I was finishing my undergraduate degree when my university suggested applying for a PhD. It seemed appealing but I didn’t know what subject I wanted to study for the rest of my life. They suggested I do some trends research to identify future trends and choose the one that I was the most interested in. I had two things, movie tourism (this was back in early 2007 so movie tourism wasn’t a big thing then); or the impact of social media on purchasing decisions.
By chance, around the same time, I was offered an opportunity to work on a project to study volunteer voice in mega sporting events. Due to budget restrictions, this work would have to be completed online – analysing blogs, forums and the social media that was around in 2007. I wasn’t a lover of social media (I’m still not) but I fell in love with the data and the rich qualitative insights you could gather. I chose social media as the focus of my PhD and the rest is history. I’ve been lucky enough to bridge both academia and commercial industries and continue to work in developing the field of social media intelligence ever since.
When I began my PhD and started analysing social media data, people regularly laughed and told me there would be no future in this approach beyond simple marcomms measurement. I’ve always advocated for a strong focus on behavioural metrics and blending of data sources, but that’s not how the industry started out. While social media intelligence still has not quite come of age, I feel vindicated for staying the course, and extraordinarily proud that I’ve played a part in its development.
A couple of years ago I came across the work of Tricia Wang in “thick data”, and it really resonated with me. I’ve always found that data can really dehumanise the people it seeks to understand, but Tricia’s work advocates revealing the social context of connections between data points.
No, not really. My work has focused on social data and other “voice of the customer” data sources. This part of the industry has always been unable to succinctly define and clearly communicate business value. It’s also largely been developed via the technology companies offering social listening or analytics solutions; however, this brings many challenges in market development and credibility.
Those working in the industry have been early in anticipating the tipping point of social media intelligence into the mainstream, but it has yet to happen. So, I’ve personally shifted my attention from practitioner to industry champion, with a focus on identifying value, developing ethics and improving accreditation to galvanise its credibility and growth.
I expect that the number and diversity of data sources will continue to rise, along with the production of more unstructured data sources (chatbots, voice etc). Like with social data, they can play a role in understanding “truths” about people and how they behave.
More widespread integration of unstructured data is already happening and blending with other data sources; this will continue, along with a progression from “vanity metrics” to business outcomes. However, more work needs to be done to identify the value of data sources, effective metrics to use (likely behavioural), and, of course, ethical frameworks.
I started my career as an academic, my own pursuit has always been about understanding human behaviour. I’ve still not lost this curiosity and passion, and it remains the biggest opportunity in data and technology. The opportunity is to understand and create new solutions to old problems and live better lives. However, as with everything, in the wrong hands it can be used for bad. My personal focus has always been on the unintended consequences of data and technology, not enough attention is brought to this.
Organisations are drowning in data. All the hype around data being the new oil has spurred businesses to gather more and more information, but the value only comes when they know what data points are meaningful, and what analysis approaches and metrics are effective, not the technology used to gather data.
Too much data can lead to too few insights, and we see organisatons struggling with this. On the one hand, they are told data will overcome their problems, on the other it brings new ones that they are not always ready for and able to manage.