Qualitative data analysis methods and wisdom of the crowd assists business development.
How does qualitative and quantitative data differ? And what does data have to do with the wisdom of the crowd?
Paul Howarth: ‘There are two types of data. Qualitative data (quant) is essentially words/pictures/videos. Quantitative data (qual) is anything attached to a number.
Computers are good at interpreting numbers. Software is, in essence, 0s & 1s. We can, therefore, develop things like spreadsheets, databases and algorithms. What’s more, numbers are easy to analyse.
It is difficult, however, to analyse qualitative data. Every word has a meaning, and meaning cannot be explained by numbers. Qual data is also called unstructured data. This is because no one knows what is in that data itself.’
Quant data signifies quantity. What does qual signify?
Paul: ‘The key to understanding qualitative data is in the words first five letters. QUAL explains a quality. Labelling a quality is always done in words. But the problem is that computers cannot grasp the context of words.’
Have times changed to support qual analysis more?
Paul: ‘Qual data was almost scorned upon in early years. This is because comments were individual’s singular opinions. And you could not take opinions for truth. Neither could you gather a general consensus.
However, with tech growth (specifically WEB 2.0) you’ve got millions of qualitative opinions. These opinions are posted daily on the internet. Let’s call this ‘The Experience of The Crowd’.
First, aggregate those qualitative opinions. Second, find a theme. Third, attribute a number. You then have an evidence-based metric.
What’s more, this metric is based on real actions. So, the metric is grounded on thousands of opinions. Rather than a singular opinion. If you want to ignore one opinion that’s ok. But ignoring the opinion of thousands may be foolish.
The “Experience of the Crowd” provides new openings for the qual data industry. Like Blockchain, the technology to analyse “The Crowd” is relatively new. The Holy Grail is to distil experiences. This means to distil Knowledge into Wisdom. And, therefore, make sense of “The Wisdom of The Crowd”.’
Can you tell us about how, historically, this data was collected?
Paul: ‘Most of the marketing of quant data was developed through surveys. A Likert style question would be asked. These included questions such as ‘Do you like this product?’. You would then score between one and five. A load of tick-box questions are often used.
A little tick box would be at the bottom of the survey. You would note down other issues here. Its positioning was last because analysts hoped that you would not fill it in.
This is because analysts could not use this data. Not in any tangible volume anyway. Analysts could read each comment, of course. But insights cannot be made without the ability to interpret comments in any volume.
Analysts were not focused on analysing the qual info. Neither were they focused on collecting it.’
How does it differ today?
Paul: ‘These days we analyse the data we should be focusing on. As such, open-ended questions should be placed at the top of every survey. We should not assume what customers/employees feel about a product/services/ place.
Typically, analysts use data to measure and improve something. This is so we make wiser decisions.’
What do you say to anyone thinking about using data analytics?
Paul: ‘Remember that qualitative data starts with QUALity.
A survey may say that 20% of your customers are not happy. But it does not tell you why they are not happy. That is the question you have to answer if you want to improve.’