Wednesday, July 23
7:45am – 8:45am Continental Breakfast & Registration – Ting Foyer
8:45am – 10:15am Session 1 – Opening Remarks and Keynote Speech – Wong Auditorium
Peter Anlyan, Anlyan Consulting (Co-chair)
Robert Lutton, Sandhill Consultants (Co-chair)
Paul Gillin, B2B Social Media Strategist (Media Chair)
Introducing 2015 Co-chairs – Rich Wang, MIT CDOIQ Program
Stuart Madnick, John Norris Maguire Professor of Information Technologies,
Sloan School of Management & Professor of Engineering Systems, School of Engineering
David Schmittlein, John C Head III Dean of the MIT Sloan School of Management
Sponsor Greetings – 30 seconds each
Keynote: “The Digitization of Capital” – Wong Auditorium
Richard Watson, Research Director for the Advanced Practices Council of the Society for Information Management and J. Rex Fuqua Distinguished Chair for Internet Strategy at the University of Georgia.
10:15am – 10:30am – BREAK
10:30am – 12:00pm Session 2 – Keynotes – Wong Auditorium
“A Holistic Systems Approach to Architecting the Future Enterprise”
Deborah Nightingale, Former Director, MIT Sociotechnical Systems Research Center (SSRC), Professor of the Practice of Aeronautics and Astronautics and Engineering Systems; Co-Director, MIT Lean Advancement Initiative
“You May Not Need Big Data After All” (Harvard Business Review, December 2013)
Jeanne Ross, Director, MIT Center for Information Systems Research
12:00pm – 12:45pm LUNCH Ting Foyer
12:45pm – 2:15pm Session 3 – Wong Auditorium “Data Standardization Lessons Learned and Best Practices”
Chair: James Meng, Deputy Assistant Secretary of the Navy, Architectures, Standards and Integration
Morteza Anvari, SES , Director, Programs & Strategy, Deputy Assistant Secretary of the Army
David Dalenberg, Senior Financial Systems Analyst, OUSD(C)/ DCFO / Business Integration Office
Peter Kaomea, CIO, Sullivan & Cromwell
Douglas Koski, Director, Information Systems and Technology, Assistant Secretary of the Air Force
Mark Krzysko, SES, Deputy Director, Enterprise Information, OUSD(AT&L) ARA
Pamela Wise-Martinez, The White House/DNI
2:15 – 2:30pm Break
2:30pm – 3:30pm Session 4A – Wong Auditorium
Report on CDO Research – Yang Lee, Stuart Madnick, Rich Wang
Abstract: A new breed of executive, the chief data officer (CDO), is emerging as a key leader in the organization. After presenting the motivation for this phenomenon, we summarize the current findings from our CDO survey research. Then we provide a three-dimensional cubic framework that describes the role of the CDO. The three dimensions are: (1) Collaboration Direction (inwards vs. outwards), (2) Data Space (traditional data vs. big data) and (3) Value Impact (service vs. strategy). We illustrate the framework with examples from early adopters of the CDO role and provide recommendations to help organizations assess and strategize the establishment of their own CDOs.
Session 4B – Room E51-145 “Data Quality Through Curation at Big Data Scale”
Andy Palmer, Tamr, Inc.
Abstract: Enterprises’ ambitious plans for Big Data are driven by the desire to “compete on analytics,” as described by Tom Davenport in his 2007 book. Two kinds of innovation stimulated the first phase of Big-Data adoption: (1) new data engines for specific analytic workloads or one size does not fit all for database systems and (2) next-generation, web-based visualization tools and systems. The next phase will be stimulated by innovation in the middle of the “Big Data” stack. It will involve a complete overhaul of traditional methods of information integration and quality management: from ETL to data cleaning to master data management. Innovation will enable a more automated, intelligent and collaborative approach to data integration, quality and curation that’s up to the challenge of Big Data: collaborative data stewardship. Collaborative data stewardship “flips” the traditional cycle of highly engineered, manual ETL – replacing it with more-automated and collaborative processes for discovery, ingest, validation, transformation, correction, consolidation, and publishing. Innovation is required because existing tools are too manual, designed for “single-user” curation, rely on trained engineers to implement point-to-point integration pipelines of data sources, and focus on efficiently moving data from point A to point B.
Session 4C – E51-149 “Data Management in Financial Services”
Mark Temple-Raston, Senior Vice-president, Citigroup
Abstract: Data Management is conceptually deep. Everything we work with is data. Therefore the data management discipline itself includes managing your mission data, as well as managing the data that you use to manage your mission data. Data Management is self-referential. To better understand Data Management, we need a mathematical foundation that is not difficult to understand and tells us “where to go next”. Mathematical analysis in financial services can be used to clarify Data Management, and with further development make Data Management more effective. We present financial services as an example.
3:30pm – 3:45pm BREAK
Industry Solutions: Manage and Exploit
Big Data and IQ
3:45pm – 5:15pm in 4 Rooms
Session 5A Wong Auditorium
3:45 – 4:15: 5A-1 “How Federal Agencies Can Prepare Now for the DATA Act.”
Catherine Ives, Data Quality Solution Expert, Citizant
Hudson Hollister,Founder & Executive Director, Data Transparency Coalition
Abstract: The DATA Act, signed into law on May 9th, builds on existing legislation to strengthen democratic accountability and public-sector management of federal spending. This session will introduce the new law, and discuss how new data standards will improve access to and the usability of federal spending data and streamline reporting via a single, consistent electronic reporting format. Learn how the new standards will improve the presentation of federal spending data so that public-sector officials, businesses, or taxpayers will be able to more fully automate the dashboards, reports or business analytics they use to do their jobs. Hear about the implementation process, including what agencies can do to prepare now, and the ways in which, beyond the law, agencies can leverage the data standards to strengthen mission effectiveness and efficiency.
4:15 – 4:45: 5A-2 “Demonstrate a Successful Information Governance Program with Maturity Models: Quick and Easy”
Tina Rosario, Vice-president, Data Governance and Management, SAP
Abstract: So you’ve started an information governance initiative. How can you tell if you are making progress? And how can you tell if one area of your business is making more progress, while other areas are challenged? Can you demonstrate success to your stakeholders? In this session, learn from the Global Data Management organization at SAP as they discuss how they measure information governance maturity across the business. Find out what metrics they use to show stakeholders quantified value. Their straightforward approach can easily be leveraged by your organization too!
4:45 – 5:15: 5A-3 “Big Data: Enabling a Modern Data Architecture”
Dan Rice – Solution Engineer at Hortonworks
Abstract: Reduction in IT spending + explosion of data growth = modern data architecture (MDA). Legacy systems are unable to sustain the amount of new data sets that are being required. Augmenting existing systems with an MDA provides a cost effective way to deal with these new types of data while allowing legacy systems to continue to do the work they have been built for. This topic will explore how companies have leveraged this MDA today including:
o Data Management
o Data Access
o Data Governance
o Security and Operations
Session 5B Room E51-145
3:45 – 4:15 5B-1 “Using 1st and 3rd Party Data for Privacy Compliant On/Off-Line Targeting”
Joel Neubert, Government and Political Account Strategist, Acxiom Corporation
Abstract: There have been several recent breakthroughs in the field of message targeting in general, and online messaging in particular. Whether you’re trying to communicate with constituents or consumers, we’ll discuss best practices in making sure your message finds its way to the right mailbox, inbox, banner or online newsfeed – while remaining privacy compliant. Acxiom is at the forefront of enabling communication specialist with the tools they need to thrive in a marketplace vying for everyone’s attention.
4:15 – 4:45 5B-2 “Optimizing Data Landscapes with Machines”
Arka Mukherjee, Founder and CEO of Global IDs Inc
Abstract : Large organizations have complex data landscapes that are costly to maintain. A machine-centric approach to systematically analyze data landscapes can potentially reduce data management costs significantly. This presentation describes a practical approach to machine-centric reverse engineering. The ROI associated with this approach will also be discussed.
4:45 – 5:15 5B-3 “The Unreasonable Seductiveness of Data”
Todd Morley, Senior Vice President of Commercial Consulting, Chief Data Officer, Mosaic Data Science
Abstract: Business executives express uncertainty and frustration about how their organizations should capitalize on big data and data science. Early experiments with Hadoop and analytics bring a recognition that transforming data into economic advantage is much harder than merely storing data or developing elementary models. Likewise, there have been some highly publicized failures of big data models to deliver their promised benefits. In this session we examine some reasons for these failures and illustrate how a return to the rigors of the scientific method can lead to sound, durable analytical models.
Session 5C Room E51-149
3:45 – 4:15: 5C-1 “Unstructured” or “Uncontexted” Big Data
Don Soulsby, Sandhill Consultants.
Abstract: Big or small, structured or unstructured, for data to be meaningful and usable in today’s massive and complex enterprises, the means to assure that the data’s consistency and interoperability is required. Increased size and complexity along with reduced structural definition of data associated with Big Data initiatives aggravate the problem. Taking a business value perspective we will look at the Big Data varieties that are character centric; Human and/or Machine generated. In this presentation we will examine this textual data as lacking context rather than being “unstructured”. It is our position that in the terms of character based data, there is always a structure. It may, however, need to be discovered.
We will introduce the Sandhill approach to practices and technologies to classify and integrate textual Big Data into traditional data management and data delivery processes.
4:15 – 4:45: 5C-2 “Great Data Only Happens by Design”
Dominic Sartorio, Sr Director, Product Management, Data Integration and Data Quality, Informatica Corp
Abstract: Great data isn’t an accident. Great data happens by design. The challenge is that data is becoming increasingly fragmented. The explosion of technologies around the Internet of things and Big Data, along with the ability of lines of business to purchase their own applications in the cloud, makes managing data and achieving great insights a challenge. This session will focus on:
· The effects of the evolving data landscape
· How achieving great insight happens by integrating across fragmented data boundaries and automating data quality process
· The characteristics of companies that are leading in use of quality information to transform their organizations and improve business performance (vs those that don’t)
4:45 – 5:15 5C-3 “Global Data Management – How to Position for Information Trust and Executive Insight”
Suzanne Anderson, Vice President Enterprise Master Data – Johnson & Johnson Health Care Services
David Woods, Principal Partner EIM Strategy – DATUM LLC
Abstract: Deploying solutions that provide increased capabilities is a human event and technology itself doesn’t provide a “silver bullet.” In successful projects, the organization shares responsibilities in ensuring that deployed solutions support both the processes and analytics that delivers the desired value creation and profitability. The Data Management challenges each organization faces are unique in their priority and severity. Therefore the structure and composition of a Global Data Organization is one of the major success factors for establishing a successful and sustainable data program. In this presentation we will walk you through how Johnson & Johnson formed a strategy that would support the business initiatives and result in a technology roadmap with a clear downstream executional plan. Based on the Johnson & Johnson experiences, we will review the developmental stages of a data organization, the models and the choices for establishing the right structure to the organization in addition to the process for selecting the team members that will produce high-performance business results.
Session 5D Room E51-151
3:45 – 4:15: 5D-1 “Unleashing Data Provenance Over Your Kingdom / Enterprise”
Mark Johnson – Chief Business Development Officer – Gavroshe USA, Inc.
Paul Bertucci – CTO – Diginome, Inc. (Gavroshe Partner)
Abstract: Attacking data provenance challenges in the Financial Services industry in order to address some of the most critical pain points around regulatory compliance (Basel, Frank-Dodd, others) and in the Public Transit industry around “revenue leakage” is of utmost importance. By layering in an automated data provenance solution that underpins the core transactional systems to provide visibility, validation, auditing, tracing, heredity, integrity validation, and predictive analysis over an enterprises data scope these challenges can be met. In this Case Study we will highlight how this was done for a major financial institution in North America, and the impact it had on their ability to be 100% compliant with industry regulation because of this innovative “data centric” approach to enabling data provenance.
4:15 – 4:45: 5D-2 “The Chief Data Officer: Really The Chief Requirements Officer?”
Justin Magruder, President & Founder, Noetic Partners
Diane Schmidt, Managing Director, Noetic Partners
4:45 – 5:15: 5D-3 “Three Keys to Building Success and Traction with your CEO and C-Suite Peers”
Cortnie Abercrombie, Emerging Roles Leader for Big Data & Analytics at IBM
Abstract: Chief Data Officers face many challenges and obstacles to success when they take the role. One of the biggest is how the organization participates or does not participate with the role. Based on findings from IBM Institute for Business Value C-suite studies, this session focuses on three keys for building traction and success: 1) prioritizing initiatives for highest impact, 2) generating traction through small successes (breaking down data siloes), and 3) creating data and analytics strategy that garners impact now and builds toward the future.
5:30pm Symposium Reception – Ting Foyer Hosted by Robert Lutton (Co-Chair) and Peter Anlyan (Co-chair)
Thursday, July 24
7:30am – 8:30am – Continental Breakfast & Registration – Ting Foyer
8:30am – 8:45am – Day 2 Opening Remarks – Wong Auditorium
8:45am – 9:45am – Session 6 “The Role of Unstructured Data in the Big Data World”
Chair: Don Soulsby, Sandhill Consultants
Bill Inmon, President & CTO of Inmon Consulting
9:45am – 10:00am – Break
10:00am – 11:00am Session 7A – Wong Auditorium “Big Data, Big Responsibility, Big Competitive Advantage”
Jennifer Barrett Glasgow, Chief Privacy Officer, Acxiom Corporation
Laura Hahn, Senior Data Governance Consultant, TDAmeritrade
Abstract: Big Data is often associated with the technological challenges of updating, managing and maintaining massive databases. However, the challenges and responsibilities associated with Big Data are not just technical. Many of these databases contain personally identifiable information, including demographic, behavioral, social and regulated data. Understanding when and where data can be used, and where it cannot, can limit a company’s exposure to reputational risk and significant penalties. At the same time, harnessing Big Data can pay enormous dividends and be a source of real competitive advantage. Acxiom will also discuss its decision and experience with AboutTheData.com, the online portal that enables visitors to review and correct consumer marketing data.
Session 7B – Room E51-145 “A Dynamic Capability Maturity Model for Improving Data Governance”
Richard Adler, Chief Architect, DecisionPath, Inc.
Abstract: This paper describes a dynamic performance management methodology for Data Governance based on a Capability Maturity Model (CMM). A CMM defines a set of metrics for measuring an enterprise’s competency in terms of a set of recognized best practices. Unfortunately, CMMs have not been widely adopted for Data Governance, in large part because they are not directly actionable: a CMM enables enterprises to benchmark their performance at a point in time, but provides no support for improving Data Governance competency. This paper describes a dynamic PM pilot software tool that extends and “animates” a CMM developed by IBM’s Data Governance Council (DG-CMM). The resulting process improvement methodology enables organizations: to (1) measure their maturity levels against the DG-CMM; (2) define goal maturity levels; (3) formulate plans to improve maturity levels to achieve those targets; (3) test improvement strategies by simulating their outcomes, permitting plans to be validated or refined prior to roll-out; and (4) monitor execution results to detect emerging problems promptly, allowing suitable mid-course adjustments to ensure success. The paper will describe IBM’s DG-CMM and illustrate the application of this methodology via a case study to improve a company’s information lifecycle management maturity.
Session 7C – Room E51-149 “A Start-up CDO Perspective on the Pivotal Role of Conceptual Data Modeling in Maintaining Information Quality in Big Data Domains”
Peter O’Kelly, CDO, ShopAdviser
Abstract: ShopAdvisor, a Boston-area start-up founded in 2011, combines and enriches data sources from a wide variety of partners and data services in order to shop-enable content in context (e.g., in print and digital magazine issues). Leveraging big data technologies including Amazon Web Services’ Elastic MapReduce and Redshift, ShopAdvisor applies both traditional and new data processing techniques to improve e-commerce experiences for consumers, advertisers, and publishers. In this presentation, Peter O’Kelly explains how ShopAdvisor relies on conceptual data modeling to address information quality goals. While conceptual modeling may seem quaintly old-school to some Web-centric system developers, O’Kelly will share actionable insights and lessons on how conceptual data modeling is in fact more important than ever, how organizations of any kind can implement their own best practices of conceptual data modeling, and how it is completely compatible with today’s agile software development practices.
11:00am – 11:15am– Break
11:15am – 12:15pm Session 8A – Wong Auditorium
Panel: “The Role of Business/IT Collaboration in Ensuring Information Quality”
Chair: William Ulrich, President, TSG, Inc., President, Business Architecture Guild
James Gilligan, CEO, Blue Cross Canada,
Rich d’Erizans, Senior Business Leader, Enterprise Architecture, MasterCard Worldwide
Raj Dolas, Portfolio Manager, United States Patent & Trademark Office,
Russ Jackson, Director, Enterprise Architecture, Export Development Canada
Abstract: Information is a business asset, which requires business and IT to work collaboratively to ensure that information is available, secure, complete, and of high integrity. Attempts to address missing, incomplete, or low quality information tend to focus on the point where the problem surfaced, not the root cause of the problem. This roundtable discussion explores the fundamentals of business / IT collaboration in ensuring information quality, particularly as it relates to the root cause of information quality challenges. The roundtable discussion will cover governance concepts as well as the role of business architecture as a vehicle for articulating shared goals related to information quality. Additional topics focus on aligning shared priorities, funding, and transformation roadmaps.
Session 8B – Room E51–145 Panel: “Delivering on the Promise of Big Data – Research and Case Studies”
Chair: Annette Pence, Senior Principal, Information Systems Engineer, MITRE Corp.
8B-1 – “DATA Act and Other Open Data Mandates: How They’ll Change Government”
Hudson Hollister, Founder & Executive Director, Data Transparency Coalition
Abstract: After a three-year legislative process, Congress has finally passed the Digital Accountability and Transparency Act (DATA Act), the first-ever legal mandate for open data. The DATA Act requires the federal government to adopt government-wide data standards for all existing spending-related reporting requirements and to publish the whole corpus of spending information online. By conferring the necessary authority to promulgate spending data standards government-wide, the DATA Act provides a crucial foundation for realizing the goals of President Obama’s May 2013 Open Data Policy in the federal spending domain. Without the DATA Act, open spending data would be nobody’s job. Congress is considering similar mandates in other domains of federal information beyond spending, including financial regulation and legislative actions. These mandates are being advocated by nonprofit transparency advocates and by the tech industry, represented by the Data Transparency Coalition. The passage of the DATA Act promises to open new avenues for democratic accountability, new opportunities for analytics in government management, and new possibilities to automate compliance requirements by grantees and contractors. Future, similar open data mandates in other domains will create similar changes in those domains. The fundamental features of open data–basic information must be first, standardized, and second, published–will solve longstanding data quality challenges in federal spending and elsewhere.
8B-2 – “Deriving Value from Partnership-Driven Data Sharing and Analysis”
Ted Sienknecht, Chief of Innovation and Analytics for MITRE Corp.
Abstract: The non-profit MITRE Corporation is helping transform the healthcare ecosystem through partnership and systems engineering. To this end, we recently extended our model for analytic partnerships to support major healthcare initiatives. In this presentation, we will provide an overview of the principles, approaches, and technologies recommended for facilitating this kind of large scale data fusion and analysis across a diverse stakeholder community. We will share insights and lessons learned, drawing on example applications such as fraud prevention. We will explore:
Why support analytic partnerships—value, risk, cost/benefit
Who should participate—roles, equities
How should analytic partnerships operate—governance, technology, security/privacy
What works—principles, success factors
Session 8C – E51-149 “Predictive Analytics for Accelerating Business Advantage”
Chair: Eric Hixson, Cleveland Clinic
Fern Halper, Director TDWI Research, Predictive Analytics
Abstract: Predictive analytics is becoming a mainstream technology for numerous organizations across multiple industries. These organizations want to use the power of analytics to adopt a predictive and proactive approach to customer behavior, business events, and other significant changes to their environments. These companies are also looking for ways to make predictive analytics more than just a specialized activity done by an elite few. Predictive analytics involves methods and technologies for organizations to spot patterns and trends in data, test large numbers of variables, develop and score models, and mine data for unexpected insights. Sources for predictive analytics are expanding to include machine and semi-structured and unstructured data, making it important to include newer techniques such as text analytics in technology portfolios. In this talk, Fern Halper, Director of TDWI Research for Advanced Analytics will present findings based on TDWI’s 2014 Best Practices Report, “Predictive Analytics for Accelerating Business Advantage,” which brought together insights from an extensive research survey and interviews with users and industry experts. She will address topics such as:
- How peer organizations are implementing predictive analytics to meet business objectives
- The skills needed for predictive analytics
- Big data and predictive analytics
- Best practices for obtaining value from predictive analytics
Session 8D – Room E51-151 “Fusing Data, Analysis, and Modeling for Greater Development Efficiency”
Ahmed Abukhater, GISP, PhD.
Abstract: Today governments and businesses aim at increasing efficiency while maintaining adequate public engagement and development agenda. However, the global financial crisis made it even more difficult to do so and created a new set of challenges including the extensive permitting and approval process, involving all interested and impacted stakeholders in the process, and the segregation in data silos and organizational workflows. It is critical to provide an integrated approach to support planning and development of organization’s workflows and assets. This comprehensive solution must integrate both critical data and operational workflows to eliminate redundancy, promote efficient management of assets, and foster a collaborative decision making process across different components of any projects. This session will provide an overview of the current industry landscape with a focus on data quality issues and challenges in managing projects and fostering collaborative decision making processes. It will also feature real life examples of how this integrated approach enables government organizations as well as businesses to successfully streamline the various project lifecycle components, with accurate and relevant data and more efficient and compatible analytical and modeling tools that allow them to generate actionable insight, strategic direction and better decision making.
12:15pm – 1:00pm– Lunch – Ting Foyer
1:00pm – 2:00pm Session 9A – Wong Auditorium Panel:
“How Data Modeling Can Support Data Quality and Big Data, Why It Frequently Doesn’t, And What Every CDO Should Do About It”
Moderator: PeterO’Kelly, Chief Data Officer, ShopAdvisor
Panelists: David Hay, Author, Data Model Patterns: Conventions of Thought;
JoeMaguire, Coauthor, Mastering Data Modeling: A User-Driven Approach
Abstract: In the day-to-day hurly-burly of business, the well attested advantages of data modeling occasionally get lost, with sometimes dire consequences. What can be done about that? In this panel discussion, two experts of data modeling theory and practice go beyond the usual platitudes, grappling with the issues—structural, technological, organizational, and practical—that needlessly compromise efforts to improve data quality and Big Data initiatives through data modeling. The discussion will be extemporaneous but not freewheeling, as the panelists harvest wisdom from their experience as consulting data experts for dozens of clients in widely diverse industries, and as the moderator—a working CDO who encounters these issues every day—drives the discussion forward. Topics might include:
- How do current best practices fall short? Can poorly applied data-modeling practice actually compromise data quality?
- How does data modeling apply to a Big Data world dominated by Hadoop and NoSQL technologies?
- How should—and shouldn’t—data modeling be integrated into data governance practices?How do changes to the technology landscape improve and/or distort modeling practices, and how can organizations prevent the latest fashion from compromising data quality?
Session 9B – E51-145 “Improving and Sustaining Master Data Quality using the CSRUD Life Cycle Framework”
Dr. John R. Talburt, University of Arkansas at Little Rock, and
Dr. Yinle Zhou, IBM InfoSphere, Austin, TX
Abstract: Master Data Management (MDM) has been widely adopted as an enterprise strategy. However, despites often large investments in software and personnel, many organizations still struggle to maintain high-levels of integrity in their systems of record (SOR). In many cases this is caused by the failure to understand and properly manage the full life cycle of master data. Major topics include:
- Understanding MDM as an Entity Identity Information Management Problem
- The Principle of Entity Identity Integrity
- The Five Phases of the CSRUD Life Cycle for master data
- The Role of Entity Resolution (ER) in MDM
- Deterministic Matching Systems versus Probabilistic Matching Systems
- Methods for assessing master data quality
Session 9C – Room E51-149 “The Value of Advanced Data Integration in a Big Data Services Company”
Flavio Villanustre, VP Technology Architecture & Product, LexisNexis
Abstract: The advent of Big Data in the last decade has challenged how companies ingest, refine and manage data. While most organizations have traditionally been used to dealing with moderate volumes of largely structured data, to relying on the structure of the data to discover the semantics and to utilizing active search, retrieval and reporting methods to extract valuable information from it, the recent increase in data volumes and the widespread need to leverage mostly unstructured data has started to push the envelope on what legacy technologies can offer. When dealing with Big Data, a significant portion of the effort goes into the data ingest process, ensuring that data quality is adequate, and the data linkage phase, where data attributes originating from diverse sources must be linked to specific entities and, eventually, those sources combined to build a consistent and homogeneous body of knowledge. During this presentation we will introduce the audience to the advanced data integration methodologies used by LexisNexis, a multi-billion dollar information services company, and the application of its open source HPCC Systems Big Data technology platform to perform the integration and linkage of tens of thousands of data sources, in a highly efficient, consistent and reliable manner. In particular, we will present the use of probabilistic record linkage and fuzzy matching methodologies, performed with the LexisNexis Scalable Automated Linking Technology (SALT), to achieve a high degree of linking accuracy with a fully automated system. These advanced data integration methodologies are used in LexisNexis solutions to serve customers in industries such as government, law enforcement, financial services, health care and industry contributory databases.
Session 9D – Room E51-151 “How to Establish a CDO Office in Your Organization”
Chair: Derek Strauss – CDO, TDAmeritrade
Brian M. Baczyk – CDO, Conning, Inc.
Eugene Kolker – CDO, Seattle Children’s Hospital
John Smits, CDO for Global Business Operations, EMC
Peta Gay Tessy, Lead Information Architect, Office of the Chief Data Officer, Board of Governors of the Federal Reserve System.
2:00pm – 2:15pm – Break
2:15pm – 3:15pm Session 10A – Wong Auditorium Panel: “Big Data in Healthcare”
Chair: James Noga, VP & CIO, Partners HealthCare
Academia: Mark Schreiber PhD, Associate Director of Knowledge Engineering at Novartis
Research: Shawn Murphy, MD, PhD – Director of Partners Research Information Systems and Computing
CIO: John Halamka, MD, MS – Chief Information Officer of the Beth Israel Deaconess Medical Center and Dean for Technology at Harvard Medical School.
Healthcare IT Vendor: Tom Yosick – Research & Development at Epic
Abstract: Big data will be a major challenge to health care organizations in the next decade. The flood of data from the biomedical research literature, electronic health records, lab, imaging, and pharmacy systems, infrastructure systems, on-site and remote patient monitoring, geospatial data, biosurveillance, email and social media, sentiment analysis, and other sources will stretch our capacity to synthesize, understand, and act. This session will discuss issues and potential use cases in this emerging field.
Session 10B – Room E51-149 “Data Quality & the Quality of Care”
Chair: Annette Pence, Senior Principal, Information Systems Engineer, MITRE Corp.
Loretta Randolph, Principal Health Care Organizational Strategist, MITRE
Lorraine Fellows, Principle Information Systems Engineer, MITRE
Abstract: The MITRE Corporation is committed to supporting the Center for Medicare and Medicaid (CMS) in achieving its core mission. This is especially true in light of CMS’s changing role in the US healthcare system from claims payer to helping drive quality of care. In this changing role, CMS has developed the first CMS Quality Strategy which lays out a plan to improve the health of the nation by providing better healthcare at lower cost. This presentation will begin with an overview of the development of the CMS Quality Strategy, and the need for a foundation of quality data. The presentation will then highlight some specific data management initiatives jointly undertaken by MITRE and CMS over the last several years to strengthen CMS’s data foundation. Highlighted initiatives include creating an enterprise data warehouse strategy, laying the foundation for Data Architecture, and standing up a fledgling Data Governance framework.
3:15pm – 3:30pm – Break
3:30pm – 5:00pm – Session 11 – Wong Auditorium Panel: “Data Collaboration in Financial Services”
David Blaszkowsky, Senior VP, Head of Data Governance, State Street Corporation;
David Saul, Senior Vice-president and Chief Scientist, State Street Corporation;
Said Tabet, Senior Technologist and Industry Standards Strategist, Corporate CTO Office, EMC;
Lars Toomre, Managing Director, Toomre Capital Markets
Abstract: The increasing breadth, pace and complexity of global regulations has made it more expensive and difficult for all sectors of financial services to comply. The maturation of semantic technologies, when combined with increased implementation of emerging industry standards, holds out the promise of easing these issues. Semantics and ontologies are key to greater data transparency and interoperability, thereby enhancing the overall trust level of the financial system. Enhanced trust benefits all constituencies:
· Financial services enterprises gain enhanced profitability opportunities along with better risk management.
· Regulators receive the information they really need to meet their statutory mandates.
· Technology vendors have clear requirements to develop products and services with incentives to innovate.
· Standards organizations enable intra-enterprise design, implementation and maintenance of software and processes.
5:00pm – 5:30pm – Closing Remarks & Open Discussion
2015 MITCDO Symposium Co-chairs: Peter Anlyan, Robert Lutton, John Talburt, Richard Wang
Friday, July 25
Bridging the Data Science Talent Gap
Symposium Attendees are invited to attend.
Please RSVP to firstname.lastname@example.org
Chair: Peter Anlyan, Anlyan Consulting
8:30am – 9:30am – The State of the Industry: “Hype vs. Reality: How HR is Bridging the Gap to Data-Driven Decision-Making”
Karen O‘Leonard, Vice President of Talent Analytics & Benchmarking Research at Bersin by Deloitte.
9:30am – 10:30am – What Is Industry Doing About It?
Paul Barth – CEO/Founder, NewVantage Solutions
Linda Burtch – CEO, Burtchworks
John Smits – CDO for Global Business Operations, EMC
Peter Wurman – Chief Technology Officer, Kiva Systems
10:30am – 10:45am – Break
10:45am – 11:45am – What Is Academia Doing About It?
Michael Rappa, Institute for Advanced Analytics, NC State University
Elke Rundensteiner, Worcester Polytechnic Institute
John Talburt, University of Arkansas, Little Rock
Andy Wasser, Heinz College, Carnegie Mellon University
11:45am – 12:00pm – Wrap Up