Finding Stories In Data For Promotion And Defense

  📍 There’s never been so much data floating around and accessible to the public and with AI the ability for anyone to analyze this data and make sense of it has never been easier.

Along with the opportunity for organizations to use data as part of their own promotional activity, there’s also the challenge of the data uncovering other stories, which perhaps the organization would prefer if the public didn’t know about. To help us navigate all of this, I’m pleased to be joined by Alex Waddington, founder of Whetstone Communications, a consultancy that helps communicators do more with their data.

Alex, great to see you again. Welcome to the podcast. Thanks, David. It’s great to be here. To start things off, Alex, could you give a bit of background to who you are, your career, and how you’ve ended up working as a data for communications consultant? Yeah, sure. I don’t think it was something that I necessarily, would have predicted at the start of my, my career.

So I always wanted to be, a writer, a journalist. That’s, from, being, at secondary school, that was the sort of path I, wanted to go down. So I did train as a journalist, University, which I loved that. I think that’s never left me in a way, even though I’ve, went over into, into, not internal communications, but in house communications, strategic comms, corporate comms, call it what you want.

I spent a bit of time in publishing, which again, you, I took a lot from, learned a lot about hitting deadlines. but. Yeah, I switched over into comms fairly soon after qualifying as a journalist. working in public sector, worked for a long time in higher education, and a number of communications roles, so ranged around internal comms, public affairs, stakeholder, so you know, one of the beauties of universities is they’re big places and you can, you can.

Opportunities come up. I took those opportunities. and I suppose it wasn’t until I ended up working for a charity in the COVID period. really great charity called the Good Things Foundation do a lot of work on digital inclusion and they did a lot of evaluation. work for the programs they ran.

So they had a kind of traditional, research team. Who did a lot of that and did a lot of research into the issue of digital, exclusion. but they also had a data insights team. and I got talking to their data insights team. It was a small team. I got talking to them about what they did.

and how they use data to help understand how well programs are working and also to understand the problem they were trying to tackle, which is digital exclusion, which is actually a massive issue in the UK. And it just suddenly, was a lightbulb moment for me in terms of, being a communicator, having done journalism, which is an arts degree and having an arts based background.

it, I’d never, I suppose I’d use data to an extent, and I’d use spreadsheets a bit and databases a bit, but It was, I’d never really thought much more about it and suddenly realized that actually I’m missing out. there’s a gap here in terms of bringing science together with art, bringing more of the data side into it and got really excited.

if you’ve ever, I had one of those moments where suddenly you see the possibilities, start to get really excited about what could be done. So I, from there, I in my spare time, taught myself to code because I realized I wanted to be able to scrape data, public data.

There’s so much of it, so much that’s there and accessible. but there’s so much that’s there on the internet, which is, yeah, it’s the world’s biggest database, the internet, just not in a very structured form. So taught myself to code and, set myself little projects. and then decided that the time was right to go out on my own and really to, my USP as such is that I’m a communicator, first and foremost, someone who understands, the challenges of communications and what’s needed, but also I’m able to bring.

the data into that. So putting the two together. I’m, I guess I would argue I’m able to help communicators use data in a realistic and an effective way, because I understand what their challenges are and, I’m not an out and out data analyst and it’s not my desire to be that not, there’s anything wrong with that.

I don’t, I’m not a data scientist. You don’t, I don’t think you need to be, to bring more data into comms. yeah. And now I run my own consultancy. I work across, still a lot in higher education, drawing on my background, do quite a lot in local government, a bit in kind of emergency services, a bit of training, that’s becoming more popular, projects where I’m helping comms teams dive into the data and help work out, how, what it tells them and then what they can do with that to improve and get better outcomes.

yeah, and really got to say, absolutely loving it. No regrets. Brilliant. And the theme of the episode is about data journalism. Can you just give a bit of an insight into what we mean by data journalism? Yeah. data, I obviously was a journalist, originally, but data journalism wasn’t, even something people talked about.

it was quite a while ago now that I did trade as a journalist. We have to say that. but It’s actually, as I, I understand it, as I’ve researched it, being around, it’s it was called something else, it was think called computer driven journalism or some, something like that.

And data journalism only became a sort of term, in the last sort of 10 to 15 years, something that’d be recognized. So what it is, it’s essentially, using, using. Interrogating and researching data to find stories. that’s really what it is in the same way that, good old fashioned journalism, you’d, when I did my training, you’d go out and you’d be sent out on your work experience and you’d be told to go, and walk around the village and go talk to people and find out what’s going on, that they, journalists don’t really do that anymore because they haven’t got the time, but that was essentially, what happened, or part of the job.

Okay. Now, obviously, everything is so much digital these days, so much power, so many data sources. You can do that, but effectively in a different way. And DataJournalism is doing that. it’s, there’s a lot of public data that’s, published. any public bodies publish a load of it.

You can get a lot of it through Freedom of Information. So all sorts of sources and, really the data journalism, the skill in that has been able to harness, has been able to, know where it is, harness it. to, to find, to, get it into the format you need.

And then basically, do the analysis on it, to see whether perhaps, theories that you might have or things that, you might have been told or, hunt just, hunches that you have about something that’s going on. does that stand up or does it not?

So it’s again, it’s the, journalism. It’s really, factual accuracy is really important. So obviously, you need to know how to use the data to get. So they aren’t, to either see whether something’s true or not, if you’re trying to test something. And is that how you start off with the process as well?

You start off with a bit of a hypothesis, let’s say, and then you go and find the data to either confirm or refute that? Yeah, I think that’s, that is a definitely, from everything I’ve learned, and I’m still learning, so I’ve got to say, I, I Data journalism’s only been something I’ve been interested in and looking at seriously for the last three years.

But yes, the big, one of the big learnings for me has been, it’s really easy to get lost in data. if you’ve ever looked at a massive spreadsheet yourself that’s, thousands of rows and lots of columns, and you don’t really know what you’re looking for, you can spend ages Not really know what to do with it.

You get lost. So yeah, having a hype, having a theory and a hypothesis, whatever that is, whether that’s about, something that you think is happening in your organization, whether it’s something about an audience that you’re trying to understand and then using the data to see whether, and it needs to be the right data.

That’s the other thing. So sometimes you might look at something and go, this actually doesn’t. tell us this doesn’t help answer that. We need this data. That’s actually, or we need more data to bring in alongside it. But yes, that is definitely my, one of my big tips in some of the training that I do.

And when I, speak to comms professionals, how to get started is have, have some theories, hypothesis, hypotheses that you want to, test. Fine. Then work out, work, work out what data you’ve got, so perhaps data mapping exercise can be really useful, and then work out which is the best data to help you test your theories and go and do that.

And then, these things aren’t binary either. That’s the other things to say, as, as often in life, it might not be you get a yes, no answer. You might get a, in circumstance, certain circumstances, that is true, but there are exceptions, you’ve got to be aware that things have, caveats and very, yeah, very much have caveats to them.

And when it comes to, uncovering interesting stories, whether it’s promotional or, perhaps negative ones, do you find that it’s Often best combine different data sources or are those stories still available just from a single source of data? It’s a really good question. I, think both. So I actually, do in terms of again, I’m a real, I’m a real evangelist really for data.

I’m real advocate. So in this, in a simple sense, just having, yeah, so let me give you an example. A lot of the work I do, or a chunk of the work I do, is around helping public sector comms teams get the best out of their organic social media because budgets are tight and organic, as many of us know, organic social media is becoming harder and harder to get, get anything from.

you’re gonna, you really need to get the best out of it. If you can understand what works. And teams do have, often have really good, tools that they schedule their social media on. You can get a lot of data at the back end of that. You loads of really good data, all your content from the last year, all the analytics, it’s all nicely structured, in a simple, a very Simple way if you’re if your team are working on a theory that we will do posts that are a certain length because we think they get a better engagement You can literally, having one spreadsheet with the text, which you can run a word count on So you can get a number and then the engagement rate, whatever, number of posts Like, click, whatever you want to use for the overall engagement rate.

and you can look for a correlation. And you don’t even need to know how to do a correlation coefficient sum. There’s a function in Excel which is dead easy, it works it out. And basically if the number is a certain, if the number is 1. That’s a perfect correlation. So it means when one, one number moves, the other number moves accordingly.

And so if it goes, if it can work either way. So that will immediately tell you just by having that one simple data set and knowing how to do one simple thing, whether, your, if your team are working on a theory that we do content of a certain length, or we do short content or long content, cause it works better, or that doesn’t work.

You can immediately see where that’s right. That’s really clear, you can see whether that stands up or not. where you can also do, I do say the more advanced, in terms of finding stories. I would say some of the really good stuff comes from combining. Definitely. And that’s what, going back to data journalism, and I listened to a lot of podcasts.

One of my big tips is, find data journalism, podcasts, you some great ones out there. can, share the links with those for, anybody listening, but, yeah, they, that’s what you take from it. That it can be a publicly available data set that, that, it could be your own, your own data set.

And if you can, if you’ve got something like a, what you’re looking for, without getting too technical often, is a way of joining them. how are you going to join them? Is it going to be by date? Have you got two data sets that have both got date information in that you could merge them on? You need some commonality between those data sets.

But if if you can think of those things, if you can find, two or three data sets that you can join, and suddenly you’ve got, you’ve got A brand new piece of data or a data set that could have all sorts of interesting stories in so you can test more theories, you can get more creative with it, basically, yeah, it makes sense because I think if there is a single data source that’s publicly available.

Either there’s, no story in there or it’s already just available to the public anyway, or someone else has found it. If you can combine that with some other information, the likelihood is it’s unique to you. And particularly if it’s your own data, then it is your own unique take, it’s your own unique story on that, that data.

Absolutely. An example that I’ve used in a previous employer, That springs to mind was, we did it in order to do some digital PR to generate some backlinks to the website. It was really effective. We did, an analysis of, NHS waiting times and Google search data for private, medical insurance in those regions as well.

And just by combining that information, uncovered something totally different. And then there’s a story there. And it means that for a software business that kind of has no right to get mentioned in national newspapers or magazines or websites with a kind of generalist or a health based interest, All of a sudden, you’re appealing to them, and you’re getting coverage, and you’re getting backlinks.

it was enormously effective. Absolutely. I think that’s a great example, David. That’s a, it’s a really good example, and I, I know that, yeah, I, I’ve seen, other bits of, really it’s quite smart. That’s, I think that’s, the point to make on that is there is an opportunity to do things like that, fairly low cost.

it’s not you don’t need to go commission a load of research. the one of the things in kind of com, comms and PR that I thought just got overdone was the, going and commissioning a survey and building a story on the back of it. You can actually get much better data sets, bigger data sets that are actually more robust.

Spending thousands of pounds on 100 people. and the BBC, again, data, drawing it back to where you could get inspiration for that type of thing you’ve just said. But I think it’s a great example. The BBC are great at this sort of thing. Absolutely, BBC Data Unit, I think they’re called the Shared Data Unit.

Look out for their stories because they did some really good stuff recently on sewage, over, this, when you get the rain and there’s the sort of these emergency sewage discharges, is the word I was looking for. And they actually correlated, they got the weather data.

And they got the discharge data, and as we know, what we’re being told is the sewage is only supposed to be discharged when it’s bad weather, that’s what we’re told, or it happens in exception. What they found was there was a correlation between discharges and days when the weather was fine. what this looks like is, it looks like we’re not actually getting the full story there, there was some kind of explanation for the water companies, but, That’s a really good example of uncovering a story that, actually getting to, getting to a, it’s a new, it’s a new angle.

And obviously, in this case, very much in the public interest. But you could think about how you could do this for your own organizations, to tell great stories. it’s something that wasn’t known, and that was simply Say simply, there’s a bit of skill in it, but putting together two datasets.

And we’ve touched on a few of the datasets, already, but are there any others that spring to mind that people might want to start looking into just to see if there is useful data there that they can combine with their own or other sources? I think, I think search data, you’ve mentioned search data.

that’s always a good one. that’s, easily available. I think, anything, if you’re more in the sort of campaign y space, there’s interesting things you can do with, public data around, parliament and government, there’s all loads of, and that’s a lot of textual data.

so don’t, I think big tip here is don’t think of data just as numbers, because if you can, perhaps two years ago, I’d have, been saying, you do need a bit of skill for this, but you’ve mentioned AI already. And in terms of textual analysis, if you can collect a big data set of texts and this could be, it could be like lots and lots of text, but, organized by, it could be search results or something like that, and you actually want to be able to analyze it.

One time you used to really have to be able to do, textual analysis using some fairly sophisticated tools. You probably get them off the shelf as well, but might be expensive, but really to do it well. It’s getting to the academic sphere of, the work that they do, analyzing big textual data sets.

but now AI, with the caveat that if you’re going to bet the house on it and, do something major with it, you need to verify that it’s actually not, hallucinated. So that’s major caveat, but it is getting so good so quickly. so yeah, definitely things like, parliamentary data, government data.

if you can, Scraping data is an interesting area. There’s kind of two sides to the argument on data scraping. If there’s anything in the public domain that’s publicly funded, I’d say it’s fair game for data scraping. me personally, I’d say there’s no problem with that. The other argument that you get is that, basically, if someone’s putting something on the internet, whatever it is, they’re putting it in the public domain, there’s a benefit to them for doing that.

you can’t really argue if somebody collects it and does something with it. Yes, and companies do argue that, you’re not allowed to automatically collect it. That’s against the terms of use, but there is a, there is, so there is a big, debate about ethical scraping at the moment, which is be interesting to see how that plays out.

There’s also, cases going on with, long running legal cases with LinkedIn, for example. So LinkedIn wouldn’t be half as successful if it didn’t have its data publicly available. If it was closed off and I’m searching for you, David, and I said, Oh, that’s, the guy I want, but then it just closes off and I can’t see any of your content.

I’d be like, that’s, no good. So in a way the argument is. Companies like LinkedIn are trying to have the cake and eat it by saying, no, you can’t collect our data. There’s companies that are obviously provide data collection services that obviously will argue against that. And, there is a really good argument that when social media companies and other companies close off their data and Twitter locking down their API, it’s hard.

So X as it is now, it’s hard now for even academic researchers to. to collect data from Twitter, but Twitter and social media is another good example of, where you can collect really good data, but it’s getting, harder. So social media, I’d say, and then, look at the amount of open data there is, there is this open data initiative, particularly in the UK, but also other countries where, you know, that there is actually data sometimes that’s published by government agencies, there’s loads of, and one thing people don’t know as well, just one final thing is.

The Office for National Statistics is obviously, the source for official statistics. They have loads of them. You can actually request additional data from them. If you, if they’ve produced a data set and they don’t actually have what you want, it’ll cost you, but it won’t cost you much. It’s quite, actually quite affordable.

if you’re, particularly if you’re doing a commission project and you’ve got some budget, you can say to them, Oh, this is really interesting. Can you provide this? and I think a lot of people don’t realize that. they think they’re just, they just pump out, they’re almost one, one way.

It’s the same data year in, year out. Yeah, it is. And it’s really good data. Don’t get me wrong. But they actually have quite granular data as well. So that’s another big tip. ONS, look at what they might be able to give you that’s not necessarily out there already. Yeah. I’m a big fan of some of the data that the government put out.

Generally speaking, I tend to think that government organizations are hugely inefficient and horrible to deal with. But I have to say the data that they make available is excellent. out of this world. When I was doing my dissertation for my master’s, it was based on the education sector. And if you want to, you can go and find out down to a school level, down to a region, national, but filter it in slice and dice that data in so many different ways about length of tenure and salaries and job roles and size of organization and all of it.

It was unbelievable and so easy and just freely available. Absolutely brilliant. Absolutely. Great. And then, in terms of analyzing the data, we’ve touched on it already. AI, I’ve been using ChatGPT recently. I know there are other tools available. something I found with ChatGPT more recently is I found it a bit harder to get it to analyze Huge chunks of data.

So something I’m doing not with them as a data journalism mind, but actually just to help with, messaging is, I’ve provided chat GPT, a spreadsheet of tens of thousands of websites to go and crawl, and then find out what sort of messaging each of these websites is communicating and marrying that up with some other interesting data.

The challenge I’ve run up against recently is, chat GPT would then process about the first 10 lines of data and then go, there you go. That’s done. And it didn’t matter what prompts I gave it, it would always stop at around 10, maybe, if I was lucky, 20 different rows of data. And the way I’ve ended up getting around that is, I’ve just found a freelancer who, I think they’re using JavaScript, I want to say?

Yeah. to actually run that more effectively. Is there, are there any other tools or skills that people might need in order to get the most out of their data? Yeah, that’s really interesting. I’ve had similar challenges. When I first came on the scene I thought, Oh brilliant, this makes my life a whole lot easier.

But it’s actually not quite turned out like that, but it’s definitely helpful. I mean I taught myself, yeah, essentially two of the main languages for Processing and scraping and acquiring data are, Python and R. And I taught myself R, only because at the time this was pre chat GPT. So you couldn’t go, how do I do this in Python or R, which you can now, which is amazing.

So in terms of learning to code, if you want to do that, you, have, literally it’s, 90 percent easier than it used to be but at the time I was borrowing code from GitHub and other places and then adapting it to do what I wanted to do and that’s how I learned to I’m not, I can’t say I’ve learned to code in R but I’ve learned to do what I need to do in R to get what I want I would, so I would always say the gold standard for if you want to, There are two options.

There are tools, so well, three options. If you’ve, you, you can hire someone to do it for you if that’s affordable and, you can find someone. there are tools, third party tools that will, collect fairly plug and play type stuff. it’s fairly easy interface and some of them are more complex and some are more expensive than others, but will allow you to, do that through a friendly web interface.

or you can learn to do it yourself and actually most stuff. Once you can do it yourself, you can handle most of, I’d say. And so I think my, it might, that might not be feasible for everyone, but the big thing I’d say is if you’ve got chat GPT and code, whatever, as I say, it’s almost now to, learn how to, run a bit of code and you, and, the beauty is as well, you can say, me this code in whatever language you want. So if you can, it doesn’t need, it, literally, you could probably almost say, I want to run some code to collect some data. What is the easiest way to do it? I’m not, I don’t want to get into downloading complex programs. I, want a way of, running some, it could almost be a web based scrape somehow.

You could run a scraper, cloud based scraper, I think like my big, you So the tip for really getting the most from data is getting enough knowledge and enough confidence and skills to be able to do it yourself, to get that data, or at least if you’re in a team, get, is there somebody in that team who’s got more of a technical background or actually is into this stuff, that’s the other thing, isn’t it?

Is there, it doesn’t have to be you, is it a collaborator, so I don’t think there’s any substitute. Thank you. Because particularly when I hear data journalists, there’s very good data journalists, they’ve all got the technical skills to be able to do it themselves without relying on, third party services or, AI.

And one thing we’ll say It gives them the confidence to report on it, If they’re comfortable with managing and analysing the data, then they can be comfortable with what they’re about to report on. A hundred percent. And sometimes for me, if I’m Getting some data for a client. I’m doing a project where we’re trying to get some insight that’s going to something, a lot of money is going to be spent based on that insight and recommendations.

I, I need to know that, a how the data is right and I know how it was collected. Therefore, I can, I can verify it myself. So it’s all I can, audit it, effectively. So, yeah, it depends. I think it’s the answers. It depends what you’re going, what you’re doing. Doing what you’re going to use it for sometimes as to how much AI comes into.

But it, but my big tip is, there’s a new tool called Julius. yeah, have, a look at that. I’ve not really had a big play with it so far, but academics who have to do very complex data analysis have been absolutely raving about it. They literally been saying, this is a game changer for my academic research.

And when they’re saying that, that it. It must be worth looking at. So for data analysis, yeah, have a look at that. I’ll go and check it out. We’ve spoken quite a bit about the, I suppose the nice part of data journalism, from the organization’s point of view of uncovering positive stories that are going to help your own promotional efforts.

There is the other side of that coin, which is, Data can be used to uncover stories that organizations might wish other people didn’t know about. can you talk a bit about that? yeah, this is, this is, I’m happy to talk about this because this is actually something, it’s almost, a conceptual thing for me that I, like an idea I’m trying to develop and get people thinking about because I’m, I started thinking about it a while ago, this idea of defensive data journalism, Defensive, reputational comms, or data driven reputational comms, whatever you want to call it, so it’s almost what you’ve got now, so you’re a corporate communicator, you’ve always been dealing with negative sort of stories, your organization, particularly if they’re public sector, things will go wrong, any organization, think Things will go wrong, but particularly if you’re in the public sector, for example, your data is available, a lot of it, or, if you publish things on your website, I guess if you’re part of, you’ve got to do reporting for compliance reasons, that sort of thing.

You will have a lot of data out there now with data journalists now being, they’re able to do things in an automated way they were never able to do. They’re able to find stories that they couldn’t find. you’re going to have to, first thing is, Why I argue communicators need to be data literate and have data comfort is, you’re going to have to deal with data driven negative stories.

And if you’re not comfortable with data, you’re not really going to be able to, I think you’re at a disadvantage. So there’s that for a start. I also think if you looked at it, In terms of how do we get ahead, right? So think about the, you have these white hat hackers who basically, you get them in to, to try and break your systems and break into them, all they, essentially this is now a job for people where, they basically security consultants are going to try and break into your systems.

Yeah. They’re probably X hackers. You could have a similar concept as you could hire someone potentially. So this might, someone might listen to this and go away and make a really good career out of this. And that’s great. what about how, you could either do it yourself or you potentially could have consultants, people who come in and go, we’re going to really, get into your data.

We’re going to start with theories about what you might be doing and we’re going to use that to try and find the bad, the stories that might be coming up that are going to trip you up. And. For me, it’s like I, that I think again, particularly if you’re a data led organization and you’ve got a lot of data, there’s probably all sorts hiding in there.

In doing it, you might also find some really good stories. so this is the other thing, right? In doing that, you might actually go, it’s a really, we’ve seen really interesting like story coming outta this data, which would be, which was really probably worth doing and helps, demonstrate.

All the good things that you’re, some of the good things that you’re doing, but also high, high, it can be, and again, I think, don’t just think numbers, think text. if you are the sort of a public organization or your organization has a lot of like minutes and, but, like this kind of meetings where controversial things have been decided, if you can think about what the keywords might be.

About the less, you know, what probably what the issues are for you and you might, someone somewhere will potentially at some point, start looking at, perhaps all these documents and seeing what themes they can find within it, or perhaps it’s attend. Perhaps you publish, forget if you’re a public body, it’s, like local councils, for example, it might be you can, scrape, you probably can’t get this as a nice downloadable dataset, but I think there’s attendance records now for councils about how many meetings they’ve attended.

is, if you scrape that together, is there a story there that might be, oh, it might be something like 20 percent of councillors, attend less than half. Of all meetings over the last year something like that, which is okay, that’s not so good Now you might not have to do anything about it.

But at least you know about it. you can’t In a way, you can’t force those cancers to turn up, but at least you’re able to go, okay, what, is our position when this comes up? Let’s think about what we’re, how we’re going to, perhaps there’s a rational explanation. Perhaps they were, they’ve been seriously ill, so you just.

It could potentially help you deal with, so those kind of not as it could be a crisis, but it might be a reputational sort of moment where you’re on the back foot and you might help you get ahead. So yeah, anybody who’s it’s, really just a bit of, intellectual exercise I’ve been going through thinking about how it might work, but, I think as data develops as a skill in comms teams, you could see that developing potentially.

I asked at the start of the interview, Whether when you’re going into one of these exercises, if you start off with a hypothesis and that was directed at the kind of let’s try and find a positive news story for an organization. Does the same apply when you’re looking at perhaps the defensive data journalism side of things too?

yeah, I think it Yeah, I think There’s always an element of, you don’t know what you don’t know, I would think, you just genuinely might not, you might look at some data and might not know what you’re going to find, but I think you’ll always have, if you work in an organisation or with an organisation, if you’re, sort of consultant or an agency, you probably, you get to know them and you might have a hunch as to what the, what they’re like, what the weak points are, where the problems might be.

And then it’s the same principle of identifying, the kind of the right data to be looking at. it’s no point looking at the, the sort of data that’s not Yeah, the wrong day to say if you like. So you do need to, it’s a mapping, understanding what data you’ve got, do you need to bring it together?

But yeah, having, I do think like having a, speaking to other people, if you have to, what it might be, what kind of, where have we had problems in the past? Is there anything, just looking around that we’re, we think might be an issue, but we don’t know because we’ve not looked at the stats, we’ve not looked at data to tell us where, is there any evidence in there that actually might suddenly make, okay, this is, sometimes the key thing is as well, I always say this, that you data can often tell you the what, but then it’s the why that’s more important.

and actually might need, they might, when you look, yeah, when you look at the why, and there might actually be a Some an explanation that isn’t necessary, it could be something not what you think basically So as I say it I think getting this early warning System in and same principle as you say, you know Kind of having hypothesis theories thinking about what might be our problems and actually having a look at the data and saying is there anything?

That really Makes that into a big story. yeah, same principle, definitely. So the reason I asked that question was, I think if, particularly for defensive data journalism, if you’re starting off with a, I suppose with a hypothesis of we’re probably not going to look good if this comes to light, and looking down in that particular path, then probably something’s happening in that organization that shouldn’t.

So the example you gave earlier of the, sewage overspills into rivers and lakes and seas, on particularly dry spells. So it’s, it can’t be explained away by, oh, there’s a deluge of water in there. It’s just runoff. If that’s being intentionally done, someone in that organization knows about it and probably thinks this, maybe we shouldn’t be doing it.

But as marketeers, if we know that we can Shine a light to the leadership of the those organizations of if I can find out this information, other people can. So you better either change your behavior or at the very least come up with some good reasons or explanations as to why, like you said, the why is important as well.

it can affect change in those organizations. The other positive thing is perhaps you do uncover something that no one in the organization knew was going on. But again, that can make some brilliant change happen in those companies. I think that’s a, that’s brilliant. Actually, I’d not something I’d not considered.

I think that, the, so again, another benefit of being able to work with data in terms of, the strategic value of marketing, of comms, showing that, Being proactive. We are, we are data led. Yes. We use it to generate, kind of sales, positive coverage, reputation, raise the, improve the reputation, but you also use it to be make a strategic contribution to the organization.

Yeah. Love that. Great. look, I think that’s a brilliant point to leave it on, but before I let you go, I’m asking everyone for some book recommendations. I don’t know if you’re able to provide one. what? I love your t shirt, by the way. I’ve only just noticed because you’ve raised up a little bit.

Yeah, there you go. That is the new bacon. Yeah, it’s one of the ones I wear when I do, some of my talks. Okay, I’m gonna go for Information is beautiful by David McCandless. So it’s a book to look at and read. David McCandless is a fascinating guy. Former journalist, or still a journalist. But, he’s a data visualization the God really.

And he’s actually speaking in London this month and I didn’t manage to get a ticket. I was a bit gutted, but hopefully might go to one of his workshops. So information is beautiful. It’s been had several iterations now. it’s been around like 10, 15 years and it’s just, it’s brilliant for so we haven’t talked about.

Data visualization and that’s a whole different topic, but it leads on from working with data and sometimes you could, the best stories are told visually, not, not with words or, certainly mainly visual and if you want to get inspiration for a get well. He does a book called News is Beautiful as well.

So News is Beautiful actually might be one to look at when we’ve been talking about data journalism because it actually will give you ideas for bringing data sources together, for how to tell stories with data. So probably get them both, I’d say. Information is Beautiful, News is Beautiful, David McCandless.

coffee table type stuff, just browse them, you don’t need to read it cover to cover. Yeah, really inspirational stuff. I’ll go and check him out. Thank you. look, Alex, you’ve been a brilliant guest. I’ve loved this topic. I could get really nerdy about data. It’s something I enjoy just getting stuck into.

how can people catch up with you after the episode? Yeah. So while I’m on LinkedIn, that’s the main social media platform I use. I don’t tend to bother the others that much these days. So if you search for Alex Waddington data, Thanks, sir. you will find me I’m sure and you’ll, it’ll be obvious from my, my profile.

at whetstonecommunications. com and alex at whetstonecoms. com will get me on, email. And just, love to hear from anybody who’s doing similar things. anybody who wants to You know, using comms on marketing and wants to become more data literate. yeah, love those. And people, people who are further on than me, you always learn so much from people have been doing this for years as well.

So if you’re a bit of a guru and you think, you can, help me develop, I’d love to hear from you as well. Great stuff. I’ll include all the links in the show notes. Thank you so much, Alex. Great to see you. Thanks, David. Thanks everyone.