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The Role of Community-Driven Data Curation for Enterprises

The Role of Community-Driven Data Curation for Enterprises



With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of ...

With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.

E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.



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  • the slides describing The role of community driven data cu ration for enterprises are very helpful to me....The different types of curations u mentioned in ur slides namely manual,automated,sheer,blended are knowledgable...Expecting more such good topics from u edward. discount coupons
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    The Role of Community-Driven Data Curation for Enterprises The Role of Community-Driven Data Curation for Enterprises Presentation Transcript

    • The Role of Community-Driven Data Curation for Enterprises
      Edward Curry, Andre Freitas, Seán O'Riain
    • Speaker Profile
      Research Scientist at the Digital Enterprise Research Institute (DERI)
      Leading international web science research organization
      Researching how web of data is changing way business work and interact with information
      Projects include studies of enterprise linked data, community-based data curation, semantic data analytics, and semantic search
      Investigate utilization within the pharmaceutical, oil & gas, financial, advertising, media, manufacturing, health care, ICT, and automotive industries
      Invited speaker at the 2010 MIT Sloan CIO Symposium to an audience of more than 600 CIOs
    • Web of Data
    • Acknowledgements
      Collaborators Andre Freitas & SeánO'Riain
      Insight from Thought Leaders
      Evan Sandhaus (Semantic Technologist), Rob Larson (Vice President Product Development and Management), and Gregg Fenton (Director Emerging Platforms) from the New York Times
      Krista Thomas (Vice President, Marketing & Communications), Tom Tague (OpenCalais initiative Lead) from Thomson Reuters
      Antony Williams (VP of Strategic Development ) from ChemSpider
      Helen Berman (Director), John Westbrook (Product Development) from the Protein Data Bank
      Nick Lynch (Architect with AstraZeneca) from the Pistoia Alliance.
      The work presented has been funded by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2).
    • Further Information
      The Role of Community-Driven
      Data Curation for Enterprises
      Edward Curry, Andre Freitas, & Seán O'Riain
      In David Wood (ed.),
      Linking Enterprise Data Springer, 2010.
      Available Free at:
    • Overview
      Curation Background
      The Business Need for Curated Data
      What is Data Curation?
      Data Quality and Curation
      How to Curate Data
      Curation Communities and Enterprise Data
      Case Studies
      Wikipedia, The New York Times, Thomson Reuters, ChemSpider, Protein Data Bank
      Best Practices from Case Study Learning 
    • The Business Need
      • Knowledgeworkers need:
      • Access to the right information
      • Confidence in that information
      Working incomplete inaccurate, or wrong information can have disastrous consequences
    • The Problems with Data
      Flawed Data
      Effects 25% of critical data in world’s top companies (Gartner)
      Data Quality
      Recent banking crisis (Economist Dec’09)
      Inaccurate figures made it difficult to manage operations (investments exposure and risk)
      “asset are defined differently in different programs”
      “numbers did not always add up”
      “departments do not trust each other’s figures”
      “figures … not worth the pixels they were made of”
    • What is Data Curation?
      Selection, preservation, maintenance, collection, and archiving of digital assets
      Active management of data over its life-cycle
      Data Curators
      Ensure data is trustworthy, discoverable, accessible, reusable, and fit for use
      Museum cataloguers of the Internet age
    • What is Data Curation?
      Data Governance
      Convergence of data quality, data management, business process management, and risk management
      Data Curation is a complimentary activity
      Part of overall data governance strategy for organization
      Data Curator = Data Steward ??
      Overlapping terms between communities
    • Data Quality and Curation
      What is Data Quality?
      Desirable characteristics for information resource
      Described as a series of quality dimensions
      Discoverability, Accessibility, Timeliness, Completeness, Interpretation, Accuracy, Consistency, Provenance & Reputation
      Data curation can be used to improve these quality dimensions
    • Data Quality and Curation
      Discoverability & Accessibility
      Curate to streamline search by storing and classifying in appropriate and consistent manner
      Curate to ensure data correctly represents the “real-world” values it models
      Curate to ensure datacreated and maintained using standardized definitions, calculations, terms, and identifiers
    • Data Quality and Curation
      Provenance & Reputation
      Curate to track source of data and determine reputation
      Curate to include the objectivity of the source/producer
      Is the information unbiased, unprejudiced, and impartial?
      Or does it come from a reputable but partisan source?
      Other dimensions discussed in chapter
    • How to Curate Data
      Data Curation is a large field with sophisticated techniques and processes
      Sectionprovides high-leveloverview on:
      Should you curate data?
      Types of Curation
      Setting up a curation process
      Additional detail and references available in book chapter
    • Should You Curate Data?
      Curation can have multiple motivations
      Improving accessibility, quality, consistency,…
      Will the data benefit from curation?
      Identify business case
      Determine if potential return support investment
      Not all enterprise data should be curated
      Suits knowledge-centric data rather than transactional operations data
    • Types of Data Curation
      Multiple approaches to curate data, no single correct way
      Individual Curators
      Curation Departments
      Community-based Curation
      Manual Curation
      Sheer Curation
    • Types of Data Curation – Who?
      Individual Data Curators
      Suitable for infrequently changing small quantity of data
      (<1,000 records)
      Minimal curation effort (minutes per record)
    • Types of Data Curation – Who?
      Curation Departments
      Curation experts working with subject matter experts to curate data within formal process
      Can deal with large curation effort (000’s of records)
      Scalability: Can struggle with large quantities of dynamic data (>million records)
      Availability: Post-hoc nature creates delay incurated data availability
    • Types of Data Curation - Who?
      Community-Based Data Curation
      Decentralized approach to data curation
      Crowd-sourcing the curation process
      Leverages community of users to curate data
      Wisdom of the community (crowd)
      Can scale to millions of records
    • Types of Data Curation – How?
      Manual Curation
      Curators directly manipulate data
      Can tie users up with low-value add activities
      (Sem-)Automated Curation
      Algorithms can (semi-)automate curation activities such as data cleansing, record duplication and classification
      Can be supervised or approved by human curators
    • Types of Data Curation – How?
      Sheer curation, or Curation at Source
      Curation activities integrated in normal workflow of those creating and managing data
      Can be as simple as vetting or “rating” the results of a curation algorithm
      Results can be available immediately
      Blended Approaches: Best of Both
      Sheer curation +post hoc curation department
      Allows immediate access to curated data
      Ensures quality control with expert curation
    • Setting up a Curation Process
      5 Steps to setup a curation process:
      1 - Identify what data you need to curate
      2 - Identify who will curate the data
      3 - Define the curation workflow
      4 - Identity appropriate data-in & data-out formats
      5 - Identify the artifacts, tools, and processes needed to support the curation process
    • Setting up a Curation Process
      Step 1: Identify what data you need to curate
      Newly created data and/or legacy data?
      How is new data created?
      Do users create the data, or is it imported from an external source?
      How frequently is new data created/updated?
      What quantity of data is created?
      How much legacy data exists?
      Is it stored within a single source, or scattered across multiple sources?
    • Setting up a Curation Process
      Step 2: Identify who will curate the data
      Individuals, depts, groups, institutions,community
      Step 3: Define the curation workflow
      What curation activities are required?
      How will curation activities be carried out?
      Step 4: Identity suitable data-in & -out formats
      What is the best format for the data?
      Right format for receiving and publishing data is critical
      Support multiple formats to maximum participation
    • Setting up a Curation Process
      Step 5: Identify the artifacts, tools, and processes needed to support curation
      Workflow support/Community collaboration platforms
      Algorithms can (semi-)automate curation activities
      Major factors that influence approach:
      Quantity of data to be curated (new and legacy data)
      Amount of effort required to curate the data
      Frequency of data change / data dynamics
      Availability of experts
    • Overview
      Curation Background
      The Business Need for Curated Data
      What is Data Curation?
      Data Quality and Curation
      How to Curate Data
      Curation Communities and Enterprise Data
      Case Studies
      Wikipedia, The New York Times, Thomson Reuters, ChemSpider, Protein Data Bank
      Best Practices from Case Study Learning 
    • Community–based Curation
      Two community approaches:
      Internal corporate communities
      External pre-competitive communities
      To determine the right model consider:
      What the purpose of the community is?
      Will resulting curateddataset be publicly available? Or restricted?
    • Community–based Curation
      Internal Communities
      Taps potential of workforce to assist data curation
      Curate competitive enterprise data that will remain internal to the company
      May not always be the case e.g. product technical support and marketing data
      Can work in conjunction with curation dept.
      Community governance typically follows the organization’s internal governance model
    • Pre-competitive Communities
      Pre-competitive collaboration
      Well-established technique for open innovation
      Notable examples
    • What is Pre-Competitive Data?
      Two Types of Enterprise Data
      Propriety data for competitive advantage
      Common data with no competitive advantage
      What is pre-competitive data?
      Has little potential for differentiation
      Can be shared without conferring commercial advantage to competitor
      Common non-competitive data
      Needs to be maintaining and curated
      Companies duplicate effort in-house incurring full-cost
    • Pre-competitive Communities
      External pre-competitive communities
      Share costs, risks, and technical challenges
      Common curation tasks carried out once inpublic domain rather than multiple timesin each company
      Reduces cost required to provide and maintain data
      Can increase the quantity, quality, and access
      Focus turns to value-add competitive activity
      Move “competitive onus” from novel data to novel algorithms, shifting emphasis from “proprietary data” to a “proprietary understanding of data”
      e.g. Protein Data Bank and Pistoia Alliance in Pharma
    • External Pre-competitive Communities
      Two popular community models are
      Organization consortium
      Open community
      Organization consortium
      Operates like a private democratic club
      Usually closed community, members invited based on skill-set to contribute
      Output data - public or limited tomembers
      Consortiums follow a democratic process
      Member voting rights may reflect level of investment
      Larger players may be leaders of the consortium
    • External Pre-competitive Communities
      Open community
      Everyone can participate
      “Founder(s)” defines desired curation activity
      Seek public support to contribute to curation activates
      Wikipedia, Linux, and Apache are good examples of large open communities
    • Overview
      Curation Background
      The Business Need for Curated Data
      What is Data Curation?
      Data Quality and Curation
      How to Curate Data
      Curation Communities and Enterprise Data
      Case Studies
      Wikipedia, The New York Times, Thomson Reuters, ChemSpider, Protein Data Bank
      Best Practices from Case Study Learning 
    • Wikipedia
      The World Largest Open Digital Curation Community
    • Wikipedia
      Open-source encyclopedia
      Collaboratively built by large community
      Challenges existing models of content creation
      More than 19,000,000 articles
      270+ languages, 3,200,000+ articles in English
      More than 157,000 active contributors
      Studies show accuracy and stylistic formality are equivalent to resources developed in expert-based closed communities
      i.e. Columbia and Britannica encyclopedias
    • Wikipedia
      Wiki platform behind Wikipedia
      Widespread and popular technology
      Wikis can also support data curation
      Lowers entry barriers for collaborative data curation
      Widely used inside organizations
      Intellipedia covering 16 U.S. Intelligence agencies
      Wiki Proteins,curatedProtein data for knowledge discovery and annotation
    • Wikipedia
      Decentralized environment supports creation of high quality information with:
      Social organization
      Artifacts, tools & processes for cooperative work coordination
      Wikipedia collaboration dynamics highlightgood practices
    • Wikipedia – Social Organization
      Any usercan edit its contents
      Without prior registration
      Does not lead to a chaotic scenario
      In practice highly scalable approach for high quality content creation on the Web
      Relies on simple but highly effective way to coordinate its curation process
      Curation is activity of Wikipedia admins
      Responsibility for information quality standards
    • Wikipedia – Social Organization
      Four main types of accounts:
      Anonymous users
      Identified by their associated IP address
      Registered users
      Users with an account in the Wikipedia website
      Registered users with additional permissions in the system
      Access to curation tools
      Programs that perform repetitive tasks
    • Wikipedia – Social Organization
    • Wikipedia – Social Organization
      Improvement of one’s reputation
      Sense of efficacy
      Contributing effectively to a meaningful project
      Over time focus of editors typically change
      From curators of a few articles in specific topics
      To more global curation perspective
      Enforcing quality assessment of Wikipedia as a whole
    • Wikipedia – Artifacts, Tools & Processes
      Wiki Article Editor (Tool)
      WYSIWYG or markup text editor
      Talk Pages (Tool)
      Public arena for discussions around Wikipedia resources
      Watchlists (Tool)
      Helps curators to actively monitor the integrity and quality of resources they contribute
      Permission Mechanisms (Tool)
      Users with administrator status can perform critical actions such as remove pages and grant administrative permissions to new users
    • Wikipedia – Artifacts, Tools & Processes
      Automated Edition (Tool)
      Bots are automated or semi-automated tools that perform repetitive tasks over content
      Page History and Restore (Tool)
      Historical trail of changes to a Wikipedia Resource
      Guidelines, Policies & Templates (Artifact)
      Defines curation guidelines for editors to assess article quality
      Dispute Resolution (Process)
      Dispute mechanism between editors over the article contents
      Article Edition, Deletion, Merging, Redirection, Transwiking, Archival (Process)
      Describe the curation actions over Wikipedia resources
    • Wikipedia - DBPedia
      DBPedia Knowledge base
      Inherits massive volume of curated Wikipedia data
      Built using information info box properties
      Indirectly uses wiki as data curation platform
      DBPediaprovides direct access to data
      3.4 million entities and 1 billion RDF triples
      Comprehensive data infrastructure
      Concept URIs, definitions, and basic types
    • Wikipedia - DBPedia
    • The New York Times
      100 Years of Expert Data Curation
    • The New York Times
      Largest metropolitan and third largest newspaper in the United States
      • nytimes.com
      • Most popular newspaper website in US
      • 100 year old curated repository defining its participation in the emerging Web of Data
    • The New York Times
      Data curation dates back to 1913
      Publisher/owner Adolph S. Ochs decided to provide a set of additions to the newspaper
      New York Times Index
      Organized catalog of articles titles and summaries
      Containing issue, date and column of article
      Categorized by subject and names
      Introduced on quarterly thenannual basis
      Transitory content of newspaper became important source of searchable historical data
      Often used to settle historical debates
    • The New York Times
       Index Department was created in 1913
      Curation and cataloguingofNYT resources
      Since 1851 NYT had low quality index for internal use
      Developed a comprehensive catalog using a controlled vocabulary
      Covering subjects, personal names, organizations, geographic locations and titles of creative works (books, movies, etc), linked to articles and their summaries
      Current Index Dept. has~15 people
    • The New York Times
      Challenges with consistently and accurately classifying news articles over time
      Keywords expressing subjects may show some variance due to cultural or legal constraints
      Identities of some entities, such as organizations and places, changed over time
      Controlled vocabulary grew to hundreds of thousands of categories
      Adding complexity to classification process
    • The New York Times
      Increased importance of Web drove need to improve categorization of online content
      Curation carried out by Index Department
      Library-time (days to weeks)
      Print edition can handle next-day index
      Not suitable for real-time online publishing
      nytimes.com needed a same-day index
    • The New York Times
      Introduced two stage curation process
      Editorial staff performed best-effort semi-automated sheer curation at point of online pub.
      Several hundreds journalists
      Index Department follow up with long-term accurate classification and archiving
      Non-expert journalist curators provide instant accessibility to online users
      Index Department provides long-term high-quality curation in a “trust but verify” approach
    • NYT Curation Workflow
      Curation starts with article getting out of the newsroom
    • NYT Curation Workflow
      Member of editorial staff submits article to web-based rule based information extraction system (SAS Teragram)
    • NYT Curation Workflow
      Teragram uses linguistic extraction rules based on subset of Index Dept’s controlled vocab.
    • NYT Curation Workflow
      Teragram suggests tags based on the Index vocabulary that can potentially describe the content of article
    • NYT Curation Workflow
      Editorial staff member selects terms that best describe the contents and inserts new tags if necessary
    • NYT Curation Workflow
      Reviewed by the taxonomy managers with feedback to editorial staff on classification process
    • NYT Curation Workflow
      Article is published online at nytimes.com
    • NYT Curation Workflow
      At later stage article receives second level curation by Index Dept. additional Index tags and a summary
    • NYT Curation Workflow
      Article is submitted to NYT Index
    • The New York Times
      Early adopter of Linked Open Data (June ‘09)
    • The New York Times
      Linked Open Data @ data.nytimes.com
      Subset of 10,000 tagsfrom index vocabulary
      Dataset of people, organizations & locations
      Complemented by search services to consume data about articles, movies, best sellers, Congress votes, real estate,…
      Improves traffic by third party data usage
      Lowers development cost of new applications for different verticals inside the website
      E.g. movies, travel, sports, books
    • Thomson Reuters
      Data Curation: A Core Business Competency
    • Thomson Reuters
      Thomson Reuters is an information provider
      Created by acquisition of Reuters by Thomson
      Over 50,000 employees
      Commercial presence in 100+ countries
      Provides specialist curated information and information-based services
      Selects most relevant information for customers
      Classifying, enriching and distributing it in a way that can be readily consumed
    • Thomson Reuters
      Curation process
      Working over approximately 1000 data sources
      Automatic tools provide first level triage and classification
      Refined by intervention of human curators
      Curator is a domain specialist
      Employs thousands of curators
    • Thomson Reuters
      OneCalais platform
      Reduces workload for classification ofcontent
      Natural Language Processingonunstructured text
      Automatically derives tags for analyzed content
      Enrichment with machine readable structured data
      Provides description of specific entities (places, people, events, facts) present in the text
      Open Calais (free version of OneCalais)
      20.000+ users,>4 million trans per day
      CNET, CBS Interactive, The Huffington Post, The Powerhouse Museum of Science and Design,…
    • ChemSpider
      Structure centric chemical community
      Over 300 data sources with 25 million records
      Provided by chemical vendors, government databases, private laboratories and individual
      Pharmarealizing benefits of open data
      Heavily leveraged by pharmaceutical companies as pre-competitive resources for experimental and clinical trial investigation
      Glaxo Smith Kline made its proprietary malaria dataset of 13,500 compounds available
    • Protein Data Bank
      Dedicated to improving understanding of biological systems functions with 3-D structure of macromolecules
      Started in 1971 with 3 core members
      Originally offered 7 crystal structures
      Grown to 63,000 structures
      Over 300 million dataset downloads
      Expanded beyond curated data download service to include complex molecular visualized, search, and analysis capabilities
    • Overview
      Curation Background
      The Business Need for Curated Data
      What is Data Curation?
      Data Quality and Curation
      How to Curate Data
      Curation Communities and Enterprise Data
      Case Studies
      Wikipedia, The New York Times, Thomson Reuters, ChemSpider, Protein Data Bank
      Best Practices from Case Study Learning 
    • Best Practices from Case Study Learning
      Social Best Practices
      Community Governance Models
      Technical Best Practices
      Data Representation
      Human- andAutomatedCuration
      Track Provenance
    • Social Best Practices
      Stakeholders involvement fordata producers and consumers must occur early in project
      Provides insight into basic questions of what they want to do, for whom, and what it will provide
      White papers are effective means to present these ideas, and solicit opinion from community
      Can be used to establish informal ‘social contract’ for community
    • Social Best Practices
      Outreach activities essential for promotion and feedback
      Typical consumers-to-contributors ratios of less than 5%
      Social communication and networking forums are useful
      Majority of community may not communicate using these media
      Communication by email still remains important
    • Social Best Practices
      Sheer curationneedsline of sight from data curating activity, to tangible exploitation benefits
      Lack of awareness of value proposition will slow emergence ofcollaborative contributions
      Recognizing contributing curators through a formal feedback mechanism
      Reinforces contribution culture
      Directly increases output quality
    • Social Best Practices
      Community Governance Models
      Effective governance structure is vital to ensure success of community
      Internal communities and consortium perform well when they leverage traditional corporate and democratic governance models
      Open communities need to engage the community within the governance process
      Follow less orthodox approaches using meritocratic and autocratic principles
    • Technical Best Practices
      Data Representation
      Must be robust and standardized to encourage community usage and tools development
      Support for legacy data formats and ability to translate data forward to support new technology and standards
      Human & Automated Curation
      Balancing will improve data quality
      Automated curation should always defer to, and never override, human curation edits
      Automate validating data deposition and entry
      Target community at focused curation tasks
    • Technical Best Practices
      Track Provenance
      All curation activities should be recorded and maintained as part data provenance effort
      Especially where human curators are involved
      Users can have different perspectives of provenance
      A scientist may need to evaluate the fine grained experiment description behind the data
      For a business analyst the ’brand’ of data provider can be sufficient for determining quality
    • Conclusions
      Data curation can ensure the quality of data and its fitness for use
      Pre-competitive data can be shared without conferring a commercial advantage
      Pre-competitive data communities
      Common curation tasks carried out once in public domain
      Reduces cost, increase quantity and quality