Botswana 30-31 Oct 2017

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. 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. 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. 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. 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. DEFINING INFORMATION QUALITY
 People need the right information at the right
time for the right purpose.

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. 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. (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. 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. 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. (Based on Chisholm, 2012)
Social understanding of data

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. 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. 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. 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
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. 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. 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. 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. 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. 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. Collected Data
Processed Data
Organised Data

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. 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