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Landmark legislation was enacted this past year – the Evidence-Based Policymaking Act of 2018 – sets the stage for more evidence-based decision-making in government. This is an important first step and the questions now are: What comes next? How will this help reduce barriers for using data in government? How will this impact data management practices?
View the presentation, What the Foundations for Evidence-Based Policymaking Act Means for Data Management
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Public Sector Innovation is a hot topic and is now more than ever since the Office of Management and Budget (OMB) identified Cross Agency Performance (CAP) goals promoting innovation. As the National Science Foundation (NSF) introduces new and innovative practices to improve efficiency, it has also seized it as an opportunity to expand Enterprise Data Governance. NSF is piloting a multi-faceted model to accelerate innovation with the objectives to insert new technology, engage and develop the workforce, improve data management and create a repeatable process. NSF’s Smart Tool Pilot connects NSF’s data to its business, amplifying the value of the data and generating leadership expectation for governance without ever mentioning the phrase “data governance”.
View the presentation, Driving Enterprise Data Governance through Innovation
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Every organization needs a guided approach to managing their data and information to obtain the maximum value for success in a challenging environment. An Enterprise Data / Information Management (EDM / EIM) framework that is based on industry standards and proven practices gives an organization the ability to deliver consistent high quality data and information to all its constituents. This session explains the frameworks that are most common in enterprise data / information management, explains their essential components, and gives the reasons that every organization, regardless of industry or size, should use one of these frameworks to create a sustained data management program to achieve lasting results. Main learning objectives: • Framework for Enterprise Data / Information Management (EDM / EIM) • Components of EDM / EIM and their alignment • Value to organizations when using an EDM / EIM framework • Continuum: Framework – Strategy – Program
View the presentation, Successful Data Management – Frameworks for Strategy through Implementation
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Accurately and consistently matching patient data records is critical to ensuring safe and effective care to patients, preventing incorrect treatment and diagnosis decisions. The Patient Demographic Data Quality (PDDQ) Framework was derived from the Data Management Maturity Model by CMMI Institute, commissioned by Health and Human Services, Office of the National Controller for Health IT. The PDDQ helps organizations develop and implement sound data management practices, establish governance, and create data quality standards and processes, contributing to minimization of the number of duplicate records across the industry, improving patient safety across the care lifecycle.
View the presentation, Improving Patient Safety: Introduction to the Patient Demographic Data Quality Framework
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The EU’s General Data Protection Regulation (GDPR) goes into effect on May 25. With it comes big responsibilities for organizations. Jeff Jonas, data scientist and Privacy by Design (PbD) advocate, will do a deep dive into this new law and its ramifications to the data management community, including some “gotchas” that will likely cause even the best GDPR programs to unwittingly fall out of compliance.
View the presentation, GDPR: Big Data, Big Responsibility
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Management concerns fall into broad categories:
– Implementing change that effectively improves performance
– Processing lots of information without gaining appreciable insight
– Securing real returns from technology investments Data, of course, is at the heart of all of these and other organizational complaints.
A triple play of investments in data as an organizational asset is
- a necessary prerequisite and
- a primary enabler – permitting management to address these troubling issues.
Transformation may require some organizational discomfort. The first step is to recruit qualified organizational talent. The second step is to significantly overhaul the manner by which most organizations seek to obtain informational value. The third step is to implement a new type of strategy – one that integrates organizational functions and IT. By approaching data management in this different way, organizations can begin to gain the leverage that they seek. This talk will address each of these topics – illustrating how this triple play of new leadership skills, revised value propositions, and a re-positioning of strategic investments can alleviate the concerns.
View the presentation, Triple Play: Leadership, Value & Strategy in Data
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Enterprises are never at a lost for challenges that need to be addressed. Challenges such as technology modernization; work force transition; application rationalization; business continuity planning are but a few examples of efforts that must be addressed. However, these problems like customers and the markets do not stay still. Using the Zachman Enterprise Ontology participants will be introduced to the use of primitive problem patterns as a means of rapidly capturing and re-using enterprise primitives to solve problems. This session will include a brief tutorial on the framework mechanics itself and then explore the nature of the framework and enterprise problems as a series of graphs. Participants will then be shown how these graphs can be used to develop patterns for both problems and solutions alternative generation.
View the presentation, Intro and Application of the Zachman Enterprise Ontology
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“Data debt” is a term based on the concept of “technology debt,” out of the Agile Development world. Data debt is a concept and metric that will reveal to leadership the huge costs in delaying doing the “right things” with data and information. This session will explain the concept, and offer suggestions as to how to apply this powerful metric to sustaining your EIM or DG program.
View the presentation, Data Debt – A significant and powerful new metric to prove the value of your data management and data governance programs
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Governments around the world are investing heavily in blockchain research and development. These projects range from data sharing and identity management to fighting fraud. As cryptocurrencies have doubled in market cap in the last several months, regulators like the Japanese FSA are recognizing the use of Bitcoin for payment. The U.S. Department of Homeland Security has given BlockCypher and other blockchain companies awards to research how data is shared. In this session, we’ll discuss:
- Blockchain use cases in government
- Using blockchain and analytics for cybersecurity
- More effective and efficient ways to combat Money Laundering
- Using big data/AI/machine learning to detect suspicious activities, potential crime/illegal activity, criminal networks
- Facilitating the sharing of data for aggregation and trend identification, protection against fraud and other crimes
- Making compliance reporting to authorities faster and more standardised, enabling data comparison and better monitoring/screening
View the presentation, The Role of Blockchain Data in the Public Sector
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Data.gov is the Federal Government’s open data site, and has been in operation since May of 2009. It features nearly 200,000 datasets from federal agencies, as well as dozens of participating state and local governments. The data available covers a wide range of subjects, from health, education, public safety, and much more. This talk will provide background on how Data.gov functions, an update on the status of open data throughout the federal agencies, use cases, and current challenges.
View the presentation, How does Data.gov function? Use cases, and current challenges and the most recent updates on the status of open data throughout the federal agencies.
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