On this week’s show Matt speaks to Caroline Carruthers and Peter Jackson about their new book Data Driven Business Transformation.
We’ve got three copies to give away. Just tweet us the answer to the question we set in the show – two books for the first two correct answers, and one to the one we judge the funniest. The decision of Matt and Chris is final.
Transcript (generated by Otter.ai)
Matt Ballantine 0:20
Hello and welcome to Episode 111 of WB-40, the weekly podcast with me Matt Ballantine and Chris Weston
what it seems like an age since you and I spoke to each other in this online medium that we call Squadcast which is how we record these shows. And that’s because it is it’s a month and we’ve had live shows we’ve had visits to the itdf we’ve had shows where I’ve cobbled it together because you guys occupied we’ve had a week off a month has gone summer’s here we’re leaderless rudderless. Watford had a horrific time at the FA Cup.
Chris Weston 0:57
How you know, it seems to have been the top flown by isn’t it and I cannot do remember the last time we did one of these it’s almost become almost an agile the habits which you must get back into pretty rapidly. I’m well, I’m okay. I suffered a bit with it with it with a bad back. So my I’m going to be more impatient and and bad temper than usual tonight. And but other than that, yes, I’m very good. Thank you. I’m a bit of a wrap up of various contracts and projects this week before I go into my new adventure next week. And also, we’ve been doing some you’ve been a holiday and it’s been some work on the podcast pad projects and is some feedback from my to do which I’m charger. Yes, sir. It’s all good, really, about you. You’ve been away.
Matt Ballantine 1:41
Yeah, I happen to have been in Italy for the last
10 days, bit of time in Rome, looking at the ancient ruins, and then down to just outside Sorento to have my the lemons and the enormous insects. Vesuvius ducking out majestically behind ominous clouds and an awful lot of ice cream. So it’s very good. And I also on the flight back today, we’re recording on Sunday. And I saw a what I’m assuming father and two teenage children who’d obviously been to the Spurs Liverpool game in Madrid. And obviously, because there was so much pressure on transport, that they had a convoluted route bag by Rome. And that just made me very grateful that I only had to get from Wembley after being asked to humiliated in a cup final.
Unknown Speaker 2:39
That’s a normal journey they doing?
Chris Weston 2:41
Yeah, there is a bit of a drug, isn’t it? To go to Spain just to play quite, quite, quite poorly. Nevermind. It’s better to have experienced that kind of thing or not. So I guess,
Matt Ballantine 2:54
exactly, and it will probably fit with the general spurs psyche. Hello, Chris king of being a bit more is about football. So that’s good. Anyway, onward. We have on the show, a fascinating interview with Karen Carruthers and Peter Jackson, about a book they’ve written called data driven business transformation. And I went to see them a few weeks ago now to talk about the book and also talk more generally about the challenges of donation organizations. So I think we should probably just get on with him to donate. Let’s go for it.
Peter Jackson 3:34
So the story started the first book, which is the Chief Data officers playbook, which was published in November 2017. And Caroline and I were both speaking at a data conference in London, and Caroline been presenting on the first 100 days of being a chief data officer. And I’ve been presenting on delivering the data strategy in the cauldron of bu, and over a cup of coffee, we decided be an awful lot easier to do our jobs, there’s a book on how to look at each answer, why don’t we write the book. So that was really the start. And that was a book that was written from our experiences being CEOs, practicing CEOs, collective experiences, our peers, who were very generous in, in leading in and helping us write the book, but very much for our community. We felt that after the success of that, we wanted to write a wider book for business leaders, again, for them to understand the power, the transformational power of data in their organizations.
Caroline Carruthers 4:32
So it really is it’s a data book for business people.
Matt Ballantine 4:36
And what you say is the barriers in business, people at the moment to
Unknown Speaker 4:43
them being able to adopt
Matt Ballantine 4:45
more data centric strategies in their organizations,
Peter Jackson 4:48
I think there’s a number of different things that we’ve found as we talking to it. One is, and it’s not just with senior business people, but a lot of people have this fear of data. And we’ve created it, because we’ve used maybe some complex terms, we talk about things in an abstract fashion. And what we’ve done is created this idea that it’s much more complex than it needs to be. Well, actually, if you’re looking at spreadsheets, if you’re searching on Google, if you’re using social media, you are interacting with data. So we need to get back to basics and help people understand that this is just an asset. And there are tools to help them use this and make their lives easier.
think also that a lot of organizations over the past 20 years have been very focused on people processes and technology. The classic triangle, yeah, and forgotten data. And in fact, no designing business processes or building operating models, without giving a mind to data is going to limit your transformational capability.
We we’ve I think in some cases, we’ve gone far beyond not seeing the wood for the trees, there’s so much of the world, we don’t value it anymore. And that’s what happens with data, we’ve got such a large volume of it. We’ve we take it for granted.
Matt Ballantine 6:02
It’s interesting the the last barriers to
the collection of
Peter Jackson 6:10
data and organizations around the cost of storage and pretty much disappeared
Matt Ballantine 6:13
yesterday. And so to an extent, I think it’s possible to say that whilst it’s not free, the the storage of data, the collection of data is not incrementally more expensive, no matter how much of it you do now. And of course, with that comes up quite fundamental problems that if I don’t see a cost associated to something, it’s often difficult to associate a value to it.
Peter Jackson 6:41
In our first book, we talk about something called the hoarding principle. So the hoarding principle talks about how what we’ve done is overload our organizations with so much data. So where we’ve become like those poor people live in homes, where they’re not homes anymore, they’re just houses because they have to creep down corridors because they’ve crammed boxes full of junk, or they can’t use their cooker, because the 1920s real magazines that they’ve been collecting, and they can’t sleep in a bed anymore, you know, effectively, that’s what we’ve done. For our organizations, we’ve overloaded them, we’ve created fat around the arteries full of data, so we can’t find the data that helps us do our work. Now, I know whenever I talk about deleting or moving data, all the data scientists out there start and have some kind of panic and tell me I’m wrong. I’m not talking about deleting it, but you move it into the bedrock or garbage, so that you can route through it when it’s useful to you. But you free up the organization. So the organization can work better. I guess
Matt Ballantine 7:40
there’s there’s a data industry, I think the thing is, specifically in terms of the software vendors, the cloud vendors for whom actually there is interest in you having completely unmanageable levels of data, because it locks you into their platforms into their licensing agreements and all the rest of it. They they see, you know, their their ambition. Often it’s about just being able to have more and more of this stuff, because it means it’s harder and harder for retirement. Yeah. And do you think actually editing down it is a necessity? I’ve heard the obviously there’s the the danger is the new oil analogy, there’s that this is a corollary to that which is danger is the new nuclear waste, which is the more of it you collect, the higher your risk comes to something horrible happening.
Caroline Carruthers 8:27
Well, you had a lovely quote about that one, didn’t you? The smelly fish
Peter Jackson 8:32
the data doesn’t age like wine, ages like fish. And there’s no point in keeping something that’s going rotten.
But I think that
there is that there is a good purpose for editing down.
I think maintaining the data that is useful and efficient for your business is a wise thing to do. That’s very good technologies emerging that can seek out redundant data and give you metrics on data is being you what demand is around that data. And then they push you to manage the whole bulk of the data better. Yeah, absolutely.
Caroline Carruthers 9:07
And also, I mean, you touched on it earlier about the cost of data. And and a lot of times in the past, we’ve talked about the cost of data and the cost of the storage of the data. But really, that is not the cost of your data, the cost of your data, is every single time somebody has to replicate the process because they can’t find the data from where it’s been done before. Or every single time somebody makes a wrong decision, because they had the wrong data in front of them when the right data existed somewhere else in the organization. So what we’re trying to do is to streamline the process. It’s a bit like the multimodal right place, right time. You have
Matt Ballantine 9:43
focuses new book on general managers, rather than sort of data professional we have. Do you think there’s a
fear amongst senior people and organizations at the moment that this might be you know, with all the news there is around artificial intelligence and the rest is it is there a fear that they will be either found
out or replaced, or
Peter Jackson 10:07
it’s a line I came out with the other day in a presentation I was doing, and it was around. I said, those those organizations that get hold of their data and really leverage their data will survive, they might even succeed. Those that don’t. Good luck to them. Because the disruptors, the competitors, the innovators are building from the basis of data.
It’s an asset, why would you not use it in the same way that why would you have people that work for you? And not ask them to do things? Why would you have buildings that are assets and not use them? It’s an asset you already have access to? Why would you not use it?
Matt Ballantine 10:48
So the first question on that would be for me, because we don’t have the skills. We don’t have to say, people capability to be able to, to
Unknown Speaker 10:57
one of the sorts of things that is organization should be trying to be able to, to build to be able to start to answer that question.
Caroline Carruthers 11:05
Okay. So for me, data literacy should be a skill that we’re actually pushing out through organizations. And it’s something that I’m a massive artifact advocate for. And I know Peter is as well. And, and for me, data literacy is nothing more than helping people learn how to understand, argue with interpret and tell stories with data. It’s a really simple scale that we should be helping utilize at all different levels of the organization. And the same way that we now put people on training courses for information security, and basic skills like that, through to helping boards understand how they can really get the best from their data. Just because somebody is very good with finance, or incredibly talented work with people doesn’t mean they’ve got some of those basic data skills. So we need to put in place things to help them and the book was one aspect of doing this. But there’s other ways and other modes of learning. We just can’t assume that because somebody reached a certain level, an organization that their bios more says have picked up skills on data.
Peter Jackson 12:10
The data literacy is a very, very wide thing. I agree with that in there. But it’s also for people in the organization to understand the value chain of data center organization, that was where data is coming in, and how is adding value to the organization as it passes through. I think there’s a lack of appreciation of that people concentrate on business processes and business outcomes out understanding the value that’s being driven by the data underneath
it. The other thing as well. And it’s something that we’ve seen more is that human beings are incredibly fascinating people, but what we do is naturally fill in the blanks. So if we read something, it’s natural for us to extrapolate what we think it means, will because sometimes reports are written or dashboards are written in a particular way. We think we know what they’re saying. But we’ve lost the art and some cases of actually challenging it. So an example of you and with a few companies recently, is putting up a dashboard and asking what they think they see. And what they tell me they see isn’t actually what the report tells them, because I don’t use any timescale on the report. But they interpreted that that must happen in a day or that must happen in a month. But then forgetting to ask the questions.
Matt Ballantine 13:22
Let’s just pick up again, on that that question about the ability to be able to interpret data and this thing about the skills that have been developed, particularly senior level around reading, effectively, Banshee, yeah.
You know, price to earnings ratios, and all that kind of stuff. That’s a very different skill set to being able to read data about things that are
Peter Jackson 13:45
money isn’t that massively different? And, you know, again, we need, we can’t assume that just because somebody is good at one, they’re really good at the other. So it’s making sure that we create an environment around data that it’s accessible. I mean, I was talking to to see a person recently. And they said, Well, I don’t think this is relevant to me, I don’t deal with data, I just look at spreadsheets. For that, you know, there’s a whole wealth of other things that they should be thinking about within their organizations. That’s not just a number on the bottom of the balance sheet. It’s some data centers, walls and the balance sheets. I mean, understanding patterns in data is really important, which you’ll never necessarily see on a balance sheet and geospatial nature of data and time series of data and behaviors reflected in data
is so much more than the balance sheet. And I think that’s why it’s important that senior execs understand that there’s value in having this sort of data presented to them alongside the balance sheet, and then being able to interpret
Matt Ballantine 14:46
that date when people talk about data and organizations. I think there’s also a
predominant focus on structured
quantitative data, obviously winning are starting to see the ability for machines to get a very different set of abilities to be able to process in,
Peter Jackson 15:07
which will speculative fashion as
Matt Ballantine 15:08
qualitative data, whether that’s, you know, natural language processing, or whether that’s some of the stuff around being an interpret images or whatever else.
How does that fit within? What’s been in the CDO role? And how does that fit within what senior execs should be
Peter Jackson 15:27
thinking about, I think the partner system CDO role is harnessing that data and making that data then accessible to other parts of business. I always say CEOs role as being responsible and accountable for data and making it available to the business to make the decisions out of so in the liquid pyramid data. The next layer of information, as I call it, we call it out collected, curated. contextualize data is the CDO role, allowing the rest of the business to do the information and wisdom piece. extrapolating that information that data out of an image or outside of a voice file, and presenting it in a way to the business, they can then use it is the CDO role. And I think
that just to label the point a little bit more, it’s all data within an organization. So facial recognition, like said geo spatial, audio, these are all different types of data. And they’re all things that could be useful with an organization, the CDO role is the one of the wonderful things about it is that it cuts across the whole organization, it’s not siloed, because data isn’t siloed. With an organization, if you’re using it right, it does flow right through the whole organization. So the CDO role can’t be limited by the silos. And it does mean that you can then find things that may be more innovative within an organization because you’re putting together disjointed parts that maybe necessarily haven’t fit
together before. Give me an interesting example, sort of from a legal and general situation or any perhaps pension provider, traditional way to perhaps look at at a pension set of data is on contributions and outflows and, and then start thinking about the the geospatial distribution of those pension scheme holders might reveal new and interesting patterns. And so I think that is somewhere where co brings bring your data scientist brings richness to the data and understanding Actually, we take that data and we plot it to spatially or we give it we combine it with another data set, for example, we might actually get new value out of
that. And the other thing to kind of bring in mind there as well, is when we talk about data scientists and how the present back to the business, part of what we talked about in the book that was actually creating a common language and part of data literacy is making it accessible and understandable. So one example would be let’s pick on retail, for as an example, you know, having a report back from data scientists on bananas, and understanding every single possible connotation of what you would ever want to know about bananas in a 40 page report, that was a wonderfully colorful dashboards, maybe not the most useful things for a bunch of board members who were trying to make decisions based on what they’ve been given. Having a half page on, if you put bananas in green boxes, you will sell more bananas, which is a hypothesis that you can check, try and do something with can create actual, there’s much more value in actually working together for the common language. That
Matt Ballantine 18:36
brings us onto
purpose. And within your book, you’ve got a model that describes the different facets, it’s almost like an extended something about the seven S model thinking about the different facets, you need to consider within an organization to be able to become more of a data centric organization. And you talk about purpose within there. There’s there’s almost two things that you’ve described there that maybe two sets of purposes that might underpin with an organization monster with data, the one is to be able to create compelling stories to be able to use data to be able to give reason to things that you want to do. And then the second is being able to use data to be able to test hypotheses,
to be able to validate in iterative processes to be able to see what works and what doesn’t. Yes. And obviously from for, you know, the modern
Peter Jackson 19:28
organizations who are
Matt Ballantine 19:30
totally data centric, the Googles of this
Peter Jackson 19:32
world, the, the Uber’s of this world, that’s very much where they’ve put the focuses
completely. And they would very much be data centric organizations
Unknown Speaker 19:41
and just explore
Matt Ballantine 19:42
those two different, those two different things and maybe the different approaches that are needed to be able to deliver on them and the, I guess, where organizations might be in their life cycle as to whether they need to do one other or both.
Peter Jackson 19:57
And I think that the boss, right, but if I pick on the one does the storytelling one, first. See that one is about really engaging the hearts and minds of people. And creating using data as a way of driving a movement or driving a change or driving engagement within an organization that really is about encapsulating how you get the people on board through the data. And we often use the phrase that, you know, data drives the digital agenda, it drives the machine learning, but actually people drive data without it. Without understanding the purpose without having them on board without getting whole army marching to the beat of the same drum, you’re gonna really, really struggle. But that’s actually a very creative skill. It’s using a lot of passion, empathy, energy, and bringing all that together to create that story. Whereas when you look at what we’re talking about, to test hypothesis and prove or disprove, looking at the much more logical side, you’re talking about more scientific way of utilizing data in a fashion. That is, I guess, more locked down in a way,
the storytelling side,
I think also fits more into some of the data science field, and the explaining of predictive analytics know why this might be happening, why this is happening, what the outcomes of these things may be. And I think the the testing of the hypothesis, and the iterative side, fits very much into what you might call data Ops, or an agile approach to delivery because I do have that constant testing, fail, succeed, move on. And I think that that is something that is used that that using data to validate an agile process, and the route from agile processes, very important use of data.
We do also have to be very, very careful about what we’re talking about, as I said earlier, you know, human beings are really good at filling in the blank. So we’re using our all the relevant data to make the arguments and not just the bits that we like, yeah, was it Mark Twain? statistics, statistics, and damn lies was the phrase he commanded.
Matt Ballantine 22:09
There’s the challenge of confirmation biases. And then
Unknown Speaker 22:12
suddenly, the,
Matt Ballantine 22:14
you know, the inclination, I interesting, I saw
Rory Sutherland from Ogilvy speak, yesterday’s got a new book coming out next month. And his, his argument is actually quite counter to some of this stuff, which is to say, actually, we are heuristic and gut based creatures, and is that for a reason, it’s because it’s done. It’s very well for evolution throughout. And that is post rationalization that we do a lot of the time. But that can then lead to problems. If you look at say the Brexit debate in the UK at the moment, what you’ve now got is two entrenched groups, who are just basically picking the numbers on their side to try to logically argue with the other and it gets nowhere, because actually, every time you throw up a logical argument, you created a logical counter argument.
So there is there’s a balance there about being able to use it to paint the vision, as opposed to try to be able to justify against the counter position, which is that can be quite problematic. It is.
Peter Jackson 23:14
But I think the interesting part about it is that if you would address it with the the want to learn, then it doesn’t actually matter. So I agree that in a lot of cases, we’ve used data in the past to justify our gut instinct. And in some cases, there’s nothing wrong with that as large of a learned from it. So if it’s a case of our gut, instinct said, x, the data showed us x, then Whoo hoo, our gut instinct was right, and how can we then maybe make it easier for people to learn to walk or instinct, if our gut instinct said x, but the data says, Why? What was correct? And then feed that back into? Do I need them to adjust my extinct? Or do I need to spend more time working with the data, as long as we treated as part of the learning virtuous circle, it’s all useful.
Matt Ballantine 24:08
And again, I guess that’s, that can sometimes be one of those barriers to adoption in the first place. I work for a few years for a marketing agency who focused on big experiential stuff, auto shows and the like. And there was this product about 10 years ago product brought to market which is like a pair of glasses, but it would track I movement in 3d space. So you’ve got these sorts of tools for doing user interface design, this could be able to show what was looked at and what wasn’t in a physical environment. And somebody floated this idea in front of the creatives to say, we could be able to get some
Unknown Speaker 24:42
very rich data here about what if
Matt Ballantine 24:44
your creative works and what doesn’t. And I can leave it to you to use, the reaction of the creators was to back with, they didn’t want to know, because it was broken what
Peter Jackson 24:54
they saw as their
Matt Ballantine 24:56
magic. And to an extent, when you see, you know, creative is driven purely by numbers, you can understand why there would be that resistance as well,
Peter Jackson 25:05
you can because I mean it like if you look at say car design, for instance, no cars that were designed before computers got involved, are many myriad, very intricate that they’re more like piece of art than cars. Now, when you have computers evolved, so we’re looking at streamlining, we’re looking at the best way, cars are kind of morphing into a very similar type of design. So I can completely understand where they’re talking about. But if I’ll swing back around to purpose, is the purpose of a car to get you from A to B, or is it the purpose to be work of art? Yeah. So by understanding what the full purpose of it is, you can then create a better product output service for the customer and the purpose that you’re trying to achieve. Yeah.
Unknown Speaker 25:49
So the last section in your model is around the tools. And what certain level Do you think senior people should be concerning themselves?
Peter Jackson 25:59
I think it’s very much that the senior people understand that there are a new breed of data tools that are specific to data to, to lead to transformation and changing business processes. For example, swapping out the Excel spreadsheet for something that is more sustainable and operation and organization, a lot of senior people might not realize there is an alternative that is available to them saying it’s really important that seniors x have their horizons broadened that model, calling out data tuning in that way, actually raises that conversation makes an important part of the conversation.
Yeah, it’s about not having to focus on the detail. Or as Peter said, it’s about then the horizons being broadened the art of the possible and understanding the brave new world that there is and how useful data can be to them.
Matt Ballantine 26:48
And if you were to say, this is a very generalized thing, but you know, you need to ditch your spreadsheets and instead move to what sorts of category of product not necessarily name products,
Peter Jackson 26:57
but also, what are the sorts of categories products that you think people should be thinking about would be data management products, how to how to combine and blend data together and analyze data in a product that’s not a spreadsheet. So you can do bigger volumes, you can do it in the cloud, you can collaborate over that, that data analysis and that data engineering.
Yep, data visualization is tools out there now that basically put what we would have traditionally put this basic data science in the hands of anybody. So that there’s different ways of, and one of the things we talked about, we talked about a lot about there being a problem hiring enough data people to surface the type of needs that organizations have now and in the future. Rather than just trying to churn out a bunch more data scientists quickly. If we actually put the hands of some of those tools in the hands of people who are really good in the finance or ops or the marketing zones, so they can do a lot of the base level stuff themselves, then that frees up the data scientists to the really complex political stuff that organizations want them to do. So understand that, that there’s those kind of tools out there that can leverage the power of data, in a more simple way, I think is incredibly useful.
It’s also important for senior execs to realize and understand the potential of using the right tools for pressure efficiency. That was freeing up people stop throwing bodies at a problem, that’s a data problem trying to fix a data problem by throwing bodies at it. Let’s throw the wrong tools at it. Yeah, don’t
build a house with screws and use a hammer.
Matt Ballantine 28:26
Right to finish up, then, for regular listeners to the show, you have heard me talking about the priority cards I’ve been working on in a number of fields. Now there’s a new set that about to be released. And I thought it’d be an interesting test of how good these cards are. So we’re just going to pick a few of these at random few minutes left, and just see what you make of the match. If nothing else, what ideas they spurs. So this is the date property set, you’d like to pick the first car,
Unknown Speaker 28:57
ladies first can
Peter Jackson 28:58
thank you so much. Ended up being the experiment phase. Right? So phase one,
use data science to gain greater insight into customer behavior and improve customer engagement. Yes, next. No.
I think that’s an aspiration that lots of organizations want. I think they want to serve their customer better get greater customer engagement, think they want to understand full customer lifecycle value and shared and that kind of thing and data science as a huge amount to offer in that space. The trick behind that, though, is tying up all the sources of data around customer is not only digital interface, now it’s experiential is in store. It’s at the station, it’s in letter magazine. If you can tie up all of those data sources, then you can get into this.
Yeah, I was gonna say it’s making sure you feed the data science in the right way. And I love the focus on customer because I don’t think we’ve got enough focus on customer and
customer. But I will we say around customer
is not only what we would think of this first and leisure reaction is customer a lot of our customers are CEOs are internal customers. Yeah.
And it’s that understanding their needs is really important. their interaction with actor. Okay,
our The only other thing I would say is, that’s a brilliant purpose. And I love it. But we have to think about the innovative side of things as well. And sometimes let people play with spending too long. And now I’m going to
use data science to understand employee engagement,
Unknown Speaker 30:29
data science.
Peter Jackson 30:32
I think that that is really interesting. We’re seeing a lot of employee engagement surveys. And I think that those surveys and themselves can be combined with other data from other data sources to give you a richness that then would be very applicable for data science. I think a lot of HR teams are very interested, it’s important thing to understand succession planning, or an employee’s key employee engagement. As you have all the support from I think HR is the one that is most obsessed
Matt Ballantine 31:02
with the idea of analytics at the moment, they’re not sure whether they’re getting anywhere with it necessarily, but the one for whom is highest on the agenda.
Peter Jackson 31:10
I think we’ve seen a lot more interest from those kinds of functions. At
the moment. I think some of the vendors are pushing that some of the vendors around these employee engagement surveys are realizing there’s an upsell to the service if they supply some of the data science
input. And it really is a win win, isn’t it? If we actually spend a bit of time and attention looking at what makes work a bit more fun for people, then everybody benefits? Okay,
not too long and care for one,
right? See from oops,
oh, establish a set of core principles for the ethical use of data. Yes, complete entirely. And what we talked about a lot is I mean, I’m a complete data geek, I love all things technology. And then I love when we talk about artificial intelligence machine learning where that could take us in the benefits of the human race, which are just mind bogglingly awesome. However, if we do not build ethical principles, into the data, which is what we will be fueling all those things with, we will be causing ourselves problems in the future. So I’m a massive advocate for actually us taking a little bit of time and attention now, to make sure we build the foundations that we need to create the future that we want.
Matt Ballantine 32:22
And that very much is something that has to happen at tops of organizations, absolutely.
Peter Jackson 32:27
There has to be that culture of just because we can doesn’t mean we should Yes, very important. And that that is a that’s a buffer zone on top of legislation of things like GDPR. Deliver organization wide education to raise understanding of the importance of data, this goes back to our data literacy, I think that if we’re going to drive real value out of data and real transformational value out of data, this is key.
Yeah, everybody has to be on board. It isn’t about having 10 data scientists sitting in an ivory tower creating some wonderful dashboards, you, you know, having every single person in your organization, or understanding the part they have to play in treating data as an asset. And using it as a valuable tool to drive forward, your organization is so much more powerful.
Unknown Speaker 33:19
So thank you to Caroline’s page, again, for making the time to have that conversation is fascinating stuff.
Matt Ballantine 33:26
There is a competition. So exciting.
Chris Weston 33:30
Exciting, we’re gonna give something away,
Matt Ballantine 33:33
we’re going to give three things away, we’ve got three copies of their book, data driven business transformation, how to disrupt innovate, and stay ahead of the competition published by Wiley, we’ve got three copies of that, to give away. We need to question
Peter Jackson 33:49
what do people need to do
Chris Weston 33:51
to read those books Matt
Unknown Speaker 33:52
what what they need to do is they need to tweet us. So if you’re not on Twitter, you need to get onto Twitter. If you’re not on
Unknown Speaker 33:59
Twitter, people,
Matt Ballantine 34:00
exactly. And then you need to tweet us at Wb 40. podcast with the answer to this question, to win one of these books, which I’m just looking at the back of it on a hold on, is it another thing? I mean, it’s it’s so valuable that haven’t even put a price on it, which is literally priceless. pages. pages, there are some it’s a very high quality public. There’s about 270 270. Exactly, actually, there we go. So anyway, you need to tweet us at Wb 40 podcast with the answer this question, f code, the godfather of relational databases, what are the stand for? They’ll be two prizes for the correct answers. And there’ll be one prize for the person who can come up with the most amusing answer to that question. So there we go. Anyway, let’s stop being like radio one in the 1970s. And let’s switch back to conversations about management technology, I thought was a really interesting conversation. I think that book is really interesting. And I think the book is really interesting, because it puts down in black and white, all the mechanical things you need to consider if you’re serious about your organization becoming a data centric organization. And I will tell you, now, there’s a lot of stuff you need to do.
Chris Weston 35:24
Yeah, these are threads, if you pull on them lead off in lots of different directions on and it’s and it’s, it’s very difficult to make anything happen in a fundamental way, if you really want to change your business, if you don’t follow a lot of these threads through to quite important data points and sources. And I guess even the way that people interpret data or the why, why they provide it, there’s loads of loads of really important things to do.
Caroline Carruthers 35:53
And I think the challenge with it, and this is something we talked about in the interview, and I talked about with them as well, off my is what they focus on primarily is the mechanical, the hard stuff. And then there’s a whole bunch of the psychological, which is that actually, humans don’t make decisions on a rational basis a lot of the time, in fact, if ever. And interestingly, hopefully soon, I’m gonna be interviewing Rory Sutherland, who takes an almost polar opposite view to the kind of real accolades a day I don’t think actually that Peter Carolina, a massive cultish? You know, data is the answer to everything, people at all. They’re very pragmatic about it. There are a bunch of people, though, for whom data is everything is the New World, etc, etc, etc. And those people, Rory Sutherland is almost like the antidote to that, because what he says is, people don’t make decisions on a rational basis. We are heuristic based creatures that make decisions on the basis of a bunch of things that evolution has given us. That made perfect sense, but they’re not rational. And therefore, you need to be able to use information amongst a number of other tools to be able to help people make decisions on the basis of how we as humans make decisions. But there’s obviously there’s the answer to this sit somewhere in between.
Chris Weston 37:22
Yeah, well, that’s true. I did, I was talking to somebody this week about who was considering a CRM implementation. And you’re right, as an experienced person will take information from a CRM, and they will use it to make decisions about how they’re going to manage their sales force, for example, sales team, not not trying to plug Salesforce as a project. And they will do that because they’ll know the context of that data. And depending on the time in a because sales people that one of the reasons CRM projects fail, salespeople don’t really like putting information to CRM, it’s made they make they make them do it. If I, if I’m selling widgets, and I bump into somebody in the Lyft, at a conference, and happen to get talking to them, and I see their name and their company, and I know that they are a potential customer. But all I do is go Oh, hello, how are you? And I’ve got, like 30 seconds conversation out of it, then I’m probably not going to put that in a CRM because even though some people, some outlets might say, absolutely, it’s got to go in the CRM, because we’ve got a conversation with somebody from a target. As a salesperson, I’m probably not gonna bother, unless, unless I’m in the very severe pressure, it’s coming up towards the end of some sort of period. And then not only would I put it on CRM, I might put 500,000 pounds of the potential sales against it, you know what I mean? Even though the conversation was exactly the same, people react differently, and they use that information in different ways. And then the people that have to interpret that data have to think about that as well. Because you need experienced that to manage the data that comes out in the machine. It’s not just something you can you could take face value. And that’s a that’s a learning, isn’t it, not something you have to fit, you have to realize, when you if you’re going to be a zealot about the fact that the data can drive decisions. You also have to understand that the data is not the data is subjective, often, it is not always often.
Matt Ballantine 39:18
Absolutely, there’s two things that springs to mind there. The first is old friends, good heart, and the other one whose name will come back to me in a minute, which are the two social science laws about how if you set numbers as being the target for some sort of change, or some sort of activity, then the meaning of those numbers will, will change, the semantic meaning of the number becomes different. So a sales lead once recorded in the CRM and being tracked against sales lead targets for individuals is no longer actually a sales lead. It’s it’s morphed into this thing that has a different meaning. You see this all the time in particularly in in policy matters within but government does, where things get latched onto as being useful measures of something and then they kind of lose their value quite quickly, because they become totemic in a way that nobody quite understands. So in a uniform police officers is a classic example waiting lists, all these things, they’re meaningless because they’ve become so loaded with meaning because of the fact they’ve been set as targets. The The other thing, though, is actually a conversation I have briefly involved with on Twitter a couple of days ago, which was somebody saying about how Wouldn’t it be great if we captured like all of the experiences of startups so that we never had to make the same mistakes again? And I said, you know, the thing, the only issue with that is that actually, it’s all contextual. So a decision that was made that killed startup a, could be the thing that makes startup be a unicorn, and you will never know. And so this idea, everything is logical and rational, it permeates so deep anything, particularly anything to do with the computing community. Because essence, you know, computing is about turning the world wild, Wooly World that surrounds us into something logical and rational. But we all know the reason why computer systems fail is because they can’t deal with the world, even though even with the artificial intelligence stuff, or so called artificial intelligence that we see at the moment. It doesn’t do very well with the complexity and chaos that is the human world around it.
Chris Weston 41:37
That’s right. And as you say, people who think that you can boil down these things to a set of decisions. Think you can model the world mathematically, you can what you can create a model and then say, if we did this, that wrong Last time, we did it right next time, then that and you cannot create a model of the world as you say, it isn’t possible.
Matt Ballantine 41:54
So what do we learn from all of that? Well, actually, most organizations are pretty crappy some fundamentals of data, I still am amazed. So few organizations are able to manage corporate data models, or any sort of level of sensible and do it in any way that gives them value. But without that, if you don’t understand what the data means you don’t understand what any of it means. Don’t over inflate the hype. If you want to do stuff with data, they’ve got to be rigorous and it’s been down, I think it’s the other bit isn’t it, you need to have some boring process, he that means that you know that your data is valued and valuable and traceable. And you can understand the provenance of it. And you know, where it comes from, and all of that kind of stuff, which is an awful lot of heavy lifting, to be able to get a new dashboard.
Chris Weston 42:48
But it’s all worthwhile, because then you can start to use machine learning. And that’s what’s really important.
Unknown Speaker 42:53
Well, there is that anyway, don’t forget, if you want to get a copy of the book, which is well worth read, you need to tweet us at wd 40. You need to answer the question, what does the E f f code stand for? One price the funniest answer two prizes for the rightist answers. Although I think there’s sort of there’s a fairly binary state of rightness, in the answers to that question drops a line.
Chris Weston 43:20
So what’s up for you this week Matt,
Matt Ballantine 43:23
I have got lots of bits. There is a project around innovation that I do for consulting company that there might be some stuff happening on Monday or Wednesday. There’s some stuff that I’ve got for a consulting firm that I might be doing what no am doing on Tuesday, there’s some stuff for podcast projects is definitely also happening on Tuesday. And then the rest of the we’ll see where it goes.
Unknown Speaker 43:46
I think I’m supposed to be meeting up with Mr. Ian Cohen this week as well, which should be great fun.
Matt Ballantine 43:52
Although I’m bothered if I can remember where that is? Oh, no, there is this on Thursday as well. So yeah, see what he’s doing. Trying to be able to turn taxes into self automating monsters, or whatever it is that he does at assembly. Now you have got a big week
Chris Weston 44:09
ahead. Yes, indeed, in that my my career direction changes is now upon me. And I’m going to get go to inside the belly of the beast, and getting located into that. And so that’s looking looking forward to that should be exciting. I’ve got a fairly busy week lined up and lots of meetings and things like that. So yeah, look forward to be able to talk about that next week.
Unknown Speaker 44:37
Do you actually know what it is you’re going to be doing in this new role?
Chris Weston 44:40
Let’s come back to that next week. And all.
Unknown Speaker 44:47
That sounds good. Hey, keep your Facebook.
Unknown Speaker 44:50
Super
Unknown Speaker 44:50
well. We will be back again next week. I think we’ve got another interview in the Canada can’t remember who it’s with. But I’m sure it’s gonna be awesome. And I’m trying to line up a few more this week as well. So that’s cool. So I hope it all goes well for you with your your new your new career, your new creative diversion, every game will call it that.
Chris Weston 45:11
I’m sure it will much. I’ve got high hopes.
Matt Ballantine 45:14
Excellent. And we will see you all listen to you or hear you or whatever it is you do and you’re on the receiving end of the podcast. Next week. Use your time use replace and look forward to that don’t forget you can find us at Wb 40 podcast on Twitter there you can send us your answers the question What was the E f and f code standing for you can win a copy of the book. You can follow us on Instagram bizarrely, and I’m so that one day will work out Snapchat to leave us a review if you’re doing that on a podcast platform because we’d like reviews