Our Ethical Approach to Market Research

In all our market research we adhere to the
following principles:

A male data scientist works with a tablet to complete tasks

Transparency and Informed Consent

All data collection activities we conduct are purposeful

Our research metrics are defined when we design the research parameters.

We set the right expectations from the start and inform partners and participants of the purpose of the research, what data we will collect and how we will use it. Furthermore, we do not fabricate, falsify, or misrepresent data we collect.

This principle of transparency constitutes the foundation of informed consent that partners and participants can expect from us during the development, undertaking and reporting of any research.

Respect

We acknowledge the rights of participants

They can withhold their consent or withdraw their participation at any stage in the research process. We will never pressure or force anyone in any way to stop them from opting out of the research.

Furthermore, we respect the right of participants’ sensitivity around cultural and personal matters. Due consideration is given in the design and conduct of research to minimise the possibility of harm to participants, whether it is in terms of physical, psychological, social, financial or digital harm.

Data Protection and Privacy

Data Agility sets the highest standards on data privacy and protection

We strictly adhere to these policies and data applications by reducing identifying information wherever possible and ensuring all collected data is treated confidentially – in terms of analysis, storage and publication.

Specifically, we aim to minimise the amount of personal data, such as demographic information, collected in order to further protect people’s right to privacy and confidentiality. Moreover, we take reasonable precautions to safeguard whatever identifiable research data we collect.

When identifiable research information is no longer required, we take steps to destroy it or ensure that it is de-identified.

Unbiased Methods and Results for Reliable Data

We adhere to processes that ensure objectivity and reliability

We avoid bias when collecting and analysing data.

Our work and data collection doesn’t start with an end conclusion in mind. We start our research with a hypothesis but do not gather data to prove our point. Instead, we seek accurate data provided by partners and participants of each research project. The data we gather may or may not prove our hypothesis. Our processes guide us to gather reliable data through unbiased research methods.

We avoid leading questions, loaded questions, double-barreled questions, absolute questions and confusing questions. Moreover, we use scales, ratings and open-ended questions to make them objective.

We share analytical duties with different team members to reassess our assumptions and questions and determine the most objective point of view.

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

Most popular insights.

Are You Collecting The Data You Need?

Are You Collecting The Data You Need?

Are you collecting the data you need? Collecting more data does not necessarily lead to more useful information or better decision-making. In fact, the overcollection of data can lead to wasted resources.

Data Thinking for Organisational Transformation

Data Thinking for Organisational Transformation

Data thinking can be applied across the four pillars approach of transformation — Data, People, Process and Technology. In part 2 of our article series, we explore data thinking across all areas of organisational transformation.