, organisational de-

cision making and decision making. He has pioneereda science based approach to organisations drawingon anthropology, neuroscience and complex adaptivesystems theory. He is a popular and passionatekeynote speaker on a range of subjects, and is wellknown for his pragmatic cynicism and iconoclasticstyle.

He currently holds positions as extra-ordinary Profes-sor at the Universities of Pretoria and Stellenboschand has held similar positions at Hong Kong Polytech-nic University, Canberra Univerity, the University ofWarwick and The University of Surrey. He held theposition of senior fellow at the Institute of Defenceand Strategic Studies at Nanyang University and theCivil Service College in Singapore during a sabbaticalperiod in Nanyang.

His paper with Boone on Leadership was the coverarticle for the Harvard Business Review in November

2007 and also won the Academy of Managementaware for the best practitioner paper in the same ye-ar. He has previously won a special award from theAcademy for originality in his work on knowledgemanagement. He is a editorial board member of se-veral academic and practitioner journals in the field ofknowledge management and is an Editor in Chief ofE:CO. In 2006 he was Director of the EPSRC (UK) re-search programme on emergence and in 2007 wasappointed to an NSF (US) review panel on complexityscience research.

He previously worked for IBM where he was a Direc-tor of the Institution for Knowledge Management andfounded the Cynefin Centre for Organisational Com-plexity; during that period he was selected by IBM asone of six on-demand thinkers for a world wide ad-vertising campaign. Prior to that he worked in a ran-ge of strategic and management roles in the servicesector.

His company Cognitive Edge exists to integrate aca-demic thinking with practice in organisations throug-hout the world and operates on a network modelworking with Academics, Government, CommercialOrganisations, NGOs and Independent Consultants.He is also the main designer of the SenseMaker®software suite, originally developed in the field ofcounter terrorism and now being actively deployed inboth Government and Industry to handle issues ofimpact measurement, customer/employee insight,narrative based knowledge management, strategicforesight and risk management.

Framing Business Problems as DataMining ProblemsAsoka Diggs, Intel Corp.

This will be a brief introduction to some of theimportant concepts that arise in data mining ingeneral and classification analysis in particular.The focus in this presentation will be helping at-tendees to develop their ability to framebusiness problems as data mining problems, tounderstand some of the terminology and mentalmodels prevalent in data mining, and to beingable to recognize situations where data miningmight apply.

Through a demonstration driven presentationformat, attendees will be exposed to a variety ofdata mining topics. Throughout the demo, at-tention will be paid to points of intersection

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with traditional data modeler work products in-cluding the data dictionary and dimensional da-ta model. I will also leave you with a variety oflinks and references for additional study on yourown.

Particular topics that will be addressed:• Formulating business problems as data

mining problems• Supervised Learning• Classification methods including Decision

Tree and Logistic Regression• Model Validation• No math!

After 15+ years of experi-ence in a variety of datamanagement disciplines,including database admi-nistration, ER modeling,ETL development, and dataarchitecture, I am transitio-ning into the world of ana-

lytics / data science. It turns out that there ISsomething even more fun than ER modeling, and I’vefound it. Today, my primary interest is in the organi-zational transformation involved in adopting analyticsas a source of competitive advantage. How does anorganization get there? What needs to be done? Whatorganization design and leadership changes are nee-ded to get the most benefit from analytics? I nowspend my work life practicing these new analytic mo-deling skills, and teaching others how to participateand contribute to predictive analytic projects.

Fact-Based Modeling WorkshopDr. Terry Halpin, Professor at INTIInternational University, Malaysia

Typically, analysis and modeling of an enterpriseor business domain is a collaborative processbetween the business expert, who best under-stands the business rules and requirements, andthe modeler whose main task is to elicit this un-derstanding and capture it in a formal modelthat can be executed (directly or indirectly viatransformation) by an information system. Ideal-ly, the model (including its representation of thebusiness rules) should be validated by thebusiness experts. Since business experts are of-ten non-technical, the model needs to be con-

ceptual in nature, cast in language that isintelligible to the business expert while stillbeing formal. Fact-Based Modeling aims to ad-dress this communication need in an optimalway.Fact-Based Modeling has been adopted by a fa-mily of related approaches, of which Object-RoleModeling (ORM) and SBVR (Semantics ofBusiness Rules and Business Vocabulary) arewell known examples. This presentation beginswith brief overview of fact- orientation in gene-ral and second-generation ORM (ORM 2) in par-ticular, highlighting its communication-basedmodeling procedure using controlled naturallanguage and visual support for conceptualizati-on of business rules. It explains ORM’s underly-ing principles, outlines recent improvements tothe methodology and associated tool support,and briefly contrasts fact-orientation with otherhigh level data modeling approaches (e.g. ERand UML). The bulk of the workshop then focu-ses on developing skills in constructing andtransforming data models by applying fact-ori-entation to practical examples of informationsystems, including advanced aspects, with theimplementation emphasis on relational databa-se systems.

Dr. Terry Halpin is a Rese-arch Fellow in ComputerScience at INTI Internatio-nal University (Malaysia),and a data modeling con-sultant. He previously heldsenior faculty positions incomputer science at TheUniversity of Queensland (Australia), and professor-ships in computer science at Neumont University(USA) and INTI International University. His prior in-dustrial experience includes many years in data mo-deling technology at Asymetrix Corporation,InfoModelers Inc., Visio Corporation, Microsoft Cor-poration and LogicBlox. His doctoral thesis formalizedObject-Role Modeling (ORM/NIAM), and his currentresearch focuses on conceptual modeling and rule-based technology. He has authored over 200 technicalpublications and nine books, and has co-edited ninebooks on information systems modeling research. Heis a member of IFIP WG 8.1 (Information Systems), isan associate editor or reviewer for several academicjournals, is a regular columnist for the Business RulesJournal, and is a recipient of the DAMA InternationalAchievement Award for Education and the IFIP Out-standing Service Award.

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Fact-Based ERM: killing three birdswith one stoneJan Pieter Zwart, HAN University ofApplied Sciences

Classic Entity-Relationship models suffer fromthree major drawbacks. The first is, that themodels show only the abstract metadata (typelevel), which makes them impossible to bevalidated by domain experts. Therefore domainexpert relevant semantics are not visible. Thesecond is, that they offer no support for thevaluable perspective of seeing the completeelementary fact types that are modeled:attributes are parts of fact types, entity types areclusters of fact types, and relationship types canbe either a fact type or no fact type at all. Thethird is, that although there are plenty of goodsources explaining what is modeled, there existsno good method for teaching beginningmodelers how to make an ER-model.

Fact-based modeling (FCO-IM, ORM, CogNIAM,….) has shown that good conceptual modelscan be built systematically starting fromconcrete examples of facts. Such an approachcan be used to draw up data models in ERM aswell. The starting point is a set of concreteexamples of the facts to be modeled (theinstance level of ordinary facts). These examplesare verbalized in sentences that capture thesemantics of the facts. The abstract data model(the type level of fact types) is then drawn up inan easy-to-learn procedure of sorting andanalyzing the sentences. This procedure yieldssentence types (predicates) that carry thesemantics of the fact types, as well as the usualentity types, attributes and relationship types ofthe ER model.

In so doing, the three drawbacks are fixed. Thefirst is fixed by using verbalizations of the factsto be modeled: these are easily validated by thedomain expert. In addition, the ER model can beeasily extended with the predicates and (perpredicate) a small example population, thusadding the instance level to the model in partswhere it is not self-explanatory. This facilitatesvalidation also for modeling experts. The second

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is fixed by extending the ER-model with thepredicates. Each predicate shows one completeelementary fact type, thus augmenting thetraditional view with the elementary-factperspective. The third is fixed by the procedureof analyzing the sentences. It is a completesystematic set of instructions for how to build anER model.

Several examples of using this method will beshown in this presentation.

Jan Pieter Zwart (1955) isassistant professor ofInformation Systems at HANUniversity of AppliedSciences (HAN UAS,Arnhem, the Netherlands).He has been active asteacher and developer inthe field of conceptual

information modeling and metadata driventransformations since 1985. He is co-author of thefirst book on FCO-IM, and has published peer-reviewed papers on further developments in FCO-IM.Jan Pieter divides his time between teaching anddoing research in the fundamentals of data andprocess models in the Model-Based InformationSystems group at HAN UAS.

Business Information Modeling:A Methodology for Data-IntensiveProjects, Data Science andBig Data GovernanceTorsten Priebe, Simplity

The talk presents an integrated methodology tostructure and formalize business requirements inlarge data-intensive projects, e.g. data ware-house implementations, turning them into preci-se and unambiguous data definitions suitable tofacilitate harmonization and assignment of datagovernance responsibilities. The presented ap-proach places a business information model inthe center -- used end-to-end from analysis, de-sign, development, testing to data qualitychecks by data stewards. In addition, the talkshows that the approach is suitable beyond tra-ditional data warehouse environments, applyingit also to big data landscapes and data science

initiatives -- where business requirements ana-lysis is often neglected. As proper tool supporthas turned out to be inevitable in many projects,software requirements and their implementationin the Accurity Glossary tool are also discussed.The approach is demonstrated based on examp-les from a number of large banking data ware-house projects.

Torsten Priebe has over 15years' experience in datamanagement and businessintelligence, of which hespent over 10 years in con-sulting. Since 2014, Torstenis CTO at Simplity, a dyna-mically developing international consultancy focusingon data warehousing and business intelligence. Tors-ten's responsibilities at Simplity include driving therequirements for the Accurity data governance soft-ware suite and guiding Simplity's consultants particu-larly in the areas of architecture and data modeling.Before Simplity, Torsten was heading the data mana-gement team at Teradata and the business intelli-gence practice at Capgemini in Vienna respectively.Torsten is co-author of three books, published nume-rous articles and talks regularly at international con-ferences. In addition, Torsten is a regular guestlecturer for business intelligence at University ofRegensburg, where he received his PhD with a thesison integrating structured and unstructured informati-on with semantic technologies in 2005.

PS-3C Modeling:proposed way of agile EDW datamodeling on the Data LakeRogier Werschkull, DIKW

The last years we have seen the rise of the ‘DataLakes’. While they are technologically suitablefor the time variant / non volatile part of the‘traditional’ data warehousing as we know it,they do not automatically cover/fix the difficultpart of solid EDW data warehousing: building asubject-oriented and integrated layer on top ofthis raw data. So even in the ‘age of Data Lakes’data modeling is still needed and maybe evenmore than before: to bring Data-science relateddiscoveries into production we must have agood solution for the ‘data wrangling’ part thattakes up most of the time ...

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To solve this problem, I propose a new (layered)modeling approach for building this integratedEDW. This modeling approach consists of twolayers:

1) A persistent staging area (PS) in the Data la-ke: structured / semi-structured data is histo-rized, based in a (technical) source Primary /Unique Key. We must strive to build thisusing a columnar-oriented (and compressed)storage method. Then, using schema-on readfunctionality, we model the integrated EDWlayer:

2) A layer based on [Business Concepts], descri-bed by [Context] and linked together by[Connector-context] tables (3C). In is inspiredon Data Vault modeling with the main diffe-rences being shared during this talk.

Currently, I am in the process of proposing thismodeling approach in a major organization in

the Netherlands.

Rogier is a DWH / BI Con-sultant with 16 years of ex-perience in the field. Hestarted as a frontend devel-oper with Business objects,moved to OLAP cube deve-lopment and ETL develop-

ment (Oracle warehouse builder, Kimball Datamarts/Data warehouse) and the last 5 years to DWH /ETLautomation (using Data Vault, build an ETL generatorat my previous employer) and DWH architecture anddata modeling. Recently he has built some experienceusing ‘Big Data’ technology (Hadoop ecosystem) andin the Data Science related field. This combined expe-rience has given me a broad perspective in data ar-chitecture in general and has given me ideas on howto improve the current way of modeling the integra-ted EDW.

Data Modeling for NoTablesThomas Frisendal, TF Informatik

It is time for a change in the way we work withdata models. The database world is changingrapidly as NoSQL (not only SQL) and new physi-cal data models such as graphs, key/value, co-lumn stores and HADOOP make headway into

database development. But data modeling isstill seen as synonymous with SQL tables, nor-malization and entity / relationship diagrams.

Graphs (as in directed graphs) is the natural re-placement for the 40+ years old data modelingdiscipline with its bias on normalization and anabstract relational model, which never quitemade it to mainstream data modeling. The lega-cy is that of SQL-tables, primary and foreignkeys etc. as opposed to relations / “relvars”,which constitute the academic foundation forrelational database concepts.

We will cover subjects such as:

• Make data models for the new world of BigData and NoSQL

• Make data models that are visually compel-ling and highly communicative (also for non-technical audiences)

• Work visually with the functional dependen-cies (and the join dependencies) to controlredundancy and consistency.

• Feel good at not doing normalization!

If you are a data modeler, or a data scientist or adeveloper doing data modeling as you go, thenthis is for you.

Thomas Frisendal is an ex-perienced consultant withmore than 30 years on theIT vendor side and as anindependent consultant.Since 1995 he has primarilybeen working with datawarehouse projects. His area of excellence lies withinthe art of turning data into information and knowled-ge. Since 2005 he has specialized in business analysis,concept “harvesting” and mapping, modeling of in-formation and data as well as design of business in-telligence solutions. Thomas Frisendal is a member of“The Data Warehouse Institute Denmark” (member ofthe board), “The International Association for In-formation and Data Quality” (IAIDQ) and “The Cogni-tive Science Society”. His approach toInformation-driven Business Analysis is “New Nordic”in the sense that it represents the traditional Nordicvalues such as superior quality, functionality, reliabili-ty and innovation by new ways of conceptualizing thebusiness. Thomas is an active writer and speaker, andalso Chief Data Warehouse Architect at SimCorp.Thomas lives in Copenhagen, Denmark.

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How to compose a fact-based modelinto any kind of schemaClifford Heath, Infinuendo

Fact-based modeling is not just a powerfulbusiness communication tool, it is the one truering of power for the data modeler. By workingwith a fully decomposed schema in “elementaryform”, we avoid making a commitment to anyparticular kind of composite schema. This pre-sentation will show how different compositionrules applied to a fact-based schema will gene-rate an entity-relational, star, snowflake, hierar-chical, object-oriented, XML or graph schema.

Clifford Heath is a computerresearch scientist who haslong experience in the de-sign and implementation ofenterprise-scale softwareproducts, and in the use offact-based modeling. He isa Certified Data Manage-

ment Professional (CDMP) at Masters level, has pu-blished a number of papers in peer-reviewed scientificjournals, holds several patents, is the creator of theConstellation Query Language and is a participant inthe Fact Based Modeling Working Group. Clifford hasfrequently presented at chapter meetings of the DataManagement Association as well as at the NATO CAXForum and the European Space Agency, and is CTOand founder at Infinuendo.

Casetalk –Data Modeling by exampleMarco Wobben, Casetalk

Q: Data modeling is described as a craft and on-ce completed the results may even seem artful.Yet outsiders may see data modeling as abs-tract, time consuming or even unnecessary. Inmany cases the data modeler interviewsbusiness experts, studies piles of requirements,talks some more, and then, hocus pocus, pres-ents a diagram with boxes, crows feet, arrows,etc… Then the slow process begins to keep thediagrams up to date, explain what the diagramsbehold, and sometimes even data modelersthemselves may get lost while maintaining agrowing set of data models and requirements.

A: Fact based information modeling is the veryopposite of abstract. Fact based informationmodeling uses natural language which expres-ses facts that are intelligible for both businessand technical people. It does not require peopleto understand the modeler’s magical languageof boxes and arrows. Although models can bepresented in several diagramming notations,they can be validated in natural language at alltimes. This gives both data modelers, technicallyskilled people, and business people the benefitof having a well documented and grounded da-ta model. Therefore the method of Fact OrientedModeling, is also known as "Data Modeling byExample".

Presentation highlights• key elements of fact oriented modeling;• data modeling with facts;• visualizing the model;• validating and verbalizing;• transforming and generating output

(E.g.: SQL, Relational, UML, XSD, PowerDesi-gner, etc.).

Marco Wobben is directorof BCP Software and hasbeen developing softwarewell over 30 years. He hasdeveloped a wide range ofapplications from financialexpert software, softwareto remotely operatebridges, automating DWH generating and loading,and many back- and frontoffice and webapplications.For the past 10 years, he is product manager and leaddeveloper of CaseTalk, the CASE tool for fact basedinformation modeling, which is widely used in un-iversities in the Netherlands and across the globe.

From Conceptual to Physical DataVault Data ModelDirk Lerner, ITGAIN

The fictitious company PennyStockInc needs tobuild a new data warehouse for their tradingdepartment. Due to past experience (project fai-lures, overtime, etc.) the BI Competence Center(BICC) decided this time to build conceptual, lo-gical and physical data models. Conceptual mo-dels to gather all information about their trading

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business, logical models to collect their businessrequirements and cover both relational databa-ses and NoSQL solutions. For the physical datamodel the BICC of PennyStockInc decided tochoose data vault due to the agility, flexibilityand the ability to integrate both relational data-bases and NoSQL solutions.

Attendees of this session will be a fictitious partof the PennyStockInc BICC Team and will workon some exercises to build the new data ware-house.

This session explores• Basic principles of

• Conceptual modeling• Logical modeling• Physical modeling

• How to model from a conceptual via logicalto a physical data vault model

• How to build and access the physical datamodel on Relational-DB (EXASOL) and NoS-QL-DB (REDIS):• Design the data layout on REDIS. Identify

the keys to represent the objects and whichvalues this keys need to hold

• Design the relational data model• Access both the key-value store and the

relational database.

Dirk Lerner is a well experi-enced IT Consultant at IT-GAIN and is as team leadresponsible for the Compe-tence Center Data Architec-ture, Data Modeling andData Vault. For around 15years he has headed BIprojects and is considered an expert for BI architectu-res and data modeling. Dirk is an advocate of flexible,lean and easily extendable data warehouse principlesand practices. As a pioneer for Data Vault in Germanyhe published various publications, is an internationalspeaker at conferences and author of the bloghttp://www.datavaultmodeling.de.

Business Information ModelingFusing the fact-based approachClifford Heath, Infinuendo

Most modeling languages are effective for com-municating only with people who have beentrained to read and interpret the details. Howe-ver, fact-based languages are deeply rooted innatural speech. They build a complete vocabula-ry for the business – not just a glossary of terms– and can be used directly with untrainedbusiness personnel. This radically improves thedepth and breadth of the business dialogue.

As each new fact is encountered, the modeler

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adds appropriate elements to an Object RoleModel diagram, which makes it easy to verbali-ze to natural language. Alternatively, the newfact can be expressed directly in the structurednatural language form of the Constellation Que-ry Language, which is immediately readable tobusiness users but can also be understood bythe computer. This makes it possible to describeany factual situation precisely, to ask or answerany factual question, and to elaborate detailedbusiness rules about possible or allowable situa-tions.

This presentation will introduce you to the ap-proach and to these two languages, peeking atthe respective software tools which generatephysical schemas. A group exercise will challen-ge you to apply it to model a sample problem.The attendee will learn to use verbalization andfact-based analysis to understand any subjectmaterial, and to communicate it at a deep con-ceptual level with those less expert in the field.

Clifford Heath is a compu-ter research scientist whohas long experience in thedesign and implementationof enterprise-scale softwareproducts, and in the use offact-based modeling. He isa Certified Data Manage-

ment Professional (CDMP) at Masters level, has pu-blished a number of papers in peer-reviewed scientificjournals, holds several patents, is the creator of theConstellation Query Language and is a participant inthe Fact Based Modeling Working Group. Clifford hasfrequently presented at chapter meetings of the DataManagement Association as well as at the NATO CAXForum and the European Space Agency, and is CTOand founder at Infinuendo.

Data Modeling forSustainable SystemsGraham Witt

Systems, like any other high-value acquisitions,should continue to work after the warranty peri-od, but far too often fail to work completely asintended. Data modelers can enhance the quali-ty and lifespan of a system if they take a broa-der view than one which simply converts

information storage and retrieval requirementsinto a data model. This workshop looks at howto add value to the data modeler’s contributionto system development or package customizati-on, including:

• identifying real-world complexity and its im-plications for the choice of data structures

• the role of generalization in managing suchcomplexity

• development of a common vocabulary for re-al-world concepts, attributes, relationshipsand processes, and use of that vocabulary insystem artifacts and user interfaces

• recognizing how the data is to be used inBusiness Intelligence and the implications ofthat use for choice of data structures

• analysis of how changes in the real world areto be recorded in the chosen data structures,with implications for process and user inter-face design

• ensuring test plans cover data update side-effects adequately

• managing data model change effectively.

The workshop includes a series of case studiesdrawn from the speaker’s experience dealingwith these issues.

Graham has over 30 yearsof experience in deliveringeffective data solutions tothe government, trans-portation, finance and utili-ty sectors. He has specialistexpertise in business requi-rements, architectures, in-formation management, user interface design, datamodeling, database design, data quality and businessrules. He has spoken at conferences in Australia, theUS and Europe and delivered data modeling andbusiness rules training in Australia, Canada and theUS. He has written two textbooks published by Mor-gan Kaufmann: “Data Modeling Essentials” (withGraeme Simsion) and “Writing Effective Business Ru-les”, and writes monthly articles for the Business RuleCommunity (www.brcommunity.com).

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Advanced Data ModelingChallenges WorkshopSteve Hoberman,Steve Hoberman & Associates

After you are comfortable with data modelingterminology and have built a number of datamodels, often the way to continuously sharpenyour skills is to take on more challenging assi-gnments. Join us for a half day of tackling realworld data modeling scenarios. We will comple-te at least ten challenges covering these fourareas:• NoSQL data modeling• Agile and data modeling• Abstraction• Advanced relational and dimensional mode-

ling

Join us as in groups as we solve and discuss aset of model scenarios.

Steve Hoberman has trai-ned more than 10,000 peo-ple in data modeling since1992. Steve is known forhis entertaining and inter-active teaching style(watch out for flying can-dy!), and organizations

around the globe have brought Steve in to teach hisData Modeling Master Class, which is recognized asthe most comprehensive data modeling course in theindustry. Steve is the author of nine books on datamodeling, including the bestseller Data Modeling Ma-de Simple. One of Steve’s frequent data modelingconsulting assignments is to review data modelsusing his Data Model Scorecard® technique. He is thefounder of the Design Challenges group and recipientof the 2012 Data Administration Management Asso-ciation (DAMA) International Professional Achieve-ment Award.

Your project is under attack! How tostrike back!Jeroen Gietema, author of “TheProjectsaboteur … and how to kill him”

The success ratio of IT projects is shockingly low.Independent studies by Gartner and theStandish group show that only 30% van de ITprojects is considered to be successful. Although

a lot of measures and new methods like Prince2,Rapid Application Development, SDM andScrum, have been introduced in the world ofproject management this number did not reallyimprove in the last 20 years.

You might think that Big Data Projects performbetter, they don’t! A study Cap Geminiconsulting published in 2015 shows that only27% of the Big Data Projects are considered tobe successful (https://www.capgemini-consulting.com/cracking-the-data-conundrum) .Let’s put these numbers into perspective: Theworldwide loss due to project inefficiency is inthe order of magnitude of $300 billion yearly.The average income in the USA is $40.000yearly  . This means that reducing the projectinefficiency loss by just 50% would be theequivalent of 3,7 million jobs.Is it possible to dramatically increase the projectsuccess ratio? According to Jeroen Gietema theanswer is YES. Start to look beyond the classicalproject fail factors, and add the human factor tothe equation. During his presentation Jeroen willintroduce you into the hidden world of theProject Saboteur, his motivation andthe  opportunities offered to him to perform hisundermining activities.Finally Jeroen will show you how to identify thesaboteur and what you can do to get rid of him(or her).

Jeroen Gietema has morethan thirty years of projectand managementexperience in the field ofinformation andcommunication technology.After studying computerscience he worked forVolmac and Capgemini. With Capgemini Jeroenworked as Division Quality manager and Deputy AreaQuality Manager. In addition to his work he studiedbusiness science. In 1997 he started to work as anindependent IT management consultant.

In his role as IT management consultant Jeroenheaded up large computerisation projects, wasresponsible for quality, requirements and suppliermanagement and consulted in the area of logisticservice provision. Jeroen specialized and is activelyinvolved in Agile development of complex softwaresolutions. He gained his experience in large financial,

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governmental, the industrial and logisticorganisations.

Since 2011 Jeroen is co-owner and CEO of Cedira B.V.,a company that develops and provides web-basedsolutions and management consulting services.

Based on his extensive experience in getting projectsto succeed, Jeroen Gietema co-wrote The ProjectSaboteur and The Project Saboteur and PRINCE2. TheProject Saboteur is available in Dutch, German andEnglish.

Why Business Analysts and DataModelers should hold hands moreGeorge McGeachie, Metadata Matters

How integrated are your business analysis anddata modeling efforts? Share the experiences ofa lifetime of modeling, and improve the coordi-nation between your business analysts and datamodelers, enabling them to build a coherent, in-tegrated, set of requirements and models, redu-cing the probability of project failure.

Some of the questions that will be answered in-clude:

• Why does a business analyst need to under-stand or contribute to data models?

• Why does a data modeler need to under-stand or contribute to business analysis?

• Why designing XML messages is NOT datamodeling, it is process modeling.

• How do Business Rules influence data mo-dels?

• Where does all this fit with other types ofmetadata, including Enterprise Architecture?

• How does this impact your project manage-ment and governance?

• What implications does this have for yourmodeling tools?

George McGeachie hasspent his working life crea-ting, managing and linkingdata models, process mo-dels, and others. He encou-rages organizations toconnect and utilize theirmetadata islands, to reco-

gnize the wealth of information contained in their da-ta models, to recognize that the creation of data

models must form part of an integrated approach toimproving their business, and therefore recognize theimportance of avoiding the creation of islands of me-tadata in the first place.

Data Modeling forPolyglot PersistencePhill Radley, BT

This presentation will give an overview of howBritish Telecom is adapting its approach to datamodeling and data architecture to support pro-ject teams wanting to store their data in data-bases that are not based on a Relational DataModel. An important element being the use FactBased Models (specifically Object Role Modelingusing the NORMA tool) to develop conceptualmodels that can be used to join data stores suchas HDFS, Hive, Hbase and Orient DB.

Agenda:

• Polyglot persistence another name for big da-ta, graph data, NoSQL....

• Back to basics - Conceptual, Logical andPhysical Data Models

• Basic Conceptual modeling using Object RoleModeling

• Hadoop (The Hive Metastore, File Formatsand Data Catalogs)

• Schema on Read and Data Wrangling (usingDatameer)

• Key Value Data Model (Time series Data inHbase)

• Graph Data Model (OrientDB and Neo4J).

Phill Radley is a PhysicsGraduate with an MBA whohas worked in IT and com-munications industry for 30years , mostly with BritishTelecommunications plc. Heis currently BT’s Chief DataArchitect at their Martles-ham Heath campus in the UK. Phill works in BT’s coreEnterprise Architecture team with responsibility forData Architecture across BT Group plc. He currentlyleads BT’s MDM and Big Data initiatives driving asso-ciated strategic architecture and investment road-maps for the business. His previous roles in BTinclude; 9 years as Chief Architect for Performance

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Management solutions, ranging from UK consumerbroadband through to outsourced Fortune 500 net-works and hi-frequency trading networks. He hasbroad global experience including BT’s Concert globalventure in USA and 5 years as Asia Pacific BSS/OSS Ar-chitect based in Sydney.

The Journey fromER Modeler to Data ScientistAsoka Diggs, Intel Corp.

In this session, we will be talking about whatdata science is and isn’t, with particular focus onthe skills that a data scientist is expected tobring to bear on a problem. We will discuss theeducational and skill expectations of a datascientist and how those relate to the skills we’vedeveloped as ER Modelers. Going by job des-criptions, data science can easily read like “jackof all trades, and master of them all too”. Howdo we break that down into something more re-asonably achievable?

• What is data science and what is a datascientist?

• What do they do?• Do ER Modelers make good Data Scientists?• Are the skill sets complementary?• And more – bring your questions and let’s

get into them.

After 15+ years of experi-ence in a variety of datamanagement disciplines, in-cluding database administ-ration, ER modeling, ETLdevelopment, and data ar-chitecture, I am transitio-ning into the world of

analytics / data science. It turns out that there IS so-mething even more fun than ER modeling, and I’vefound it. Today, my primary interest is in the organi-zational transformation involved in adopting analyticsas a source of competitive advantage. How does anorganization get there? What needs to be done? Whatorganization design and leadership changes are nee-ded to get the most benefit from analytics? I nowspend my work life practicing these new analytic mo-deling skills, and teaching others how to participateand contribute to predictive analytic projects.

smartDATA and Industrie 4.0Dr. Siegmund Priglinger,dr.priglinger consulting

smartDATA for Industrie 4.0 is based onCAD/CAM, PLM and SE models, where differentmodeling methods/languages and tools arecombined and monitoring approaches for ex-changing and communication between the dif-ferent systems at the engineering and shop-floorlevels will be taken into account. Rigorous DataEngineering is a must. Formalization of schemamapping and matching is the key factor for In-dustrie 4.0.

More than 30 years expe-rience as product and so-lution provider, companyfounder, IT Manager andConsulting Principal for ICTin different industries (Ma-nufacturing, Trade, Tele-communikation, IT, Energy,Healthcare, Public). Contact person for the manage-ment, the business divisions and the IT concerning“Business Intelligence” , “Data Governance” and “In-formation Management”. My current job is “to adviceother advisers” in this areas while I am project leaderof complex data migration or BI projects. I designednew methods to accelerate the migration of applicati-on models and to compare the quality of schema anddata of these application models. My vision is BI3:“Business Intelligence requires Best Information re-quires Best Integration.”

Evolutionary Big Data StrategiesMichael Müller, MID

Or „How to use the power of NoSQL togetherwith the existing relational Warehouse and stilllive to tell where which data can be found”

The tools and products in the NoSQL sphere aremoving fast. The concept of schema-on-read ispowerful and there are amazing things that canbe done with it. Parallel to this there is a lot ofexisting data that is ready to use. There is amuch higher grade of automation available forstructured data and handling this data is muchcheaper in an RDBMS Environment.This is the time for a highly adaptablearchitecture, that allows using the new

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techniques for new use cases and integratethem with the existing world. That allows tocombine structured and unstructured data easily.This new architecture has to be able to integratenew tools, especially as the NoSQL tools are stillmoving fast. And it should be able to changetools of an existent layer whenever it isconvenient.The data model becomes the center in anapproach like this. The metadata of the datamodel makes an approach like this possible. Itcreates insight regarding where which data islocated. And it helps tremendously changingplatforms.

We will cover the following topics:• Schema on read and how data scientists work• JSON as metadata for NoSQL• Data Warehouse Automation• Management of Keys across multipleplatforms• Data Models and NoSQL• Architecture and change

Michael Müller models anddesigns Data Warehousesnow for 15 years. He workscurrently as principalconsultant for MID andusually works as linkbetween user and IT.Starting from requirement

and stopping with the design of the warehouse. Aspart of a huge longtime data warehouse project heknows about the impacts of changes to a warehousefirsthand. He is very passionate about using metadatato speed up the development process and automationin general.

Positioning Data Modelingin the Big Data AreaMartijn Imrich, Xomnia

So we arrived in the area of Big Data, and ever-yone is talking about Hadoop, Data Lakes andwhy we should have a data warehouse or dodata modeling anyway. And there’s no shortageof technology. Every day new techniques arrivewith fancy names such as Spark, Splunk, Hunk,

Hive, Pig, YARN and so on. But how do we de-sign an effective Big Data Architecture? Andwhat is the role of Data Modeling in the Big Da-ta area?

A so called Reference Architecture has beencreated in many domains, ranging from applica-tions to Business Intelligence. Martijn will sug-gest a Reference Architecture for Big Data, andwhere data modeling fits.

Martijn Imrich is helpingcompanies to be more DataDriven since the nineties.Today he is a Partner atXomnia, an Amsterdam ba-sed Big Data startup. Hismain activities are in inno-vation workshops, educati-on and designing (Big) Data Architectures. He’s thefirst known to publish a Big Data Reference Architec-ture, positioning both Data Models and Technology.Before joining Xomnia, Martijn held positions likeLead Business Analyst at Royal Philips to develop theConceptual, Logical and Physical Data Models to sup-port the new Philips application landscape in Heal-thTech services. As Product Owner at Randstad, heintroduced Randstad to predictive analytics in Marke-ting and HR.

Modeling of Reference SchemesDr. Terry Halpin, Professor at INTIInternational University, Malaysia

This presentation illustrates the different ways inwhich a variety of data modeling approaches(ER, UML, ORM, RDB, OWL) support the import-ant task of identifying individuals of interest tothe business domain. In so doing, it also expo-ses the limitations and hurdles that might needto be overcome when transforming from oneapproach to the other. It addresses the followingcategories of reference scheme: (1) simple refe-rence schemes; (2) compound reference sche-mes involving multiple components; (3)disjunctive reference schemes involving optionalcomponents; and (4) context-dependent refe-rence schemes, where the preferred identifier foran entity varies according to the context.

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In natural language, one usually refers to indivi-dual things by means of proper names (e.g. ‘Ba-rack Obama’) or definite descriptions (e.g. ‘Thecurrent president of the United States’). Langua-ges for data modeling and/or ontology modelingvary considerably in their support for such natu-ral reference schemes. An understanding of the-se differences is important both for modelingreference schemes within such languages andfor transforming models from one language toanother. This presentation provides a critical andcomparative review of reference scheme mode-ling within the Unified Modeling Language (ver-sion 2.5), the Barker dialect of EntityRelationship modeling, Object-Role Modeling(version 2), relational database modeling, andthe Web Ontology Language (version 2.0).Using illustrative examples, it identifies whichkinds of reference schemes can be captured wi-thin specific languages as well as those refe-rence schemes that cannot be. The analysiscovers simple reference schemes, compound re-ference schemes, disjunctive reference and con-text-dependent reference schemes.

Dr. Terry Halpin is a Rese-arch Fellow in ComputerScience at INTI InternationalUniversity (Malaysia), and adata modeling consultant.He previously held seniorfaculty positions in compu-ter science at The University

of Queensland (Australia), and professorships in com-puter science at Neumont University (USA) and INTIInternational University. His prior industrial experi-ence includes many years in data modeling technolo-gy at Asymetrix Corporation, InfoModelers Inc., VisioCorporation, Microsoft Corporation and LogicBlox. Hisdoctoral thesis formalized Object-Role Modeling(ORM/NIAM), and his current research focuses on con-ceptual modeling and rule-based technology. He hasauthored over 200 technical publications and ninebooks, and has co-edited nine books on informationsystems modeling research. He is a member of IFIPWG 8.1 (Information Systems), is an associate editoror reviewer for several academic journals, is a regularcolumnist for the Business Rules Journal, and is a reci-pient of the DAMA International Achievement Awardfor Education and the IFIP Outstanding ServiceAward.

Benefit from automationpossibilities of Data VaultsStefan Kausch, heureka e-Business GmbH

Beside the advantages in building andmaintaining a data warehouse, Data Vault offersa huge potential for automations through itshighly structured architecture. The talk namesthese possibilites and showcases the usagebased on a standard modelling tool. Key aspectsare the creation and maintenance of datamodels for the different layers in a warehousearchitecture and the production of ETL logic forthe Raw Vault.

Stefan Kausch is the CEOand founder of heureka e-Business GmbH, acompany focussed on IT-consultancy and softwaredevelopment. Stefan hasmore than 15 years’experience as a consultant,trainer and educator and has developed anddelivered data modelling processes and DataGovernance initiatives for many different companies.He has successfully executed many projects forcustomers, primarily developing application systems,Data Warehouse solutions and ETL processes. StefanKausch has in-depth knowledge of applicationdevelopment based on data models.

Even non-relational databases haverelationshipsPascal Desmarets, Hackolade

The JSON-based dynamic-schema nature (akaschemaless nature) of NoSQL document datastores is a fantastic opportunity for applicationdevelopers: flexibility, fast and easy evolution,ability to start storing and accessing data withminimal effort and setup.

With increased scale and complexity of the data,is it still good enough to find data structuresdescribed tacitly in the application code? Witha bigger team -- including analysts, architects,designers, developers, and DBAs – don’t thedifferent stakeholders want to engage in afruitful dialog about the evolution of theapplication and the data?

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A database model (or map) helps evaluate designoptions beforehand, think through the implicationsof different alternatives, and recognize potentialhurdles before committing sizable amounts ofdevelopment effort. A database model helps planahead, in order to minimize later rework. In theend, the modeling process acceleratesdevelopment, increases quality of the application,and reduces execution risks.

But the traditional methods and tools of the old‘relational’ world are no longer valid for NoSQL. Asa matter of fact, this argument has sometimesbeen used as a justification against the adoptionand implementation of NoSQL solutions.

In this session, after a review of the differentmodeling alternatives, you will learn how toreverse-engineer an existing instance so you canenrich the documentation and constraints. Youwill also learn how to develop a model fromscratch to produce a Mongoose schema orMongoDB 3.2 validator script.

Pascal Desmarets has beendesigning applications anddatabases for a couple ofdecades, but has embracedMongoDB and its dynamicschema approach. In thecourse of designing a socialnetwork built on MongoDB,

he realized that he was missing some of the toolspreviously at his disposal in the old relational world.After a thorough inventory of the different solutionsavailable on the market, he embarked on the design anddevelopment of a new application targeted at analysts,solution designers, architects, developers, and databaseadministrators. Users can visually design, model, define,and create documentation for Mongo databases. Moreinformation is available at http://hackolade.com

Business Keys, Surrogate Keys, HashKeys – Best Practices of BusinessIntelligence Key ManagementDr. Michael Hahne,Hahne Consulting GmbH

Dealing with Business Keys is one of the corecrucial aspects of data warehousing in generatingintegrated data repositories. Business keys mightchange in the operational systems and therefore

the management of changes over time applieshere as well. Above and beyond that thesebusiness keys are the links across differentbusiness processes and there will be an impacton the successful usage in analytical applicationscovering data from different business processes incase of changes in the operational system.More different types of Service Level Agreements(SLA) come up in conjunction with 24x7operations and resulting in more pressure ondata load efficiency. Domain concepts providearchitectural solutions for heterogeneous BIrequirements.

You will learn• Domain concepts in data warehousing• Definition of business keys and surrogate keys• Managing keys over time• Usage of hash keys for key generation• Keys in the Core Data Warehouse• Impact of time stamping and keeping history

on key management

Dr. Michael Hahne is thefounder and ManagingDirector of HahneConsulting GmbH, acompany focusing onBusiness IntelligenceConsulting. He has beenworking as Vice Presidentand Business Development Manager at SANDTechnology, an international provider of intelligentsoftware for information management that specializesin solutions for corporate and large data warehouses.Michael previously also worked for seven years as AreaManager and CTO for Cundus AG, an IT servicecompany devoted to business intelligence. Michael hasmore than 10 years of experience in the area ofimplementation and optimization of data warehousesolutions. He is a TDWI Certified Business IntelligenceProfessional (CBIP) and honored TDWI Fellow. MichaelHahne offers Business Intelligence and DataWarehouse Consulting.

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Growing yourself as a DataManagement Professional:Challenges we face todaySue Geuens ,President of DAMAInternational.

You don’t hear kids saying when I grow up Iwant to be a Data Management Professional.The job doesn’t rank nearly as exciting or exoticas Fireman or Policeman. Even when we aregrown up, we generally don’t go into our firstjob thinking that we are a Data ManagementProfessional. The work of “doing data” kind ofcreeps up on us, unnoticed and unseen until onemorning you wake up and realise you are a dataperson.So now what? There was no degree atuniversity; you can’t find a college to give you adiploma; yet your boss still wants you to excelat this data stuff.From a wealth of experience and having thisactually happen in “real life”, Sue brings apractical and slightly irreverent slant to facingthe challenge of becoming that DataManagement Professional you have alwayswanted to be but didn’t know.Come and hear how DAMA can help you getthere.

Sue is a Senior DataManagement Specialist whohas been customer facingfor the past 18 years.During this time she hasfocused specifically in thefinancial (banking,insurance, pensions) andtelecommunications sectors,

gaining immense knowledge and expertise in both.Each year she attends a number of Data Managementconferences giving presentations both locally andoverseas. Her initial step into the world of data cameabout in the form of designing and implementing thefirst registration system for the NHBRC. Since then shehas moved on to various businesses and enterprising,always working toward Data Quality and Integrity,which is her passion. Sue was elected President ofDAMA SA during January 2009 and was the drivingforce behind the Inaugural Meeting which was heldon 18th February 2009 at Vodaworld in Midrand. Justcompleted implementing Data Governance at a largeSA Telco, Sue has moved her focus to responding tothe many challenges facing SA companies with theirdata. Sue has just been voted in as the DAMA IPresident for the 2014/ 2015 term.

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