Conference Full Day Workshops: 10 November 2016

09:00-16:30
FULL DAY
Maximising Business Value Using Predictive Analytics, Self-Service and Collaborative BI
Mike Ferguson, Managing Director, Intelligent Business Strategies
09:00-16:30
FULL DAY
Making Enterprise Data Quality a Reality
Nigel Turner, Principal Information Management Consultant EMEA, Global Data Strategy 
09:00-16:30
FULL DAY
Successful Implementation of a Master Data Management Programme
Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners
09:00-18:15
FULL DAY
Preparation for the Certified Data Management Professional (CDMP) Exams and Exams Leading to CDMP Certification
Chris Bradley, Information Strategist, Data Management Advisors Ltd, DAMA CDMP Fellow & President, DAMA UK
10:30-10:45 Break, 12:15-13:15 Lunch, 14:45-15:00 Break
Full Day Workshops
Full Day
Workshop
09:00-16:30
Maximising Business Value Using Predictive Analytics, Self-Service and Collaborative BI
Mike Ferguson, Managing Director, Intelligent Business Strategies

An Overview of Predictive Analytics and Machine Learning

As we move into the era of smart business, looking back in time is not enough to make good decisions. Companies have to also model the future to forecast and predict so that they can anticipate problems and act in a timely manner to compete. Predictive analytics is a therefore a key part of any BI initiative and should be integrated into analysis, reporting and dashboards. This session introduces predictive analytics and how shows how it can be used in analysis and in business optimisation

  • What are predictive analytics?
  • Technologies and methodologies developing predictive analytical models
  • Using supervised learning to develop predictive models for automatic classification
    • Popular predictive algorithms e.g. Linear regression, decision trees, random forest, neural networks
  • Clustering data using unsupervised learning algorithms
  • Deploying predictive analytical models in analytical databases
  • Implementing predictive analytics in-Hadoop
    • E.g. Spark MLlib, Mahout and commercial analytics
  • Accessing in Hadoop machine learning algorithms from data mining tools
  • Integrating predictive analytics with event stream processing for automated analysis of high velocity events

Self-Service Data Discovery and Visualisation Tools

Self-service Data Discovery and Visualisation tools are frequently sold into business departments so that local business analysts can start building their own BI applications without having to wait for IT. This means that development often starts without any IT guidance and quickly spreads to other parts of the business with little thought for integration or re-use. The result is that inconsistency and chaos can quickly set in. This session looks at best practices in deploying Self-service Data Discovery and Visualisation tools to maximise business benefit in existing BI/DW environments

  • What are Self-service Data Discovery and Visualisation tools?
  • Interactive analysis and automatic charting using in-memory data
  • The Self-service Data Discovery and Visualisation tools marketplace e.g. Qlik Sense, Tableau, Tibco Spotfire, SAP Lumira, Information Builders, SAS Visual Analytics, Yellowfin
  • Accessing predictive analytics from self-service BI tools
  • Accessing Big Data from self-service BI tools using SQL on Hadoop
  • Best practice steps in deploying self-service BI applications
    • Steps to developing self-service BI in a business led BI development environment
    • Removing complexity of data access using data virtualisation
    • Using templates and components for rapid self-service BI application development
    • Prototyping and bookmarking valuable insight
    • Handing over self-service applications for ‘IT hardening’
    • Publishing self-service BI applications for business use
    • Securing access to self-service BI applications

Sharing Bi Content through Collaborative Bi and Storytelling

One of the key requirements in the smart enterprise is being able to easily access and share BI content with others both inside and outside the enterprise. To make this possible, BI platforms need to simplify user interfaces while adding collaborative and storey telling capabilities. This session looks at how collaborative computing and BI come together to facilitate easier sharing and communication of insight.

  • The challenge to older hierarchical ways of working
  • The Facebook revolution – New technologies for enterprise collaboration
  • Why use enterprise collaboration and social computing?
  • Analytical communities in the enterprise
  • Decision making at strategic, tactical and operational levels
  • Why Collaborative BI?
    • Executing business strategy through dynamic alignment and formation of communities
    • Empower information consumers for mass contribution to business goals
  • Requirements for collaborative and social BI
  • Types of user - information producers vs. information consumers
  • Collaborative BI authoring for information producers
    • Creating stores from BI dashboards and visualisatons
  • Using collaborative BI for joint decision making and knowledge sharing
    • Sharing BI content and stories in a net meeting
    • Attaching threaded discussions to BI content
    • Voting and polling for joint decision making
  • Empowering the masses to create, share, search and collaborate over BI and related content
  • Collaborative BI technologies, e.g. Antivia, IBM Cognos Business Insight, LyzaSoft, , Panorama Necto, Tableau, SAP Lumira, Yellowfin
  • Using portals together with collaborative BI

Mobile Bi – Extending the Reach to New Devices

Now that mobile devices have made great strides in their rich user interfaces, one of the hottest new areas is business intelligence is Mobile BI. This session looks at how modern mobile devices can now connect to BI platforms to access insight from inside or outside the enterprise. It also looks at how dis-connected users are now supported and how mobile workers can participate in collaborative BI environments and act on business insight to improve business performance.

  • Popular Mobile BI use cases
  • What should be in a Mobile BI Strategy
  • Types of BI user and mobile device usage
  • How have BI platforms been ex- tended to support mobile BI?
  • The mobile BI marketplace
  • Authoring mobile BI content
    • Dos and Don’ts on Building content for mobile devices
  • Mobile BI Security – what to look for
  • Evaluating mobile BI for the information consumer
    • What can a user do with mobile BI
    • Accessing dashboards and alerts from a mobile device
    • Alerting and KPI drill down off a mobile device
    • Using predictive analytics for mobile BI action recommendations
    • Catering for disconnected access to BI content
  • Integrating mobile BI with other applications and services
    • Integrating mobile BI into a collaborative BI environment

Acting on Mobile BI via integration with operational business processes and applications.

Featured Speaker:
Mike Ferguson      Mike Ferguson
Managing Director
Intelligent Business Strategies
 
Full Day
Workshop
09:00-16:30
Making Enterprise Data Quality a Reality
Nigel Turner, Principal Information Management Consultant EMEA, Global Data Strategy 

Many organisations are recognising that tackling chronic data quality (DQ) problems requires more than a series of tactical, one off improvement projects. By their nature many DQ issues extend across and often beyond an organisation.  So the only way to address them is through an enterprise wide programme of data governance and DQ improvement activities embracing people, process and technology. This requires very different skills and approaches from those needed on many traditional DQ projects.

If you attend this workshop you will leave more ready and able to make the case for and deliver enterprise wide data governance & DQ across your organisation. This highly interactive workshop will also give you the opportunity to tackle the problems of a fictional (but highly realistic) company who are experiencing end to end data quality & data governance challenges. Attending this workshop will enable you to practise some of the techniques taught in a safe, fun environment before trying them out for real in your own organisations.

The workshop will draw on the extensive personal knowledge & experience of Global Data Strategy’s Nigel Turner who has helped to initiate & implement enterprise DQ and data governance in major companies including BT Group, British Gas, Intel and many other organisations.   The approaches outlined in this session really do work.

The workshop will cover:

  • What differentiates enterprise DQ from traditional project based DQ approaches
  • How to take the first steps in enterprise DQ
  • Applying a practical DQ & data governance framework
  • Making the case for investment in DQ and data governance
  • How to deliver the benefits – people, process & technology
  • Real life case studies – key do’s and don’ts
  • Practice case study – getting enterprise DQ off the ground in a hotel chain
  • Key lessons learned and pointers for success
Featured Speaker:
Nigel Turner      Nigel Turner
Principal Information Management Consultant EMEA
Global Data Strategy 

 
Full Day
Workshop
09:00-16:30
Successful Implementation of a Master Data Management Programme
Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners

This workshop focuses on the key elements of an MDM programme that are needed for overall success. It gives practical recommendations while at the same time providing a conceptual understanding of what is involved in these recommendations. Both governance and management are covered, and emphasis is placed in how MDM fits into a larger business strategy and architectural setting. MDM programmes are rapidly evolving as new data possibilities emerge and enterprises demand more from MDM than they have previously. These emerging challenges of MDM are addressed in detail.
  • What is Master Data, how does it differ from other classes of data?
    • What the practical implications are for governing and managing Master Data.
    • How do you control scope on your MDM program
    • Dealing with business justification, and managing expectations
    • How strategies like customer-centricity require MDM
    • How new MDM-driven business models are emerging, and their implications
  • Architectures for MDM
    • The traditional MDM architectures (hubs) and their pro’s and con’s
    • The separation of Master Data creation and distribution
    • Deciding the scope of Master Data, particularly static versus profile data
    • What MDM vendors can do for you and what they cannot
    • The need to put MDM into a bigger architectural picture to achieve business results
  • How to work with the business to be successful in MDM
    • Examples of how different Master Data entities require different overall approaches, and the implications for an MDM program.
    • How to deal with the Operations business community and meet their needs
    • How to deal with the Analytical business community and meet their needs
    • Getting governance needed for MDM into the business
  • Dealing with data integration, changed data capture, and data quality successfully
    • What is the right approach to data integration?
    • How to implement continuous production data quality monitoring
    • Inferring history, versus capturing historical events, and how to store this
    • The role of business rules in driving MDM
  • Mastering Master Data Semantics
    • Dealing with the different “types” of Customer, Financial Instrument, etc.
    • Capturing knowledge of the Master Data
    • The vital role of reference data in MDM
    • The roles of generic data models and specific data abstraction layers
  • Emerging Areas in MDM
    • Dealing with legal, privacy, and compliance issues of Master Data
    • Sourcing Master Data from outside the enterprise
    • Social Media fraud
    • The role of Big Data
Featured Speaker:
Malcolm Chisholm      Malcolm Chisholm
Chief Innovation Officer
First San Francisco Partners
 
Full Day
Workshop
09:00-18:15
Preparation for the Certified Data Management Professional (CDMP) Exams and Exams Leading to CDMP Certification
Chris Bradley, Information Strategist, Data Management Advisors Ltd, DAMA CDMP Fellow & President, DAMA UK

This full day workshop covers an overview of the process, tips and techniques of successful CDMP exam taking. In this interactive and informative session, you will learn:
  • What is the CDMP certification process
  • The DAMA-DMBOK & CDMP data exams alignment
  • What topics comprise each exam’s body of knowledge
  • Concepts and terms used in the CDMP exams
  • A Self-assessment of your knowledge and skill through taking the sample exams

VERY IMPORTANT: You will need to bring your own computer which can connect to the internet. The exam is taken online you will need to register a minimum of 2 hours before the exam at www.dama.org. Your test (and live) exam results and performance profile can be viewed immediately. 

Attendees of the half day workshop will also receive some refresher tuition covering several of the most common topics seen in recent examinations. Note however this is not a substitute for past experience and education. In the afternoon there will be three 90 minute exam sessions. The schedule for the day will be as follows:

09:00-12:15: Workshop Preparation for the Certified Data Management Professional (CDMP) Exams
13:15-14:45: Exam 1
15:00-16:30: Exam 2
16:45-18:15: Exam 3

Workshop attendees will take the certification exams on a "pay if you pass" basis (passing is 60% for associate & 70% for practitioner). If you take and pass all three certification exams, you would leave EDBI 2016 with a CDMP credential.  Note exam fees are payable directly to DAMA.

EXAM

  • 3 * 90 minute examination sessions (in the afternoon).
  • Each exam is 90 minutes in length and has 110 multi-choice questions
  • Your score is immediately known after exam is taken
  • Exam fees for ED&BI attendees - there is a fee payable for each CDMP exam, with a ‘pay only if you pass’ agreement for attendees of this workshop”
  • Passing at Associate level requires 60% or higher in one exam. Practitioner level is attained by passing 3 exams at 70% or greater.
Featured Speaker:
Chris Bradley      Chris Bradley
Information Strategist
Data Management Advisors Ltd
DAMA CDMP Fellow & President, DAMA UK