Post-Conference Full Day Workshops: 5 November 2015

DW/BI Workshop
09:00-16:30
FULL DAY
Maximising Business Value Using Predictive Analytics, Self-Service And Collaborative BI
Mike Ferguson, Intelligent Business Strategies
Enterprise Data Workshops
09:00-16:30
FULL DAY
Evolving Your Information Architecture: What? Why? How?
Peter Aiken, Data Blueprint
09:00-16:30
FULL DAY
Getting to the Next Maturity Level with Information Governance: Delivering Accuracy and Trust
Jan Henderyckx, Inpuls
Big Data Workshop
09:00-16:30
FULL DAY
Driving a Data Culture with Data Science
Jon Woodward, Microsoft
Andrew Fryer, Microsoft
Amy Nicholson, Microsoft
10:30-10:45 Break, 12:15-13:15 Lunch, 14:45-15:00 Break
DW/BI Workshop
Full Day
Workshop
09:00-16:30
Maximising Business Value Using Predictive Analytics, Self-Service And Collaborative BI
Mike Ferguson, 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
Intelligent Business Strategies
Enterprise Data Workshops
Full Day
Workshop
09:00-16:30
Evolving Your Information Architecture: What? Why? How?
Peter Aiken, Data Blueprint

All organizations have information architectures. The question is how effectively do organizations use them. This tutorial teaches how to evolve and use your organization's information architecture. It describes the information architecture ’s strategic drivers and how to enhance components of the information architecture so they can become more useful without having to ask for budget and other resources.  Other important related learning objectives include how to:
  • Build repository functionality without justifying the cost of a traditional repository.

  • Justify data-centric development practices and architectural solutions.

  • Use existing legal and financial motivations to guide individual efforts.

  • Employ architectural patterns and data profiling to jump start efforts to surface, understand, and make use of discoverable architectural components.

Upon completion of this tutorial, attendees will be in a position to develop and make use of the organization’s information architecture components in today's environment.

Featured Speaker:
Peter Aiken      Peter Aiken
Data Blueprint

Full Day
Workshop
09:00-16:30
Getting to the Next Maturity Level with Information Governance: Delivering Accuracy and Trust
Jan Henderyckx, Inpuls

We have evolved from the age of automation to the information age. Proper information management and insights have become a linchpin that act as a catalyst for the execution of your business strategies. Information can be supporting or defining your business model. Having the data in your organisation is not enough as the true value comes from your ability to turn the data into operational information and insights that allow you to create business value and make strategic and tactical decisions. Aligning your information requirements with strategic business objectives is critical.
  • Linking your business strategy to information flows
  • Architecting the business semantics
  • Information Enablement, establishing the information capabilities
    • Capabilities required to support your information strategy:
      • Persistency: Column Based Storage, Appliances, In-memory Computing, NOSQL, Hadoop, ..
      • Positioning the information management patterns; virtualisation, Extract-Transform-Load, Enterprise Application Integration, Web services, Enterprise Service Bus, Change Data Capture, …
      • Managing the information life cycle: ILM platforms
  • Managing Accuracy and Trust
    • Delivering quality and security
  • Getting the business buy-in
Featured Speaker:
Jan Henderyckx      Jan Henderyckx
Inpuls

Big Data Workshop
Full Day
Workshop
09:00-16:30
Driving a Data Culture with Data Science
Jon Woodward, Business Lead – BI & Analytics, Microsoft
Andrew Fryer, Evangelist – Data Science, Microsoft
Amy Nicholson, Evangelist – Data Science, Microsoft

Unlock the Value of Data Culture with Data Science and Machine Learning: Data culture within your organisation can deliver significant competitive advantage, drive additional revenue and savings, and help find creative consensual solutions to real-world company-wide concerns by enabling all employees to make better, fact-based decisions that enhance rather than clash with their intuition and instincts.

Getting Started with Data Culture: Executives from Finance, HR, Marketing, Operations, Sales as well as board-level IT ought to embrace putting data and data-driven next-generation business applications at the core of their strategic thinking.

Data is growing exponentially and it’s now possible to mine and unlock insights from it in new, often unexpected, yet at-last reliable, accurate and verifiable ways. Data science and machine learning have matured to the point of being relatively easy to use, and have left the domain of pure experimentation. Now is the best time to discuss and to consider the value and the opportunity of this combination of algorithmic thinking, statistics, data mining, predictive analytics, big data, and the more mundane issues of data quality in your organisation. Companies that carefully balance a portfolio of strategic and high potential data science projects with the key operational and supporting needs of day-to-day use of data will gain a competitive advantage from these approaches whilst managing the risk to their operations.

Data Dividend: IDC research, commissioned by Microsoft, shows that better outcomes from data and analytics projects correlate with greater competitiveness of an organization in its industry or enhanced ability to fulfil its mission in the public sector. Although correlation does not equate to causation, a growing body of research shows financial and productivity benefits directly linked to better data-driven decision making enabled by business analytics solutions.

Four characteristics distinguish leaders in analytics—the use of more diverse data types and sources beyond social media to include the Internet of Things (IoT), adoption of more diverse analytical and self-service tools, methods, and metrics suited to broad range of users from data scientists to business analysts and executives, distribution of insights to a more diverse audience of business users, and the right-time application of most timely data.

Business success is determined not by whether an organization invests in business analytics, but by how it invests in them and supports those initiatives. Leadership in business analytics requires competencies in data management, technology, human capital, process management, and strategy, budgeting, and resource allocation. 

In this one day workshop learn the key issues to consider when embarking on Driving a Data Culture with a focus on leveraging Data Science within your organisation.  We will also get hands on with the Azure Machine Learning tool and look at the Cortana Analytics Suite to understand the art of the possible.

Featured Speakers:
Jon Woodward      Jon Woodward
Microsoft
  Andrew Fryer Andrew Fryer
Microsoft
           
Amy Nicholson   Amy Nicholson
Microsoft