Making sense of your UX and CX research data
Let’s be realistic. The answer for your struggle to make sense of your research data is not a one-day practice in a one-day workshop or masterclass.
The best you can get out of a one-day workshop that promises you a deep synthesis skill or other UX or CX skills, and coming up with an effective research conclusion is a confirmation that someone somehow is selling a snake oil. You would be better off spending your time and money to develop your skill with something more practical, useful and achievable, such as planning and evaluating content for digital products and services.
How do I know?
During more than 15 years, I have worked with multinational companies, academic institutions and non-for-profit organizations to research, develop, and improve their products and services in four continents. My experience with research data ranges from planning data gathering, collecting data in the field, organizing data, validating data, analyzing and synthesizing data, modeling and vizualising data, translating data into actionable insights, opportunities and solutions.
Before the Big Data and the Internet of Things fever became buzzwords, I have worked with the challenges to make sense a vast array of data collected from actuators and sensors, in particular Radio Frequency Identification (RFID) and Near Field Communication (NFC) technologies, synthesizing them with business, market data and user research data collected from the field using ethnographic-inspired user-centered approaches.
Working collaboratively with different stakeholders, I align actionable insights gained from the data with business goals, improve business processes, improve products and services, create new products and services, and help businesses save millions of dollars and increase their bottom line.
Throughout my career, I have been experiencing different challenges in making sense of research data. Unfortunately, none of them can be answered by practicing in a one-day workshop or masterclass. A deep synthesis of research data is often complex and contextual, depending on which vertical industry you work in. There is no shortcut.
If there are things that I have learned about making sense of research data, they would be the followings:
- Do your pre-research, and do it well. Whether you are an expert in an area or not, there is always something new that you need to learn if you work in a fast-pace vibrant industry. You may have to learn specific terms or regulations in a vertical industry, or you may need to understand how the macro system and the micro system may influence your research outcomes. For example, I have recently done a lot of research in Indonesian human resources, finance and taxation to understand how the Indonesian labour regulation and taxation has influenced and shaped the emerging sharing economy and entrepreneurship in Indonesia. Do you know that the fixed component of benefits in one’s salary in Indonesia should be equal or less than 25% of a basic salary?
- Understand your research goals. When you embark on a research, it is important to differentiate the purpose of your research. For example: When you are assigned to create new products and services, you would mostly use exploratory research approaches. On the other hand, if you want to improve the current products and services, you may want to employ both evaluative and exploratory research approaches.
- Plan your research carefully. There is nothing worse than getting skewed or invalid research data. You have to get things right from the beginning. You need to choose your research instruments wisely, align your options with your budget and time allocation, and most importantly talk to your stakeholders.
- Brush up your statistical skills. At some point, when you have to work with complex research data, especially quantitative data, you will need statistical skills to get a better picture of what your data is telling you. If you have not used your statistical skills for a while, you can start brushing up your skills from Khan Academy — https://www.khanacademy.org/math/probability.
- Learn from your mistakes. Once in a while, we all do make mistakes. It is okay to make mistakes. Think of your mistakes as an opportunity to improve yourself. Life is too short to worry. Your response to a situation is more critical than the situation itself. Think of your mistakes as are part of your life-long learning and progress.
- Practice, practice and practice. I have always encouraged mentorship and internship because I realise that UX and CX skills, including research skills, are gained over time through experience and exposure to different challenges. If you have not had an opportunity to get your hands dirty on a project, do some pro-bono works.
In summary, translating research data into actionable insights is a skill gained through mastery of theory, practice and experience. So next time, a snake oil company sells you an unreasonable promise to practise “deep synthesis” in a one-day workshop, give it a miss, because you know better than them.
If you are interested to learn more how to translate your research data into sensible information, the following resources of introduction to Design Synthesis may help:
- Design Synthesis — http://www.slideshare.net/frogdesign/design-synthesis
- Methods of Design Synthesis: Research to Product Innovation — https://www.udemy.com/methods-of-design-synthesis-from-research-to-innovation/
- Jon Kolko — Design Synthesis — https://vimeo.com/3945848
Originally appeared at http://uxindo.com/making-sense-your-research-data/
