10 Analytics & Data Science Fundamentals Anyone Should Know

Jasper de Vries, Kadenza

Play time is over. Data Scientists need to leave their data labs and start delivering real added value. The techniques and models have been around for decades and recent technologies lower the barriers to use them. But then what? How to make sure they really add value to your company so they can deliver the transformational power everyone expects?

Whether you fit the description above or you are just thinking about getting started with Data Science, this highly interactive workshop offers you insight into critical elements of value added Data Science. Derived from experiences by clients in finance, healthcare, automotive, human resources, leisure and retail, these lessons learned will be applicable across all sectors and make sure you get where you need to be.

Key take aways:

• What Data Science can do for you
• Insight into crucial preconditions for an effective usage of Data Science
• Best practices of Data Science

Technology for Big Data and Fast Data Explained

Rick van der Lans, R20/Consultancy

With the introduction of big data and later on fast data, a tsunami of new technologies for data storage, processing, and transportation has been introduced. Hadoop, Spark, Kafka, NoSQL, MapReduce, Hive, SQL-on-Hadoop are just a few of the countless technologies that have become available for developing big data systems. And with streaming data and the Internet of Things fast data has attracted the attention of many organizations as well. It’s great to have so many technologies available, but which ones do you pick? Due to this waterfall of new technologies, it’s becoming harder and harder for organizations to select the right tools. Which technologies are relevant? Are they mature? What are their use cases? These are all valid but difficult to answer questions. This tutorial gives a clear, extensive, and critical overview of all the new key technologies for developing big data and fast data systems. Technologies are explained, market overviews are presented, strengths and weaknesses are discussed, and guidelines and best practices are given.

• New analytical needs, including data science, investigative analytics, and streaming analytics
• Differences between semi-structured, poly-structured, multi-structured, and unstructured data
• The Hadoop stack: HDFS, MapReduce, Hive, Kafka, Spark, HBase, YARN, ZooKeeper, Pig, HCatalog, and so on
• Big SQL Solutions: SQL-on-Hadoop, NewSQL, analytical SQL Database Servers, and Streaming SQL Databases
• Technologies for streaming data: Apache Kafka, Apache ActiveMQ, Amazon Kinesis, Kestrel, RabbitMQ, and ZeroMQ

Blockchain Fundamentals

Anders Brownworth, Chief Evangelist, Circle

Blockchain technology has garnered much hype recently while proven use cases remain few. Much of that is because the technology is not well understood. Anders Brownworth of Circle, a person to person payments company which leverages blockchain technology, will present a half-day session designed to get you familiar with the technology and help you sort the useful bits from the hype. Designed for a non-technical audience, we will start from a clear conceptual model and build up to some of the more recent advancements such as complex smart contracts and state channels. After this session, you will have a firm grasp of the core concepts and be able to identify viable uses for blockchain technology.

Information Management Fundamentals

Chris Bradley, Data Management Advisors

This workshop provides an introduction to the disciplines across the complete Information Management spectrum. Additionally, this course provides a solid foundation for students considering an Industry professional certification such as IRMS, ICCP CDP or DAMA CDMP.

Learning Objectives:

This workshop is intended to provide you with the knowledge, methods and techniques required to analyse, mature and implement information management solutions within your organisation. Areas covered include:

• Data Governance
• Data Quality Management
• Master and Reference Data Management
• Business Intelligence & Data Warehousing
• The essential role of Data modelling
• Data Lifecycle Management
• Metadata Management
• Risk, Security & Regulatory compliance
• Data Operations
• Content & Records management
• Data Integration & Interoperability

At the end of the course, delegates would have gained the following:

• Learn about the need for and application of Information Management disciplines for different categories of challenges
• Explore concepts such as lifecycle management, data modelling and data virtualisation and why they are important
• Understand the critical roles of Master Data Management and Data Governance and how to effectively apply them
• Learn about the different Data Management disciplines and their interrelations.

A comprehensive version of this course over 3 days is offered by IRM UK – for full details click here

Big Data Governance

Jan Henderyckx, Inpuls

Big data governance is not just about making sure that you efficiently use your Hadoop cluster or assuring that you work on the relevant use cases. With the democratisation of big data capabilities and the wider access to data, questions arise on the regulatory- and ethical compliance of the data usage. Locking all data down is not the answer as we would lose too much value.

This presentation focuses on the steps you need to take to get sustainable and compliant value out of your big data.

What delegates will learn from attending the session:

• What is the distinction between Information and Big Data Governance
• Catering for the dynamics of data on-boarding and usage flows
• Towards policy-based classification and access
• Use case governance vs Critical Data Elements
• Impact of the Big Data Governance requirements on the architecture

Getting Started With Data Quality – A Primer

Jon Evans & Nic Jefferis, Equillian

Today, more than ever, the quality of data, underpinned by a robust approach to Data Quality Management, is critical to the success of every organisation. Unfortunately, it is a topic that is still impenetrable to many through the use of unfamiliar jargon and too much emphasis on technology.

In this half-day workshop, Equillian’s Jon Evans and Nic Jefferis seek to redress the balance, by taking the audience on a journey from first principles right through to advice on establishing a Data Quality Programme. Along the way, both beginners and those already familiar with the topic will benefit from a business-focused approach, based on industry best practice coupled with many years of experience helping organisations tackle their Data Quality challenges.

The session will be structured around 4 key topics:

• Why should I care about data quality?
• Monitoring data quality
• Improving data quality
• Developing a DQ Programme

What Does MDM Have to Do With Innovation

Lars Nordwall, Neo Technology

“Your Master Data Is a Graph”. Whether it’s the organisation master or a product master involving complex hierarchies and relationships, Master Data invariably takes the form of a graph or network, and is best modelled, stored and queried using a native graph technology. Whether you are using a packaged MDM solution or a building a custom MDM solution, a Graph Database can help you get a higher ROI by reducing complexity, increasing agility and improving the speed and efficiency of your Master Data initiative.

Join this session to learn how a Graph Database fits into your MDM solution and how market leading organisations like Pitney Bowes, Cisco and UBS are gaining significant competitive advantage by adopting different MDM implementation styles to incorporate graph technology into their solution portfolio.

Topics to be discussed include:

• Understanding how a graph database complements MDM – from personalised product & service recommendations to websites adding social capabilities
• Identifying the benefits of different MDM implementation styles – ranging from using Graph Database as the primary repository for your Master Data to using a Graph Database to build a metadata registry
• Learning from industry-proven best practices in adopting Graph Databases

Agile Methods and Data Warehousing: How to Deliver Faster

Kent Graziano, Snowflake Computing

Most people will agree that data warehousing and business intelligence projects take too long to deliver tangible results. Often by the time a solution is in place, the business needs have changed. With all the talk about Agile development methods like SCRUM and Extreme Programming, the question arises as to how these approaches can be used to deliver data warehouse and business intelligence projects faster. This presentation will look at the 12 principles behind the Agile Manifesto and see how they might be applied in the context of a data warehouse project. The goal is to determine a method or methods to get a more rapid (2-4 weeks) delivery of portions of an enterprise data warehouse architecture.

Real world examples with metrics will be discussed.

• What are the original 12 principles of Agile
• How can they be applied to DW/BI projects
• Real world examples of successful application of the principles

Digital Data Strategy

Pieter den Hamer, Strategist, Alliander

Digital business is fueled by data. Therefore, managing data is a critical enabler of many business optimisation & innovation initiatives. However, most organisations are actually not very good in their data management. Typically, both data quality and interoperability are seriously hampering data-driven business opportunities with high costs and long time-to-market of new solutions. In this workshop, we will address the urgent need to improve your data management capabilities, including data governance & architecture, aligned with digital business strategy, and focused on data that matters most. Given the current maturity of your capabilities, how do you set priorities and design a roadmap for future development? How can we make data management less like a corporate dinosaur and more like an agile enabler of business change? And what standards, frameworks, roles and technologies are there to support your digital data strategy?

• Align your digital data strategy with digital market & technology trends, digital business strategy and innovation
• Determine your organisation’s current and expected “data footprint” to focus your data management strategy and identify future needs
• Assess the maturity of your current data management capabilities and generate a roadmap to bridge the gaps, working towards agile data management to enable your digital business

Data Modelling in a Big Data Environment

Chris Bradley, Data Management Advisors

This half day workshop will explore Data Modelling for Big Data together with the techniques and uses for data models beyond Relational DBMS development.

In the modern era, the volume of data we deal with has grown significantly. As the volume, variety, velocity and veracity of data keeps growing, the types of data generated by applications become richer than before. As a result, traditional relational databases are challenged to capture, store, search, share, analyse, and visualize data. Many companies attempt to manage big data challenges using a NoSQL (“Not only SQL”) database and may employ a distributed computing system such as Hadoop. NoSQL databases are typically key-value stores that are non-relational, distributed, horizontally scalable, and schema-free.

Many organisations ask, “do we still need data modelling today?” Traditional data modelling focuses on resolving the complexity of relationships among schema-enabled data. However, these considerations do not apply to non-relational, schema-less databases. As a result, old ways of data modelling no longer apply.
This workshop will show Data modelling approaches that apply to not only Relational, but also to Big Data, NoSQL, XML, and other formats. In addition, the uses of data models beyond simply development of databases will be explored.

What you will learn:

At the end of the workshop, delegates would have gained the following:

• Learn about the need for and application of Data Models in Big Data and NoSQL environments
• See the areas where Data modelling adds value to Data Management activities beyond just Relational Database design
• Understand the critical role of Data models in other Data Management disciplines particularly Master Data Management and Data Governance.
• Learn the best practices for developing Data models for Big Data and NoSQL environment
• Understand how to create data models that can be easily read by humans

Preparation for the Certified Data Management Professional (CDMP) Exams

Mark Humphries, DAMA & Katherine O’Keefe, Castlebridge

This half 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

Attendees of the half day workshop will 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.

Attendees will be able to take the exams according to their conference attendance schedule on 21 November. 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 ED&BIA 2017 with a CDMP credential.

Note exam fees are payable directly to DAMA.

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.

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.

Data Storytelling

Dirk Morgenroth, Senior Consultant Business Intelligencce, Atos

Analyzing data is only one side of today’s data professionals’ coin. Communicating that data in a compelling way is the other side. Typically, there is a particular need of verbally sharing the data with the management. Storytelling techniques are an effective mean to deliver your information and they offer many compelling reasons for making use of them. This workshop provides the context of data and storytelling and introduces to its fundamentals.

• Get sensitized to the power and potential business contexts of narratives
• Understand the importance of context and audience
• Learn important Do’s and Don’ts for visualizing your data

Grooming Data Stewards

Sue Geuens, DAMA International

Data Stewardship is very tough to sell. You are expecting individuals inside your organization to take on an accountability that they may not have full control over and do that on top of their day job.

This presentation is aimed at providing a how-to case study on what you need to do to first sell the concept and second groom colleagues to become great data stewards and sustain data governance in your organization:

• Selling the Concept
• Defining the Good vs. Not Good for a Data Steward
• Training the Data Steward to become a Change Agent
• Branding the Data Steward for instant recognition

Mastering your Master- and Reference Data

Jan Henderyckx, Inpuls

In the age of data science, AI and big volumes one could wonder why we need to care about the little data. Wouldn’t it be just enough to define the critical data points and setup data sharing agreements? Practice has however shown that properly managing your master- and reference data is the cornerstone of being a data enabled company. The word data can be a bit deceptive as it will also be necessary to address the organisational- and technological aspects and obviously we need to consider data migration and cleansing. This presentation focuses on getting control over your master- and reference data. The practical approaches and examples draw from Jan’s years of hands-on experience in MDM/RDM-projects.

What delegates will learn from attending the session:

• How to build a business case
• Understand the various architectural MDM-styles
• Blueprints for common use cases
• Integrating an MDM-system in your existing application and data landscape
• Setting up MDM/RDM organisational models
• How to select a tool that fits your needs
• Properly planning for the launch of the solution

Plenary Keynote - The New Literacy: The Skills and Insights You Need in the Information Economy

Donald Farmer, Principal, TreeHive Strategy

For all our complaints, we are fortunate to live in highly literate, educated societies. When it comes to data literacy, though, it’s more like the Middle Ages—a time when only the elite few had access to the necessary skills and materials to learn to read.

However, as business users in the enterprise increasingly help themselves to powerful devices, apps, and data services, new forms of literacy are developing. We’re learning to navigate complex visualizations, to understand the tentative language of probability and prediction, and to browse ever greater volumes of data.

How can we help these newly data literate users in their work? What tools do they need? How can we provision data for their use? Moreover, how do we cope with the explosion of insight and the ensuing debates that literacy always brings to societies and organizations?

In this session, Donald Farmer will explore the uses of data literacy in the modern organization, as well as the accompanying potential, pitfalls, and unexpected implications.

• The impact of improved data literacy on individual and organizational achievements.
• The importance of understanding and communicating ambiguity.
• The new role of the analyst in an organization with self-learning technologies and artificial intelligence.

To Cloud BI in Three Months Flat

Ian Turfrey, CIO, British Medical Association

Can you swap an ageing Oracle system for integrated Microsoft BI in the cloud in a matter of weeks?

Find out how the British Medical Association (BMA) did just that, and how data now informs business strategy at every level. The BMA, the trade union and professional body for doctors in the UK, used to rely on incomplete and disparate data. It needed a better way to see trends, spot opportunities, and most importantly, serve doctors.

That’s when Ian Turfrey, Chief Information Officer, joined the BMA with a plan to scrap the old system and move to the cloud. In this session, he will talk about strategy and the challenges of an extensive BI overhaul in a short time frame.

You’ll get practical tips on:

• Taking a cloud and SaaS-first approach to BI
• Gaining C-suite support
• Finding quick wins
• Implementing two-speed IT (maintaining while innovating)
• Getting the right skills capability

Future Blockchain

Anders Brownworth, Chief Evangelist, Circle

With all the hype surrounding blockchain technology these days, you might have missed what steps have happened in the ecosystem, why they are important and what to look for in the future. Anders Brownworth of Circle, a person to person payments company which leverages blockchain technology, will present on what blockchain technology is, the state of the industry and where the technology is headed.

Using Sensor Data From Trucks to Improve Profitability

Stijn Roelens, Enterprise BI Architect, A Leading Automotive Company

Hear why a leading multinational company that manufactures trucks, buses and construction equipment has consolidated their disparate data warehouse into a single Enterprise Data Warehouse and learn how truck sensor data is helping them to stand out from the competition and improve profitability.

• The Challenge: This company needed to combine sensor data with operational data to deliver compelling offers, provide greater customer service and improve profitability.
• The Solution: An integrated Enterprise Data Warehouse with a single global modelling standard. Automated and managed by WhereScape.
• The Benefit: The company gets value from their sensor data, shortening times to market and delivering BI solutions faster than ever before.

Integrity Behind Intelligence- How JLL Drives Business Results Through a DQ Program for Our Global Clients

HoChun Ho, Global Head, DG and Management, JLL

This presentation is a case study of JLL’s data quality program for global clients. At JLL, they managed the data used by our corporate clients across many different industries and countries. The data must be accurate, thorough, consistent, reliable and widely understood. They have implemented a common data quality engine and a global rule catalogue that deliver configurable data quality scorecards and remediation panels. It leverages and monitors board-approved critical data elements, standard reference data and related mappings, and the data standards supported by global data stewardship programs. This program is completed with a client on-boarding model, standard operating models and a data help desk which is an award-winning implementation of a commercial data governance tool. This Data Quality Program enables JLL to offer data governance as a commodity service to their clients. It is a business user’s entry point to the full scope of enterprise data governance with deep industry and domain knowledge to jump start their clients’ data governance programs. This program goes beyond the technical implementation of a shared platform; it will include the end-to-end data governance best practices. They will also demo the tool to explain the features and challenges, and its connections with data standards, master data and data stewardship.

IoD: Internet of Data

Pieter den Hamer, Strategist, Alliander

Will the ‘Internet of Data’ (IoD) allow us to reap the benefits of the ‘Internet of Things’ (IoT)? Or: How the IoT helps our business to survive in an ever more complex and networked reality, while the IoD keeps us from drowning in more and more data, more types of data and in more and more real-time data? If integrating the data from your known internal data sources on a daily basis is difficult as it is, then how will you manage when the IoT will flood your organization with much more data from numerous and varying external sources, that you will continuously want to integrate, enrich, analyze and translate into meaningful decisions and actions? The IoD – as a pragmatic and AI-driven reincarnation of the semantic web – promises to behave better. Concepts and techniques, such as linked (open) data, ontologies, OWL, RDF and SPARQL can help to link data in an ‘agile’ way, but then, focused towards domain specific applications, instead of on a global scale. Nevertheless, the Tower of Babel continues to interfere – fortunately AI and (deep) machine learning seem to be increasingly capable to overcome differences in semantics and language. We can observe The Internet of Data in real world initiatives like smart cities and smart societies. And who knows, might the ‘Intranet of Data’ invoke the end of the trusted enterprise data warehouse?

• How the ‘Internet of Things’ leads to the ‘Internet of Data’: the growing need for agile data sharing and integration
• The problem of semantics: why IoD may fail (as well) and how AI may come to the rescue
• From enterprise data warehouses to enterprise linked data networks: the Intranet of Data
• State-of-the-art technology & tools
• IoD smart grid, smart city & smart society examples

Faster, Smarter Data Delivery at Generali Insurance

Zoltán Csonka, Head of Data Warehouse, Generali Insurance

We all know that today business users want better time to market and to achieve this goal they need faster and smarter decisions by using “faster” data. It is even bigger challenge to do it in a cost-effective way. In 2016 Generali Hungary introduced its new data warehouse using Data Vault modelling methodology and a custom ETL tool which supports agile development and better ROI, then the traditional 3NF models and hand coding. Data Vault modelling, code generation and the agile approach allow us to provide faster delivery, but the “shiny” technology is not enough. To give better services and improve customer satisfaction it is necessary to change the way IT and business work together. To fulfill the requirements of “Self Service BI” and avoid chaos new rules and new kind of services must be established.

This session concentrates on showing real life solutions on how we get more value out of our Data Warehouse.

• The way our IT works vs. business requirements
• How to resolve conflicts and get better results on budget.
• How to get more working product in a short time. Sandboxes, prototype based and multi speed developments.
• Explaining the technical challenges, resource issues, methodologies and processes which supports the agile approach

Implementing Master Data Governance: A Common Sense Approach 

Paul Lucas, Head of Master Data Governance, Yara

Yara started its Master Data Governance Strategy in 2014. It uses the Corporate Data Quality Management Framework. Yara has used many components that are readily available within the organization. Over the last three years, it has implemented MDG solutions for Finance and Customer, it is in progress with Supplier, and Material has started.

Delegates will understand:

• How to execute the MDG strategy based on components that they have in their own organizations
• The Importance of data metrics and continuous quality improvement
• Organizational change management and training is vital to success

Session & Speaker TBC

TBC

Harnessing Big Data Analytics with Data Democracy

Jason Perkins, Head of Business Insight & Analytical Architecture & Nick Reid, Decision Support Chief Architect, British Telecom

Join this session, to hear about how BT are empowering the business through data democracy. Enabling self-service using Data Analytics to exploit our information assets via a multi tenanted big data repository. Providing insight to drive informed decision making in an increasingly connected world. In this session Jason and Nick will take you on a journey through real world examples of using analytics to better understand business challenges and predict outcomes:

• User Stories – Customer Experience, Operational Excellence, Self Service analytics & Innovation Analytics
• Data Strategy & the Logical Data Warehouse (Hadoop vs. Analytics Databases)
• Data Management – foundation for insight from 2,500 structured datasets to billions of non-relational data.
• Breadth of Information usage – over 15,000 users across discovery, data science, reporting & visualisation.

Explainable AI – the Most Crucial Part of Our Future Technology

Jasper de Vries, Lead Consultant Analytics, Kadenza

When it comes to the impact data has on our lives, we haven’t seen anything yet. Advancements in Artificial Intelligence make sure we won´t be the only ones taking actions in the near future. The development of self driving cars is one of the most prominent cases where insight into the reasons behind a left or right turn matter to us all. But the usage of Deep Neural Networks (DNN) will stretch far beyond this isolated example and will be put to use within almost every service and product. Understanding DNN´s is increasingly complex while, at the same time, European legislation demands we are able to explain its inner workings. Apart from possible ethical dilemmas. That’s a challenging situation to deal with.  Key take aways:

• Why Explainable AI matters to you
• The state of Explainable AI and Deep Neural Networks
• How you should prepare yourself and your organisation

Lessons Learned from the IRM UK CDO Executive Forums

Jan Henderyckx, Inpuls

Four times over the last couple of years a selected group of CDO’s have met up for the IRM UK CDO Executive Forum. The objective of the forum is to define best practices and exchange ideas on the role of the CDO in an organisation and to increase the impactfulness on the business outcome. As proper information usage is not just limited to the C-level the group decided that we wanted to share our findings and recommendations with a broader audience. Hence this session which is a compilation of “the best of” from our previous meetings.  What delegates will learn from attending the session:

• How information can be used as a business enabler
• How to position your CDO in your organisation
• What capabilities are required to be successful

The next IRM UK CDO Executive Forum will take place on 22 November during this conference – if you would like to apply to attend please e-mail Jeremy.hall@irmuk.co.uk

Big Data & GDPR: An Incompatible Marriage or an Opportunity?

Christoph Balduck, Managing Partner, DataTrustAssocates

By now most companies are implementing GDPR – a new and broad EU data privacy & data protection regulation with a large number of topics to be taken into account.

Two years ago big data and GDPR were quite incompatible – especially when using big data storage & processing capabilities like Hadoop or NOSQL. Not only did we often ignore the principles of proportionality, there was also little insight into the data captured data-sets, it’s category, risk,… & in many cases the purpose of processing & storing big data was only determined after insights were gained instead of determining a specific purpose upfront.

Furthermore, complying with the rights of the data subjects such as right to access, right to data-portability, right to rectification and restriction of processing etc. was difficult to apply to data in a so called data lake.

Over the last 2 years though, we’ve seen a tremendous acceleration of data & information mgt. tooling to support the implementation of GDPR. Big data tooling was no different from that & can now be seen as an opportunity to become GDPR compliant. Besides big data tooling, other traditional capabilities such as MDM, DQ, Metadata Mgt, etc. are essential parts of a GDPR reference architecture.

Delegates will learn:

• The risks associated with big data in a GDPR context
• How Big data can be used as an opportunity and accelerator to implementing GDPR.
• Understand all components of a GDPR reference architecture including both traditional and new data & information management components.

Business Intelligence & Analytics Keynote: Six Stubborn Myths on Data

Rick van der Lans, R20/Consultancy

There was a time when every concept we used in computing was very clearly and some even formally defined. Not anymore. Many concepts are introduced during marketing campaigns and are barely defined at all. We just have to understand what they mean intuitively. Irrespective of their poor definitions, some of these concepts become popular, they become trending topics, hypes, are being oversold, and they lead to myths. And with that come the misunderstandings. The effect is that organisations invest in products and technologies that don’t deliver resulting in project failures. Some of these myths, related to data, big data, and data lakes, are critically discussed during this keynote. Are data lakes really what the data scientists ask for? Is big data really unstructured? And does open source software for data processing make an organisation really vendor independent?

• The 3 V’s of big data are flawed
• Data can’t be a game changer
• Data processing is not our core business
• Open source software makes you vendor independent
• Big data + analytics = disruptive
• Data lakes are good for data scientists

Enterprise Data Keynote: Big Data for Audience Personalisation at the BBC: Experiences in Wrangling Lots of Data, Evolving Technology and Changing Regulation

Janani Dumbleton, Head of Data & Leyla Khalili, Executive Product Manager, BBC

The BBC Audience Platform has been transforming how the BBC engages with it’s audience by delivering more of the content they love. The platform is responsible for delivering more relevant content to our audience, by understanding what they do across the entire BBC online and broadcast estate. We do this by delivering a cloud based API suite of personalisation and participation services that fuel our big data solutions. Our data collection and analysis is transforming the BBC into a data driven organisation, and with the frameworks for this delivered, our mission moving forwards is to continue driving this transformation throughout the BBC.

Innovating practical and tangible experiences using the variety of data we collect has been a very interesting and satisfying journey for the people involved, with each challenge and success proving a great learning experience on big data, underlying technologies, and making exciting and innovative products. The Audience Platform Data team has the responsibility of balancing the privacy promise, regulatory compliance and relevant data governance and information security obligations while continuing to innovate our data use and the technology stack that underpins it. This session covers about how the BBC audience platform teams have embedded principles of governance when developing data products and services, while supporting data driven insights and interactions for our audiences and helping our products build more relevant features and content.

The session will provide an overview of big data platform, the types of data, and methods used to collect, transform and deliver data services. It will explain how data governance, security and privacy controls have been embedded into product development and data life cycles. It will cover actual challenges the teams have faced, across technology, data and processes, with some practical ways they have been overcome.

Delegates will learn from attending the session

• Learn practical applications of using big data for audience facing innovation.
• Learn how data governance, information security and privacy by design principles have embedded into product development and data life cycles.
• Understand the challenges faced in the process of developing the big data platform, and approaches used to mitigate them.

Empower Your Data and Think Differently with Data Virtualisation

Emanuel Chiavegato, Technical Product Manager, Royal Bank of Scotland & Erica Langhi, Solution Architect, Red Hat

Organisations are sitting on treasure troves of data and BI teams with the right tools can mine this data and get insights that could lead to the creation of new services and improve customer service and retention rates. In financial services, it’s key to obtain sophisticated information, captured from different systems (transactional, risk, ledger, static) to be delivered on multiple channels. With a data virtualization layer data are offered in real time, ensuring the accuracy and reliability of the data. In this session, we’ll discuss strategies to deliver real-time data looking at how Royal Bank of Scotland used Data Virtualization to implement a data access layer and support real-time data decisions within their organisation.

Key takeaways:

• Use Data Virtualisation to avoid the need to copy data to construct data marts or data warehouses
• Operate in real time
• Reduce development time, work with agile prototyping

Everything Looks Like a Graph: Data Modelling Using Property Graphs

Thomas Frisendal, TF Informatik

People working with graph databases experience that lifting a data model off the whiteboard and into a graph database is easy. Because “everything looks like a graph”. But that must mean that graph data modelling can be used in many contexts? Yes, it does; even if the data store is not a graph database! This holds true for most NoSQL stores, and even for SQL-databases! In this presentation Thomas will look in some detail at data models with high degrees of connectedness (an Email data model, for example). You will see that property graph data modelling is a very versatile approach across the board. It handles the classic data modelling issues such as normalization in a visual manner, and graph data models communicate intuitively with the business folks. In addition, some real, and common, modelling challenges are solved most elegantly using graph models. The presentation is targeted at a modelling audience with little or no exposure to graph data models, and it will illustrate how property graphs fit into NoSQL as well as SQL. It will also illustrate what cannot be (easily) done in data stores, which are not graph technologies, and which data modelling differences there are.

Starting Enterprise Information Management Using DAMA DM-BOK

William (Bill) Carroll, Executive Manager – EIM, Kuwait Finance House

Starting up Enterprise Information Management requires a current situation assessment, developing a strategy and road map, building team competency, and guiding the implementation of Data Governance, Data Quality, Data Architecture, Metadata, and Master Data Management.

While the DM-BOK provides exceptional inspiration, you should infuse ideas from other thinkers in the different topics, and adapt best practices to your situation in order to achieve a working, successful program.

You will learn how to:

Use the DAMA DM-BOK for your maturity assessment

Adopt/Adapt ideas on Data Governance

Set up a Data Governance Program

Set up and populate a Metadata Repository (Business Glossary)

Set up a Data Quality Program

See how Data Governance, Metadata and Data Architecture facilitate starting an MDM Program

Propagating Data Principles

Andrew Newman, Data Policy Manager & Lisa Allen, DG Manager, Department for Environment Food & Rural Affairs

Defra group has a simple but ambitious aim: to create a great place to live in. They have a rich seam of data on the environment and things that affect the environment. In 2016, working together across ten organisations, they made over 13,000 data sets available as open data. This was their first step in their data transformation.

They now have a data transformation programme leading work across all of Defra’s organisations to realise the vision of Better Data, Better Used; helping their people to treat their data as an asset, whilst demonstrating how they can be more data driven and open by design.

To drive this approach they now have a single set of Data Principles. Their 9 Data Principles are easy to understand and apply to all data whatever its size, structure or format. As part of their Enterprise Architecture Principles, they have to be considered in all ICT projects.

In this session delegates will learn:

• About Defra’s Data Principles
• How they have rooted our Data Principles within their organisations
• What difference this has made
• How the principles are enabling transformation and enabling them to break down organisational data silos.

Building a Digital Publishing Analytics System

Adrian Wiles, Enterprise Data Architect, Financial Times

Adrian will share the story of how and why the Financial Times came to build its own real-time cloud based Web Analytics system. This presentation will cover:

• Key features that were considered essential but missing from off-the-shelf solutions
• Building a data enrichment and analytics pipeline able to cope with peak news events
• Supporting developer flexibility and multi-varient testing, as well as traditional web analytics
• Maintaining a single version of the truth whilst providing real-time dashboards and audited business reports
• Migrating trusted metrics from an incumbent system

One Small Step for Machine, One Giant Leap for the Market: Lessons Learnt from a Real World Machine Learning Project

Paul Nicholson, Head of Business Performance, Hastoe Housing Association

Hastoe Housing Association is taking part in a unique 3-year project with the University of Surrey funded by Innovate UK to apply state-of-the art Data Analytics to the housing sector. By applying some clever “machine learning” techniques to our data we can now predict when a home will become vacant, if a tenant will fall behind with their rent and whether it’s better to replace or repair a faulty boiler.

This is all very novel for social landlords and persuading sector experts to ‘make the leap’ to using predictive tools is proving to be the biggest challenge.

What you’ll learn from the session:

• Lessons from a real world machine learning project and selling advanced data analytics in an emerging market
• Introduction to some techniques like “Non-negative matrix factorisation”
• …and how we’ve applied them to gain deep insights into our business.
• How Knowledge Transfer Projects (KTP) can benefit your business

Moving Towards GDPR Compliance in a Complex Organisation

Norbert Eschle, Lead Information Architect, IFDS

Standardisation of data management and governance solutions across an enterprise, however, provides significant value with regards to e.g. efficiency and auditability. However, business units within complex organisations often travel at different speeds, making such standardisation difficult to achieve. To provide appropriate data management and governance solutions to an organisation with different levels of readiness and need, an enterprise architecture function should adopt a pragmatic approach to solution and data architecture. Delegates will benefit by learning about:

• Project challenges and pitfalls
• Approach taken to identify enterprise and business area requirements
• Approach to solution governance

Merging Enterprise Data and Measuring Its Value

Suzanne Coumbaros, Head of Data Governance, The Co-operative Bank

The activity of bringing data together can be costly and can be overlooked when organisations make business decisions. This is particularly true when organisations grow through mergers / acquisitions and when they try to reduce their costs, by simplifying their Enterprise Data landscape.

By understanding the inherited Enterprise Data effects of these decisions, organisations can be better informed of issues and costs that they should consider.

If you attend this session you will gain an understanding of what Enterprise Data you should pay close attention to, what data you should bring together, what data issues you need to look out for and how to measure the value of your data.

• Enterprise data considerations
• What data to merge
• Data issues to consider
• How to measure the value of data

How to Create Massive IMPACT and be an Effective Zoo Keeper

Nigel Risner, Motivational & Inspirational Speaker

In an ever-changing world and with pressures that come from a global source how do we make sure our teams are “in the room” and making an IMPACT. Nigel will share his 6-stage approach for keeping people energised, focused and most importantly achieving results.

He will also include a fun inter-active communication session that will have delegates talking about it for days, weeks and months to follow.

In his unique style, he will identify everyone in the room and share with them how to manage the animals in their workplace by being an effective zoo keeper.

• The power of focus
• The cost of internal terrorists
• The importance of communication

Logical Data Lake and Logical Data Warehouse: Two Sides of the Same Coin?

Rick van der Lans, R20/Consultancy

For the popular data lake concept many definitions exist. Many of these definitions are like “A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.” It makes sense to have one environment where all the data can be found in its rawest form. Especially for data science and for investigative analytics a data lake is incredibly useful. But the question is does it really have to be a physical repository of data? Isn’t it sufficient that users can access a system that gives them access to all the data? In other words, why not a logical (or virtual data lake)? The technology exists to develop them. Data virtualization servers are mature enough to develop data lakes. It would avoid copying massive amounts of big data from their source to the date lake.

But what’s the difference between a logical data lake and a logical data warehouse? Don’t they do the same thing? Are they not the same thing? Both present a heterogeneous set of data sources as one logical database to the users. This tutorial explains how the two concepts of virtual data lake and logical data warehouse can be merged together and still run typical data lake and data warehouse workloads. They are really two sides of the same coin. One integrated architecture is presented that covers both concepts.

• What are the limitations of a physical data lake and what are the benefits are of a logical data lake?
• How do we set up one integrated architecture for a logical data lake and a logical data warehouse?
• How easy it is to make new data sources available for reporting, analytics and data science?
• How can big data stored in Hadoop and NoSQL systems be made available to analysts and data scientists easily and transparently?

Cutting Diamond - the Art of Big Data

Jane Chang, Enterprise Architect, British Gas

There is no shortage of technologies in the support of data analytics and big data exploitation. The success of big data mining does not rest only on the superiority of the technologies but the art of mining. It takes the knowledge of techniques as well as the business intelligence of the objective to create true impact.

In this talk, Jane Chang is going to discuss her experience in Data Analytics using different tools and platforms and how true value can be gained only when the right level of business intelligence is applied. She will be sharing her view of the success factors, in particular, some of the learning outcome from an Innovate UK funded research project.

Don’t Become a Data Quality Slave

Cristina de Salas, Data Quality Expert, Zurich Insurance

All companies want to claim that they have good data quality. But what is good quality? Even Data Quality should have quality limits, otherwise you become a slave of your solution. Data Quality just for the sake of it is counter productive, and it should not be forgotten that it only offers value added when it is designed to serve the business: Zurich is already hands on managing its exposure data quality. This session will focus on

• Myths of Data Quality
• Limits of Data Quality
• Insights into the Data Quality approach of Zurich

Open for Business: a Collaborative Approach to Developing Data Standards in the Environment Agency

Becky Russell, National Lead for Data Standards, Environment Agency & Nigel Turner, Global Data Strategy

The Environment Agency is at the forefront of the UK government’s Open Data agenda. There are many challenges in releasing a large number of data sets into the public domain, but ensuring that data is collected, processed and presented consistently has been recognised as critical. To meet this and other needs, the Environment Agency has put a strong focus on the development and implementation of data standards, and their enforcement through data governance and IT lifecycle management. This case study will highlight how to implement effective data standards in a large and complex organisation. A key message of the session is that a business led, collaborative approach is essential. It will do this by covering:

• The primary drivers behind the Environment Agency’s focus on data standards, including Open Data and other requirements
• The business and IT problems that data standards are addressing
• A problem focused approach to the creation and development of data standards
• The need for active business and IT participation through data governance
• The relationship between data standards and data modelling
• Lessons learnt and advice for other organisations trying to implement data standards and data governance

Data Warehousing: Today and Beyond

Kent Graziano, Snowflake Computing

The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in today and beyond.

• What are the traditional/historical approaches
• What have organizations been doing recently
• What are the new options and some of their benefits

Mining Your Data for Gold at Barrick Gold Corporation

Ed Humphries, Head of Digital Transformation, Barrick Gold Corporation & Melanie Mecca, Director, DM Products and Services, CMMI Institute

Barrick Gold Corporation (Toronto) is the world’s largest gold mining company, creating wealth through responsible mining for people, communities, and countries. Barrick is pioneering transformation of the mining industry through technology and innovative exploitation of data assets. “Digital Barrick” depends on data-driven discovery of business opportunities through advanced analytics and machine learning. Because effective management of data assets is essential, Barrick selected the Data Management Maturity (DMM) Model to precisely evaluate current practices and implement policies and processes critical for Digital Barrick’s success. They will discuss:

• Barrick’s vision and the Common Data Platform
• How the DMM creates a foundation for success

Panel Discussion: Benefits that Improved Data Exploitation Can Deliver an Organisation – Both Tangible and Intangible

Julian Schwarzenbach, Data and Process Advantage Ltd & Chair, BCS DMSG

Data is an input and enabler to most business activities, however, the quality of data and the effectiveness of its exploitation does not always support organisational objectives.

This panel debate will explore:

• Types of benefits
• Data to support benefits delivery
• Organisational exploitation of data

The Future of Data Governance: Data Governance in the Data Lake

Michael Davis, DG and DQ Leader, Voya Financial

This presentation highlights the key differences between Big Data Governance and traditional Data Governance. Michael will explore the new approaches to data governance by surveying the current landscape and offering real world, practical solutions that enable organizations to make the leap from traditional to Big Data Governance organizations. Attendees will walk away with the knowledge on how to quickly develop an Enterprise Data Management strategy that takes advantage of the emerging Big Data technologies and governance solutions that enable quicker and better organizational decision-making.

The following will be covered:

• Implementing a Big Data Governance and self-service analytic strategy in your organization to fuel sustainable data-driven insights and solutions
• Leveraging Unstructured and Structured data to build data products and solutions
• Best practices and principles for implementing a Data prep and MetaData management strategy

Business Intelligence & Analytics Keynote - Edge Analytics: The Next Frontier in Smart Business

Mike Ferguson, Intelligent Business Strategies

For many years companies have been building data warehouses and data marts for reporting and analysis with only a handful of professionals doing data mining and statistical analysis. However, the arrival of big data shone a spotlight on predictive and advanced and we are now at the point where they are considered strategic the boardroom. Today data science is mainstream but still mostly focused on machine learning on data stored centrally. However, the demand to analyse low latency streaming data and the emergence of the Internet of Things (IoT) is leading many to ask why is it that we have to analyse all data at the centre? Why not at the edge, closer to where the data is being generated? With so much data being generated and much more to come, would pushing analytics into the network not scale better? This keynote looks at why companies now need to develop models and rules centrally but deploy them anywhere all the way out into the network. It looks at why edge analytics is fundamental to being able to scale to manage IoT and how streaming data and distributed execution of an integrated suite of analytics can enable the ‘always on’ intelligent business.

• The explosion of data and things that are emitting it
• Prevention and opportunity – use cases for streaming analytics
• Why do we have to move all data to the centre before analysing it?
• Fast data and fast action edge analytics – develop centrally and deploy anywhere

Enterprise Data Keynote:Are We the Baddies? The Ethical Wakeup Call for Information Professionals and Data Provocateurs in the IoT Age

Daragh O Brien, Castlebridge

The pace of change and evolution in information management appears to accelerate year after year, with each generation promising newer and better ways to improve our lives. Whether it is making trains run on time or some other panacea to a social ill, our technology nirvana is always just one more release away.

But technology is neutral. The dark side of that panacea is that one person’s technology enabled dream can be another’s digitally enhanced nightmare, from ethical bias in sentencing systems, to business models built on the digital servitude of people, to fake news, to simple issues of work place safety in the digital age.

We don’t have to reinvent the wheel or throw the baby out with the bath water to embrace the opportunities posed by the Ethical Information Management Future as many of the lessons we need to learn have already been taught (we just haven’t been paying attention).

• Get valuable insights on the reality of consumer attitudes to privacy
• Understand how proven principles and practices can support Ethical Information Governance
• Find out if you are really one of the baddies or not.

Addressing the Need for Data Agility in the Insurance Industry

Ranjeet Athwal, MI Architect and Mark Bendall, Nova Programme Architect, Enstar

The traditional RDBMS approach to data warehousing has often proved costly, protracted and is not responsive to change. Using a modern BI architecture, we will demonstrate how Hadoop and complementary technologies can be used to deliver a platform that is secure, flexible, cost effective, and delivers early business benefit with reduced IT intervention.

We’ll also describe how we overcame some of the challenges, for example, new design patterns when using an immutable file system to manage data that is susceptible to change. Plus the desire to move away from a plethora of reports and extracts to guided self-service data exploration using visualisations. Finally we’ll describe how we are using the same platform to support more advance predictive analytics using data science toolkits that have always been inherent in insurance but in the past needed specialist solutions.

Delegates will learn:

• The benefits of a simple yet effective design pattern using big data technologies combined with the appropriate controls delivers a cost effective BI platform
• A sample of the considerations addressed and key design decisions taken on our journey to evolving this platform
• A summary of the challenges faced introducing this technology including resources / skills, new BI paradigm, and impact on existing business processes

Predicting Social Sustainability of Global Supply Chain for a Better World

Anis Radianis, Business Intelligence Manager, Foreign Trade Association

There is growing interest to reap the benefits of artificial intelligence (AI) technology but in reality the challenge to incorporate AI into existing business intelligence (BI) technology is not an easy endeavour. Therefore, the purpose of this presentation is to provide the audience some real world examples of how FTA leverage their existing BI technology to optimize the new AI technology in order to help business with their most challenging issues, predicting social sustainability.

Attendees will learn:

• How to leverage existing BI technology & processes that could help to leverage AI
• How to use AI technology & processes to achieve predictive capabilities
• What are the technological & business benefits

An Insight to the Introduction of Gamification within an Enterprise DQ Solution

Dan Griffiths, Lead Data Analyst, BAE Systems

Having moved into a Lead Data Quality role in order to deliver solutions that would vastly improve the quality of data across the enterprise, Dan struggled with audience engagement in some of his early implementations. After experiencing some of the more traditional delivery methods he used in his Business Intelligence role fail he took a gamble and implemented gamification into his Data Quality solution. This session takes you through the Journey Dan took and shares some of the successes and failures experienced both during and after the implementation. Employee Motivation and engagement are huge factors in any Data Quality implementation; attend this session to see if gamification could help to increase your chances of delivering successful Data Quality Solutions.

• Benefits of implementing gamification into your data quality solution
• Why this can work within your workforce
• When not to use gamification

Driving Business Growth from Data – Moving from Notional Value to Deliverable Initiatives

Mike Maddock, Director, Kader Technology Ltd

With the explosive growth in Data over the last ten years, it is arguable that few businesses have yet to exploit the true potential value of their Data. With the convergence of technology trends and the real cost of technology reducing, organisations face the risk of being overtaken by smaller more agile companies. How can organisations mitigate this risk and move the internal debate from conceptual-level discussions of value and to well-defined, concrete initiatives that play to the strengths their business? What are some of the opportunities around data that emerging technology now presents? This session will discuss these considerations and provide insight into how other organisations have leveraged their data for commercial advantage, highlighting some of the challenges to be resolved in the journey towards being a truly data-centric company.

In this session, the delegates will learn:

• How to move from abstract notions of the value of data, to specific and concrete areas aligned with business strategy
• How converging technology trends now provide transformational opportunities for business
• What are the key challenges businesses are likely to face in their journey to exploit their data

Putting Your Most Valuable Data Asset to Work - Current Challenges in Storing, Handling, and Working with Customer Data

Timo P. Kunz, Data Scientist, Catawiki

Is your organisation in the fortunate position that it is not only capable of capturing data but that it has also managed to recruit capable people that know how to tackle the most daunting  data challenges? Further, are ideas how to turn insights and model outputs into action plentiful? While this clearly puts you ahead of the curve, the next steps towards the success of your data operation are not less mission critical.

This talk will discuss challenges in storing, handling and working with what is most likely your company’s most valuable data asset: customer data. We address the organisational and UX challenges in capturing user data, the legal implications imposed by GDPR, as well as tradeoffs and compromises when designing a robust infrastructure that does not stifle the creativity or velocity of your analysts and data scientists.

In a Chaotic World, How Do You Solve a Problem Like Analytics?

Bernard Panes, Analytics Solution Architect & Bill Dawson, Innovation Program Ops Lead, Accenture

Nature is amazing, it solves complex information problems elegantly, and there isn’t a leader or a plan in sight…

In a digital world ‘chaos’ is the new normal, and turning data into actioned insight becomes exponentially harder to manage…

So, what can we learn from nature and its principles like replication, mutation, and competition?

We would like to share a pioneering approach to analytics transformation using Liquid Architecture and Lean Startup techniques; experimenting and rapidly evolving product designs through fast learning cycles.

You will learn:

• Why you should make a case for a value focussed, and lean approach to your Analytics project and why it will probably be more successful; delivering greater value, less waste, a faster time to outcome, and reduced risk
• What analytics components you should focus your design, build and delivery effort on when proving value… and equally importantly, what you should stop doing
• How to structure and staff an analytics initiative from Ideation through to fully scaled production. What the skills, behaviours, and platform enablers required for this approach
• We will share project examples of how decentralized evolutionary systems have inspired us to change our approach and what we are learning from the experience

Enterprise Wide Data Quality Programme

In 2016, we started a DQ programme within ABN AMRO. We started with 13 different lists of DQ issues, with no guidance or bank wide policy on DQ. Currently, we have implemented one overview of all our DQ issues, bank wide governance on three different levels, bank wide awareness around DQ, bank wide tooling implemented for DQ, Reference & Master Data and are now taking the next step of DQ. Accelerating our DQ programme.  This session will discuss:

• How the implementation of a bank wide DQ programme went
• How to create awareness about DQ
• Impact of reference and master data management on DQ

Data Modelling is Not JUST for DBMS’s

Chris Bradley, Data Management Advisors

Have you ever heard any of these comments regarding Data Modelling?

• What’s the point of data modelling?
• We don’t need models as we use packages
• We’re an agile shop, no need for models.
• We don’t build custom DBMS’s so don’t need Data models.

Unfortunately, these and other similar comments are still heard across organisations worldwide.  In part the problem is the way in which Data modelling has been taught with its focus on the development of technical solutions. Although Data Modelling has been around for over 30 years, its original roots were firmly in the DBMS world but the World has moved on. Today’s Business systems landscape isn’t just about developing “new” DBMS based systems from scratch. Yet this is all too often how Data Modelling is taught and promoted. In most organisations today the IT portfolio contains a variety of additional components such as: COTS packages (ERP, CRM, ECM etc),  BI & DW systems, NOSQL, SOA & XML message based systems, Communication with the business, Data Quality & Governance and more.

So, is DATA important for these systems? – you bet.

Has data modelling moved on to cater for these? Well – that’s what this talk is about!  We’re all probably familiar with how to create a database from a logical and physical data model. But how do the rules change when we’re dealing with an ERP package or XML in SOA applications?  How can we leverage our existing logical data models for this new audience?

This session will re-emphasise the “traditional” place modelling has in the DBMS design lifecycle.  It will then go on to show how data modelling can be used and why it’s vital in other areas of the application portfolio. Chris will describe why Data modelling is NOT just for use in DBMS design, in fact it hasn’t been for a long time. Also how the techniques we learned in the 70’s and 80’s for the pre-relational era are useful again now, and why data models are essential for COTS package implementation.

Data Warehousing – NoSQL? No Way!

Jason Robson, Solution Architect, Equiniti Riskfactor

Do you wonder if NoSQL can be used effectively within a data warehousing environment?

Equiniti Riskfactor produce a data warehousing and business intelligence solution targeting risk management and fraud detection within the commercial finance industry.

For many years we have utilised a traditional relational database management system, but have now begun to investigate an approach that uses NoSQL database technologies.

This has been driven by several factors; the need to provide lower cost entry points, improve application performance and to harness the elastic scale of the cloud.

The talk will describe the results of this work, and covers:

• The characteristics of applications that can benefit from a NoSQL approach, including an overview of some of the available NoSQL options
• Ideas as to how our solution architecture could be modified to accommodate the use of MongoDB
• The conclusions drawn from the evaluation project

Attracting the Best Customer Base in Higher Education Using Intelligent Data Mining

Jagdev Bhogal, Senior Lecturer in Data Technologies, Birmingham City University & Guy Garrett, CEO, Achieve Intelligence Ltd

Higher Education Institutions (HEI) need to have a strong position in the competitive market of the Education Sector. The new funding model for UK Universities, relies on student fees. Recruitment and retention are important to maintain reputation and financially advantageous. Data Mining (DM) and Business Intelligence (BI) are established tools in industry that can help improve decision making in HEI. This session discusses the challenges being faced in student recruitment and describes the development of a prototype application to investigate how data mining can be used to identify important factors related to student recruitment. Jagdev and Guy investigate how to analyse HE results, enrolment and graduation data to identify, profile and target students that are most likely to succeed on a variety of their undergraduate courses. Attendees will learn:

• The Higher Education sector can benefit from the way Data Analytics has been used in business
• Student Recruitment and Retention go hand in hand to improve student progression
• Profiling can be used to recruit the “ideal” students for each course

AI Dream, Winter, BI, Machine Learning and What is Next

Thiago Assuncao de Faria, DataOps & AI Consultant, LINKIT

Artificial Intelligence was the future and dream in the 60’s. Then it was almost a curse word, ignored by many and feared by investors. Then it came Data Mining, BI, Machine Learning, Deep Learning and AI is blooming again. What can we learn from it?

• The history of AI
• The current hype
• Next steps and challenges

Using a Digital Assistant to Proactively Manage Enterprise Data as an Asset

Delphine Clement, Senior Business Program Manager & Erman Oral, Data Engineer, Microsoft

More and more, the Enterprise needs data to run its business at the pace of a digital world. This data must be connected, appropriate, reliable, readily available and accurate for assisting the business teams with intelligent decision making.

• Learn how My Data Health, a Hackaton born Enterprise application, is a digital assistant that provides the business teams with proactive, contextualized data quality recommendations available at their fingertips.
• Learn how business feedback is crowd-sourced for continuous active learning and adjustment of recommendations’ relevance.
• Learn how recommendations consumption, business satisfaction and My Data Health product backlog is driven through UX design and telemetry.
• Learn how My Data Health digital experience was designed with the business teams through usability studies.
• Learn how Cloud Services and Agile Delivery empower real time and continuous business transformation.
• Learn and leverage for starting or pursuing your own data transformation journey!

Data Management in a Cloud Computing Environment

Mike Ferguson, Managing Director, Intelligent Business Strategies

As the adoption of cloud computing continues to grow and we are now at the point where many companies may have deployed applications both off-premise on public clouds on-premise on private clouds. They may even be using off-premise infrastructure to extend their private cloud environments. As this investment continues to grow, there is now a demand to seamlessly manage and govern data in a consistent way irrespective of its location in a cloud computing environment. This session looks in detail at the challenge of consistently managing data in a cloud computing environment and looks at what is needed to keep data consistent across off-premise and in-premise systems. In particular, it looks at important data management disciplines such as maintaining data privacy, data access security, data quality, data consolidation, data virtualisation, replication, master data management and data synchronisation across on-premise and off-premise clouds and what is possible today. It also looks at hybrid data lakes and explores concerns about the added complexity that off-premise multi-tenancy brings. In addition, it will highlight problems that still need to be solved to get to a point where companies can confidently and freely manage off-premise and on-premise data in a seamless manner. The session looks at the following:

• Pros and cons of deploying on the cloud?
• Public versus Private clouds
• Deploying systems on public clouds – what are the options
• Deploying systems on private clouds
• Cloud-based data storage
  o Multi-tenant databases
  o Cloud object storage, e.g. Amazon S3, Azure Storage, Openstack Swift
  o Managing multiple databases in the cloud Vs managing multi-tenant databases
  o Data warehousing with cloud-based analytical databases and cloud BI
  o Big Data in the cloud
  o MDM in the cloud
• Managing data governance across cloud and on-premise systems
• Ingesting data in the cloud – streaming and batch ingestion
• Data quality on the cloud
• Cloud-based data integration
  o Integrating cloud and on-premise data – options available
  o Integrating off-premise data sources into on-premise data warehouses
  o Integrating MDM with on-premise and off-premise systems
  o Data virtualisation across on-premise and off premise data sources
• Building information services in a cloud
• Managing data privacy in a hybrid cloud computing environment
• Managing data access security in a hybrid cloud computing environment
• What works and what doesn’t?
• Do’s and Don’ts
• Getting started with cloud-based data management

Modern Data Warehouse Architectures: From A - Z

Rick van der Lans, Independent Analyst, Consultant, Author and Lecturer, R20/Consultancy

There was a time when a data warehouse architecture consisted of a chain of databases all running on one or two machines in our own data centre. Handwritten ETL programs were used to copy and transform data from one database to another. But so much new technology offering innovative opportunities has become available, there are so many new BI requirements, and we have new ways to design our data warehouse architectures. Data warehouse architects are struggling with all these new developments. They have to find answers for an almost endless list of questions. Should the data warehouse be developed with Hadoop? Do we still need data marts if the BI tools read data into memory? Can we use Spark as query performance booster? What does it mean to design datavault-based data warehouses? How does data streaming and the IoT work together with the data warehouse? Should we move the entire architecture into the cloud? Can we replace the data warehouse by a data lake? What is the role of the logical data warehouse? Will an analytical SQL database server solve all our query performance problems? And so on, and so on.

This full day workshop covers all the architectural and technical developments. How are they interrelated? How to migrate to a modern architecture? What are the pros and cons of all these developments?

You will learn:

• What the use cases are of Hadoop and Spark in a data warehouse architecture?
• To distinguish between five levels of BI in the Cloud and how they differ.
• What the advantages are of using datavault as design technique.
• Whether data warehouse automation is a hype or reality.
• How Spark can be used to boost query performance and may even replace data marts.
• How a logical data warehouse and virtual data lake can work together.
• How analysis of streaming data can be embedded in a more classic architecture?
• Why operational BI demands a new architecture.

Making Enterprise Data Quality a Reality

Nigel Turner, Principal Information Management Consultant EMEA, Global Data Strategy

Many organisations are recognising that tackling data quality (DQ) problems requires more than a series of tactical, one off improvement projects. By their nature many DQ problems 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. This will enable you to practise some of the key techniques in a safe, fun environment before trying them out for real in your own organisations.

Run by Nigel Turner of Global Data Strategy, the workshop will draw on his extensive personal knowledge of initiating & implementing successful enterprise DQ and data governance in major organisations, including British Telecommunications and several other major companies. 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 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 maxims for success

GDPR One Day DPO Intensive: Key Skills for the Data Protection Officer

Daragh O Brien, Leading Consultant, Educator and Author, Castlebridge

The role of the Data Protection Officer (or Chief Privacy Officer for our North American cousins) will increasingly be a critical one in organisations processing personal data. The General Data Protection Regulation (GDPR, coming into force on 25th May 2018) makes it a mandatory role in certain circumstances, but it is generally recognised as a good idea in organisations to have someone with responsibility for the oversight and governance of Data Privacy issues and obligations.

This workshop will take you through a detailed overview of the DPO as a Data Governance role. It will look at the key skills and knowledge a DPO must have. Combining a whistle-stop tour of the Data Protection law principles, the workshop will then:

• Examine Article 29 Working Party guidance on the role of the DPO and how that maps to good practice in Data Governance
• Look at the role of the disciplines of the DMBOK wheel in effective Data Privacy Compliance
• Demonstrate how Data Quality principles, practices, and methods can be applied by a DPO to support Privacy Impact Assessments and demonstrate effectiveness of compliance
• Provide an overview of how effective data governance and stewardship practices are key to ensuring alignment of day to day information management with the requirements of data privacy compliance
• Examine how Agile approaches to Governance and Master Data Management can help ensure a responsive and proactive data privacy governance environment for the DPO in your organisation.