Page 47 - Trust in ICT 2017

Trust in ICT 1

6.2 Trust Provisioning Processes

6.2.1 Data Collection Strategy

A significant amount of trust related data needed to be collected and handled into an intelligent way. There
are many strategies for big data collection and big data storage that can be used in the Trust Agents for
reputation information, interaction history, sensor data, user related data, service/app related data, and
context related data.
Each service or application will require its own strategy with elements of complete enumeration and
sampling. Over time some aspects of a data collection strategy may move from complete enumeration to
sampling (or vice versa), particularly as knowledge is developed and requirements or resources change.
Sampling strategies are often punctuated by complete enumeration from time to time in order to re-evaluate
baseline data.
It is not feasible to construct a perfect strategy for any one fishery or subsector that will meet all
requirements for all time. Flexibility and the adoption of alternative approaches must form a key component
of any strategy, whether it is designed for assessment of fish stocks, the evaluation of markets or the
assessment of community dependence on fisheries.

In general, however, any strategy will require the following steps:
• Evaluate existing data sets in relation to the objectives of the programme, including accessibility of
the data.

• Describe the operating characteristics of the sector or subsector.
• Decide on the approach to be taken: complete enumeration or sampling, including cost-benefit and
cost effectiveness analysis and an evaluation of operational considerations.
• Design methods according to the approach adopted, including the form of stratification to be used
in sampling;

• Implement a test phase to validate the method, including participation by other stakeholders;
• Establish a continuing feedback mechanism between data sources and data users to ensure that
data types, quantity, quality and origin are consistent with the requirements for determination of
the performance indicator.

It is needed to understand big data strategies and the techniques used with each strategy. For example in
the Figure 2 the first dimension is labelled business objective. When developing big data capabilities,
companies try to measure or experiment. When measuring, organizations know exactly what they are looking
for and look to see what the values of the measures are. When the objective is to experiment, companies
treat questions as a hypothesis and use scientific methods to verify them.

The second dimension is labelled data type. In their normal course of functioning, companies collect data on
their operations (e.g., sales) and capture it in their database that has a structure or schema. It is called as
transactional data. In other instances, companies deal with data that come from sources other than
transactions and are typically unstructured (e.g., social media data). This combination results in four
quadrants, each representing a different strategy: performance management, data exploration, social
analytics, and decision science.

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