How you gather data matters.
The data gathering experience and the data input experience matters and will shape the insights you gain from your data analysis.
I’ve seen data collection experiences that will influence data gathering. Some examples:
- There are errors in the values offered for selection (see image above)
- Data categories are confusing
- The way data input user interfaces are laid out and designed influences data input
- Instructional text is ambiguous or confusing
- Data labeling is confusing
- Data collection requires too much input
- Data input is required at the wrong time in the data collection process
- Relationships and dependencies between different data input areas are not mapped out correctly
- Considerable user recollection is required to input data
- Data input requires translation of data (sometimes across formats, systems or processes — sometimes not commensurate)
- Significant data processing is necessary before input
- Data input demands correlation with external resources (ease of access to external resources)
- The data input context and physical environment will influence the data input process
- Data input that require human factors considerations. Factors like culture, psychology, human abilities and human behaviors, because they will influence data input (and data analysis)
After the data is gathered, we sometimes use algorithmic processes to process data. These processes have their own inherent risks. When we are using mathematical or algorithmic models to process data, we have to be careful that we are not being too reductive, since this would influence data analysis and insights.
In other cases, the data is more complex and calls for an analytical approach that considers human factors.
There are human factors at play within data collection and data analysis — most of the time, we fail to acknowledge these until we find hard to explain data anomalies. You have to be able to look at these data anomalies and determine whether there are issues in the data collection process or data analysis process — then you have to be able to fix the problem.
Data collection and data analysis processes are experiential, and these experiences influence the quality of data you collect and its interpretation.
Today, vendors are rushing to create data collection and data analysis tools without thinking through the user experience. As an user of these products, you have to be able to identify their strengths and weaknesses to use them better than your competitors — or to select a better tool altogether.