S.02: The Connected Consumer: E.05

Grassroots Data

with Tim Lyons

April 4, 2018 • 20:46 min


  | Grassroots Data (w. Tim Lyons )


A conversation with Tim Lyons, Chief Marketing Officer of QSR International, based out of Melbourne Australia on the human side of big data. As humans living and working in the digital age, we produce massive quantities of data every day. Data is being applied to products and solutions used by humans, so it stands to reason that it should be informed by human perspective. But human insight on the big data scale is often unstructured and unpredictable, making it difficult to capture and hard to analyze. So what's a company, or city for that matter, to do? 


Welcome to Season 2 of It's Worth Doing Right. I'm your host, Olivia Hayes: passionate pragmatist, and the Head of Product Strategy at Accomplice. As humans living in working in the digital age, we produce massive quantities of data every day. The term "big data" attempts to quantify and express the magnitude of the data we produce. But many big data applications fail to consider the human side of that data. This data is being applied to products and solutions used by humans, so it stands to reason that it should be informed by human perspective.



The human insight on the big data scale is often unstructured and unpredictable, making it difficult to capture and hard to analyze. In this episode, we're going to talk about looking at the human side of data (or qualitative data) through the lens of big data. How do companies capture and analyze this kind of data? What are some of the practical applications we currently have for these types of insights? To clarify the finer details of the human side of big data, we called in a seasoned professional who came armed with a case study to help us understand.



I'm Tim Lyons, the CMO at QSR International. We published software called NVivo, which is designed to help anybody that uses qualitative data - or wants to find out the power of their qualitative data - to do so easier, and from a variety of of different sources.



Architecting big data solutions is a new exercise for most organizations, so mistakes are inevitably going to be made. What are some of the most common missteps companies make when taking on big data initiatives without fully considering qualitative data?



Quantitative data is very helpful to give us a linear view of what's going on, but what we miss out on is the human data perspective of what your sample tells you. That is definitely the largest mist that people experience. Often, it happens because qualitative data is traditionally seen as hard to collect and understand because there are so many sources of data flowing around in the world now. 95 percent of the data created in the world currently is qualitative, so if you leave that off of your list when you're doing your study or commercial exercise, it is going to impact the quality and the robustness of your results.



The landscape has completely changed in the last handful of years when you start to consider the explosion of social media, and when you start to consider the way that people choose to communicate - and that they can opt in and out of communications. When you look at that from a research perspective, the amount of opportunities for people to share their opinions has grown significantly - and what you don't want to do is rely on what would be now considered "traditional channels". If you do, you are going to miss some very important "grassroots" parts of the conversation.



So to piggyback onto that: in this connected marketplace, are there any disadvantages to leveraging qualitative data in big data endeavors? I'm thinking about the concept of noise. Does being connected mean there's just a ton more noise on the data side?



There's absolutely no doubt that in the modern world, there is more noise. And often, qualitative data can be mistaken for that noise. The reality of that situation is you need to know how to deal with your qualitative data. You need to use the correct tools and the correct methodology to ensure that you can organize, understand, and draw conclusions from that data. Increasingly, there are tools and products out there that help you do that, of which of course NVivo is one. But there are a multitude of avenues that you can take with qualitative data to make sure that it becomes much more than just noise. I think it would be a huge mistake if your listeners thought that qualitative data was in fact just noise. You would be giving away a very rich seam of insight. The thing that people need to consider is what they are trying to get out of that data, and where the value of that data can be. You want to make sure that from the start, you have a very clear picture of what you're trying to achieve as an outcome. Otherwise, it does risk become noise. Either quantitative or qualitative, it doesn't really matter: when you're getting into big data numbers, in terms of your data set and your sample size, the risk is that if you don't set up your projects and your your methodology correctly, the outcomes will be compromised.



You've mentioned the importance of data strategy in the past. And I think sometimes our risk with qualitative data is that people feel like there's a much looser way of interpreting it. But you have stressed the importance of an organization having a data strategy for that reason.



Absolutely. I think there's a number of things that an organization needs to consider when it comes to their data sets. The first is that collecting it is not a novelty, and it's certainly not enough. The reality is that every organization is collecting data, whether they are doing it overtly or not. They're sitting on a lot of data, and they can get a lot of value out of that. But having a strategy and an understanding from the top-down within your organization of what it is that you're trying to find out, and what you want to get from the data that you're collecting, is ultimately going to be the key. What we'd really love to see with our partners, with our clients, with our customers, is that within organizations, they're creating a data culture that really impresses the value of data that sits within an organization. And if you can get to that point? Regardless of the size or the frequency that you interrogate that data, you're going to have a much clearer picture that ultimately gives you a competitive advantage.



If an organization wants to leverage their qualitative data, what do you feel like is one simple thing that most organizations could do to start leveraging that qualitative data?



I believe the most significant thing that they can do to leverage their data is focus. Organizations across the world are sitting on huge piles of data. Qualitative, especially. What they need to really have down is understanding what it is that they're trying to do with that. Let's not try and boil the ocean by analyzing old data sets all the time. Focus on what your core competencies are within your business - what you want to take as a competitive advantage - and ensure that that data is healthy and flowing in the way that it needs to. Start with the analysis of that, and then move on from there as you master the technique and the art. Then you can start to expand the value that you're getting.



Let's talk about the application of this a little bit. You've seen the advantages of leveraging big data - and qualitative insights, specifically - to solve civic challenges, such as rebuilding a town after a natural disaster. Can you tell us a little bit about that situation?



Sure. The example that we're talking about here happened back in 2011, in a town called Christchurch, New Zealand. In 2011, they experienced a devastating series of earthquakes, and it basically leveled the city. There was a huge humanitarian effort, and a lot of the population had to leave their homes, the city, and that part of the country, because of the destruction that had happened. What the council did subsequent to the clearing and relief effort was to start to think about how to rebuild the city of Christchurch. And the easy way (I guess, if there is an easy way to conclude a natural disaster) that they could have approached this was just to replace it - just to rebuild exactly what Christchurch looked like in the first place.



But what they chose to do was, in the face of a crisis, to take an opportunity. And the opportunity was to really listen to their population and constituents, and to reimagine what a modern Christchurch might actually look like. So they went out with traditional survey methods, but understood the importance of getting insights from all of the population, regardless of how they wanted to interact. They definitely used traditional things like surveys, and phone polls, and things like that. But they also wanted to open up the conversation to a non-traditional or modern source data as well. So the great example from Christchurch certainly was that they got over 100,000 responses. Pieces of qualitative data that came in through their particular channels. Everything from traditional surveys to other methods. They were taking tweets, they were receiving emails, they were making phone calls. They even had an example where somebody had taken the time to build a lego model of what they wanted Christchurch to look like, and sent that in.


[09:09] All of these things are very disparate when it comes to their type of data. But the conversation in the main was common. They were engaged constituents

wanting to have their say about what their city should look like moving forward. So the council used Nvivo to collect all of that qualitative data, and then start to organize it in a way that helped them understand the common themes of what their constituents were telling them that they wanted the modern city to look like. The net result was that they were able to collect all of those pieces of data, and understand that regardless of one of the pieces of data being a 140-character (or now, 280-character) tweet, and one of the pieces of data being a Lego model, they were actually saying similar things. They were able to group that data together and start to understand what all of their constituents were saying to them - not just the ones that were able to access traditional means of research.



The conclusion to that project was a completely reimagined city. What they found out was that because of that natural disaster, that community feeling had grown so significantly. Pride and the patriotism for that area of the country had increased so significantly that people wanted more meeting spaces. They wanted more green areas. They wanted more community activities. Christchurch has a central point, which was a very old cathedral, and the importance of that came through - so the rebuilding effort and the centralizing of the city around that area became particularly important. They started to be able to to address public transport concerns. They looked at where their sporting facilities were located, and where their community spaces were located, and started to reorganize those so they were accessible and built on areas that were expandable. They started to look at the housing density and the types of housing, and the approval of those based on the modern view of what they wanted the city to be. The great story here was that a local municipality had been able to use the power of qualitative data to step forward in the face of something that was absolutely terrible, in terms of a natural disaster.



Why did the city decide to use qualitative data versus going with typical zoning, municipality, or census data?



The power of the people's voice comes into play here. The council realized that the actions that they take and the money they spend comes from the public purse. It also has political consequences, in all reality, going forward. The case here displays the fact that we should listen to what would be loosely termed "your customer." In the case of Christchurch, the customers are the people that live in the city and pay the taxes. In the commercial context, it's about listening to your customer, however they want to communicate with you. The landscape of, of how they want to communicate is changing. The days of an organization being able to dictate those terms and that conversation have long gone. The organization that's going to succeed in the future is already customer centric. And truly being customer centric means that you've got to ensure that your data collection, and your interpretation of that data, is facilitated in the areas that customers want to interact with you - not the other way around. That is critical for business growth, and super critical for business success.



What challenges did the governing body face when they were trying to integrate this type of feedback?



The biggest challenge is how to interpret it. The tools that are available to analyze qualitative data advancing quite quickly, and are very powerful. However, you still need somebody at the end of that process who is able to manipulate that data and understand it, and take those themes, and make conclusions that are relevant. The next big thing for an organization is making sure that those conclusions are defensible. The collection of data and the interpretation is one thing, but ensuring that the conclusions that are drawn at the end are justifiable is certainly important. The Christchurch example is a good one. As we discussed before, public offices are answerable to the people. So if they make a conclusion and they built an entire city on the back of that information that they've gotten, and they've taken the time to go out and get that intonation correctly, then inevitably there'll be questions on decisions and justifications. You have to make sure that the qualitative data that you've collected and analyzed is organized in a way that you can easily reference the conclusions that you've drawn.



One of the major benefits of qualitative data is that it can be mined to mobilize voices that may not ordinarily have the chance to be heard. In what ways do we find that qualitative data channels are especially equipped to be able to do that?



I think this gets back to what we would probably term "grassroots data and information." The reality, as you touched on before, is that some people are not motivated to answer a paper-based survey or answer a phone call. And if they're not doing that, what you are getting are typically the extreme voices: the people that are are enthusiastic, or negative towards the conversation. What you missing out on is the rich scene of people that sit somewhere between those two points. So what we need to ensure as an organization is that we are truly listening to all of our customers and understanding what they've got to say, rather than the "vocal minority", if you will. There are countless examples, especially in the political space, of where that traditional data has misled polls and likely results because what we've missed out on is the heart of qualitative analysis, which is a people science. It's a social science, and it's the story of humanizing data. And if we miss out on on that, we are likely to make missteps in our conclusions.



You mentioned the negative sentiments, which is equally as valuable. It can be difficult for organizations to listen to negative feedback, but it sounds like that's also a really valuable part of this qualitative data.



Absolutely. Otherwise you're not getting a full picture and you end up, in a commercial sense, drinking your own kool-aid. If you're only listening to the people that are particularly positive or vocal, then you're missing out on a stream of data that could help you avoid a pitfall. Commercially, that makes a huge amount of sense and presents a huge amount of value too. You're looking at building products and investing our funds into particular areas, and you want to ensure as best you can that you are making the right decision for your shareholders, and that you're making the right decision ultimately for your customers to respond to, and have a positive result.



In what ways can leveraging big data alongside human insight support innovation? What are the possibilities that you see for the future there?



We live in a world today where we're creating more data per day than we've created in the previous decades. What this gives organizations is an unparalleled opportunity to essentially see into the future. If we can harness the qualitative data and the human insights that come from that, it will give your organization a huge competitive advantage. What we need to ensure though is that we're focused on the critical things that your organization needs to successfully execute on their business strategy, their growth goals, their revenue targets, or their public outcomes. What we want to ensure is that we're using that data to make our business succeed. And that's not trying to boil the ocean. It's trying to ensure that the data that we're examining is going to be relevant to moving our organization into the future. So what we're getting with the use of human insight is a complete understanding of what our customer base is looking for, sometimes before they even know it themselves. So looking at the sentiment, both the positive and negative sides, to what your data is telling you is going to help you ensure as best you can that you're making the right strategic and financial bets for your organization.



What projects are you working on personally or professionally right now that you're excited about?



One of the things that that is occupying a lot of my professional time at the moment is QSR International's product development division. We have published Nvivo software for many years now, and we're just about to release the twelfth version of it - which is a huge market release for us, coming up early in 2018. One of the other exciting projects that QSR International has recently launched is a product called Interpris, a product that is is purely pushed into the government and the public sector space. It's really focused on qualitative data and surveys, especially. The example that we talked about with Christchurch is a very exciting one, because the product is designed to help municipalities especially listen to the voices of their constituents and make better decisions based on that qualitative data.



As we move into a future where consumers will continue to generate exponentially more data, harnessing human insights will be critical for gaining a competitive edge. Crafting a data strategy will help your organization to understand what your sources of qualitative data are, and how to properly analyze that data. Keeping your focus on the data that you've deemed strategically important to your organization will help you from being overwhelmed and distracted by the interesting, but irrelevant. Understanding human insight data is critical in helping you to design products that meet the needs of those humans that you're designing for.



Thanks for joining us for today's conversation. To see more content from the Accomplice team or leave us feedback, visit us at iwdr.wpengine.com, or drop us an email at podcast@itsworthdoingright.com. And remember: if it's worth doing, it's worth doing right.