Let Data guide you to the solution

In today’s time, we are going beyond macro view, and entering a layer where data is becomes increasingly important. Large service companies have begun to develop complex and intelligent systems for content recommendation and evolving experience, such as Netflix, 3M, Disney and Spotify.

In order to deliver delivering solutions and experiences that are more coherent and able to keep up with people’s needs, it is important to first understand the importance of the data, and how this access to relevant information impacts our discipline. There are two types of data:

• Quantitative data: Data that tells you WHAT is happening (or not happening). Usually, it’s a numerical data.
• Qualitative data: Data that tells you WHY this is happening. Qualitative insights aren’t numeric.

Both these datas are important as they need to be together to really understand your product’s usage patterns. Armed with quantitative and qualitative data, you can make a more informed decision.

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Limitation: Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.

Strength: Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Limitation: Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions may have for those participants.

Strength: Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective, and rational

Conclusion: The use of data in products should be seen as a tool to evidence behaviors, because a project does not end after its launch, and having smart metrics is essential to plan, improve and evolve your product or service constantly with the organization’s goals.