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£120.00 / year

Virtual Summit Academy - Solo User

Description

The first Data Quality Pro Virtual Summit (DQPVS #1) features an outstanding panel of expert authors, speakers, coaches and practitioners within the fields of Data Quality, Data Governance and Master Data Management.

Learn new insights, techniques and methods from some of the world's leading experts on demand from your smartphone, tablet or computer - at home or at work.

  • Learn from some of the world's most experienced authors and practitioners
  • Buy a virtual ticket at any time - the summit runs continuously, 365 days of the year
  • Watch presentations on demand from your computer, tablet or smartphone device at work or at home
  • Lifetime access and 30 day money back guarantee

Expert Practitioners

Expert Practitioners

The presenters for the virtual summit include some of the world's leading authors, experts and practitioners in the fields of Data Quality, Data Governance and Master Data Management.


Presentation 1:

Getting in Front of Data Quality by Tom Redman

This fast-paced presentation crystallizes what it takes to improve data quality.

The unfortunate reality is most data quality efforts fail. But those who approach the opportunity properly, put the right people in place, and use proven methods, can produce great results, relatively quickly and easily.

This presentation covers:

  • Approach: Why eliminating the root causes of error is the only way to go.
  • What You Need to Do: Including understanding customer needs, measurement, control, and improvement.
  • Who Does the Work: An important first step involves getting responsibility for data out of IT. And everyone who touches data has a role to play!
  • Why: Bad data have always cost time and money. Lots of both. Today, as organizations seek to leverage big data and become data-driven, the costs are even greater. But subtle.
  • Culture: Every manager can improve data quality within his or her span of influence. Leadership is decisive.

Presentation 2:

Data Quality in Emerging Markets by Gary Allemann

Emerging markets such as those in Africa, South America and Asia present unique data quality challenges that frequently overwhelm Euro or US centric approaches and technologies.

These challenges include:

  • High levels of illiteracy amongst both customers and staff
  • Immature IT environments
  • Lack of standards and variety from country to country
  • Multilingual populations
  • No fixed addresses (or no standards)
  • and many more

These economies together represent almost 50% of the global population and present opportunities for growth that make them attractive to global businesses.

This paper will focus on some of the unique challenges experienced in these environments by the speaker, and present strategies for dealing with these.


Presentation 3:

Superb Data Governance is not the Outcome by Lisa Allen and Michael Rose

The Environment Agency is a public sector organisation. This session will explain our experiences of implementing data governance.

We will outline how we structure our governance and monitor, measure and track progress through a data management maturity model. We will show the clear linkages between good data governance and direct benefits to the end users both within and outside the organisation. By getting governance right our data can deliver more than before.

This session will cover:

  • Why data is important
  • How we look after our data
  • Benefits of looking after the data
  • The impact of opening up our data to wider uses
  • Top tips for any data governance programme

Presentation 4:

Master Data Management: Defining MDM Maturity with a Continuous Improvement Approach by Mark Allen

Common to any MDM strategy is the need to transition master data from an insufficiently managed state to a highly managed and controlled state necessary to achieve MDM goals.

Achieving MDM goals requires a well orchestrated plan and set of key milestones spanning various disciplines, practices, capabilities, and improvement areas. Capturing the essence of this as a high level, measurable program strategy is what a maturity model is for and why continuous improvement needs to be a key focus throughout the maturity process.

This presentation covers how to define an MDM maturity model, how to apply maturity measurement consistently across an MDM program, and provides examples of various maturity milestones that should be tracked. This presentation will also discuss reasons MDM maturity can hit a wall, what are maturity inhibitors, and how to maintain momentum through a well focused continuous improvement approach.


Presentation 5:

Top Data Governance Mistakes and How to Avoid Them by Nicola Askham

Data governance is getting a reputation for being complex and extremely challenging to implement, but it need not be.

Learn from others experiences and do not repeat their mistakes. Join Nicola Askham, The Data Governance Coach, to hear about the many pitfalls that you could face, how you can deal with them and more importantly avoid altogether if you take a structured approach to your data governance initiative.

Join this session to learn:

  • What not to do in your data governance initiative
  • What to do to overcome some of the common challenges
  • Tips and best practice advice to make your data governance initiative successful

Presentation 6:

Data Quality Strategy in a Big Data Analytics World by Alex Borek

Big Data is no longer an abstract concept. It is today’s reality companies compete in.

Organizations around the world move rapidly towards capturing, integrating and analyzing data at massive scales, varieties, and at higher speeds. Structured and unstructured data is processed and infused as information insights into all key business and decision making processes in organizations.

There is a multiplicity of new technological platforms like Hadoop, NoSQL and new information architectures that support organizations to be able to handle Big Data.

But once Big Data technologies are implemented and used, companies have to think about the next important step: Making sure that the revolutionized systems and platforms deliver trusted information to business users.

The challenges are manifold, as well as the opportunities.


Presentation 7:

Setting up a Data Quality Risk Management Program at your Organization by Alex Borek

Poor quality data often leads to major risks in all parts of an organization, affecting operational efficiency, bottom-line results, customer satisfaction and strategic decision making.

Monitoring, measuring, and quantifying how information quality impacts business objectives on an enterprise wide scale – both financially and non-financially, can become, however, a tricky task.

Based on best practices from several industries and key insights from his book, “Total Information Risk Management: Maximizing the Value of Data and Information Assets,” Dr. Borek shows step-by-step how a data quality risk management program can be set up successfully and how data quality risks can be systematically identified and mitigated. As a result, a value tag can be put on data quality, which allows higher executive buy-in to data quality initiatives to reduce related risks.


Presentation 8:

Metadata Management as a Key Component to Data Governance, Data Stewardship, and Data Quality by Dalton Cervo

For many organisations, their data landscape is one of disparate and fragmented silos, resulting in conflicting data. These conflicts cause considerable operational waste and severely limit the strategic performance of leaders.

The answer, of course, is to adopt modern attitudes and approaches to data management. However, many organisations fall into the trap of delivering disparate data management disciplines that fail to align, and so the problem repeats itself.

Metadata Management is a core method that needs to be aligned with every aspect of your Data Quality, Data Governance and Data Stewardship initiatives. In this presentation, globally recognised practitioner and author Dalton Cervo provides a detailed account of how to make this alignment a reality.


Presentation 9:

Data Quality Requirements: How to Define Measurable Characteristics of Data by Laura Sebastian-Coleman

To manage the quality of data, you must be able to measure it. To measure it, you need to define what you mean by quality data.

This presentation outlines an approach for defining measurable characteristics based on dimensions of quality.

It also discusses the relationship between data assessment and requirements definition, and the application of criticality and risk assessment to make better decisions about what data to measure and how to measure it.


Presentation 10:

Through the Looking Glass: An Analytical Approach to Targeting Data Governance by Jon Evans

When a global entertainment and media company decided they needed to start taking Data Governance seriously, one question rang out loud and clear – where do we start?

Whilst DG was recognised as an essential capability they needed to develop in order to tame their ever-growing information estate, the initial challenge came from the sheer size, scale and diversity of their business.

  • Where were the most serious issues currently being felt?
  • What capabilities did they already have that could be leveraged more widely?
  • How did Data Governance maturity vary across the business?

This session will explain how these questions were answered by using an innovative approach that brought together techniques from the world of data governance, business intelligence and enterprise architecture.


Presentation 11:

Using Lean Principles to Manage the Data Value Chain by Mario Faria

Creating and managing a data office is not an easy task.

The reasons for so many problems in streamlining a data strategy come from lack of data ownership, lack of a data roadmap and the data processes not clearly defined.

Using the Lean Principles that come from the Toyota Production System is one method that has been proved to be quite successful.

This session will explore how it can be done.


Presentation 12:

Destination Data Town – A (Fun) Path to Better Data Quality by Bill Florio

A key challenge for data quality leaders is ensuring the business is aware of the benefits of improved data quality and the risks of doing nothing.

In this presentation, Bill Florio talks about how a data quality initiative within Teach for America achieved a range of key benefits. His presentation focuses on:

  • How to ground your employees in what data quality means and why it matters
  • How Teach for America addressed its data quality challenges
  • Creative approaches that both involve and motivate users in tackling data quality issues

Presentation 13:

Aligning Data Quality with the Business in Energy Retail by Mark Humphries

Data Quality is not an end goal in its own right. It needs to support the business.

In this presentation, Mark explains how as Data Manager for a European Energy Supplier, he established a framework and a culture for maintaining Data Quality by starting with the business model.

Because Mark started with the business and not with the data, he established a working Data Governance framework without explicitly calling it as such; an approach that he calls Data Governance by Stealth.


Presentation 14:

Enabling Greater Data Intelligence Through Grass Roots Data Quality Efforts by Don Jenkins

In this presentation, Don Jenkins leads you on an insightful journey of grassroots Data Quality Improvement at EnerNOC, an award-winning provider of intelligent energy management solutions.

EnerNOC have witnessed incredible gains from leveraging Data Quality to help improve the performance of vital operations across the organisation.

Don provides detailed case studies on how to tackle issues such as:

  • Achieving quick wins but keeping the big picture in mind
  • Ensuring your data quality metrics resonate with the business
  • Maintaining momentum by winning big on smaller initiatives
  • Building operational data quality processes that can be handed over to Business as Usual
  • Massively reducing effort while increasing speed and revenue retention

Presentation 15:

Data Quality Management on a Shoestring Budget by Dylan Jones

Implementing a Data Quality Management Strategy can be a challenging undertaking at the best of times but for a small organisation the task can seem insurmountable with limited funding, staff and technology.

In this presentation, veteran Data Quality practitioner Dylan Jones (editor and founder of Data Quality Pro), takes you step-by-step through a case study of a small organisation which was suffering extensive waste and strategic deceleration as a result of poor data quality.

Following some creative uses of existing staff and simple methodology changes, the organisation witnessed a startling turnaround.


Presentation 16:

It’s What’s Inside That Counts: How Your People Can Lead Your Data Governance by Charles Joseph

In this presentation Charles Joseph provides an account of how specialist insurer, Beazley, is taking a people-centric approach to Data Governance.

Charles focuses on a range of key issues that companies need to address such as:

  • Selling complex data governance and data quality initiatives to the business
  • Engaging with supporters and evangelists to gain maximum traction
  • Assigning roles for data governance
  • Understanding the exact questions you need to address in order to be successful

Presentation 17:

Competing with High Quality Data – Building and Executing a Successful Data Quality Approach by Rajesh Jugulum

Because of the adverse impacts that poor quality data can have, organizations have begun to increase the focus on data quality in business in general and they are viewing data as critical assets like others such as people, capital, raw materials and facilities.

Since the data quality field is experiencing explosive growth, many companies have started to realize the importance of having a dedicated data quality (DQ) program to ensure that the data is fit for the intended purpose.

This presentation aims at outlining a holistic DQ approach that businesses can adopt to energize their DQ innovation processes, perfect their data gathering and usage practices and ensure robust and reliable data is available to make judicious decisions.

The proposed DQ approach that is based on DAIC (Define, Assess, Improve and Control) project life cycle, is a structured, systematic, and disciplined approach to ensure that we are getting satisfactory levels of data by minimizing cost of low quality data. This in turn, strikes a balance between rigor and creativity resulting in reduced DQ assessment and issue resolution times.

The presentation also includes some case studies from large organizations.


Presentation 18:

The Three Legged Stool – How Data Privacy Compliance is Supported by Data Governance and Data Quality Practices by Daragh O Brien

Data Privacy is a growing area of importance.

Apart from increasing international regulation (101 laws and counting), increased penalties (EU proposing 5% of turnover), and increased media coverage, customers are increasingly concerned about privacy as a quality characteristic of data-driven businesses. Add to this the growth of “Internet of Things”, Big Data, and the ubiquity of Cloud computing, and there is a complex juggling act to perform.

This session will look at the role of Data Governance principles and Data Quality practices in helping organisations of all sizes addressing their obligations under Data Protection principles and legislation.


Presentation 19:

Making Data Quality Metrics Meaningful to Business Sponsors and Stakeholders by James Phare

How do you make data quality metrics more accessible, intuitive and relevant to a business audience?

This is the problem that experienced data quality practitioner and former Head of Information Management, James Phare, tackles in his first Data Quality Pro Virtual Summit Presentation.

James tackles a range of issues such as:

  • How to close the gap between what metrics a data practitioner values and the metrics a leader needs
  • The phases of data metrics maturity and how to move your organisation up the curve
  • Understanding the different types of cost and how they impact metric selection
  • The importance of prototyping and simulation to deliver faster value to the business

Presentation 20:

Data Quality and Its Impacts on Decision-Making by Christoph Samitsch

The cornerstone of my presentation will be a discussion about the relationship between data quality and decision-making. Not only is there strong evidence that data quality influences decision-making performance, but the time it takes to make a decision may also be a variable affected by that.

In the first place, I will shed some light on what constitutes data quality. This will include a presentation of the most important data quality dimensions as identified from past research efforts.

Furthermore, I will be discussing the decision-making process and how data quality comes into play when talking about an increase or decrease of decision-making performance. An example scenario for which this assumption could be applied is the usage of Decision Support Systems, which are often times being used either in a professional manner or for personal purposes. As an example, Google Maps is heavily used by users as a spatial Decision Support System with the aim to make decisions more efficiently.

Finally, I will go over the experiment I have conducted and the research data I could gain from it. This will also include the most important hypotheses I have attempted to confirm within the settings of my experiment. I will round this up with the main findings of my thesis and will also go over some suggestions for future research.


Presentation 21:

Welcome to the Data Zoo! by Julian Schwarzenbach

The behaviours of staff towards data can have a critical impact on the data used by an organisation. Sometimes users, with best intentions, do the craziest things with data.

Our popular Data Zoo concept explores the ways that people interact with data, the good and bad points of these interactions and, most importantly, the organisational factors that influence data behaviours.

The Data Zoo has been updated to reflect the latest thinking and also has had a visual make over and will form the basis of this presentation.

Do you know where your Data Squirrels store their data? Do you know how to deal with Data Anarchists? Do you know why you should have a Data Evangelist?


Presentation 22:

Applying Data Quality and Confidence in the Environment Agency by Angie Wills and Rebecca Strickland

The Environment Agency is a public sector organisation.

With increasing data transparency from public bodies, data quality is taking centre stage.

This session outlines our experience of using a pragmatic approach linking data quality and confidence to impact in our outcome focused organisation.

We will discuss:

  • How we worked internally to identify and prioritise data quality and the risks when data fails to meet our expectations
  • How we linked data quality to outcomes in an organisation that isn’t revenue driven
  • The data quality issues we faced
  • The benefits we realised

Course Includes

4 Quizzes

39 Texts

20.0 hrs

What's included?

Applying Data Quality and Confidence in the Environment Agency by Angie Wills and Rebecca Strickland

Competing with High Quality Data – Building and Executing a Successful Data Quality Approach by Rajesh Jugulum

Data Quality and its Impacts on Decision Making by Christoph Samitsch

Data Quality in Emerging Markets by Gary Allemann

Data Quality Requirements by Laura Sebastian-Coleman

Data Quality Strategy in a Big Data Analytics World by Alex Borek

Destination: Data Town – A (Fun) Path to Better Data Quality by Bill Florio

Enabling Greater Data Intelligence Through Grass Roots Data Quality Efforts by Don Jenkins

It’s What’s Inside That Counts: How Your People Can Lead Your Data Governance by Charles Joseph

Making Data Quality Metrics Meaningful to Business Sponsors and Stakeholders by James Phare

Master Data Management: Defining MDM Maturity with a Continuous Improvement Approach by Mark Allen

Metadata Management as a Key Component to Data Governance, Data Stewardship and Data Quality By Dalton Cervo

Setting up a Data Quality Risk Management Program at your Organisation by Alex Borek

Superb Data Governance is not the Outcome by Lisa Allen and Michael Rose

The Three Legged Stool – How Data Privacy Compliance is Supported by Data Governance and Data Quality Practices by Daragh O Brien

Through the Looking Glass: An Analytical Approach to Targeting Data Governance by Jon Evans

Using Lean Principles to Manage the Data Value Chain by Mario Faria

Welcome to the Data Zoo! by Julian Schwarzenbach