Conference Sessions - June 29, 2015
| Wednesday June 29 7:30–8:30 |
Registration and Continental Breakfast | ||||||||||||||||
 8:00
- 8:50 CONCURRENT SESSIONS |
|||||||||||||||||
Wednesday
|
Tips from the Trenches-Lessons Learned from Successful Practitioners PANEL Moderator: David Loshin, President, Knowledge Integrity Panelists: Maggie Hubble, Global Data Governance Office, General Motors Michael Kelly, Chief Data Officer, University of South Carolina Kate Wood, Data Governance Director, Dr Pepper Snapple Group Michele Koch, Director, Enterprise Data Management, Navient This panel discussion will focus on real life experiences and challenges encountered by practitioners in starting, deploying and sustaining data governance and data stewardship programs Topics include:
Level of Audience |
||||||||||||||||
Moderator:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Ethics
in an Information Management Context Katherine O'Keefe, Data Governance & Privacy Consultant, Castlebridge Associates From recent high profile failures in ethics like the Volkswagen emissions scandal, to countless articles about the call from data scientists for clarity on ethics, to the European Data Protection Supervisor is calling for a focus on Ethics in Big Data there is a growing consensus that "something must be done". However, much of the discussion of Ethics takes place in the abstract, and the real challenge in commercial and not-for-profit organizations is ultimately what happens in reality when the organization is faced with the power of modern information management capabilities. Ethics risks being seen as another "tick box" item to be taken care of by the "Ethics people", just as Information Quality is often seen as the role of the "Quality department". Key takeaways for this session include:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Organizing
your House - Digging out of your Data Landfill Cynthia Parsons, Consultant, IT Analysis, Nationwide Insurance Scott Peachey, Consultant, IT Analysis, Nationwide Insurance Understanding the 360 degree view of your data involves being engaged in every aspect of the data's lifecycle. This presentation will include the following:
Level of Audience |
||||||||||||||||
Speakers:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Enriching
the Quality of Your Data Sally McCormack, Data Quality Practice Lead, Datasource Consulting Organizations today understand the need for data quality, but what can be done to continuously improve the quality of data and add value? The answer is to enrich your data. This session will discuss how geographic, demographic, behavioral, psychographic, and census information can be used to enhance and improve the quality of your data. In this session, attendees will learn:
This presentation is targeted at the more experienced data quality practitioner. Instead of discussing how to establish a data quality initiative, this presentation assumes the audience already possesses this knowledge and has attained this experience. Instead, this presentation will highlight how an organization can scale existing data quality practices for more meaningful information and actionable insight. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Data
Quality Quantitative Assessment Dennis Huss, Data Governance Team Lead, Federal Government Jennifer Kish, Data Governance Program Specialist, Federal Government Ever wonder how good the data is, when you’re reading some report? Or how good the data needs to be for the report conclusions to be believable and usable? We all know that high data quality is good. But how good does it need to be for a specific business purpose, and at what cost? How much should be invested to assure good quality data? Can we focus our efforts on particular aspects of data management to maximize the effect of our investments? This session presents an algorithm that uses eight aspects of data management:
By assigning impact weighting, an expected level of quality is derived. Finally, the algorithm is continually improved through comparison of estimated level of error to actual level of error. As time and the virtual distance between the data consumer and the data producer increase, data needs a succinct, robust rating that assesses the work of the data producer and provides the data consumer with an indicator of how critical a purpose the data can be put. If the consumer is going to Court, the data must be absolutely correct. If the consumer simply wants to find a general trend from billions of normally distributed data points, a million errors are likely insignificant. A pre-development assessment of expected data error based on business management and technology application can help determine where investment is most needed to produce data that serves it purpose. Level of Audience |
||||||||||||||||
Speakers:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Creating
Master Data Governance at Stanford University Kathleen Warmoth, Data Stewardship Coordinator, Stanford University Stanford University defines master data as data that crosses over all business units and schools of the university. Historically the university’s data governance program has concentrated on defining and compiling the metadata for elements used in BI reporting. As the program matures there is mission-critical need to establish policy, process and protocol for managing the master data. Join Kathleen Warmoth, Data Stewardship Coordinator, as she shares Stanford’s approach to governing their master data: how they are creating effective policy by using the steering committee as the master data managers, and how they are continuously improving their master information to support business intelligence and decision support initiatives. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
KEYNOTE: How to Establish Information Leadership for the Digital Enterprise Will Crump, CEO & President, DATUM LLC According to analyst firm IDC data is the core of digital transformation. One consistent best practice for success from digital leaders is to have an information strategy that directly maps to business capabilities and goals. In this session learn the 3 essential steps for creating that strategy and establishing information leadership. Information success is less about managing and more about leading. This session will include examples from real companies in order to demonstrate the business value that can be realized when adapting an information strategy to deliver digital success. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 9:35 - 10:15 Break & Exhibits Open | |||||||||||||||||
| 10:15
- 10:45 DATA GOVERNANCE AND IQ SOLUTIONS |
|||||||||||||||||
Wednesday
|
Governance 2.0: Enabling Complete Business Process Governance Rex Ahlstrom, Chief Strategy Officer, BackOffice Associates Enterprise IT landscapes contain a mixture of on-prem and cloud-based systems, business and IT users, and multiple data types. To be successful in Information Governance, users must be able to set and enforce data governance policies across this diverse landscape. Forrester describes this as “Governance 2.0” and Gartner recently published research on an emerging product category called “Information Stewardship Applications.” Join Rex Ahlstrom to learn about the importance of setting and enforcing governance policies and how dspConduct™, BackOffice Associates next generation business process governance solution, extends beyond traditional MDM to enable complete Information Governance. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 10:15
- 10:45 DATA GOVERNANCE AND IQ SOLUTIONS |
|||||||||||||||||
Wednesday
|
Adding Data Quality Functions to Microsoft Excel Victor W. Fehlberg, Chief Technology Officer, Aim Dynamics Too often data stewards are forced to depend entirely on IT for data quality systems because the tools in the marketplace are complicated to use. Aim-Smart is an easy-to-use Microsoft Excel companion that provides powerful data quality algorithms such as profiling, matching, deduplication, parsing, address verification and more, within a tool users are already familiar with – Excel. Users can leverage all of Excel’s features – filtering, sorting, coloring, functions, etc. and then, using the same tool they can employ the power of a full-featured data quality engine. This tutorial will show how to leverage this new Excel Add-In to make the most of your data. We will cover how to:
Come see what this new tool can do for your business! Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 10:15
- 10:45 DATA GOVERNANCE AND IQ SOLUTIONS |
|||||||||||||||||
Wednesday
|
Combining Best-practice Data Management Methodologies with Data Governance Technology Darius Clayton, Founding Partner, Diaku Sethuram Bommireddipalli, Practice Director, Trinus Data governance is at the center of any data management strategy. When done right, it connects the various data disciplines and in doing so strengthens each offering while providing a rich view of data and its use. In this session, you’ll hear tales from the trenches taken from decades of experience implementing data governance programs in organizations of different sizes in many industries, and how new data governance technologies are able to make data management greater than the sum of its parts. Level of Audience |
||||||||||||||||
Speakers:
|
|||||||||||||||||
| 10:45 - 11:00 Room Change | |||||||||||||||||
| 11:00 - 11:50 CONCURRENT SESSIONS | |||||||||||||||||
Wednesday
|
Metadata-Driven
Data Validation Case Study Jay Smith, Managing Director, Blue Cross Blue Shield Association This presentation is a case study of an internally developed solution at the BlueCross BlueShield Association headquarters in Chicago. BlueCross and BlueShield Health Plan licensees across the US submit cost data on a variety of medical treatment categories for central analysis. Basic formatting and code value errors were not found until late in the Data Warehousing process, leading to delays and the need for Plans to re-submit their data long after their initial submission, which in turn led to dissatisfaction with the process. We were able to leverage our existing metadata infrastructure, which stores data descriptions, data types and valid code values when appropriate to develop a data validation solution that could be run immediately upon receipt of the inbound files. As a result, errors are exposed and can be corrected early in the process, leading to a reduction in re-work and higher overall satisfaction with the process. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
How
Smart Cities Handle Data Governance Christoph Kögler, Head of Innovation, T-Systems Multimedia Solutions GmbH The ongoing digitalization of urban space entails new challenges in data governance. Smart Cities involve enormous amounts of data the evaluation of which holds the key to creating sustainable and efficient structures. Data must be stored securely and legal issues must be clarified. Who owns which data and who is entitled to access it? Which data can be made accessible for the general public in the context of an open data initiative and which data must be kept under lock and key because it enables people to be identified or constitutes a security risk? To answer questions like these, new processes, tools, and mechanisms are needed to manage data and ensure its integrity. Christoph Koegler, Head of Innovation at T-Systems Multimedia Solutions, provides compelling insights into the topic and best practices from actual Smart City projects. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Global
Customer Data Governance - General Motors Maggie Hubble, Global Data Governance Office, General Motors This session will provide a look into General Motors Customer Data Management journey. Where we came from, where we are today and what the Data Strategy looks like as we take our initiative to the Enterprise. Topics covered include:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Successful
Beginnings: a Data Quality Success Story W. Michael Armbruster, Data Quality Manager, Jeffco Schools Curtis Lee, Executive Director for Data Quality and Ed Tech Support, Jeffco Schools Where do you begin with Data Quality? The answer is not obvious when you work In a world of third party applications mixed with home grown applications, a young Data Governance structure, tight budgets, limited resources, systems integration in progress, no official data models, multiple DBMS platforms and operating systems, and systems users spread across 154 Schools, 86,500 students, 86,500 Parents, and over 14,000+ teachers (4700) and administrators. Here's one idea that worked and has built a solid foundation for future successes and Data Governance efforts. This session will explore:
Level of Audience |
||||||||||||||||
Speakers:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Agile
Analytics-as-a-Service Startup Accelerates Success with the DMM Melanie Mecca, Director of Data Management Products and Services, CMMI Institute Roberta Lingau de Oliveira, Enterprise Data Governance Program Manager, Neoway Business Solutions Neoway Business Solutions creates market intelligence through its scalable platform for big data analytics. By gathering, cleansing, aggregating, analyzing and modeling, Neoway turns big data into smart data. The value of effective Data Governance and a solid Data Quality program is high; however, startups have a steep learning curve in grappling with data quality improvements and establishing data governance. Typical challenges are:
Neoway selected the Data Management Maturity Model to gain competitive advantage through proficiency in managing critical data, implementing end-to-end governance, and improving data quality. You will learn:
Level of Audience |
||||||||||||||||
Speakers:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
What is your 'data-type', are you an Emmet or a Wyldstyle? Lisa Dodson, Manager, Data Management Practice, SAS One of the biggest challenges organizations have today is communicating about data. We all have different data perspectives based on how we interact with data, understand data or use data. In this presentation we will use an analogy provided by the LEGO movie to better understand our own and, hopefully, our co-workers ‘data-types’. Knowing your own and your co-workers’ ‘data-type’ will lead to better communication about data within your organizations. Join us and find out if you are an Emmet or a Wyldstyle. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 11:50 - 1:15 LUNCH | |||||||||||||||||
| 1:15 - 2:05 CONCURRENT SESSIONS | |||||||||||||||||
Wednesday
|
Data
Naming Architecture – How to Define and Name Data According to Standards Karen Sheridan, Data Management Senior Business Consultant, The Hartford The Hartford has developed a repeatable data naming methodology that, when followed, results in creation of a consistent enterprise vocabulary. A standardized naming process helps to minimize redundancy and prevents the use of alias terms. Data Stewards leverage this process to create business names and definitions to load into our metadata repository via our data dictionary template. The resulting business glossary provides the foundational building blocks for standardized and consistent data naming throughout the enterprise. The gradual development of one name for one business concept and one piece of data enables better communication across the enterprise by both people and systems and provides users the ability to understand relationships between data elements. A good name should provide an indication of the purpose and meaning for the data without dependency on a repository. Topics this session will include:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Governing
Data for Privacy in the Internet of Things Age Robert S. Seiner, President / Publisher, KIK Consulting / TDAN.com Data is the fastener that connects everything through the internet. The transfer of data between machines will drive the future of everything we do in our personal and professional lives. This data includes personally identifiable information (PII), personal health information (PHI) and intellectual property (IP) and it must be governed to assure that all privacy rules are being followed. This session with Bob Seiner will examine the relationships between governance, privacy and the Internet of Things. Bob will share information from clients that have focused their data governance efforts on privacy issues inherent to transferring data through non-traditional means. The IoT age is upon us and there is a lot to learn about governing data for privacy. In this session Bob will talk about:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Aligning
your Enterprise Data Governance Program to Beyond Regulatory Affairs Kevin Shannon, Global Head of Enterprise Data Governance, Dun & Bradstreet Some companies have enterprise data governance programs that are now turning focus beyond reaction to regulatory requirements. What should be next for your enterprise data governance program to sustain relevant, current business value? In this presentation we will discuss areas of focus to keep your enterprise data governance program relevant, such as:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Data Governance, Customer Loyalty, and the Mobile Pay Disruption John Talburt, Chief Scientist, Black Oak Analytics, Inc This session discusses the competitive advantage of extending data governance to include customer communication and preference management. Research by the Aberdeen Group has shown that on average companies interact with their customer over 9 different channels. The research also shows that companies using customer communication management technologies obtain more cross-sell and up-sell revenue, higher lifetime customer value, and higher returns on marketing investments than companies without these technologies. The session describes the policies, strategies, and technology required to bring customer communication management under enterprise data governance as “Customer Communication and Preference Governance” or CCPG. In addition, the session will discuss the crucial role that CCPG will play in helping merchants improve customer loyalty, a key strategy in overcoming the disruptions to marketing analytics arising from the rapid adoption of mobile pay applications. Points discussed include
In addition, this session will also discuss how CCPG advantage companies in handling the increased disruption in marketing analytics being caused by mobile pay applications. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Turning
a Cruise Ship with a Plastic Oar, the Cultural Impacts of Data Governance Shannon Fuller, Director, Data Governance, Carolinas HealthCare System The culture of your organization will dictate the manner and speed at which you can implement a Governance program. Understanding the impacts of implementing a Governance program on the organization is a key element in designing your roadmap, communication strategy and implementation plan. Key Questions:
Introductory |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Best
Practices for Implementing a Data Quality Monitoring Initiative Hillary Bliss, Senior Manager, Data & Analytics, Protiviti There are several popular tools on the market for automated data governance monitoring and proactive detection of data quality issues and you can also set up your own processes using almost any database or ETL tool, but it requires significant investment to develop and implement business rules that produce meaningful results. Before spending months and months of business and IT resources developing content, make sure you have a winning strategy that will lead to a successful implementation. We will share best practices and lessons learned from dozens of successful data governance jump starts for framing, scoping, communicating, documenting, developing, and implementing an automated data quality monitoring process, with a focus on the business user and data quality manager. Key Takeaways:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 2:05 - 2:20 Room Change | |||||||||||||||||
| 2:20
- 3:10 CONCURRENT SESSIONS |
|||||||||||||||||
Wednesday
|
Too
Fragile to Flex? Kimberly Nevala, Director, Business Strategies, SAS Best Practices This session will explore the Impacts and potential implications of key analytics and data trends on the practice of data governance. Join us to learn what the digital enterprise, the tech- and data-savvy consumer, the rise of the machine and all those things imply about the future of governance. What practices, roles and approaches will stay, go, or must adapt to meet new patterns of information creation and consumption? You will learn:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
DIY Data Governance: NewYork-Presbyterian Hospital's Online One-Stop, Self-Service Governance Shop Tran Ly, Data Strategist, NewYork-Presbyterian Hospital NewYork-Presbyterian Hospital’s new strategy for formalizing data governance promotes open communication and collaboration by centralizing & standardizing metrics, terminology and other resources online. We took a home grown, DIY approach in creating various resources including systems mapping, and tools that help us correct misattributed metric values. We documented work & communication processes, and culled a thorough contact list to increase transparency and foster shared ownership of governance responsibilities across roles and departments. The self-directed nature of the website allows us to set up a governance infrastructure in an unobtrusive way, but its ease of use belies its sophisticated operations. The site also serves as an unambiguous show of the hospital’s commitment to governance and a data driven culture. Results of the Data Governance Suite’s launch are forthcoming, but early feedback suggests our systematic yet colloquial approach has been effective at giving users explanatory information about their data. Topics include:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Governing
Reference Data in a Large Enterprise Saad Yacu, Manager / Delivery Lead, Allstate Insurance As the demand for advanced analytics and data quality initiatives increase, a key pillar into supporting these initiatives is to have a solid enterprise reference data organization to enable these capabilities. Without having a solid governance process over the reference data management process, the organization will risk having a huge amount of low quality reference data which reduces the confidence with the resulting analytics or provide incorrect data manipulation. This presentation will discuss the challenges and alternative approaches taken to govern the various components of reference data and making sure that there is a sustainable approach to keeping the content current. Some of the topics covered in this presentation are:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
The
PPT of Data Quality, Process People & Technology Estrella Gallegos, Data Integrity Manager, Rackspace Learn how to enhance the quality of your data by leveraging your organization's PPT (Process, People, & Technology) with a practicing Data Therapist. In this session you will hear real world examples along with many tricks of the trade on how you can use what you have, and deliver solid results. Hear some simple things that can be done immediately to help reduce duplication and increase accuracy and completeness. As well, you will learn approaches for getting everyone on a mission of making data driven decisions. The speaker has experience implementing data quality programs in healthcare, financial services and technology companies. What attendees will learn:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Keep
Calm and Protect Your Data: Rapidly Creating an Effective Data Protection
Program Michelle Fleury, Corporate Senior Director, Data Protection Officer, Cisco Systems Thought it’s all over the news, way too many companies are surprised by and utterly unprepared for a data compromise when it happens to them. Because the threats are varied and unpredictable, many companies find it difficult to decide where to start -- so they don’t! Unfortunately, the consequences for inaction can be disastrous: recovery and remediation costs, regulatory fines and ongoing monitoring (sometimes up to 20 years!), reputation impact – even the complete interruption of your business. This session will teach you how to rapidly establish an effective data protection program. Using Cisco as a case study, we’ll explore the essential elements of an effective program, the business case for data protection, the critical steps to developing and deploying a program, and tips for getting your program up and running quickly. Top 3 Takeaways:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
Wednesday
|
Identity
Resolution for Data Quality and Master Data Management David Loshin, President, Knowledge Integrity Despite one ’s best efforts at controlling the quality of data representing those critical entities such as customers, products, vendors, and other business concepts, there is no system that controls the introduction of errors. Misspellings, added characters, extra words, etc. all confound the ability to uniquely identify, search for, and differentiate entity data, ultimately impeding consistent reporting and analysis. No data quality or master data management program can be complete without using processes and technologies for identity resolution, record linkage, and matching. Yet most data practitioners do not fully comprehend how identity resolution works nor how best to tweak the algorithms to achieve desirable results. This session reviews the basic approaches to identity resolution and it is used for data cleansing, data quality, and master data management. Attendees will learn about:
Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
| 3:10 - 3:30 Coffee Break | |||||||||||||||||
| 3:30 - 4:20 KEYNOTE | |||||||||||||||||
Wednesday
|
KEYNOTE: Common Knowledge Graeme Simsion, Bestselling Novelist and Former Data Management Professional When you take on a new project, a new job, or a new career, what can you bring to it from your past experience? Or, turning the question around, what capabilities and skills should you work hard to develop, knowing that they will serve you throughout your working-and perhaps personal-life? Graeme Simsion is well-placed to answer these questions-particularly for those in the data management profession. He has achieved success as a technical professional in data modeling and data management, as a business founder and manager, as a researcher (data modeling again) and, most recently, as a screenwriter and best-selling novelist. Level of Audience |
||||||||||||||||
Speaker:
|
|||||||||||||||||
|
|||||||||||||||||
