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Rome, June 25-26 2015Residenza di RipettaVia di Ripetta, 231
INTERNATIONALS U M M I T2 0 1 5
T E C H N O L O G Y T R A N S F E R P R E S E N T S
Data Management,Analytics and
Business Intelligence
Data Management,Analytics and
Business Intelligence
A B O U T T H E S U M M I T
Most companies today use Business Intelligence (BI) reports and dashboards to mea-
sure Business Performance at strategic, tactical and operational levels. However today,
business is demanding much more than just descriptive BI. Many organisations today
want to go beyond this by implementing predictive and prescriptive analytics. To that end,
many companies are now establishing advanced analytics teams in business depart-
ments to help develop new advanced and predictive analytics that can be deployed in
real-time and in historical environments to produce new insights for competitive advan-
tage. This is happening both in traditional Data Warehouse and in new Big Data envi-
ronments where Data Scientists are analyzing new multi-structured data sources to pro-
duce new models and insights. Also Business Analysts are using these analytics in visu-
al data discovery tools to help predict and forecast the future. In addition analytics are
being embedded in applications to help embed recommendations, alerts and forward
looking insights in processes and applications to optimize business operations.
Another challenge is the number of data sources that companies are now accessing to
capture data for analysis to produce deeper insights. Clickstream data, social network
interaction data, weather data, sensor data, location data and news feeds are just a few
of these. The question is what should companies do with this data? How should it be
organized and stored? The emergence of Hadoop has seen data cleansing and inte-
gration being offloaded from Data Warehouses, to cheaper lower cost Hadoop environ-
ments but is this at the expense of Data Governance? How do you govern data in Big
Data environments and traditional Data Warehouses with confidence? What if struc-
tured data is brought into Hadoop? How secure is your data in Hadoop?
This Summit examines trends in Business Analytics and Business Intelligence and
examines how organizations should manage and govern all this data going forward.
A B O U T T H E S U M M I T
Examples of topics that will be covered include:
• New Analytics Architectures – The Role of A Data Reservoir and Data Refinery
• The Impact of Self-service Data Integration – Data Chaos or Data Governance?
• BI Organisation 2.0 – The expanding role of Data Scientists and Business Analysts
• Agile Data Modeling Techniques for Tradi tio nal and Big Data Analytical Environments
• Best Practices in Data Discovery and Visualization
• Advanced and Predictive Analytics for the Big Data Enterprise
• Panel: The impact of self-service on traditional IT BI/DW
Five Levels of BI in the Cloud
• Actionable Intelligence Using Storytelling and Collaborative BI
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Once filled to be given to:Technology TransferPiazza Cavour, 3 - 00193 RomaTel. 06-6832227Fax [email protected]
SPEAKERSSPEAKERSMike
Ferguson
ClaudiaImhoff
BarryDevlin
Rickvan der Lans
Is Managing Director of Intelligent BusinessStrategies Limited. As an analyst and consultanthe specialises in Business Intelligence, Analytics,Big Data, and Data Management. With over 33years of IT experience, Mike has consulted fordozens of companies, spoken at events all overthe world and written numerous articles.Formerly he was a principal and co-founder ofCodd and Date Europe Limited – the inventors ofthe Relational Model, a Chief Architect at Teradataon the Teradata DBMS and European ManagingDirector of DataBase Associates.
MikeFerguson
Is Managing Director of R20/Consultancy basedin The Netherlands. He is an independentanalyst, consultant, author and lecturerspecializing in Data Warehousing, BusinessIntelligence, Big Data, Data Virtualization, andDatabase Technology. Mr. van der Lans hasadvised many large companies worldwide ondefining their Data Warehouse, BusinessIntelligence, and SOA Architectures. Mr. van derLans has lectured all over the world for overtwenty five years and has written a number ofpopular books, including Introduction to SQL and
SQL for MySQL Developers, that have beentranslated into many languages and have soldover 100,000 copies. Recently, he has publisheda very successful book Data Virtualisation for
Business Intelligence.
Rickvan der Lans
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PARTICIPATION FEE
Euro 1400
The fee includes all seminar documentation,luncheon and coffee breaks.
HOW TO REGISTER
You must send the registration form with thereceipt of the payment to:
TECHNOLOGY TRANSFER S.r.l.Piazza Cavour, 3 - 00193 Rome (Italy)Fax +39-06-6871102
PAYMENT
Wire transfer to: Technology Transfer S.r.l.Banca: CariparmaAgenzia 1 di RomaIban Code:IT 03 W 06230 03202 000057031348
within June 10, 2015
ROMEJune 25-26 2015Residenza di RipettaVia di Ripetta, 231
Registration feeEuro 1400
GROUP DISCOUNT
If a company registers 5 participants to thesame seminar, it will pay only for 4. Those whobenefit of this discount are not entitled to otherdiscounts for the same seminar.
EARLY REGISTRATION
The participants who will register 30 days beforethe seminar are entitled to a 5% discount.
CANCELLATION POLICY
A full refund is given for any cancella tion re ceivedmore than 15 days before the seminar starts.Cancellations less than 15 days prior the eventare liable for 50% of the fee. Cancellations lessthan one week prior to the event date will beliable for the full fee.
CANCELLATION LIABILITY
In the case of cancellation of an event for anyreason, Technology Transfer’s liability is limitedto the return of the registration fee only.
SEMINAR TIMETABLE2 days: 9.30 am - 1.00 pm
2.00 pm - 5.00 pm
first name
surname
job title
organisation
address
postcode
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telephone
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Stamp and signature
InternationalSummit
2015
The Summit is for IT Executives,Professionals, Managers andArchitects who wish to take a
detailed and practical look at thelatest developments in Data
Management, Business Analytics,and Business Intelligence.
Is among the foremost authorities on BusinessInsight and one of the founders of Data Ware-housing, having published the first architecturalpaper on the topic in 1988. With over 30 years of ITexperience, including 20 years with IBM as aDistinguished Engineer, he is a widely respectedanalyst, consultant, lecturer and author of theseminal book, Data Warehouse–from Architecture
to Implementation and numerous White Papers.His new book, Business unIntelligence–Insight
and Innovation Beyond Analytics and Big Data
(http://bit.ly/BunI-Technics) was published inOctober 2013. Mr. Devlin is founder and principal of9sight Consulting. He specializes in the human,organizational and IT implications of deep BusinessInsight solutions that combine operational,informational and collaborative environments. Aregular tweeter, @BarryDevlin, and contributor toITKnowledgeExchange and TDWI, Barry is based inCape Town, South Africa and operates worldwide.
BarryDevlin
Is a thought leader, visionary, and practitioner,Claudia Imhoff, Ph.D., is an internationallyrecognized expert on analytics, Business Intelligence,and the architectures to support these initiatives.Mrs. Imhoff has co-authored five books on thesesubjects and writes articles (totaling more than 150)for technical and business magazines. She is also theFounder of the Boulder BI Brain Trust, a consortiumof internationally-recognized independent analystsand experts. You can follow them on Twitter at#BBBT or become a subscriber at www.bbbt.us.
ClaudiaImhoff
O U T L I N EFirst Day
Session 1New Analytics Architectures – The Role ofa Data Reservoir and Data Refinery
Barry Devlin
New technology, especially in the Hadoop space, has becomeincreasingly popular and pervasive in the past few years. In or-der to make sense of these tools, vendors, consultants andeven customers have begun to sketch architectures to positionthe pieces and fit the function together. Thus, we have seen theemergence of architectural diagrams of Data Reservoirs (alsoknown as Data Lakes) and Data Refineries, to name but a fewof the more common. However, this is still very early days. Thedefinitions remain as varied as their sources and their claimscontroversial. This session sheds light on these new analyticsarchitectures:
• What is a Data Reservoir/Lake and how does it compare to aData Warehouse?
• What is a Data Refinery and what is its relationship to ETL?• What are the benefits and drawbacks of these new architectu-ral components
• Do Reservoirs and Refineries replace or complement traditio-nal architectural thinking?
• What technologies and tools are needed?
Session 2The Impact of Self-Service Data Integration– Data Chaos or Data Governance?
Mike Ferguson
Most medium and large-scale businesses today have traditio-nal Data Warehouses and Master Data Management systemsin place with ETL processing established to capture, clean andintegrate OLTP data to populate these systems. In traditionalData Warehouses and MDM systems, schemas are defined up-front and IT professionals manually build the ETL data clean-sing and integration jobs. However, for many companies, timeshave changed. The thirst for new internal and external data
sources continues to grow including semi-structured and un-structured data. These new data can be very high in volumeand created at very high rates. As a result, Big Data platformshave emerged to support high velocity data ingest and explora-tory analysis of these new sources. In addition, new light-wei-ght, easy to use, self-service ‘Data Wrangling’ tools have emer-ged that automate data preparation tasks, provide in-place vi-sual data transformations as well as data lineage. These toolsare aimed at expert business users to encourage them cleanand integrate data without the need for IT. The question is, if bu-siness users are using self-service Data Integration tools whathappens to traditional ETL? How can data be trusted if usersare all doing their own Data Integration? What will self-serviceData Integration do to IT Data Governance initiatives? This ses-sion looks to answer these questions.
• The emergence of self-service Data Integration• Data Wrangling tools – what are they, how do they work andwho are the vendors?
• Smart Data Management – statistics, analytics and automation• Traditional Data Management platforms and self-service DI • Why collaborative Data Governance is now needed • Metadata integration across self-service and traditional envi-ronments
Session 3BI Organisation 2.0 – The expanding roleof Data Scientists and Business Analysts
Barry Devlin
For more than twenty years, businesses have striven to defineand deliver BI according to a well-defined organisational modeltypically called BI Centre of Competence (or Excellence). Thismodel provides a largely singular and formal approach to deli-vering consistent and controlled data. Today, faster businessneeds have led to an increasing emphasis on data discoveryand instant self-service analysis. Big Data technologies and ex-ternal data sources have reduced focus on traditional databaseand Data Management. The result has been suggestions thatradical, new ways of organising and managing information arerequired. This session examines the evolution of this thinkingand shows how:
• The roles of Business Analysts and Data Scientists are chan-ging and expanding
• A balance of formal control and innovative data use can beachieved
• A new Adaptive Decision Cycle can support the transition fromexploration to production
• Business and IT must define a new, symbiotic approach to col-laboration to derive maximum business benefit from informa-tion and technology
Session 4Agile Data Modeling Techniques for Tradi -tio nal and Big Data Analytical Environments
Rick van der Lans
There was a time when Data Warehouses and Data Marts weredesigned using classic database design techniques such as nor-malization and star schema modeling. But the world of Data Mo-deling has moved on and had to move on because of technologi-cal changes. For example, the Data Vault Modeling techniqueswas introduced to develop Data Warehouses that offer data mo-del extensibility (so that new information needs can be imple-mented easily) and report reproducibility (to support compliancyrequirements). With NoSQL database servers new databaseconcepts were introduced. These products can offer a high velo-city data ingest, but only if the databases are designed in a spe-cific way. NoSQL and Hadoop also offer the schema-on-readconcept which has a major impact on Data Modeling. In addition,Data Virtualization has made it possible to develop virtual DataMarts. Designing virtual Data Marts is different from designingphysical Data Marts. For example, it allows for nested virtual ta-bles. To summarize, Data Modeling has changed and this ses-sion discusses all these new aspects of Data Modeling.
• The building blocks of Data Vault modeling: hubs, links, and sa-tellites
• Differences between schema-on-read and schema-on-write• How to design for NoSQL database servers• Modeling virtual Data Marts for Data Virtualization• Combining Data Vault and Data Virtualization leads to agile BIsystems
Second Day
Session 5Best Practices in Data Discovery andVisualization
Claudia Imhof
Gartner first recognized the market for Data Discovery in 2011as the “new end-user-driven approach to BI”. Today there are anumber of vendors who are positioned as ‘Data Discovery’ ven-dors. These “independent” software companies have rapidly mo-ved into the territory once belonging to the BI mega vendors. This movement is driven by business users who want ease ofuse and who are exerting more influence over BI purchasing de-cisions than in the past. Data discovery has historically beenviewed as an adjunct to traditional BI but is now increasinglybeing sought as a standalone alternative.Attendees will learn:
• The difference between Data Discovery, Data Visualization andBusiness Intelligence
• The need for Data Discover/Visualization in the era of Big Data• Best practices and pitfalls in implementing Data Discovery/Data Visualization
Session 6Advanced and Predictive Analytics for theBig Data Enterprise
Mike Ferguson
For most organisations, descriptive BI looking at past businessactivity is no longer enough. The demand is now for deeper insi-ght to predict the future and to guide the business into makingthe right decisions. In addition, new more complex Big Datasources are demanding more powerful advanced analytics toprocess semi-structured and unstructured data. This sessionlooks at how organisations can use Predictive Analytics and hownew advanced algorithms can be used to deepen insight in a BigData environment. It also looks at how these analytics can be in-corporated into existing analytical environments.
• An introductions to advanced and Predictive Analytics• Types of analytic algorithms and their uses • The importance of data preparation• Approaches to using advanced and Predictive Analytics e.g in-database, in-Hadoop, in-stream and in-memory
• Using analytics in self-service BI tools and analytics applica-tions via predictive APIs
• Big Data analytics use cases – IT infrastructure and applicationperformance management, customer engagement, risk pre-vention and operations optimization
• Getting started – do’s and don’ts, model management, align-ment with business objective and organizational issues
Session 7Panel: The impact of Self-Service on traditionalIT BI/DW
Speakers and Vendors
Session 8Five Levels of BI in the Cloud
Rick van der Lans
Business Intelligence and Analytics are about improving andsupporting decision-making processes. To develop reportingand analytical capabilities, organization must install, optimize,operate, and manage all the technical components, such asData Warehouses, ETL tools, Hadoop clusters, disk storage,appliances, and so on. But do organizations want to do thatthemselves? Do they want to have all the required technicalspecialists on their payroll? If not, BI systems can be outsour-ced. They can be moved to the Cloud. But which form of BI inthe Cloud is best for an organization? This session discusses indetails the differences between five levels of BI in the Cloud.These levels differ in how far they unburden an organization.For example, with DataWarehouse-in-the-Cloud organizationsare still responsible for most of the tasks. Whereas with BICC inthe Cloud, almost all the work is done by the vendor. Thismeans the organization can focus for a large part on reportingand analytics.
• Overview of file levels of BI in the Cloud: resource in the Cloud,database in the Cloud, Data Warehouse in the Cloud, BI in theCloud, and BICC in the Cloud
• Behind the scenes of a BI in the Cloud vendor• Using proprietary or public tools?• Staying independent of the Cloud solution• Privacy and security aspects of storing data in the Cloud
Session 9Actionable Intelligence Using Storytellingand Collaborative BI
Claudia Imhof
“Opinions without facts are just opinions”.“Absolute numbers by themselves are useless”.
These are quotes from high-level executives in major enterpri-ses – and they illustrate the frustration these Decision Makershave with Analytics and Data Science today. While Analytics andData Science are still darlings in our industry, significant pro-blems are bubbling up regarding the real value of their efforts. Data Storytelling and collaborative BI put drama and com-prehension into dry analytical results. These improve not only in-terest but also a better understanding of what the numbers meanto the Decision Makers in the enterprise.Attendees will learn:
• The definitions of Storytelling and collaborative BI• Examples of both capabilities• The infrastructure needed to support these initiatives• Getting started with Storytelling and collaborative BI
