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Demand-Driven Open Data
More info:
Contact:
http://ddod.us
David.Portnoy@HHS.gov, @DPortnoy
Introduction to DDOD for
Data ...
“Demand-Driven Open Data” (DDOD) is
a framework of tools and methods that…
Provide external data users[✽] with a
systemati...
Why DDOD?
What happens when there is no
feedback loop on the value delivered?
The Problem
Without measuring value,
rational individuals deliver datasets that are
1. Easiest to generate
2. Least risky ...
The Opportunity
HHS can create additional economic, health and social value by
changing the way it measures progress on Op...
Prior to DDOD, if you wanted to influence the data HHS provides there were primarily
two extremes: participate in one-off ...
So we need a mechanism that…
Enables systematic, ongoing and transparent signaling of relative
value of data in a way that...
• No systematic feedback to
influence data available
• Limited by short durations and
often unproven business models
Decis...
DDOD is positioned to
1. Maximize value, innovation and discovery using existing
data assets
2. Achieve an engaged, active...
Moving from...
Build first and then
see if anyone will use it
Directive driven
By understanding the “market” for its data,...
DDOD complements and reinforces the existing efforts for
HealthData.gov and Health Data Leads
Health Data Leads DDOD
“Push...
Processes for administration of use cases, such as
• Encouraging responsiveness, transparency and documentation
• Ensuring...
Each of the participants (Data User, DDOD Admin, and Data Owner) is responsible for
enabling a specific set of milestones
...
DDOD relies on signing up Data Users to advocate for their use cases, participate using
DDOD tools and provide effectivene...
Qualitative Quantitative
Prioritization based on self-reported descriptions
from questions provided on a form with each ne...
Evaluation & feedback
Completed
use cases
ImplementPrioritized
use cases
PrioritizeIncoming
use cases
Prioritization is at...
Implementation of a use case could fall into one of 3 categories
Time to execute
Cost/Effort
Improve
Promote
Add
Facilitat...
Decentralized implementation, with
allocated team in each organization
Centralized implementation
team with department-wid...
Communications
method
Knowledge
Base
Data Processing
& Storage
Workflow
engine
There are 4 core components to the DDOD too...
Outreach is a core component of DDOD
Reasons that high level of participation from Data Users is needed:
● To help Data Ow...
DDOD also serves to enhance content and discoverability for HealthData.gov, as well
as ensuring the relevant system of rec...
DDOD initiative can be categorized by phases along the Development and
Engagement dimensions
Development
Pilot
1
Engagemen...
There are related initiatives that would make DDOD more effective...
One measures the usefulness of existing data,
while t...
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Intro to Demand-Driven Open Data for Data Owners

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This document is intended for use by data owners within government to learn how Demand-Driven Open Data (DDOD) could benefit their organizations.

DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, NIH, CDC, FDA, AHRQ and others) to create additional economic and public health value.

DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.

DDOD website: http://ddod.us

Published in: Health & Medicine

Intro to Demand-Driven Open Data for Data Owners

  1. 1. Demand-Driven Open Data More info: Contact: http://ddod.us David.Portnoy@HHS.gov, @DPortnoy Introduction to DDOD for Data Owners
  2. 2. “Demand-Driven Open Data” (DDOD) is a framework of tools and methods that… Provide external data users[✽] with a systematic, ongoing and transparent way to tell HHS what data they need ...To be managed, measured and executed in terms of use cases, enabling allocation of limited resources based on value What is it? ✽ Such as industry, researchers, nonprofits, media and local governments
  3. 3. Why DDOD? What happens when there is no feedback loop on the value delivered?
  4. 4. The Problem Without measuring value, rational individuals deliver datasets that are 1. Easiest to generate 2. Least risky to release And the growing volume of datasets makes locating useful ones hard
  5. 5. The Opportunity HHS can create additional economic, health and social value by changing the way it measures progress on Open Data efforts... from number of datasets released to value in terms of use cases enabled
  6. 6. Prior to DDOD, if you wanted to influence the data HHS provides there were primarily two extremes: participate in one-off events or attempt the regulatory path ...But each had significant limitations • No systematic feedback to influence data available • Limited by short durations and often unproven business models Decision process isn’t transparent No access to restricted use data • Costly and requires access • Long lead times • Uncertain outcomes • Battle parties with competing interests Influence process isn’t fully transparent Gap in feedback options One-off Methods (Bottom-up attempts) • Challenges, • Hackathons, • Meetups, conferences, • Crowdsourcing Regulatory (Top-down approach) • Lobbying, public comment, • FOIA, • Leverage associations and consortiums ExamplesLimitations Missing potential for creation of economic & public health value
  7. 7. So we need a mechanism that… Enables systematic, ongoing and transparent signaling of relative value of data in a way that’s inclusive of all types of participants That’s “Demand Driven Open Data” (DDOD) 1. Systematic 2. Ongoing 3. Transparent 4. Inclusive
  8. 8. • No systematic feedback to influence data available • Limited by short durations and often unproven business models Decision process isn’t transparent No access to restricted use data • Costly and requires access • Long lead times • Uncertain outcomes • Battle parties with competing interests Influence process isn’t fully transparent Gap in feedback options Demand-Driven Open Data Need a mechanism that’s systematic, ongoing, and transparent Not limited to arbitrary time frames and short durations of one-off methods Mitigate the long lead times, expense and uncertainty of influencing legislation Gain transparency on how your needs are weighed against competing interests and costs Use an approach more compatible with gaining access to restricted use data One-off Methods (Bottom-up attempts) • Challenges, • Hackathons, • Meetups, conferences, • Crowdsourcing Regulatory (Top-down approach) • Lobbying, public comment, • FOIA, • Leverage associations and consortiums ExamplesLimitationsDDOD fills the gap and addresses many of the limitations
  9. 9. DDOD is positioned to 1. Maximize value, innovation and discovery using existing data assets 2. Achieve an engaged, active user community 3. Help guide prioritization of open data efforts
  10. 10. Moving from... Build first and then see if anyone will use it Directive driven By understanding the “market” for its data, HHS can better allocate resources by migrating to a “Lean Startup” methodology To... Make sure there are customers before building Demand driven
  11. 11. DDOD complements and reinforces the existing efforts for HealthData.gov and Health Data Leads Health Data Leads DDOD “Push” by mandate “Pull” by need Hosting, Indexing, Discovery Expert Driven Demand Driven
  12. 12. Processes for administration of use cases, such as • Encouraging responsiveness, transparency and documentation • Ensuring use cases and resulting datasets are indexed in HealthData.gov Specialized tools for administering use cases • Workflow engine, communications method, knowledge base • Data processing, storage, hosting, versioning Proactive outreach to industry and academia for a thriving community DDOD provides 3 core services to Data Owners
  13. 13. Each of the participants (Data User, DDOD Admin, and Data Owner) is responsible for enabling a specific set of milestones ...But all implementation decisions ultimately are made by the Data Owner The DDOD Admin only facilitates the process when needed
  14. 14. DDOD relies on signing up Data Users to advocate for their use cases, participate using DDOD tools and provide effectiveness feedback. For Users, the process looks like this: Get started by simply adding your use case [✽] We’ll get you going, starting with a discussion that covers: ● Requirements for your use cases ● Criteria you use for prioritization As we go about working on your use cases, you’ll leverage the DDOD tools and processes for requirements management, voting and community engagement You submit verbal and written evaluations of the DDOD tools and processes ✽ First search HealthData.gov to see if the dataset or use case already exists EvaluateParticipateOnboardAdd
  15. 15. Qualitative Quantitative Prioritization based on self-reported descriptions from questions provided on a form with each new use case or feature While absolute quantitative valuations might be difficult, it’s possible to use known objective factors to assess relative value 1. What’s the value to your organization? 2. What’s the value to industry? 3. What’s the value to public health? 4. How consistent is it to the mission of the agency? 5. How time sensitive is the request? What are the impacting factors? 1. Cost already spent on a procured study or survey requested 2. Revenue from cost recovery programs 3. Avoided costs from FOIA requests, manual periodic releases, etc. 4. Crowdfunding-style cumulative pledges from multiple parties as a proxy for value There are both qualitative and quantitative methods for prioritizing use cases
  16. 16. Evaluation & feedback Completed use cases ImplementPrioritized use cases PrioritizeIncoming use cases Prioritization is at the level of program owner Consider implementation cost, savings from avoided future requests (such as FOIA), revenue opportunity for future cost recovery, risk of PII/PHI, risk of misinterpretation Including strategic relevance, agency mission, org priorities, recognition The decision to implement is not binary. It involves requirements management for potentially multiple interested parties ① ② ③ All prioritization and implementation decisions are made by Data Owners. We found there are typically 3 drivers.
  17. 17. Implementation of a use case could fall into one of 3 categories Time to execute Cost/Effort Improve Promote Add Facilitate deployment of ● New datasets ● New APIs For existing datasets ● Add needed fields ● Improve data quality ● Add / improve metadata ● Add / improve API If datasets already exist in legacy systems, make them more available and discoverable ● publicize availability ● index to HealthData.gov and Data.gov Current State
  18. 18. Decentralized implementation, with allocated team in each organization Centralized implementation team with department-wide buy-in Centralized DDOD manager Centralized dev team Health Data Leads, Program owners Dev team at level of program or data owner Who executes the Use Case? The circumstances around the execution of each use case is different... Typically, implementation by Data Owner’s organization is most efficient, due to the depth of domain knowledge required. But depending on the organization’s resources, capabilities and priorities it may be better executed with the help of a centralized development team.
  19. 19. Communications method Knowledge Base Data Processing & Storage Workflow engine There are 4 core components to the DDOD tool set.. Their use is guided by DDOD process, policy, and best practices Transparency Discussions & decisions around use cases must be visible to the public. Solutions Use case requirements and their solutions must be editable by all parties. Data Provide data processing and storage capabilities where needed. Tracking Track and manage the status and assignment of use cases These tools continue to evolve with an eye towards lowering the learning curve and improving ease of use for both Data Users and Data Owners. Minimizing the need for assistance from DDOD Administrators makes it possible for DDOD to scale.
  20. 20. Outreach is a core component of DDOD Reasons that high level of participation from Data Users is needed: ● To help Data Owners with prioritization it by providing sufficient information about relative value of datasets ● For both Data Users and Data Owners to gain confidence that DDOD is a sustainable marketplace that enables productive interaction Methods of proactive and sustained outreach to the user community: ● Targeted online publications ● Participation in conferences and user group meetups ● Collaboration with healthcare-related industry groups, accelerators and incubators ● Collaboration with universities Scalability for DDOD depends on building: ● Brand awareness through outreach ● Brand equity through the completion of use cases
  21. 21. DDOD also serves to enhance content and discoverability for HealthData.gov, as well as ensuring the relevant system of record is entered in the EDI Data User runs search on HealthData.gov Data User creates / updates use case DDOD Admin engages Data Owner on use case • keywords, subject • data dictionary HealthData.gov Data Dictionary Dataset Inventory EDI* Use Case DDOD Admin ensures changes to EDI get propagated to HD.gov * Enterprise Data Inventory (EDI), which is a catalog of HHS “Strategically Relevant Data Assets” Data Dictionary Dataset Inventory DDOD Admin enters use case on HD.gov with links to specifications Data Owner adds entry to EDI, including metadata DDOD Admin curates entry & ensures SLAs DDOD Admin creates repository for use case Process of adding a new DDOD use case
  22. 22. DDOD initiative can be categorized by phases along the Development and Engagement dimensions Development Pilot 1 Engagement Idea Prototype Market Small scale trial Vision Pilot 2 AwarenessActionTransformation Scope of 1-year DDOD launch Source: Deloitte analysis of Challenge.gov
  23. 23. There are related initiatives that would make DDOD more effective... One measures the usefulness of existing data, while the other enables users to discover new data DDOD ✓ Signaling of demand Enables ① systematic and ② ongoing and ③ transparent signaling of relative value of data for the ④ full range of market participants Data maturity scorecard, Data activity scorecard Usefulness of supply Has a feedback loop on the usefulness of existing and future data Full metadata inventory Discovery of supply Enables users to discover the possible applications for data, regardless of its privacy classification or availability

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