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Data as a service: a human-centered design approach/Retha de la Harpe

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Botswana 30-31 Oct 2017

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Data as a service: a human-centered design approach/Retha de la Harpe

  1. 1. Retha de la Harpe Associate Professor Faculty Informatics & Design Cape Peninsula University of Technology South Africa Data as a Service: a human-centered design approach
  2. 2. My position • I am a researcher who deals with research data in practice • This presentation only deals with interpretive research using qualitative data • Most of our work is with communities • We consider the introduction of technologies in their situation • We recognise the capability of anyone to participate in designing relevant solutions for their situation • I am sharing the experiences and challenges we experience in collaborating in international projects
  3. 3. Global Context versus Local Voices • Culture • Safety • Language • Religion • Identity (personal, community, national)) Different values and behavior Divide between political powers and needs of people Out of touch with ordinary people’s dreams and aspirations - who are the spokespersons? Burden of disease Unemployment Poverty Safety Inequality
  4. 4. Responsible Research and Meaningful Engagement Enter ethics Engage Leave Reflect: • Impact • Feedback • Leave behind Prepare: • Ethics • Liaise with community • Propose Research Intention • Sensitise Researchers • Plan engagement • Concrete objectives for engagement Engage • Equal & active participation • Use appropriate methods & tools • Strengthen relationship Feedback Leave behindCommunity Research Fatigue “Many people came to take our voices but nothing came out”
  5. 5. The contextual relevancy of the right information for the right person at the right time, for the right purpose in an open data open science environment How much data is crystalised into meaningful and responsible knowledge?
  6. 6. DEFINING INFORMATION QUALITY  People need the right information at the right time for the right purpose.
  7. 7. DEFINITION OF INFORMATION QUALITY • The right information means that it must have:- Meaning Recipient Access Appropriate R-Information Recipient R-Time R-Purpose in the context of use
  8. 8. CIO 2009 8 Data, information and knowledge What is data? Raw facts (letters, numbers, images, sound, etc.) What is information? Processed data? Data with meaning? Information does not exist What is knowledge?
  9. 9. (Chisholm, 2012) Data Quality is Not Fitness for Use The special problems of the relationships between data and what it is used for will require a different set of approaches and should be called something other than “data quality” Malcolm Chisholm Information Management Online, August 16, 2012
  10. 10. From Figure 1, we can see that the interpreter is independent of the data. It understands the data and can put it to use. But if the interpreter misunderstands the data, or puts it to an inappropriate use, that is hardly the fault of the data, and cannot constitute a data quality problem. Data quality is an expression of the relationship between the thing, event, or concept and the data that represents it. This is a one-to-one relationship, unlike the one-to-many relationship between data and uses. Therefore, I would propose the definition of data quality as: “the extent to which the data actually represents what it purports to represent.” •The interpretant misunderstands the data. •The interpretant uses data for a purpose that is incompatible with the data. •Data is faked and used for illegal or unethical purposes Problems with the “Fitness for use” definition of data quality
  11. 11. Any piece of information, in order to be useful, should be… Knowable. Nearly everything (but not all, as Heisenberg[1] taught us) is knowable, although sometimes very difficult to learn or discern. Recorded . In some sharable, objective medium and not just in some human brain. Accessible (with the right resources and technology) Navigable (it may be there but is it easy to find?) Understandable (language, culture, technology, etc. ) Of sufficient quality (for the intended use) Topically relevant to needs (perceived needs and unknown needs) (otherwise, it is noise) Utility characteristics of information
  12. 12. (Based on Chisholm, 2012) Social understanding of data
  13. 13. CIO 2009 13 Data stakeholders Data stakeholders have: • Knowledge • Skills • Technical • Adaptive • Interpretive When interacting with data they: • Communicate • Improvise • Reflect-in-action • Collaborate Data roles: • Data producer • Data consumer • Data custodian • Data manager
  14. 14. An Open Data Repository Collected Data Processed Data Organised Data Observations Answers Transcriptions Translations Images Narratives Codes Categories Sub-themes Themes Knowledge claims Findings Results Conclusions Further Research Record Document Anonymise Analye, Interpret, Reflect Design Report, Disseminate Present Data activities Data elements
  15. 15. Researcher in Data Role Collected Data Processed Data Organised Data Data Consumer Data Producer Data Prosumer Data ManagerData Custodian Collect, record, capture data Curate data (access, format, standardise, backup, securing) Read, Analyse, Interpret Present, Disseminate Communicate Plan, Organise, Monitor, Direct)
  16. 16. Semiotics • Semiotics theory refers to how signs and symbols are used to convey knowledge with relations between: – syntactic as the relationship between sign representation (structure) – semantic between a representation and its referent (meaning) – pragmatic between the representation and interpretation semiotic levels (usage) • The process of interpretation, called semiosis, at the pragmatic level depends on the use of the sign by the interpreter in the case of data, the data consumer. • The sign (data) is not a representation of an objective reality but depends on the shared understanding in the context of the communication process 16
  17. 17. 17 Human Information Functions SOCIAL WORLD – beliefs, expectations, commitments, contracts, law, culture, ... PRAGMATICS - intentions, communication, conversations, negotiations, … SEMANTICS - meanings, propositions, validity, truth, signification, denotations,… The IT SYNTACTICS - formal structure, language, logic, Platform data, records, deduction, software, files, … EMPIRICS - pattern, variety, noise, entropy, channel capacity, redundancy, efficiency, codes, … PHYSICAL WORLD - signals, traces, physical distinctions, hardware, component density, speed, economics, … Semiotic Levels
  18. 18. Knowledge Contributions Type of Knowledge Conceptual knowledge (no truth value) • concepts, constructs • classifications, taxonomies, typologies, • conceptual frameworks Descriptive knowledge (truth value) • observational facts • empirical regularities • theories and hypotheses }causal laws (Niiniluoto 1993 Prescriptive knowledge (no truth value) Design product knowledge Design process knowledge: Technological rules (Bunge 1967b) Technical norms (Niiniluoto 1993)
  19. 19. Data Service • A Data Service is where data in an optimally administered repository can be produced or consumed based on the needs of end-users in the roles of data producer, consumer, custodian and manager to support activities and decision- making • A service path consists of different touch points where data users, administrators and managers interact with data • Data service stakeholders are those who has an invested interest in the data stored in a data repository
  20. 20. Data Touch Points from the Researcher’s Perspective • Conceptualise research (problem, approach, what do do, where, how and why) • Role of literature (status) • Propose research • Plan data management • Plan data collection (methods) • Engage with research setting (Initiate contact, permission) • Research setting (get permission) • Data source – collect • Analyse & Interpret • Manage data • Disseminate
  21. 21. Contextual aspects • Cultural • Language • Literacy • Methods used to collect data – capture details of methods • Interact with people • Mechanisms to unlock the context (research fatigue) Metadata
  22. 22. Data as a Service - Stakeholders • Researcher / Scientist / Data Scientist • Research Institution • Scientific Audience • Gatekeeper (organisation, community) • Research Participants • Research Project Team Members • Collaborators • Funding Agencies • Publishers • Conference Organisors • Libraries & Repositories • Sources and Beneficiaries of Research (Government, Civil Society, Industry (research uptake) Relationship networks to create value (opportunity intent)
  23. 23. Collected Data Processed Data Organised Data
  24. 24. DESIGN THINKING (Emergent) Right answers Right questions Expert advantage Ignorance advantage Rigorous analysis Rigorous testing Telling Showing Presentations and meetings Experiments and experiences Headquarters In the field Avoid failure Fail fast Subject expert Process expert Arm’s length customer research Deep customer immersion Periodic Continuous TRADITIONAL THINKING (Directed) Planning of a flawless intellect Enlightened trial and error Thinking and planning Doing If you build it, they’ll buy it If they inspire it, they’ll buy it An Introduction to Design Thinking| Presented at Laurea University of Applied Science | January 2013
  25. 25. Plan and Prepare to enter the research setting Enter Activities Methods 1 Ethics • Obtain ethics clearance and data permission • Plan informed consent activity 2 Liaise with community • Identify “gatekeeper” • Make initial contact • Propose research intent • Manage the relationship 3 Community engagement planning • Define concrete objectives and proposed outcomes • Communicate with community partners • Plan the field trips (logistics, materials, workshop plans, etc.) 4 Preparation of researchers • Sensitise researchers towards cultural practices of the community context • Identify roles and responsibilities 5 Community Engagement plan • Plan the community engagement objectives and activities 6 Documenting • Plan documenting of activities and reflections

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