Smart MDM - a Best Practice Guide to Design and Implementation

Mike Ferguson, Managing Director, Intelligent Business Strategies

This workshop focusses on the end-to-end implementation of master data management and tries to address the hardest problems that arise in an MDM project. It looks at the broader picture of information governance, data quality and metadata management before applying these to an MDM project. It also addresses how machine learning and artificial intelligence are helping to automate discovery, profiling, semantic tagging, and cataloguing of master data that is scattered across many different data sources and how it can be classified to know how to govern it.

It then looks at the design of MDM systems looking at distributed maintenance of master data while still maintaining integrity, inbound integration of master data to consolidate it into an MDM system and outbound synchronisation of master data to supply both operational and analytical systems. Different implementations are considered including master data virtualisation when you have a hybrid state of some master data consolidates and some not. In particular it looks at what needs to be considered when dealing with data integration and data synchronisation to achieve best practice in design and implementation.

The session covers the following:

• An introduction to data governance
• An operating model for governing master data across the enterprise
• Introducing a shared business vocabulary
• Metadata management
• Enterprise data quality and data integration
• Why Master Data Management? – building a convincing business case for MDM
• Smart MDM – Leveraging machine learning to automatically discover, profile, tag, catalog and classify master data
• The role of the information catalog in MDM and reference data management
• The main approaches to implementing MDM
• What kind of MDM system are you building? – a System of Record, Centralised Master Data Entry System or both?
• Understanding master data maintenance in your enterprise
• Best practices in designing master data consolidation
  • Data capture techniques
  • The benefits of standardising inbound data to an MDM system
  • Should history be kept in an MDM system?
  • Approaches to cleansing, and matching
  • Consolidation vs. virtualising master data to create an MDM system
  • MDM in the cloud – is this a better option?
  • Enriching master data using Big Data Analytics
  • Matching at scale – leveraging Hadoop and HBase for scalable master data matching
• Best practices in designing outbound master data synchronisation
  • Integrating an MDM system with an enterprise service bus for outbound synchronisation of operational systems
  • Schema and integrity synchronisation problems that can occur and what to do about them
  • Conflict resolution on outbound synchronisation
  • Design considerations when integrating MDM with ETL tools for synchronising data warehouses and data marts
• Distributed master data maintenance – can blockchain or NewSQL distributed relational databases help with master data integrity?
• Accelerating master data queries using graph query processing and graph analytics
• Maximising the use of data virtualisation in MDM
• The implications of switching to centralised master data entry
• The change management program imposed by centralised master data entry

Speaker Biography 

Bootstrap your Data Governance Programme

Jan Henderyckx, Managing Partner, BearingPoint

You’ve been assigned the task of implementing data governance in your organisation. What should you do in the first phase of your mission? Should I simply create a board of data stewards and hope for the best? Data Governance has now been around for quite a while now and we are starting to see patterns that explain the difference between success and failure. Understanding what the value proposition of data governance can be is a great starting point for scoping your programme. Defining the vision and mission that is understood by the business will help you embed your data governance activities.

The basic set of tasks that are required will strongly depend on the use case you are addressing. Supporting analytics is not comparable with setting up master data management or providing your management with reporting capabilities. With the proposed agile deployment approach, you can start delivering value in a matter of months.

Attendees will learn:

• How you demonstrate data governance success and sell to your management
• What use cases can benefit from data governance
• How to define a good vision and mission for your programme
• Applying agile data governance
• How you can put KPI’s in place
• What roles are required at the beginning of your journey
• What collaboration options are efficient for accelerating your progress
• Governing Big Data projects and Data Lakes
• Using Data Catalogs and automatic classification
• Positioning your data office

Speaker Biography

Using Data Profiling for Proactive Data Quality Improvement

David Plotkin, Director, Head of Metadata Services, MUFG Union Bank

As your company moves to proactive data quality improvement, data profiling provides a robust methodology and toolset to discover quality issues before your customers do. This presentation discusses the advantages of proactive data quality improvement, how to set up an infrastructure (including stewardship) to support the effort, the gathering and documentation of data quality rules, what data profiling is, using data profiling for existing and new data elements, and what to do when you do find data quality issues.

You will learn:

• What data quality issues profiling helps you find
• How to build templates for collecting data quality rules and a repository to store those rules in.
• The pitfalls of working with the business people-what works and what doesn’t.
• The key role of data stewards in managing and improving data quality
• An iterative methodology for finding and then reviewing the results of data profiling.
• Implementing data quality rules during a data load

Speaker Biography

Data Governance 3.0 - Agile Data Governance: What You Need to Know

Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners

Data Governance emerged around 2005 and has since undergone two main phases: an initial ad hoc response; and subsequently a more focused top-down approach.  This tutorial examines a new generation of Data Governance which is now emerging – Data Governance 3.0, or Agile Data Governance.  This new generation is heavily influenced by the need to support data users across that enterprise, which has been driven by the increasing availability of end-user data management tools, such as those supporting self service analytics.

Known as “data democratization”, this movement has placed much greater demand on Data Governance units to provide information about physical data assets.  At the same time, the realization that production data landscapes of most enterprises cannot be understood by top-down efforts has spurred an investment in tools that use automated means (e.g. Artificial Intelligence and Machine Learning) to gather and make sense of the needed metadata.  In addition, there is much greater emphasis on individuals contributing to this knowledge base, and crowdsourcing is an important feature of Data Governance 3.0.  This tutorial provides a comprehensive description of the new generation of Data Governance, describes how existing Data Governance Units can move towards it, and explains how tools are now essential.

Attendees will learn:

• What Data Governance 3.0 is and how it compares to Top-Down Data Governance
• The use cases that are driving Data Governance 3.0, and how these can be solved
• The need to focus on physical data assets, and how modern tools help Data Governance Units to do this
• The role of the individual in providing information about data assets, and how individuals can collaborate to address data-related needs.
• How the best elements of previous generations of Data Governance are factored into Data Governance 3.0

Speaker Biography 

Artificial Intelligence for MDM in Practice– Basics, Use Cases, PoC System Demos and Lessons Learned

Thomas Geyer, Head of Enterprise Data Management, Aleksandra Baumann, Lead of Global Community for AI in MDM, Dr. Faried Abu Zaid, Data Scientist, Camelot Management Consultants AG

Artificial intelligence (AI) is at it’s peak of inflated expectations, still it is worth to consider data science based solutions for classical information management challenges, as the approach provides unique benefits for Master Data Management (MDM). The workshop introduces AI with a general overview about the topic and some key approaches like Machine Learning.

Based on that we will dive deeper into the topic by explaining fundamental concepts, supported with practical MDM examples and live demos. In addition, we will share insights and lessons learned from previous and ongoing AI&MDM projects. Finally, we will discuss and apply what we have learnt against the background of MDM topics to ideate new AI MDM use cases. The workshop, that leverages design thinking elements, will be a balanced mix of presentations, discussion, demos and work session involving all workshop participants.

• Basic understanding and deep dives with focus on AI for MDM.
• Lessons learned and insights from AI&MDM practitioners
• AI use case ideas for your company’s MDM challenges

Speaker Biography: Thomas Geyer

Speaker Biography: Aleksandra Baumann

Speaker Biography: Dr. Faried Abu Zaid

Data Debt and Other Key Metrics – Prove the Value of Your Data Management and Data Governance Programs

John Ladley, Sonrai Solutions & DGPO, Consultant, Author & Advisor

Peter Drucker said it best, “You cannot manage what you do not measure.” So how do you provide actionable measures to demonstrate that your data management and governance programs are working? You need to measure value and progress. Both are required.

Data debt” is a term based on the concept of “technology debt,” out of the  Agile Development world.  Data debt is a concept and metric that will reveal to leadership the huge costs in delaying doing the “right things” with data and information.

You need to provide actionable measures that confirm your data programs are working.  In addition you need to provide clear accounting for the results of all data –related activities.  If data is a business asset, it needs to somehow impact financial statements.

This session will explain how to identify and implement key metrics for measuring the success of your DG program. Topics to be covered include:

• What types of metrics are there?
• What is data debt and how can we use it to sustain DG?
• How do we assemble and use metrics?
• How can I communicate effectively with stakeholders to maintain alignment and commitment?
• How can I implement a measurement process focused on creating business value?

Speaker Biography

Data Quality as a Way of Life

Gerard Bartley, Manager Global Master Data, Jacobs Douwe Egberts and Ieva Langenfelde, Data Consultant

Having been at the sharp-end of data management and governance implementation, Gerard and Ieva have in-depth experience of improving data quality in businesses.  The aim of their workshop is to share that practical knowledge with the attendees, so that they can improve data quality in their own organisations.  They will speak about their hands-on experiences of:

• Identifying common data problems
• Bringing data quality as a sellable message to organisations, plus building a business case
• Implementing prevention, detection and correction processes (both as part of general data collection and specific data quality improvement techniques)
• Data governance as a quality enabler (plus how to set it up)
• Tools to support data quality
• Measuring data quality
• Sustaining data quality long term

with real life examples of data quality activities.

Delegates will take away:

• Clarity on key data quality issues
• Useful tips on how to get the buy-in from the business & stakeholders
• Practical methods on how to change their processes to support better quality data
• Best practices plus dos/don’ts on how to set up data governance to support them
• Better insights and understanding on how data quality tools work
• Ideas how to keep the implemented data quality practices sustainable in an organisation

Speaker Biography: Gerard Bartley

Speaker Biography: Ieva Langenfelde

Leading 360 Customer Engagement and Compliance Best Practices

Mehmet Orun, Sr. Director, Product – Customer 360, Salesforce

With the increase in digital channels and privacy landscape, achieving 360 and managing 360 understanding of customers have become increasingly complicated.  Effective solutions must:

• Demonstrate understanding of information lifecycle intricacies
• Bridge traditional PII contact point and digital engagement channels
• Empower employees in data stewardship
• Built with consent and compliance in mind

In this working session, we will share leading data architecture, user experience, and solution best practices to create a 360 customer view, that balances persistence with real time access, with a focus on business use cases:

• Importance of Understanding Information Lifecycle in Customer 360 Solutions
• Empowering Party and Consent Management Data Model Patterns
• Canonical Model Driven Data Governance and Metadata Management
• Creating and Maintaining Contextual Global ID and Customer Profiles
• People Driven Data Stewardship for Exception Handling

Speaker Biography 

Successful Reference Data Governance and Management

Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners

Reference data – often simply known as codes, lookups, or domains – is an area of enterprise data management that is becoming increasingly important.   However, many enterprises have difficulty formulating governance programmes and management practices for reference data.  This tutorial explains the overall structure needed for both reference data governance and reference data management.

The very different roles need to manage external reference data (sourced from outside the enterprise) and internal reference data (produced wholly within the enterprise) are described.  The options for environments for producing and distributing reference data are compared and contrasted.  The significant role of semantics in reference data is also examined in detail, together with practical ways in which knowledge of reference data can be successfully managed.  Additionally, the special aspects of quality in reference data are described.

Attendees will learn:

• What reference data is, how it differs from other classes of data in its governance and management needs
• The structures needed for successful reference data governance management
• How the semantic needs of reference data can be addressed
• How to deal with data quality in reference data content

Speaker Biography 

Managing the Changes - How to Make Your Data Governance Programme Sustainable

John Ladley, Sonrai Solutions & DGPO, Consultant, Author & Advisor

Data governance equals change, and managing change is the number one obstacle to Data Governance success.  Managing change is required for a sustainable program, so the changes associated with data governance need to be identified, and the organization needs to be carefully led from current state to future state.

Data Governance is more accepted now than in prior years.  However, the occurrences of companies who have tried data governance several times, or have stopped trying is uncomfortably high.  Whether data governance is triggered by compliance or a large data project, it begins with great enthusiasm and excitement.

Then, for a number of reasons, the excitement wanes, and the challenges appear.  For certain, data governance reduces risk and adds value when done thoroughly. And many companies have even launched Data Governance more than once!

How do you sustain the excitement and value proposition? How do you address the many challenges that occur?

In this tutorial we will talk about the challenges of making Data Governance a “going concern” in your organization and how to sustain a program for the long term.

This tutorial will cover :

• The Top Ten reasons DG programs stumble
• The fundamental concepts of organizational change
• Why the changes from DG cause issues
• Prescribe activities to deal with resistance, obstacles and issues
• How to plan for managing the change in your organization
• How to build and manage the change team, from sponsors to stakeholders
• Re-energizing your program after in light of staff changes
• Keeping DG relevant after the data project is done
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
• How new topics and hot topics, like governing the data lake, AI, global privacy concerns and others, will affect data governance

Speaker Biography 

The Data Governance Professionals Organization (DGPO) Meeting: A Fun Look at the Six Core Areas of the DGPO Data Governance Framework

John Ladley, Sonrai Solutions & DGPO, Consultant, Author & Advisor

If you are just starting a data governance program, you are steadily growing and maturing one, or you are re-starting the efforts again for the nth time, the Data Governance Professionals Organization (DGPO) is here to help.  In supporting our vision, we have established a formal framework to be the primary, authoritative resource for data governance practices. This framework, the DGPO CAP (Core Areas of Practice), unofficially referred to as the DGPO Hexy, represents the six core areas of practice and is the foundation for all the content and best practices the DGPO will continue to share going forward.

Join us as we discuss these six practices and how they are core to all data governance programs regardless of industry, size, or maturity.

What you will learn:

1. Fundamentals – The foundational elements necessary for any data governance program.
2. Organization – Organizational considerations for data governance, both within and external to the data governance program.
3. Communication – Marketing the purpose (and general existence) of your data governance program, sharing wins and payback, and providing regular status reports. It also covers training and development and change management.
4. Process — From documenting parameter-establishing guidelines to the detailed steps of tactical execution, the process area of practice covers the “Why, What, and How” of data governance.
5. Metrics – Metrics and measures necessary for continual improvement across the business and for communicating where the business has been, where it is now, and where it is headed.
6. Stewardship – From business to technical stewards and even data custodians, this practice area helps to define who they are and what they do.

Speaker Biography 

Data Governance Conference and MDM Summit Co-Chair Opening

Jan Henderyckx, Partner, Bearing Point & Gerard Bartley, Manager Global Master Data, Jacobs Douwe Egberts

Plenary Keynote: Challenges of Developing an Enterprise Data Marketplace

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

There is a new kid on the data block: the data marketplace. In a data marketplace business users shop for the right data products. Examples of data products are predefined KPI’s, reports, files, and data services. The data marketplace is a supply-driven architecture in which data products are developed before the business requests them. This is very similar to how most shops operate: products are researched and developed before there is any guarantee that they will be bought. One of the key goals of the data marketplace is to let organizations benefit more from the investment they have made in data over the years.

In this Keynote, Rick explains how an enterprise  data marketplace differs from a data warehouse and a data lake. Additionally, the challenges of developing and maintaining a data marketplace are discussed. Because it’s another data delivery system developed to supply business users with the right information at the right time, some incorrectly think it’s the old data warehouse, but with a twist. Rick will address this common misunderstanding, in general the data marketplace extends the capabilities of existing data delivery systems, such as data warehouses, data marts and data lakes. The keynote also addresses how the data marketplace fits in the unified data delivery platform in which all data delivery systems are combined.

• Are our assumptions for developing data warehouses still correct?
• The challenges of developing a data marketplace
• Unifying data lakes, data warehouses and data marketplaces

Speaker Biography 

Data Governance In an Agile Environment

Guy Harvey, Systems and Data Lead, HS2

In order to keep up with the pace of competition and rapidly changing technologies, many companies are having to adopt agile ways of working as part of core manufacturing, construction, distribution, engineering, finance and service provision. As such, implementing data governance following a traditional command and control approach has largely been unsuccessful as it didn’t match the business model of operation.

Data Governance needs to adopt those same agile methods and work alongside the pace of the business if it is to be successful. There are simple processes to adopt that can get data governance up and running and keep up with the ever-changing world of business. In this session, you will get a clear understanding of how to successfully implement data governance with a large degree of agility.

Speaker Biography 

Data Governance Roles

Dennis Slattery, CEO, EDMworks/Sandhill

Digital transformation or regulatory compliance are examples of strategic activities for many organisations.   “Is data fit for purpose?” is a common question arising during these activities.  Data is an outcome of business processes so a better question might be “Is the organisation fit for purpose?”

Change is needed.  Trusted data is the NATURAL OUTCOME from an organisation that understands what needs to be done and has the capabilities to do it.  For most organisations, that means (1) adapting existing leadership and staff roles to embrace relevant data management activities and (2) creating and integrating key data governance roles (CDO, owners, stewards etc.) into the fabric of the organisation.

In this presentation we describe an effective governance framework and explain the roles and responsibilities of business leaders, operational, development and governance personnel so that the organisation generates “trusted data.”

Key takeaways include:

• “Fit for purpose data” is a natural outcome of a “fit for purpose organisation.”
• Business leadership has a critical role in successful data governance. People make things happen.
• Governance must remove the road-blocks and let people get on with it.

Speaker Biography 

Real World Management of Data Assets – Move Beyond MDM by Mastering Life Cycles and Supply Chains

John Ladley, Sonrai Solutions & DGPO, Consultant, Author & Advisor

The good news is data is being recognized as a powerful asset. There are many “points of light” that show the value of data.  Now the recognition is setting in that organizations simply need to do better at handling their data. Isolated MDM is only a start. Many organizations desire to be “data driven” but are simply not at all capable of fulfilling the vision. How do they get there?   They have to position themselves to manage their data life cycles.

The management of data as an asset, or any other asset, means understanding and directing the entire life cycle of that asset within your organization.  John will define the critical aspects of managing data life cycles, and how this approach creates a consistent and sustainable framework for data asset management.

Speaker Biography

The Role of Data Governance in Master Data Management

David Plotkin, Director of Metadata Services, MUFG Union Bank

One of the most important aspects of Master Data Management is identifying when two records (whether they be customer, product, or some other key entity instance) actually represent the same real-world item. In order to make this work, you obviously need some sophisticated tools. But you also need the ability to make decisions (and thus have designated decision makers) in order to identify what attributes must be used, the quality needed for those attributes, what sources can be used to enhance the data, and so forth. And who makes those decisions? The answer, at least in a company with a robust Data Governance function, is the data stewards, with their efforts coordinated by Data Governance. This presentation explains the basics of how identity resolution works, and the key roles that Data Governance plays in reaching decisions about what data to use and what quality is needed.

You will learn:

• They key processes in identity resolution
• How to get to the identifying attributes using Data Governance techniques
• Processes for examining the quality and reliability of the available data
• How decisions get made and documented
• How to extend this information to determining enterprise-wide reference data
• How to determine the sensitivity to false positives and negatives

Speaker Biography

Building the Foundations for Data Maturity. More than Just Bricks and Mortar

Douglas Silverstone, Head of Data, Analytics and Information Security, Metropolitan Thames Valley

Rewind to April 2016 – We had no data quality, control or any form of governance, Our staff could not trust the data we presented and our critical systems were teaming with inefficiencies.

Now 4 audits, 3 years, 2 mergers and 1 MDM later what does Data governance look like for us? We want to lead the housing sector on how we can use data and fundamentally change how we use information to support our business. We set out on a journey to revolutionise how we manage and surface data to our users.

We made some mistakes along the way and were still not perfect, but for a medium sized business we’ve made some good progress. Join us as we review what has worked what didn’t and a look at what we still want to achieve.

Learning points:

• What does our data governance maturity path look like.
• 3 year review of a ground up data governance program.
• A outline of our key data governance building blocks.

Speaker Biography 

Good Data Hunting - 6 Elements of Successful Data Governance

Mark Taylor, Enterprise Data Architect, Primark

We all know that implementing enterprise data governance involves identifying and agreeing some structure of data owners and stewards with defined accountabilities for enterprise data. So why do data governance initiatives often fail to deliver tangible results even when they have senior management support?  In this presentation we look at some of the less obvious though critical elements that enable practical, pragmatic and most importantly, effective data governance to work – and thus to deliver measurable improvements in the quality of key enterprise data.

Topics covered in this session include:

• Separating data from technology
• Creating good data definitions
• The difference between data governance and data management
• The importance of tracking data lineage
• Measuring the right things
• Making a practical start with data governance

Speaker Biography 

Data Stewardship - Surviving the Perfect Storm

Jocelyn Sheriff, Data Quality & Metadata Manager & Ellie Fitzpatrick, Data Framework & Policies Lead, PwC

This talk explores PwC’s recent journey to set up a Data Stewardship team in the UK.  It will focus on why PwC needed data stewards and the drivers that led us to undertake the journey, including the approach establishing the team and the challenges we faced. It will then describe what has been delivered and how this has enabled PwC to continue on a broader Data Strategy, Governance and Management journey. We will then reflect on the learning to date and consider what could have been done differently.

What delegates will learn:

• How to structure a programme to set up a Data Stewardship team
• How to implement an onshore / offshore model for a Data Stewardship team
• What pitfalls to avoid

Speaker Biography: Jocelyn Sheriff

Speaker Biography: Ellie Fitzpatrick

Data Architecture Models for MDM

Petter Larsen, Head of Enterprise Data Architecture, NETS

Modern enterprises must live with a continuously changing enterprise architecture. This is true for all the four enterprise architecture domains; Business architecture, Application architecture, Data architecture and Infrastructure (Technical architecture). All the enterprise architecture domains have an impact on the business, but when it comes to Data architecture, not only does it have impact on the business, it should be completely driven by business requirements.

In this presentation we will look into the following topics:

• MDM fundamentals – Master data vs golden data records
• Different data architecture models (or “patterns”) with examples
• IT application roles and requirements related to MDM
• What makes an IT system into a MDM solution? Its role in the data architecture or its functional capabilities?
• MDM and Data architecture impact on your business processes

Speaker Biography 

How to Effectively Leverage Pre-Mastered Content from Third Party Data Sources for Your DG and MDM Initiatives

Scott Taylor, The Data Whisperer

Learn insider tips and tricks to manage 3rd-party data providers and how to explain the strategic value of pre-mastered content investments to your business stakeholders.

Reference data is core to MDM but managing and maintaining the RD content can be a thankless endeavor and hardly ever seems aligned to your company strategy.  More and more enterprises are looking for new ways to use “pre-mastered” content that is clean, structured, dependable, and well-governed by external 3rd-party sources. But what do you need to know about dealing with these data providers? And how do you explain the strategic value of this investment to your CEO?

In this workshop you will learn and review:

• MUST USE criteria to evaluate potential data suppliers
• The things your data provider never wants you to ask!
• What role can standards can play? (there are so many to choose from!)
• The state of syndicated taxonomies – use them if you can find them
• Mapping 3rd-party data funding to C-Level strategic urgency

Speaker Biography 

Towards the Information-Centric Culture: Dropping Barriers

Oleksii Khakhaliev, Data and Client Marketing Director, Auchan Retail Ukraine & Anna Ovchinnikova, Senior Business Analyst, PerfexLab

Becoming an information-centric organisation embraces many dimensions, but first of all, it requires a shift in culture and attitude toward information and data.

This shift suggests a workplace environment that employs a consistent and repeatable approach to decision-making through evidence acquired from actual information.

This shift also requires educating the organisation employees on how to use, access, analyze and succeed with information.

But even prior to that, this shift demands to resolve the issues with visibility, accuracy and relevance of the information intended to support business decisions.

In this presentation, speakers will discuss the frequently missed first measures for gaining trust in business information: organising for visibility, defining for accuracy and verifying for relevance.

The delegates will learn:

• Why visibility, accuracy and relevance are the three main pillars of trusted information;
• Where to start with raising trust in information and how to sustain it through governance;
• A pragmatic approach to building a foundation for trusted information: a case study.

Speaker Biography: Oleksii Khakhaliev 

Speaker Biography: Anna Ovchinnikova

Data Governance Keynote: World Class Data Governance - What it is, Why You Need It and How You Can Make it Work for You

Mark Allen, Head of Data Governance, Capital One

World class data governance is not an aspirational target – it is essential and we must all start on the road to it now.  Join me in this lively session to find out why we should change our attitudes to the way we manage data.

We will review what great data management and governance are, and the benefits they provide.  We will find out what world class data governance looks like and use bottles of water, balls of string, planes and spacecraft to illustrate why we must aim high.  Then, we will check out the 5 steps that can set you on the road to world class data governance.

And finally, we will look into the near future, to a world that will look very different from today and see that world class data governance is not only achievable but absolutely essential.

Key learning points:

• What is world class data governance and why is it essential
• What are the 5 steps you can take to get on the road to world class data governance
• How is the world about to change and what does this mean for world class data governance

Speaker Biography

MDM Keynote: AI & RPA for Next Generation MDM – a View Behind the Curtain and Lessons Learned From Experience

Thomas Geyer, Head of Enterprise Data Management & Dr. Faried Abu Zaid, Data Scientist, Camelot Management Consultants

Nowadays Artificial Intelligence (AI) and RPA are almost omnipresent in the media, the literature and all over the internet. However, most of the companies are still trying to find the right access to AI. Especially when it comes to classical Master Data Management, MDM teams are still trying to tackle DQ issues by defining deterministic business rules, manual and intense data crunching activities and reactive error management. We will show how innovative companies are leveraging AI for MDM, we will explain why you don’t need good master data to start with AI and what you can expect from AI solution.

• Clear overview about what is possible and what not
• Use cases for AI & RPA for MDM
• Lessons learned, and insights form our projects

Speaker Biography: Thomas Geyer

Speaker Biography: Dr. Faried Abu Zaid

The Impact of Culture and Maturity on Your Data Governance Implementation

Ole Busk Poulsen, Head of Group Functions Information Architecture & Data Governance, Nordea

Implementing data governance is not a one-time exercise, it is a transformation of the way you work with information in the daily work in the line organization. To do this transformation efficiently you need to take the culture and maturity of the organization into account. Doing this leads to numerous dilemmas, discussions and fundamental decisions. In this session several of the dilemmas faced, and consideration done in the implementation of data governance at Nordea will be presented.

From a culture and maturity perspective the following topics will be discussed:

• The operating model and integration of data governance in the daily work
• The drivers of data governance at different maturity levels
• The focus of data governance – control and/or alignment
• Ownership of terms and physical data
• The relation to other disciplines and roles – information security, data protection and application owner
• Increasing maturity

Speaker Biography

Data Governance – The Journey from Theory to Practice

Lisa Bozek, Head of Information Management, Climate-KIC

Getting started on the data governance journey is no simple task. In this session we share our real-world experience, including how we gained executive support for the programme of work, how we chose a data governance approach, formalised a data governance organisation structure, and involved the business on a practical level. The session ends with a summary of pitfalls, roadblocks and, as the Head of Information Management, what I wish I had known before starting.

Walk away from this session with the understanding that:

• The data governance frameworks on the market today can be boiled down to a few basics,
• Your company is already governing data – build on what is there rather than changing everything,
• Success requires clarity about what data you are governing and why.

Speaker Biography

Greenfield Master Data Management – Back to Basics

Sherine Anis, Director, Quovadis Business Solutions

This presentation will provide attendees with a roadmap to launch and manage a successful Master Data Management program for companies that are either in the process of implementing one or are in a greenfield state. The talk will demonstrate how to:

• Define a clear vision for the MDM Program – What problem will be addressed, and what is the key benefit to the organization?
• Create a data governance program – This step is necessary for a successful MDM implementation. Bypassing this step will force organizations to return to it when issues arise.
• State the purpose of the MDM program – Analytical or Operational.
• Establish data ownership, including roles and responsibilities.
• List the data sources involved.
• Define the types of data to be mastered, including relationships and grouping.
• Create an implementation plan.

Speaker Biography

The Emerging Role of Artificial Intelligence and Machine Learning in MDM Implementations

Ashok Nayak, Managing Director, Analytics and Global MDM Lead, Accenture

As organizations continue to focus on digital transformations, the role of Master Data Management continue to be a growing phenomenon. Traditional MDM tools have also been enhancing the features related to core mastering concepts such as, but not limited to match, merge, standardization, enrichment and data cleansing using the latest technologies and probabilistic theories. However, organizations struggle with the growing need for data stewards for proactive data quality management. This topic will discuss use of AI/ML to enhance the master data management solution and reduce the efforts related to data stewardship.

The participants will learn:

• Use of AI/ML in the MDM solution
• A few usecases with demonstration of AI/ML in action
• The future of MDM

Speaker Biography

Managing the Complexity of Data Volume and Variety in a Fast Pace High-Tech Environment

Jeroen Vermunt, Domain architect data governance, ASML

In the highly complex world of semiconductor manufacturing vast amounts of largely varied data are generated every day.

ASML, world-leader manufacturer of machines for the production of semiconductors (chips), is implementing a central data lake to capture this data and make it accessible in a central environment.

Managing all this rapidly changing data imposes very challenging data governance requirements:

• How do users find information in this data lake without drowning in the amount and complexity?
• How can users be enabled to understand the data they want to consume, including the lineage of the data?
• How do we make sure that the data can be trusted?
• How can we control and monitor that access to data is secure?

This session presents some real-life examples of how we are approaching these challenges within ASML.

Speaker Biography

Data Governance Across Government

Regine Deleu, Government Enterprise Architect, New Zealand Government

This case study recognises information as a key economic resource and one of organisation’s most important assets. Despite gaining recognition as an asset in its own right, information has not being managed as any other asset. This case study looks at the characteristics of information as an organisational asset and how to setup your organisation to get the most out of your information.

The presentation will show:

• The 7 characteristics of information as an organisational asset
• The cost and risk if you manage your information wrongly
• How to increase the value of your information
• How to calculate the quality of your information
• And how to setup your organisation to manage your information in an optimal way.

Speaker Biography

Data Governance Panel - What I Wish I had Known

Moderator: Mike Simons, Associate Editor
 CIO.co.uk, ComputerworldUK and Techworld

Panellists: Joanne Biggadike, Metro Bank & Mark Wilson, Handelsbanken

‘Data is you organisation’s Crown Jewels’ This phrase is both a modern cliché and a truism. Organisations are producing more data than ever. CEOs and the boards are demanding immediate business value from that data. Line of business leaders are launching programmes that use company data, sometimes with scant regard to security, compliance or data integrity. Meanwhile partners and competitors are trying to access your data in ever larger volumes. At the same time customers, staff and, increasingly, regulators are expressing concern at the way data is aggregated and the uses to which it is put. In this session we will share some war stories to highlight the pitfalls that data professionals must avoid.

Moderator Biography: Mike Simons

Panellist Biography: Joanne Biggadike

Panellist Biography: Mark Wilson

Ghetto MDM

Remo Biagioni, Head of Business Intelligence, TransUnion

MDM programmes are notorious for requiring extensive investment in both time and money whilst delivering slowly. At TransUnion (previously Callcredit) we’ve grown up very fast: our internal systems all deal with slightly different versions of the truth. We understand good data management and focus our investment on the management of our core, consumer, data assets. Rather than start a large master data programme to tame our customer and product data, we decided to invest in new financial and CRM systems. Neither system, though, can be implemented overnight leaving a need for consistent data to support sales reporting, product management, investment decisions and the implementation of the new CRM system.

This talk will demonstrate how we used the principles of MDM, the tools we had available and piggybacked on existing projects to deliver many of the benefits of a much larger MDM programme using stealth and a ghetto mentality.

Speaker Biography

Howdens Joinery’s Digital Journey; Powered by MDM

Andy Rollins, Solution Architect, Howdens Joinery & Sam Goss, CMO, Comma Group

Howdens Joinery is the UK’s largest manufacturer and supplier of fitted kitchens, appliances and joinery products. After having just gone live with their new digital platform, their Solution Architect (Andy Rollins) will be discussing with Sam Goss (CMO Comma Group) how STEP has been a key component underpinning their Omni-Channel Strategy and how digital is transforming traditional B2B manufacturing industries, and how Comma supported them along their journey.

Delegates will learn about:

• The transformational effects of becoming digitally driven in manufacturing
• Implementing an Omni-Channel Strategy
• How working with partners strengthens and benefits the whole digital programme

Speaker Biography: Andy Rollins

Speaker Biography: Sam Goss

From Data to Insights - Building a Business Centric Data Strategy

Dana Julinschi, Master Data Governance & Projects Manager, FrieslandCampina

Businesses are evolving faster than ever and the expanding digital opportunities put an increased amount of pressure on both data professionals as well as their internal stakeholders.

Whether you are part of an already mature data organization or are simply at the beginning of your journey, you must have a coherent and business centric message when it comes to your data strategy and its delivery in order to remain a relevant partner for your stakeholders. The key in doing this is to understand what drives your business, what are the key wishes, initiatives and challenges and channel these towards not only allowing your company to function, but towards providing the valuable insights your stakeholders are after.

Delegates should be able to take away advice on:

• How to discover and distil the business insights you wish to deliver
• What are the key elements to build a business driven data strategy
• What constraints one has to be aware of when developing and delivering this
• How to bring the “paper” strategy alive in the business and get buy-in

Speaker Biography

Leveraging Data Stewardship: a Case Study in Petrobras’ Geological Data Management Department

Dean Melo, Geologist, Petrobras / University of Aberdeen

The overload of concepts on data stewards in terms of accountability is a problem faced by many companies.  In other words, the question “who is responsible for this data” should be answered clearly and the same data steward shouldn’t say “yes” so many times. Problems are discovered when regulations require specific outcomes, for instance, changing how some data must be disclosed.

Sometimes the same data steward is responsible for many concepts, some data haven’t a data steward or there is an overlap among them which can lead to decisions without alignment. Therefore, an approach to make it clear has been adopted in an Oil and Gas company applied to geological data. It is based on the data conceptual level (not instances) and uses the notion of granularity to find a solution to the mentioned problem. The main results were: a clear connection between data stewards and the concepts which they are responsible without “grey area” (represented as accountability matrix); a scalable method to fit new data stewards as new concepts (consequently new data) emerge. We adopted four levels as the best granularity using the terminology (from the lowest level to the highest of details): disciplines, domain, subdomain, group and elementary data.

The motivation to carry out a case study which involves all steps related to how implement an effective data stewardship is based on the assumption that “data steward practices are primarily people- and process-driven activities that need to be specially fitted within business models” (ALLEN & CERVO, 2015). The same authors emphasise that no commercial applications are able to cover models and process that fit the necessities of data stewards. Furthermore, beyond the private companies, the capacity of researches (in Universities and Research Centres) in play data steward role has been hotly debated and the lack of resources and support have been pointed out as a pivotal factor (Hartter et al., 2013).

What delegates will learn:

• How connect data stewards and data elements using a conceptual level;
• The results are useful to avoid the overload of the data stewards.
• Some idea about how display data stewardship as a accountability matrix being easy to access and search.

Speaker Biography

Minority Report - A Veteran [Data] Professional's Advice From the Field

Mehmet Orun, Sr. Director, Product – Customer 360, Salesforce

As business professionals, we want our problems solved, opportunities realized and know we have a team we can rely upon to maximize success.  As data professionals, we speak of patterns and anti-patterns to solve said business challenges.  However, we seldom talk about the personal and professional journeys that lead us to success or lessons we learnt along the way.  This session is about the professional journeys each of us are on, to provide a personal perspective and create an opportunity for dialogue.  Bring yourself, your stories, curiosity and sense of humour.

Speaker Biography

Regulated Information - Not So Common a Concept in MDM

Alicja Agnieszka Dys, Data Architect, European Chemicals Agency

Although master data – the most valuable and shared information – is relatively easy to identify, its definition and scope can pose a challenge for an organisation. In European Chemicals Agency (ECHA), a chemical substance is a pivotal element of each regulatory process and activity. In order to enable substance-centric view and provide the status of related activities, substance identity has to satisfy the needs of all the regulations within the organisation’s merits. The challenge of defining such an abstract and scientific term as ‘chemical substance’ in regulatory contexts led to the idea of ‘regulatory substance’. Aiming for increased transparency of regulatory processes sets high requirements on the identification and definition of common concepts in public administration.

In this presentation:

• A concept of ‘regulated substance’ as Master Data
• Implemented MDM solution
• Opportunities for improvement

Speaker Biography

Multi Enterprise MDM

Henrik Liliendahl, Co-founder and CTO, Product Data Lake

Doing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future. But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus we will have a need for working on the same foundation around master data.

This presentation will take you through:

• Why business ecosystem wide MDM will be on the future agenda
• What exactly is multienterprise MDM
• How does it apply to party master and what about data privacy and data protection
• How can multienterprise MDM be used within product MDM and what is the link to IoT (Internet of Things)
• Learn from a concrete use case encompassing product information and AI (Artificial Intelligence)

Speaker Biography

Linking MDM Strategy to Digital Transformation Strategy

Sachin Dhoble, Head of Data Management, Maersk Line

Most organizations currently are at some stage of their digital transformation journey as they try to stay aligned with evolving customer experience, data driven business insights and decision making like product enhancements, marketing and support, and impact of external variables like compliance and regulation. In order to truly achieve a data driven culture and exceed the expectations of customers and business stakeholders, establishing a trustworthy data foundation is essential, and Master Data Management strategy plays a key role in it. This session covers learnings from a the digital transformation journey that Maersk undertook where Master Data Management was established as a critical component for achieving the desired objectives by successfully linking the MDM, Data Quality and Governance strategy with the strategic outcomes of the digitalisation journey.

The attendees will learn:

• Approach for linking MDM Strategy with Digital transformation strategy
• Setting up Data Governance and Quality management framework to achieve a sustainable MDM setup to cater to the evolving needs of digital transformation
• Key learnings around business value proposition of MDM, scoping and efficient implementation approach

Speaker Biography

IQ International: Your One-Stop-Shop for Data Quality Professionals

Christiana Klingenberg, Director, IQ International

Come and hear about ongoing collaboration sponsored by IQ International to document industry specific data quality metrics. Working groups have been established to collect, analyse and document these metrics and by the time of the conference Christiana will be able to discuss some of the preliminary findings at this session. IQ International will be able to offer metrics for Finance, Manufacturing, Consulting, Banking and Healthcare. This will help organisations to:

• Enable faster DQ program implementation
• Ensure consistency of best practices across organisations
• Enable future benchmarking (comparison)
• Standardises communication within and between industries

IQ International (often referred to as ‘IQ Int’ for brevity) is the not-for-profit professional association for people passionate about improving Information Quality. IQI is focused on leadership in the IQ field through education, networking and solving data problems.  IQ International offers: Peer Group Networking, Webinars, International Journal, A Knowledge Library, Professional Certification – IQCP credential and Working Groups (DQ Metrics).  IQ International invites all data quality professionals to join and become part of a thought leadership community.

• IQ International offers a one-stop-shop for all data quality professionals
• International working groups elaborate a pool of industry specific data quality metrics
• IQ International is looking for members with thought leadership ambitions and interest in joining the DQ metric working groups

Speaker Biography 

Plenary Keynote: Unified Data Delivery – Shortening Time To Value in a Digital Enterprise

Mike Ferguson, CEO, Intelligent Business Strategies

For most organisations today, digital transformation is creating a data deluge with new data sources appearing almost daily. This has led to a complex data landscape with hundreds, if not thousands of data sources and multiple workload specific analytical data stores. In addition, with so much data pouring into the enterprise, business users are eager to analyse it and are buying their own self-service data preparation tools to integrate data independently of IT.  It seems everyone is integrating data with no attempt to share what they create. More and more silos are therefore appearing making it harder to manage and trust data.  Unaware of the complexity, business executives are demanding more agility. to remain competitive in a digital economy where customers can churn at the touch of a mobile phone screen.    So how can companies remain agile and reduce the time to value when data complexity in on an upward trajectory?  In this keynote session Mike will look at this problem and show how companies can establish a strategy for delivery of trusted, unified data that speeds up data and analytical processing and shortens time to value.

Speaker Biography

Consent Management for GDPR

Martin Oberhofer, Executive Architect, IBM & Lars Bremer, Senior Software Engineer, IBM

The General Data Protection Regulation (GDPR) is in effect now and companies still struggle to implement urgently needed capabilities to comply with this privacy regulation. This session will focus on the consent management aspects of GDPR. While there are many point solutions out there to capture consent – most of them don’t manage the consistent 360° view individual for which consent is captured. This could lead to multiple duplicate individual records for which across channels conflicting consent is captured without being noticed due to lack of matching capabilities possibly leading to consent violations.

This session thus shows:

• Overview of relevant consent management capabilities
• How to manage processing purpose definitions for consent as part of your data governance processes
• Intersection of master data management and consent management and how they relate to each other
• Benefits of using them together

Speaker Biography: Martin Oberhofer

Speaker Biography: Lars Bremer

Our Digital Transformation: Data Governance as a Foundation for a True Data-Driven Enterprise

Karima Makrof, Data Governance Manager, Volvo Car Group

Beating the Competition by Optimizing Product and Supplier Data Onboarding with MDM and Data Governance

Allard Mes, MDM Consultant, Blokker & Alfred Kayser, Lead MDM Architect, Viqtor Davis

This presentation will present best practices and lessons learned from the successful MDM implementations for product and data supplier onboarding optimization for a large European retailer chain, Blokker. In the fast-moving consumer market, moving fast on product data has become crucial, especially in fast moving retail market in Europe. Specific approaches, solutions, data models, workflows and processes will be detailed and discussed for their merits and pitfalls. By pushing responsibility for the data product to the suppliers, and having optimized product onboarding processes, allows retailers and wholesalers to have a very short time to market, whilst increasing overall quality of the assortment. Next to this, by integrating data governance in the entire process, throughout the whole chain from supplier to the consumers, data quality is improved in both quantitative and qualitative terms.  The presentation will follow the successful roadmap implemented at the Blokker organisation, depicting how MDM enabled Blokker’s growth in product assortment, customers and revenue.

• Optimized product and supplier data by integrated data governance from supplier to consumer
• Best practices and lessons learned from a wide range of MDM implementations
• Success story from one of the large European retail organisations

Speaker Biography: Allard Mes

Speaker Biography: Alfred Kayser

Panel Discussion: The Future of MDM

Moderator: Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners

Panellists: Michael Evans, CTO, Comma Group, Salah Kamel, CEO, Semarchy & Dr. Scott Schumacher, Distinguished Engineer, IBM

Master Data Management (MDM) has been mainstream for many years now, with a strong vendor ecosystem, much written about it, and implementations in a wide swathe of enterprises.  Yet the field is not standing still.  New trends are emerging, new technologies are becoming available, and new demands are being made of MDM.  This panel discussion explores what this means for the future of MDM, with the panellists bringing their own experiences from a wide diversity of backgrounds.  Potential themes include whether MDM will fragment into specific solutions for specific master data entities (e.g. Product vs. Customer), the significance of graph databases in MDM, privacy and legal concerns, how enterprises organize around MDM, and so on.  Questions will also be taken from the audience, so this will be a chance for attendees to get answers to topics that are important to them.

Speaker Biography

The Impact of Culture on Data Change Initiatives – Don’t Mistake Motion for Action

Kiran Gill, Head of Data Services & Operations , Lloyd’s

Moving onto new laptops, investing in state of the art data management software or technology, introducing a data governance framework  – sound like the perfect environment for properly managed and governed data? It’s likely. Does this mean that the users are making use of the new technology and applications? Maybe. Are the users, people who control the data and impact the quality of the data doing the right things? Not always.

Although people may be equipped with the right tools, there’s nothing to say they are using them properly. Using a spanner to hammer in a nail is the perfect way to describe someone who knows they possess something useful, but don’t quite know what to do with it. Adding further to the problem is the user who doesn’t want to learn how to use it. In this talk we discover how traditional attitudes affect modern use of data. People are the key to data success and just because there is motionje, doesn’t mean there is action.

Speaker Biography

Finding Insight in the Spaghetti – a Case Study on How We Sorted Out Our Data

Claire O’Connell, Head of IT, Well Pharmacy

With over 10,000 reports running daily and a mess of  un-governed legacy data Well Pharmacy were not set up for success to start utilising data to drive the business forward.  However, with a challenging environment for Pharmacy, with pressure on NHS budgets and a Digital Transformation underway it has become increasingly important to generate business insight from our data, including understanding our patients.  This session will describe how we addressed the challenge and what we learned along the way in a context of highly sensitive personal data.

Key Takeaways:

• You have to sort data out from the bottom up – there had been several attempts to resolve it before without putting in place proper data governance and architecture in place.
• You need to take an ‘agile’ approach to deliver value quickly, because it is a long and tedious job and stakeholder will lose interest if they don’t get something quickly.  You need to involve a lot of people across the business, because Data affects almost every role.
• Build a programme with data governance and technical delivery working side by side, or you might end up with yet more mess to deal with!

Speaker Biography

Data Governance - For Compliant Innovation

Michael McMorrow, Principal, MMM Data Perspectives

One of the biggest data challenges facing many organisations is how to balance the obligations of Data Protection (for example GDPR) with the opportunities of Data-centric Innovation. Left unmanaged this can result in seriously dysfunctional internal behaviour and conflict. Meanwhile, Data Protection Regulation is here to stay, and Data-centric Innovation is growing ever-more exciting. Data Governance provides the underpinning framework for managing this challenge.

This session will cover how Data Governance can lead on:

• Creating a culture of Compliant Data Innovation – break down silos excessively biased towards one or other perspective
• Embracing and extending GDPR “Record of Processing Activities” to sustainably improve Enterprise Metadata, including transparency on all data uses and benefits
• Ingraining ‘Compliance Thinking’ across the design, development and testing of data analytics for consistent delivery of reliable, ethical and legitimate innovation.

Speaker Biography

Data Migration Governance – a Neglected Area

Lars Oberink, Consultant, Data Shepherd

Data migration is a tough area. It is complex, costly, and has a large impact on the overall project success. Most data migrations eventually “burn up” – they take longer, cost more and never really achieve their original targets. During the course of the project, the data team often finds itself in perpetual trench warfare against the rest of the business. Eventually at best mediocre results are achieved leaving everyone frustrated and exhausted. Why is this? Firstly, there are huge and often undocumented and unexpected technical challenges to overcome, before even a byte of data can be transferred from source to target. This involves plowing through layers of neglect, sidestepping all the nasty politics, and then painstakingly analyzing the data structures, designing the migration scenarios for each object, formulating extraction and  transformation rules, and building and testing the migration software.

The main challenge in data migration however is not even the software, but the management of people, time and resources. Somehow the data migration métier continues to attract obtuse characters. It also never fails to bring project managers to their boiling point. When the phrase data migration problem is uttered, project stakeholders instantly lose patience and become belligerent. Data migration consultants are quite often replaced or sidelined. One needs come to terms with this reality.

The bottom line is that the data migration need effective governance. Data migration requires a healthy dose of realism. The fundamental problems of data migration will most likely never be solved. But by having a form of governance in place, combined with an ounce of goodwill and perhaps a stroke of luck, you will protect the project against total data failure while providing at least a decent result.

Data migration governance describes what the targets are, how the data migration is structured, how responsibilities are assigned, what deliverables must be produced.

The presentation contains key learnings on three governance areas:

1. Project Governance – migration approach and plan – how to start a migration effectively, build a functioning team, and keep the different stakeholders involved / not too unhappy.
2. Design Governance – migration scope, design and build – how to cope with the unique planning challenges due to the “migration squeeze effect”.
3. Execution Governance – mock conversions and data validation – how to survive the chaotic first Mock load, and then slowly work towards a successful cutover, by driving the migration from a clear process.

Key takeaways:

• Data migration should be approached as a sub-project within the larger project or program, especially for large phased rollouts involving many countries or sites. Manage the anger factor in data. (data is emotion)
• Data migration will never have sufficient resources and time assigned to follow your process – that’s why you need a strategy to cope with the chaos that comes with it.
• Data migration must protect itself against bad data quality (in the broadest sense) – the key is having a good validation approach and using clearly defined validation rules.

Speaker Biography

Redefining the Way We Think About Energy, Redefining the Way We Think About Data

Panagiotis Tsaknis, Information and Enterprise Data Management Architect & Jane Morrison, MDM Consultant, Agile Solutions

RES (Renewable Energy Systems) core business is to develop, construct and operate large-scale, grid-connected renewable energy projects worldwide for commercial, industrial and utility clients’. As RES grows as a multinational and given the significant growth of the wind and solar markets over the upcoming years, RES not only generates energy but also generates data. Standardization of information and systems therefore is essential to grow and compete as a multinational, consequently we have aligned our Information and Data Standardization approach to RES’s core values.

• Passion: Collect, Clean, Optimise and potentially Monetise data
• Accountability: Assign ownership of the data and define a data governance framework
• Collaboration: Collect data from all business units and functions and provide a single point of reference
• Excellence: Remove bottlenecks and allow users to focus on their core business activities.

Key Takeaways:

• Steps on how to Introduce MDM to the Business
• Challenges faced
• Lessons learnt so far

Speaker Biography: Panagiotis Tsaknis

Speaker Biography: Jane Morrison

Data Governance Keynote: How Do We Move from Governance to Value Generation

Adam Preston, SCIB Chief Data Officer, Santander Corporate and Investment Banking

The CDO community has been focused on ‘organising and stocking the larder’ with good quality timely and consistent data, now we are ready to work with the ‘Chef’ in order to deliver value. Opportunities (recipes!) are born from both market analysis idea generation and/ or from mining the data assets to uncover trends or insights – how does the CDO support both these approaches and ensure their efforts are rewarded?

Summary:

• How does the CDO role systematic support value propositions
• How does the CDO role expand into a Value generating organisation
• Creating a Data Marketplace

Speaker Biography

Data Strategy Keynote: HS2 - Delivering a High Speed Railway Using Data and Digital Technology

Robert Kirkwood, CIO, HS2

HS2 will be the new high speed backbone of our rail network connecting the city centres of London, Birmingham, Manchester and Leeds. Once fully completed, it will have built around 553 kilometres of new high speed track (equivalent to over half the length of Britain Lands End to John O’Groats as the crow flies).

When fully operational, up to 48 HS2 trains with up to 1,100 seats each will be running every hour on the UK’s rail network.

This is a huge undertaking; Britain’s first new intercity railway north of London in 100 years; Europe’s largest infrastructure project.

The right information delivered in the right way will enable significant productivity and output uplift at every stage of HS2’s Lifecycle – from Design through Build and into Operations.

The opportunity exists to bring structure and insight from the vast, diverse data sources and information opportunities that delivering HS2 will open and expose.

New models for Construction, Safety and Asset Management will evolve and sensoring will open the door to analytics , machine learning and AI.

With Quantum computing on the same delivery horizon the door to innovation and delivering real time data intelligence and AI decision making just opened.

Speaker Biography

Fake Governance

Gerard Bartley, Manager Global Master Data, Jacobs Douwe Egberts

This session will walk the audience through the concept of fake governance, how to realise you may have fake governance in your organisation, the impacts of it (including a case study), the main steps in setting up a targeted/low impact master data governance organisation to fix and then find out how that case study issue was fixed.

The topics covered will include:

• Signs you’ve got fake governance in your organisation
• The real impact of fake governance
• A deep-dive into a particular data area which often causes issues
• What is data quality?
• Starting up a targeted data governance programme

Delegates should be able to take away:

• The ability to assess if their current governance organisation may not be working
• How to give management a picture of the impact of data issues
• Practical guidance on building a targeted master data governance programme

Speaker Biography

Implementing Data Governance in Daily Operations in Copenhagen Properties and Procurement, Municipality of Copenhagen

Janni Conradsen, Head of Digitalisation, Municipality of Copenhagen & Lars Frederiksen, Senior Consultant, Strand & Donslund

As Copenhagen Properties and Procurement (CPP), is becoming more and more dependent on data, it is obvious that genuine governance of data is a must. Depending on local Excel sheets and individuals knowing everything is not good enough. Based on former experiences and experiences presented by other companies and organizations round the world, CPP have realized that to govern data and make it a persistent part of daily operations, you must govern all the factors that influence the way data is managed; Process, Business Rules and it-systems.  Another important finding is that for governance to become a part of daily operations, managers must be given responsibilities and authority that cross existing organizational borders.

CPP have developed a concept for Data Governance that also includes and takes the other aspects of Business Architecture into consideration, defines roles and responsibilities and describes governance process to insure lasting governance in a changing business.

You will learn:

• How Data Governance is linked to Business Process, Business Rules and Application Architecture
• Why Data Governance demands responsibilities that cross traditional organizational borders
• What the difference is between implementing Data Governance and running Data Governance as a part of daily operations
• What CPP have done to implement Data Governance and make it a part of daily operations

Speaker Biography: Janni Conradsen

Speaker Biography: Lars Frederiksen

MDM vs Governance Models

Petter Larsen, Head of Enterprise Data Architecture, NETS

When a modern enterprise decides to improve its data maturity and its data management capabilities, it will have impact on its other governance models.  The new Data Governance model must operate alongside the existing IT Governance and Process Governance models (being the other two general governance models needed by an organisation).  This needs careful thought, planning and management, so this presentation will look into the following topics:

• A data centric view on the three governance models – how do they all contribute to improved data maturity and its data handling capabilities?
• Governance models vs MDM – what is the difference between centralizing data stores vs. centralizing processes? The general trend is to move action as close to the source as possible (“self service”) but how does this fit with your MDM initiative and data architecture target models?
• What are the pitfalls? What do you need to look out for? What to do if one of the other models are weak or not even missing? How to avoid gaps and overlaps?
• Why is data governance important also for IT and process governance?

Speaker Biography

How to Build an MDM Strategy During the Digital Transformation Era

Yaniv Naor, Master Data Management Business Lead, HUGO BOSS

As of today many companies implement an MDM solution , should the MDM strategy be a continuous process , or is it a one time job?

Does MDM change with time ? Does social media play a role in MDM implementation ?

With today’s digital transformation reaching every business, the way managers of today think is different from the ways managers have thought a decade back.

What does this really mean ?

Key takeaways include:

• Digital transformation – MDM learnings
• Correlation between MDM and Analytics
• Visualization of data profiling – how to analyze data

Speaker Biography

The Data Diplomacy – a Key to Avoiding Data Governance Bureaucracy

Håkan Edvinsson, CTO, Informed Decisions

Speaker Biography

Sustainable Data Governance?

Michael Bendixen, Data Governance Manager and Head of IS Data Management Solutions & Services, Grundfos

Since 2012, Grundfos have been running Data Governance globally, and have continued to incorporate new areas. During this time, Grundfos have identified some of the factors that are key to keeping Data Governance running. These are the critical elements, which have made a real difference in obtaining and maintaining executive management attention and sponsorship on Data Governance in Grundfos since the beginning. But keeping Data Governance running does not come easy, and there are still challenges to deal with.

In this session, delegates will learn about:

• The journey Grundfos have been on since 2012
• The Data Governance Framework in which they operate
• The key factors to keeping focus on Data Governance – including data quality reporting as a vital component
• The challenges they are dealing with
• The focus areas going forward (and input to what is missing?)

Speaker Biography

Foundation of a Data-Driven University

Kim Setälä, Master Data Management Development Manager, Aalto University

How Data Quality Boosted Corporate Performance Management

Bart-Jan van Dijk, Enterprise Data Steward, Evides & Rolf Emmens, Managing Consultant Data Management, RGP

As the Dutch 2nd largest Drinking Water company Evides has social responsibility to provide high quality drinking water to 2.5 million clients in the south-west of the Netherlands with 24/7 availability.

To produce drinking water a License to Operate (LTO) is required. It is a complex process to purify and distribute water from a source (the river Maas) to households and companies. This process is characterized by governmental ownership and oversight.

The drinking water creation processes are highly automated and generate huge amounts of data (time-based measurements). For all of this to be well-managed, a (drinking water) company needs to have the best performance measurement controls available. The importance of data and information is made very explicit in the Evides strategy 2017-2021.

In 2017 Evides started a Data Governance project to ensure data that is used for KPI reporting is managed from the creation until the presentation in the KPI.

Key take-aways are:

• Even though the amount of data increases rapidly and the value of data doubles every year, data is only important for management if explicitly used in their KPI;
• As with information, good water can only be derived from cleansed and managed sources. Did you always want to find out exactly how much data is needed to produce 1 m3 of drinking water? Here’s your chance!
• Data has more value when seen in broader context than applications and departments. This turns out to be especially true in CPM.

Speaker Biography: Bart-Jan van Dijk

Speaker Biography: Rolf Emmens

Governing and Managing Semantics in Reference Data

Malcolm Chisholm, Chief Innovation Officer, First San Francisco Partners

For decades enterprises have invested in data modeling, and most enterprises would not dream of instantiating a physical database without basing it on a sound data model.  Reference Data contains about the same amount of business semantics as is found in data models, but it is largely ungoverned and unmanaged. This presentation describes the nature of Reference Data (also known as code tables and lookups) and shows how it needs to be governed and managed for its semantic content.  The benefits of this approach are described, such as drastically reducing coding errors during data entry.  A framework is presented that outlines the governance and management tasks required for the semantics of Reference Data.  Emphasis is placed on what happens when the semantics of a Reference Data element change.  This is a situation that is rarely seen in data models but is quite common in Reference Data and can disrupt areas such as Data Analytics if it is ungoverned.

Attendees will learn:

• What Reference Data is and why its semantic content is so important
• What the benefits are of governing and managing Reference Data semantic content
• Approaches to governing and managing the semantic content of Reference Data
• How to handle changes in the semantics of individual Reference Data entries

Speaker Biography

What is Enough Data?

Liz Henderson, Associate Director, Global Data Governance Lead, Turner & Townsend

How do you know when you have enough data to “go live” to provide the best quality service to your clients, when you do not have time to migrate all historical data?  This session will provide an insight into the different techniques for defining data sufficiency and how Turner & Townsend achieved data sufficiency for their digital programme.

• Methodologies for calculating data sufficiency
• Process for collecting data
• Lessons learnt during data collection

Plenary Keynote Panel - Where Do We Go From Here?

Jan Henderyckx, Partner, Bearing Point & Gerard Bartley, Manager Global Master Data, Jacobs Douwe Egberts

The Complete Guide to Data Stewardship

David Plotkin, Director of Metadata Services, MUFG Union Bank

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.  The business needs of master data are described, including strategies for meeting manual and procedural needs. The more technical details are fitted into this framework.  The role of tools in MDM is described, both the tools that are used to implement MDM, and the tools that indirectly support MDM through metadata management.  MDM programmes are evolving from improving back office efficiency to being a central part of business models.  These emerging challenges of MDM are addressed in detail, including how MDM supports data scientists and the relationship between Big Data and MDM.

Attendees will learn:

• What Master Data is, how it differs from other classes of data, and what its special needs and challenges are.
• The structure of an MDM programme, including how the business needs to be aligned to data governance and data management within an MDM programme.
• How to deal with integration, semantic, history, quality, and other requirements in an MDM programme.
• MDM architectures, including what tooling can offer, and how these vary with different master data entities.
• The relationship of master data with reference data, and emerging areas of MDM.

Speaker Biography

Data Governance – A Strategic Approach

Jon Evans, Information Strategist & Founder, Equillian

Data Governance has received increasing attention in the past couple of years as regulations such as GDPR place greater emphasis on data compliance. But organisations that view Data Governance as merely a tick in the box to keep the regulators at bay are missing the point.

Contrary to what some people think, Data Governance isn’t about policing your data – it’s the glue that binds all your data-related capabilities together. Mitigating your data risks is only half the equation; the more interesting half is the value Data Governance generates by helping you get smarter about the way you define, manage and exploit your data assets.

In this full-day workshop, Equillian’s Jon Evans will show you how to place Data Governance at the heart of your Enterprise Data Strategy. He will dispel some of the common myths and misconceptions about Data Governance and explain the key building blocks – the essential ingredients of a successful Data Governance recipe.

Both beginners and those already familiar with the topic will benefit from Jon’s business-focused approach, based on industry best practice coupled with many years of experience helping organisations develop their Data Governance approach. With plenty of group and solo exercises designed to instil a deeper level of understanding, attendees are sure to take away lots of ideas to try out after the conference.

The session will be structured around 6 key topics:

• Why do you need a Data Governance strategy?
• Exploring the data continuum
• A framework for Data Management
• A closer look at Data Governance
• Assessing your current maturity
• Developing a roadmap

Speaker Biography