Pre-Conference Tutorials and Conference Sessions -
June 27, 2016
| Monday June 27 7:30–8:30 |
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| 8:30 - 11:45 MORNING TUTORIALS | |||||||||||||||||
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T1: The
Best Place to Start is HERE - Getting Started Governing Data Kelle O'Neal, Founder and CEO, First San Francisco Partners This tutorial will be a practical step-by-step process to help any organization get started governing data. We will talk about how to identify starting points in your organization and what roles are typically involved across an organization. We will talk about how to link data governance to other high priority initiatives within a company, such as Customer Experience or a Back Office Transformation. After this session, you will be able to:
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Monday
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T2: Governing the Business
Vocabulary to Achieve a Shared Organizational Data Understanding Peter Aiken, Founder and President, Data Blueprint Karen Akens, Data Consultant, Data Blueprint Organizations with multiple data sources often have one or more of the following data challenges: Lack of clarity across the enterprise about authoritative sources of data, various views on the values of data elements, lack of agreement on names and definitions, and various ways of calculating the same term These challenges cause misunderstandings that result in lost time, lost opportunities and lost revenue. A Business Glossary is a critical, first step in any Data Governance program and a tool that can address these challenges. In this session, Data professionals will learn how to develop a Business Glossary and how to overcome common challenges. Using government and commercial case studies as examples, they will also learn how to:
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Monday
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T3: Hacking Data Ted Harrington, Executive Partner, Independent Security Evaluators Data governance exists to ensure the availability, usability and integrity of what has become a massively valuable asset for businesses, and ecurity professionals often talk about the desire that adversaries have to replicate and steal data. However, modern adversaries often attack companies to not only steal the data, but also to make it unavailable, unstable, or worthless, thereby undermining the very objectives of a data governance program. Presented by the elite security research organization who was first to hack the iPhone, Android, Diebold eVoting machines, and pioneered car hacking, this session explores security issues in a data governance context. Through both lecture style and hands on modules, this session will analyze who the adversaries are, what their motivation would be to undermine the principles of an effective data governance program, and how to calculate asset value. Through analysis of attack anatomies, this session breaks down real-world attacks in order to extract lessons about how data governance professionals can best understand the adversarial perspective in order to consider defense accordingly. Attendees will leave with immediately actionable guidance that they can bring back to their organizations to drive towards data governance programs that better account for modern adversaries and how they operate. Level of Audience |
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Monday
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T4: Building Your DG Team
and Effective Communication Techniques to Promote Your DG/DQ Programs Barbara Deemer, Chief Data Steward, Navient Michele Koch, Director, Enterprise Data Management, Navient It is a well-known fact that strong communication is critical to the success of any Data Governance (DG) or Data Quality (DQ) Program. In this tutorial, Michele Koch (Director of the Data Governance Office) and Barbara Deemer (Chief Data Steward) will present Navient’s experiences and resulting best practices while implementing and sustaining their DG and DQ programs. Michele and Barbara will also share how to build your DG team. Navient’s enterprise DG program won the 2011 Data Governance Best Practice Award. This session will cover:
While the ideas are not revolutionary, the real world examples will help to trigger lots of ideas for your own company. Level of Audience |
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Monday
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T5: Understanding and Applying The Dimensions of Data Quality Malcolm Chisholm, President, AskGet Inc The dimensions of Data Quality lie at the heart of this important practice area of data management. Yet many practitioners struggle with what they mean. In this tutorial we critically examine the dimensions and break them down into meaningful components. These components can in turn be used to direct aspects of Data Quality work. Without understanding the dimensions of Data Quality, a Data Quality program can becomes confused and misdirected. How the dimensions can be used to govern a Data Quality program is also examined in the tutorial. Another topic that is covered is how quantitative aspects come into play with the dimensions, and how meaningful metrics can be created. Qualitative aspects are also examined, and certain dimensions appear to have more qualitative than quantitative aspects. The importance of how the dimensions affect data issue resolution is also examined, with a discussion of the practical implications. Attendees will learn:
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Monday
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T6: The Art and Science of
Data Quality Metrics Danette McGilvray, President and Principal, Granite Falls We know that a data quality monitoring process will not guarantee quality. However, once root causes are identified, measures to prevent data quality problems are put into place, and existing data errors are corrected, it is often helpful to implement on-going data quality metrics. Metrics provide visibility to data quality issues so we can react quickly when they arise. Those same metrics can show us where things are working so we can let us turn our attention to other priorities. There is also an art to metrics – understanding that metrics change behavior and designing them so you promote the behavior you intend. If data quality is important to your data governance efforts, join us to learn important points to successfully design and implement data quality metrics:
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| 12:00
- 12:30 DATA GOVERNANCE AND IQ SOLUTIONS |
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Monday
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A Framework for Technology-enabled Data Governance Steven Zagoudis, CEO, MetaGovernance Solutions LLC Effective Data Governance offers meaningful solutions towards enabling the business and technical goals of accurate, accessible and trusted data and information. Business advantage requires the optimal use of information assets while simultaneously eliminating the waste and uncertainty (risk) in these assets. The relationship between business processes and the information created or consumed can be defined in an overarching Data Governance architecture. Business processes, governance stakeholder roles, information sources, business attributes, data elements, data dictionaries, data controls, etc. are but a few of these components. This session demonstrates a technology-enabled governance framework that provides a classification of the various data and information assets of an organization. The framework is then extended to show the association of processes, workflows, systems, controls, security implications, and people to provide a manageable view of Data Governance. Data and process noise becomes governance and compliance news. The mantra of “know thy data” becomes organizational reality. During the session you will learn:
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Monday
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Embracing a Two-speed IT Paradigm Michael Berard, Senior Account Executive, Experian Data Quality The dichotomy of speed versus endurance is deeply engrained in our culture. We see the two as mutually exclusive; if you want speed, you have to sacrifice stability. This is very much how IT departments have functioned for many years. But in today's rapidly evolving digital environment, one thing is certain: to be successful, organizations need to balance speed and stability. To do this, many organizations have structured their IT departments into two teams that work independently, yet synergistically, to meet ongoing business needs. Enter the two-speed IT paradigm. In this session, we will explore how you can implement the two-speed IT model in your data management strategy in order to achieve optimal business results. This session will cover:
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Monday
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TopBraid EDG - Addressing the Data Governance Needs of Dynamic Enterprises Ralph Hodgson, CTO, TopQuadrant Data governance is increasingly diverse in its scope. Are your tools up to the job? Do they provide flexibility to seamlessly modify and extend what governance has to address and the types of assets being governed? In this short presentation we showcase TopBraid Enterprise Data Governance (EDG). As a modular, extendable solution, it provides a model-driven approach to integrated governance over multiple types of assets, including business glossaries, reference data, crosswalks, ontologies, taxonomies, data, policy and lineage models and other business, technical and operational metadata. Governance of "linked assets" enables capabilities that make data "meaningful". We will show how TopBraid EDG delivers:
Using standards-based graph technologies, TopBraid EDG supports governance across the ever growing numbers and types of assets and governance requirements. Level of Audience |
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| 1:30 - 4:45 AFTERNOON TUTORIALS | |||||||||||||||||
Monday
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T7: Superheros
Wanted…Capes Optional: Building a Strong Data Stewardship Community within
your Organization Erik Ferrone, Sr. Mgr F&A Data and Process Governance, TIAA Michael Nicosia, VP, F&A Strategy, Planning & Data Governance, TIAA Why are superheroes super? The answer to this question has been debated since the first comic book was created. Interestingly enough, the characteristics that make a superhero, super are the same type of characteristics (criteria) that you should look for in your Data Stewards. Now, more than ever, it is clear that a ground swell for effective data stewardship is upon us. Strong stewardship is what enables effective and sustainable data governance, and without it, your governance efforts will fail, at worst, or be marginally successful at best. This tutorial will provide insight into:
This tutorial will provide attendees the opportunity to learn about business-led data stewardship, explore a different approach to building the community, and learn about different ways they can keep the stewards engaged for the long-haul - all of which will provide greater insight into different ways in which one can be successful at implementing data governance within an organization Level of Audience |
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Monday
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T8: The Chief Data Officer
Crash Course: Learning What You Need to Get to the C-Level Anthony Algmin, Chief Data Architect, Uturn Data Solutions Taught by the first CDO (former) for the Chicago Transit Authority, this class pulls back the curtain on how to achieve a chief-level data role. Like each CDO job is different, each person’s path to get there will vary. There are, however, patterns to this journey. This tutorial examines those patterns and will help you determine whether pursuing the CDO role is the right adventure for you. First question to answer: is the CDO a technology role, a business role, or something else? We often debate business versus technology – it’s not about that. The CDO is about strategically uniting all of these aspects of our organizations that we spend so much effort separating. Business processes, technology applications, data – these are all crucial. But mostly the CDO job is a people job. Building teams, translating business and technology language, and leading our organizations to a better future by getting better at what they do by using data. It’s a simple concept, but to achieve it you need to know: data management, data governance, procurement, master data management, data quality, project management, metadata, risk management, programming, data warehousing, leadership, cloud technologies, marketing, statistics, communications, data lineage, sales, finance, human resources management, recruiting, program management, big data, internet of things, public speaking, strategy, and legal/compliance considerations. Being a bit of an entrepreneur would help, too. Our tutorial won’t be able to teach you everything, but we will explore the foundational skills and talk about what else you may want to consider studying. Will you really need to be an expert in all of them? Of course not. Will you find all of them useful if you want to become a great CDO? Absolutely. This session will also cover:
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Monday
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T9: Governing the Big Data
Lake including Information Security and Tooling Sunil Soares, Founder & Managing Partner, Information Asset In this session, Sunil Soares will review key considerations in governing the Big Data Lake including stewardship, data ingestion, metadata, tooling and model governance. Topics include the following:
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Monday
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T10: Driving Your Data Governance Program from a Business Viewpoint:
Best Practices, Direction and Advice Kelle O'Neal, Founder and CEO, First San Francisco Partners If you believe information is truly an asset, then engaging the entire business is mandatory. If managing information assets is a business issue, then data governance is a business program. Research shows none of the critical success factors for data governance have anything to do with technology. It is a key success factor that DG is aimed at providing business results, not IT results. It is also a proven fact that strong sponsorship is crucial for the success of any transformation in an organization, and EIM and DG is no different. Because so many DG programs are being initiated by business demand many participants and stakeholders find themselves in new territory. When business leaders are called upon to "do governance,", they need to learn about concepts like stewardship, data quality, culture change and information management, all while still accomplishing their day-to-day responsibilities. This workshop will walk the attendees through the assessment, definition, design and deployment of a data governance program from a business view. This presentation is intended for business leaders or managers that are new to data governance, or for data governance functions that are having trouble sustaining themselves. The workshop will cover:
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Monday
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T11: Metadata Management
for Data Governance and Data Quality Strategy Malcolm Chisholm, President, AskGet Inc Increasingly both Data Governance and Data Quality strategies are coming to rely on metadata for the processes they run and measurements of success. This tutorial describes a number of practical approaches that can be taken to manage this metadata. The most important classes of metadata area described, along with how they can be managed. For Data Governance, semantic and lineage aspects are emphasized, while for Data Quality, business rules are dealt with. How to organize a federated metadata architecture to rationally deal with all the different classes of metadata is described. The tooling requirements to deal with Data Governance and Data Quality Metadata, and the linkages between tools are also detailed. An aspect of metadata that is frequently overlooked is the quality of the metadata content that is produced. It is this quality that is by far the most important factor in metadata being of use to Data Governance and Data Quality programs, and practical measures to attain it are presented. Attendees will learn:
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Monday
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T12: From Root Cause
Analysis to Resolution Danette McGilvray, President and Principal, Granite Falls Have you ever felt caught in a loop of assessing data quality, seeing the problems, but not quite getting to resolution the way you would like? You see the problems but haven’t gotten to root causes and implementing real improvements. If that’s the case, this tutorial is for you. Join us to learn:
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| 5:00
- 5:50 Conference Sessions |
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Monday
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Caterpillar’s
Customer Data Governance Journey -- from an Analytical to Operational Model Mary Schmidt, Customer Master Data Owner, Caterpillar In 2015, Caterpillar embarked on a journey to shift from an analytical to operational MDM style. An important milestone in this journey was linking the customer master to Caterpillar's CRM system, enabling "real time" access to and creation of new customer records. This project would not only make finding and entering customer information easier for CRM users, but also eliminated separate data silos and increased the focus on data integrity. The exposure of this data and the opening of the customer data system to a user interface presents a new set of challenges and requires a refined set of data governance policies and procedures. A fully realized data governance model was executed in order to afford this new functionality. In this presentation, we will address the following:
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Monday
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A
Novel Approach Towards NoSQL Data Governance Donovan Hsieh, Sr. Enterprise Data Architect, eBay Corp. Although NoSQL databases have been adopted and used in Enterprise running mission critical applications, data governance for NoSQL is still lacking in their implementation. This immaturity is primarily due to NoSQL’s unique schema-free or flexible schema structure. It is further compounded by divergent types of NoSQL, e.g., Key Value pair, Big Table / Column Family, Document and graph model, which make them difficult to provide coherent data governance policy and practical solution. In this presentation, the speaker will talk about various challenges faced at eBay and propose a novel approach to support NoSQL data governance in Enterprise environment Level of Audience |
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Managing
Stakeholder Expectations for Data Governance Programs - Lessons from the
Trenches Iqbal Khan, Managing Director, Bintellica Program Directors responsible for managing Data Governance frameworks need to keep stakeholders engaged for continued buy-in and funding flow. They need to partner with teams managing data movement and transformations, to identify results of good data practices. As Chief Data Officers try to get their Data Governance projects off the ground, it is important to continue to have business unit stakeholders on board. In many cases the business is asked to fund some of these projects without a tangible near-term benefit. Highlighting the benefits of formalizing existing good practices can help in keeping the stakeholders interested until the target state is achieved and full benefits start to get realized. This case study will show how this was achieved by regularly reporting and publishing ongoing activities and events such as:
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Data Stewardship: Becoming Business as Usual Natalie Feinberg, Manager, Data Stewardship, Fannie Mae Christopher Trimble, Manager, Data Stewardship, Fannie Mae A key aspect of any Data Stewardship program is to ensure business ownership of data. One of the first steps to achieve this objective is to have the business community define standard business terminology for use across the enterprise, including ownership of each term. During this presentation, Natalie will share how the Enterprise Data Stewardship program has driven business engagement and a cross-functional enterprise artifacts interaction model, which has resulted in Enterprise Business Glossary (EBG) content now being a key deliverable for all strategic software development initiatives. She will also highlight the challenges encountered along this journey, including multiple sources of demand for EBG content, staying aligned with strategic initiatives, and getting ahead of agile development. Level of Audience |
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Addressing the Data Governance Challenges in Health Care Moderator: Peter Aiken, Founder and President, Data Blueprint Panelists: Shannon Fuller, Director, Data Governance, Carolinas HealthCare System Tran Ly, Data Strategist. NewYork-Presbyterian Hospital Holly Lee Sefton, Senior Business Analyst, Independent Health Jay Smith, Managing Director, Blue Cross Blue Shield Association This panel of practitioners working in healthcare will discuss the challenges they are addressing in implementing data governance in their organizations. Topics include:
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Data
Quality Practice in an Agile Environment Kim Olson, Enterprise Data Architect, Grinnell Mutual Reinsurance Company This session will show how to integrate Data Quality into an Agile Environment. We will review identification, prioritization, process and governance to align to business critical items. Data Quality in an Agile Environment can be implemented to show continued progress, improvement and evolution. It provides a way for the data quality backlog intake to come from multiple avenues for a full perspective in order to prioritize based on business objectives. The multiple avenues consist of intended data profiling discovery, business rule verification, implementation team findings, business needs, reporting pre-analysis conclusions and fraud requirement detection. This process provides a Data Quality Practice that will streamline, provide consistency and improve quality through collaboration between business and information technology teams. Quality items are quickly captured, refined and worked through in order to be classified and prioritized. Agile provides a way to tightly integrate into day-to-day processes making quality visible within every aspect of the business. Data Quality is given focus to keep company data assets in the highest quality so decisions are made based on accurate, complete data. The session outline consists of:
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