To kick-start our ‘Introduction to Data Analysis Month’, we have organised an interview with the CEO of Pansensic, Paul Howarth. In this discussion, Howarth hopes to shed some light on the often daunting and ambiguous field of qualitative data analysis methods for organisations wishing to improve.
So then, to start with the basics, what are the differences between qualitative and quantitative data, and what do you utilise here at Pansensic?
Paul Howarth: “There are two types of data. There is qualitative data, which is essentially words, pictures, videos, sounds, and there is quantitative data which is, fundamentally, anything that can be attached to a number. The whole quantitative data industry has really been exploited, maybe even approaching its potential over recent years. However technology doesn’t stand still, and the introduction of “Blockchain” is going to open up a new world for the quantitative data industry, and it will fundamentally change our world too.
“Computers understand and are good at interpreting numbers. After all, software is essentially 0s & 1s and we can, therefore, develop things like spreadsheets, databases and algorithms. Analysing qualitative data, on the other hand, is very, very, difficult. For example, every word has a meaning, and that meaning cannot easily be explained by 0s & 1s, as a word isn’t a 0 or a 1s. Qualitative Data is also called unstructured data, as no one knows what is in that data itself. If there is a conversation in a piece of data, for instance, nobody knows what’s in that conversation.
“The key to understanding qualitative data is in the first five letters of the word- QUAL. Qualitative explains a quality, and if you don’t analyse the explanations of that quality, you can’t understand or define WHAT that quality is, or WHY it is what it is. Describing a quality is always done in words, but the problem for a computer is that it cannot understand words or the context of words and their meaning in that context, so over the years what’s happened is that everybody has majored on analysing and making sense of “the numbers” at the expense of analysing and making sense of “the words”.
“In the early years the qualitative data was almost scorned upon by industry because comments were merely an individual’s subjective opinion, as such, you could not take that opinion for the truth or as a representation for the general consensus. Recently, however, with the development of technology and, more specifically WEB 2.0, what you’ve got is millions of qualitative opinions posted daily on the internet- let’s call it “The Experience of The Crowd”. Now if, and it’s a big if, you can aggregate those qualitative subjective opinions and find a theme and attribute a number to them then you now have an objective, evidence-based metric. What is more, it’s a real metric based on a real experience. So, rather than it being one person’s opinion, it is thousands of people’s opinions, and if you want to ignore one that’s ok but if you are going to ignore thousands of people all saying the same thing, that may well prove to be foolish.
“In a similar way to Blockchain opening up a new world for the Quantitative Data industry “The Experiences of The Crowd” will open up a new world for the Qualitative data industry, and fundamentally change our world too. Like Blockchain technology, the technology to analyse “The Experiences of The Crowd” is in its infancy whereby The Holy Grail is to distil those experiences (their Knowledge) into Wisdom and thus make sense of “The Wisdom of The Crowd”.
Can you tell us a little about how, historically, this data was collected?
“So, if we think about it historically, most of the sales and marketing of quantitative data was developed through surveys. We would ask a Likert Scale question, i.e. ‘Do you like this product?’ and then ask you to score it from one to five. Very often you get a load of tick-box questions, and at the bottom of the survey, there would be a little box in which you could write any other issues that could not be answered/said via a 1 to 5 system. Now, in actuality, that empty little question box was put at the end of the survey, as opposed to the top, for a reason. Its positioning was due to the fact that the people doing the survey hoped that you would not fill it in at all.
“Even if you wrote a useful, detailed, clear and concise message they would not have been able to do anything with this rich information in any appreciable volume. Of course, they could read each comment, but without the ability to make sense of those comments in volume, no insight could be made. We were not focused on analysing the qualitative information, and thus we were not focused on collecting it. These days it is different; we can analyse it and we should be focusing on it. Rather than leaving a little space at the very end of a survey, these open-ended questions should be placed at the top of every questionnaire. We can’t and should not assume what our customers and our employees feel about a product, or services, or a place of work or anything else for that matter.
“Typically, we analyse data so that we can measure and improve something or understand something better so that we can make wiser decisions. Those are the primary reasons why we collect and analyse data.
“When we analyse data, we are looking at helping people make better decisions, by helping them become better informed. Now that can be a strategical decision, that can be a metric, so they understand where they are, or that can be an improvement.”
Finally, is there a message you would like to say to anyone thinking about using data analytics to better understand their business, their clients, or their products?
“My message to them is that if you are looking to improve the quality of any aspect of your organisation, your products and your services, remember qualitative data starts with QUAL-ity. And if you don’t analyse the explanations of quality, you can’t understand why.
“So, if you want to improve your organisation, you have to understand why, and the only way you can do that is to analyse the qualitative data.
“A survey may say that 20 percent of your customers are not happy, but it does not tell you why they are not happy, and that’s the question you’ve got to answer if you want to improve.”