No Bull Marketing Ideacast \ Season 2 Episode 2

Transcript

PRE-INTRO:

AM: I believe it’s something like a scary [amount], 97% of businesses do not have themselves set up for success when it comes to data quality.

If you’re not on top of it, which a lot of businesses aren’t, and don’t monitor the data quality, 20 – 30% of your data will go bad each year.

INTRO:

BH: Hello and welcome to the No Bull Marketing Ideacast. I’m Becky Holland.

You are listening to the No Bull Marketing Ideacast. A great way to spend approximately 20 – 30 minutes, hearing interesting people talking about inspirational stuff. Except…OK, so, this episode is actually all about data quality. Well, we will try to make it fun.

DISCUSSION:

BH: Hello and welcome to the No Bull Marketing Ideacast.

AM: Thank you for having me. Very kind of you. Always a pleasure, never a chore.

BH: Always, cool! So, my guest today is Alasdair Moore. Alasdair is the CEO and Co-Founder of IDS who are a, I want to say, very exciting, fascinating data quality business. Which might sound like an oxymoron. But, it’s true, data quality is very, very important. For the world. For CEOs. For marketeers. For everybody.

So, talk to us a little bit about what’s going on. Why is data so important at the moment? We’re in a world where stuff’s changing all the time. But data can never be up-to-date surely? It’s always changing so we just don’t need to worry about it?

AM: The only thing that’s ever consistent in life, Becky, is change. Like you said, everything is constantly changing.

You started out by saying data is important. I think that sentence needs to be changed to ‘the right data is important’. I put a post out, the other day on LinkedIn, around hiring people and saying ‘the right people are essential to your business, not ‘people’ are essential to your business’.

What I mean by that, is you’ve got to have, like you have the right people on the bus to go to your destination with your business, you need the right data, or the right level of data quality and accuracy of data, to make sure you can make the right decisions, as a business. To grow, scale and be robust.

It’s exactly the same with anything you do in life. It’s about quality, not quantity.

BH: But, isn’t that just good business practice? The audience here, on this podcast, are CMOs, Marketing Directors, people that deal with data every day. They’re sending emails, looking at the CRM, they’re looking at sales data. It has to be set up right in the first place in order to be able to even function. So, isn’t that just the baseline?

AM: I suppose when you say ‘the baseline’ if you went, and Gartner have done surveys, and other organisations have done surveys, I believe it’s something like [a] scary 97% of businesses do not have themselves set up for success when it comes to data quality.

BH: 97% don’t have themselves set up for data quality. That’s nuts!

AM: Well, you say it’s nuts but I want to check my facts, because obviously this is a live recording. I’m just going to go on to the good old Google.

BH: Elevator music while we wait for the stats!

[ELEVATOR MUSIC PLAYS]

BH: This is a challenge right. If you’re listening to this podcast, see if you can come up with the answer quicker than we can! Make your own podcast about data quality. Or send it to us!

[ELEVATOR MUSIC CONTINUES]

AM: A survey from Experian found similar results. You can expect 20 – 30% of your organisation’s contact data to go bad each year. So technically [it’s] saying that EVERY business, in the world, has data quality issues. Effectively. By what they’re [Experian] are saying. They’re saying, ‘if you’re not on top of it [data quality]’ – which a lot of businesses aren’t, and don’t monitor the data quality – ’20 – 30% of your data will go bad each year.’

BH: This is a full-time job.

AM: It is a full-time job. So, saying your baseline is set up, or it should be a baseline – yes it should – but organisations don’t have it [data quality] as a baseline.

I’m just seeing what other stats come up, or anything else comes up. ‘Only 3% of companies meet basic quality standards.’ ‘Nearly half of business leaders – 40% - say that their data is too siloed to make any sense of it.’

BH: OK, so we’ve got some stats, we’ve got some facts. So, the data’s not very good.

AM: Yep.

BH: Let’s just say, for the sake of argument, I’m a bank, I’m a big bank, an HSBC, right? My data’s going to be good enough surely? It’s got customer data, people managing their finances, if there was a problem with their data, if your bank balance was wrong you’d be on the phone straight away saying ‘it wasn’t right’. So, I’m just trying to understand what the problem is. Is it the marketing isn’t [being] done right? Is it that we haven’t got the insight? What’s the actual problem that we need to fix?

AM: So, data, and data quality, are such a massive area of where it can impact a business for good. So, when you talk about your bank accounts, are people being hacked? Yes. Is that a data breach? Yes. Is that due to data quality and data safety and the ability to move data and silo it, safely and securely?

Yes, that is a problem. People do get hacked every day. There are data breaches, which can be affected by, how you look and manage and look after your data. How much you have of it. Where do you leave it? Where is that data stored? Is it [in] a legacy system that isn’t necessarily required but you only keep it because you’re concerned about the data quality in your current systems. Which means that those legacy ones have to be kept running just in case you need to get back to them.

BH: I’m really worried this podcast is going to get really boring really shortly.

AM: I know, I know! Look, data quality ain’t sexy! How do you make data quality sexy? You don’t!

[INTERLUDE]

AM: Look, you hit the nail on the head. Data quality is not sexy. Hence why, there’s a big problem. CEOs, CMOs, CTOs, turn off when they heard that word. Data quality. [And] I understand why. I really do. To them it’s ‘well, that’s not AI, that’s not ML, that’s not a big shiny new tin of SAP, that, if it works, is going to make me look phenomenal.’

Some, not all, some don’t realise, that if they don’t get the data quality right, that shiny new object that’s going to be really sexy and make a difference to their organisation won’t work.

BH: I suppose the other problem with it is that, because it’s not very sexy, let’s say you do put all of your time and energy into fixing the data and making it…Imagine standing up at the board meeting and going ‘I’ve got 100% data quality!’ and everybody’s looking at you like ‘yeah and what’s for lunch? Where’s the sandwiches? Want some more coffee?’

It’s like ‘I’ve done this thing!’.

AM: But, if they tie it into, ‘we’ve got 100% data quality, that means we’re targeting the right customers, with the right marketing messages and our emails aren’t bouncing and we’ve improved revenue by 40, 50%’, then they stop and stop eating their sandwiches.

BH: Show me the money! The revenue!

[INTERLUDE]

BH: How do you fix it? How do you fix this problem?

AM: It’s like all problems. It’s [involving] the right people. With the right processes. [And] the right tools to fix the problem. Like any other problem.

How do you fix your house? You make sure you get an architect who produces the right plans to put the right process in place for a builder to then come along and BUILD whatever you want building or fixing or sorting out. [And] then you maintain it – either yourself or get someone else in to do that for you.

So, it’s like any other problem in the world. Data quality is a problem that is easily fixed, if you bring in the right people, IF you have the right processes and if you have the right tooling. [And] you can, in some scenarios, combine all three. It won’t be any surprise to know that we, as a business, do that. Or we can bring in people who will TEACH you how to ‘fish’, for want of a better word. Of which, again, you can get done by ourselves or other consultancies.

BH: I’m guessing, you know, the bigger the organisation, the bigger the data quality-

AM: No guessing Becky we have certainty in this world! Data certainty, no guessing, thank you very much, not on this podcast.

BH: [I’m] too bloody serious! Right, I’m HSBC again…

AM: Christ you’re HSBC all the time!

BH: OK, well I’ll change, I’ll be the Bank of England. I’m going to be a bank.

AM: We can’t do the Bank of England because we work with the Bank of England. No, no, let’s move on, another company!

BH: OK, I’m going to be…Revolut. I’m going to be a sexy fintech brand. Have I got a data scientist? Have I got a team of ten, 20, 50 data scientists? Like, what do I need? Do I just need to hire a few people, get some tools, Bob’s your uncle?

AM: So, I don’t know who Bob’s uncle is, but he sounds like a nice chap! But, in reality, if you’ve got a team of data scientists, they don’t want to do data preparation.

We talked about it earlier. They do not see, again, data prep, data quality, as sexy. You’ve got a choice. You can either bring in tools to speed up that process for them, so they don’t find it as painful. There are products out there – I think there’s a really good one called iData somewhere…

BH: This isn’t a sales pitch!

AM: But it is!

BH: You can’t do it! Nobody will listen if you keep selling your product. Nobody will listen to my podcast! Chris [Attaway] will cut you out anyway.

AM: No, he won’t. I’ll pay him a tenner.

[INTERLUDE]

AM: But the point is, you’ve got to allocate someone who’s going to do this. Either be an advocate or [someone to] quality gate it [data], or whatever you want to put in place. You need someone who’s going to govern the process.

That can be a Data Scientist, it can be a Data Engineer. But, it’s got to be someone who puts the flag in the sand and goes ‘I’m going to make sure that we put in these processes, that we’ll get out [and achieve] the objective of having data quality at the end of it. You have to have someone to do that.

Cause, otherwise, it’s like reading a book. Unless you put it into practice what you’ve read, that knowledge is worthless. Because you’re not using it and you will forget it over time.

Like marketing! I’m sure you have marketing books that you’ve read over time, and you’ve probably forgotten half the information you’ve learned. Because you’ve not used it in practice.

BH: Quite possibly.

AM: That’s a yes. You need certainty on these podcasts!

BH: You and your certainty. So, look, this sounds to me like it’s relatively simple to fix then. So, you’re an enterprise organisation. You have an internal champion, who’s your CTO, your Chief Data Officer, whoever it is. You’ve got a team of people, data scientists, and other people. You select some tools. You fix your data. You know that 20 - 30% of it goes wrong every year. But those people fix it and it’s fine.

So why is this topic of conversation…? I appreciate I’ve invited you on this podcast to talk about this, but why is this a conversation? Again, why can’t we just fix it and then get on with the sexy stuff?

AM: So, like we just said. Especially with all this change over COVID and the pandemics, we’ve become very digitally-driven. [And] the intake of data has increased, multiple times than what it was.

As you’re bringing in more and more data, the increase, risk and bad data getting in, because we are humans and humans do make mistakes when they input things or when they put things on forms, or when they update a system to do with a call they’ve just had to do with customer service, because everything’s now online, it builds up the problem.

Now, what you can do is get your system to the point where the data’s quality is good, and then you can have a system in the background monitoring it, that flags up to your data governor or your data advocate or your data sponsor, when these mistakes happen, and they can re-correct them and you can automate the process.

You will never, ever solve data quality, you can only maintain it and keep up with it. Because it’s an ever-changing landscape.

BH: You could fix everything! All the world’s issues with data.

AM: Everything is data. There’s even data on you as a human being. There’s data on this table that I’ve got this laptop set up on. There’s data points about this chair I’m sitting on. Everything now is data documented. If it turns into bad quality data, we will start making bad decisions.

Great example: COVID, good old Boz [Boris Johnson] and the government, started at the beginning, [by] not being able to ingest the data and analyse that data properly because they were getting A: poor data. By the way their structure and holding wasn’t correct either. They gave false information to the general public and the general public didn’t like it and didn’t respond well to it. Actually, in the end, didn’t trust it.

Without that trust in the government, they couldn’t implement changes or sanctions. Once they started proving that they could ingest that data better, and reflect and show it and the data quality was good, then the people started listening and the country started to turn around the situation with COVID. Whereas, other countries, without naming any, were unable to do that, and were behind the UK on the curve. We’ve come out [of the pandemic] stronger than most nations around the world due to the fact that we’ve got a good handle on data quality and data ingestion and data analysis.

That was all in one breath!

BH: I’m very impressed! Very impressed indeed. So, look you and I obviously know one another we work together, through BH&P…

AM: OK, we’ll talk about data quality in a real-life scenario that you will know about recently.

BH: Go on then.

AM: So, we have a marketing requirement. It is given over to yourself, BH&P, to deliver a brochure or a manual or a transcript to do what we need as a business. What we’ve told you is data, [hypothetically] we’ve gone blah, blah, blah, blah, blah, we need X, Y & Z. Please deliver.

BH: True.

AM: You go away. You create the document. You send it back. We go ‘that’s not what we want’. You go ‘well this is what you’ve given us, this is the email’. To us [we are] not giving you the right information in the first place. Data, [hypothetically] the delivery that’s come back, isn’t fit for purpose.

We give you more information that goes back to you. That is a waste of time, and it’s not getting back to what we need to implement quickly. Therefore, it’s not efficient, it’s more costly.

That’s data!

BH: It’s a very good real-life example of why that process doesn’t work. So, in the nature of the conversation you and I, generally have, is less about ‘can you make me this brochure’ and more about ‘we want to hit £5 million, £10 million revenue, we want to hit £100 million valuation, how do we get to it?’ and that’s a much better conversation because you’re talking about the right sort of information. The data needs to drive it, but you’re starting with that information output.

AM: If that data’s bad data, we will start making bad decisions. We will get rid of suppliers we shouldn’t be getting rid of. We’ll be not working with partners that potentially are better than others, but we’re not getting the right data, or the quality of data that isn’t being kept up to date to ensure that we are.

So, there’s so many knock-on effects, but not a lot of people are doing anything about it. Those who do, by the way, will be way ahead of the game. Amazon. Great example.

BH: Go on.

AM: [The] biggest company in the world, is a data company.

BH: This is true.

AM: They know how to process data. They know how to ingest it. They have good quality processes.

BH: What percentage of data quality do you reckon they’ve got? Finger in the air. Amazon’s data.

AM: It’s going to be a long finger give me one second…

BH: Oh, don’t Google it!

[ELEVATOR MUSIC PLAYS]

AM: My honest answer is, and I don’t know what Amazon’s data quality is like, but I do know they are ahead of the field when it comes to looking at data quality cause they reference it quite a bit.

For them to [be] where they are, they must have a certain level of data quality. Or else they wouldn’t be able to do as well as they’re doing.

BH: I mean, if you have the wrong stuff delivered to your house, just going back to the very basic Amazon example, you’d be complaining. That doesn’t happen. The reason people use Amazon is cause you know ‘oh I need dog food tomorrow morning.’ Three clicks, dog food arrives. Done.

AM: I’ll give you a good example recently. James Briers, the other co-founder of IDS, orders some dog food.

BH: Go on…

AM: It ended up at my father-in-law’s house!

BH: Why?

AM: Because he must have – for whatever reason – put in a[n] order. You know how it does the auto-fill sometimes? From Google? For whatever reason – cause we were living at the cottage in the grounds – and it [auto-fill] must have automatically put two and two together, confirm[ed] the offer, and off it went!

I got a phone call from my mother-in-law saying: ‘I’ve got some dog food for your business partner.’

BH: So, the CTO at a data quality company has entirely failed!

AM: Well, he’s failed in the fact that the process that he used to order dog food didn’t take data quality into full consideration. Therefore, it didn’t marry up and go: James Briers. Wrong address. Send out. [And] it’s cost James x-amount for the dog food cause he’s not got that, and Lindsey and Richard have gained by getting free dog food and fed their dogs.

BH: So, did Lindsey and Richard keep the dog food?

AM: James very kindly said…It’s not raw food and it’s not freezable food so you’ve got to eat it within the next three days. Therefore, James living two hours away, [for him] it wasn’t cost-effective to drive over to pick up the food to go back.

BH: It’s a good job they had a dog!

AM: Exactly!

BH: From your perspective, can you just explain a little bit about what your understanding was of Venture Marketing and why you signed up to it?

AM: Because we are a firm that’s looking to grow and scale, but we are still in our young journey, so we’ve only been going [for] seven years. The idea of working with someone who understands a Venture Marketing Model, means they understand the pains of a company that’s trying to grow.

BH: Risk vs. reward.

AM: Correct. There’s the alignment there. If you win, we win. Partnership. Completely. It also helps from a cashflow perspective. Cause, obviously, we pay less if you don’t succeed, but we pay more if you do. But, if we’re succeeding, it means you’ve succeeded. So, it’s, again, risk and reward is all there.

It’s very difficult, as a starting out company, to get people to understand what you want to achieve from a marketing goal. Because it’s so hard to gauge the ROI back on it. It’s not that easy. Apart from saying ‘we want to achieve this in revenue, how do we get there and has marketing contributed towards it?’. Having those clear, defined goals.

I still don’t think the model’s quite there. I think it’s working. I think it’s got progress to work better, and it is a good model for companies like ours. But like anything, it’s a journey and you go on it together and you come to a resolution that works for all parties. [And] that’s how it works.

I’m sure many podcast [listeners] have heard of Gary V [Vaynerchuck] on LinkedIn.

BH: Oh, God don’t…

AM: They have. They have. The modern-day youngster or aspiring Marketing Executive will have heard of him. He wouldn’t be right for us. It just wouldn’t be aligned. The expectations would be different. I’m not saying he wouldn’t understand where we’re coming from, but it wouldn’t be the right [fit] for us.

We needed, what we need, and we do get this from BH&P currently, is that alignment of what we’re trying to get done.

BH: OK, so, tell me about the future. So, IDS has got a solution, you’ve got some tools, you’ve got a methodology, you’ve got an approach, you understand about the importance of data quality. What does the future look like?

AM: Having a nice bottle of wine with Elon Musk, on planet Mars, talking about how to solve the data quality issues on Mars.

BH: OK, [I] wasn’t expecting that!

AM: I think, where we’re trying to get to, is a point where, globally, organisations who buy into what we’re doing, have data certainty, which creates data trust within their organisations. Which means, at Board level, all the way down, everybody believes in what they’re trying to achieve through data.

BH: Job done!

AM: Job done. It’s as easy as that. As I sit here smugly laughing to myself of what a challenge we have ahead.

BH: [And] then you can retire on your farm with your pigs and your sheep and your goats. Are you having goats?

AM: Hell no. Look, I’m Clarkson [Jeremy] to a certain point, but not all the way. It’s interesting cause you as a business owner, myself as a business owner, and talking to other business owners, we all have plans of what we want to achieve. [And] how we’re going to get there is…everyone’s different, their route to exit is always different. Always unplanned.

What is very similar is [it’s] full of ups and downs and – I know you can’t see my fingers wandering up and down on the podcast – but we are all going to have good days and bad days. Easy days, tough days. Big wins, big losses. It’s part and parcel of it.

What I do know is, if you have good data, data certainty, data trust, however you want to badge it up [or] pass it as a marketing thing, you will definitely get to your end of route quicker, easier and faster. By having that data quality in place.

So, what is the end goal? Making a lot more people happier because they’ll actually achieve want they want to achieve in life.

BH: So, data quality is happiness?

AM: Data quality equals happiness.

BH: There you go. It’s not sexy but it will make you happy.

AM: Yep. Whoever said money’s the root of all evil doesn’t have any!

BH: Thank you so much for joining me on the podcast today. I hope we have managed to make data quality sound a teeny-weeny little bit interesting. I’m not sure it’s sexy but we’ve managed to laugh while talking about data quality which has got to be a good thing.

AM: Yeah. It’s a serious topic, but it can be fun.

BH: Cool. Well, thank you so much for being my guest today.

AM: We do get a discount off the next invoice though?

BH: How much do you want?

AM: F*** all it’s fine!

BH: See, a client that doesn’t ask for a discount! Please note everybody. This is BH&P.

AM: It’s just all about the love. All about the love.

BH: It’s all about the love. The love, and the trust and the lunch.

AM: Exactly. Exactly that.

OUTRO:

BH: That pretty much wraps it up for this episode of the No Bull Ideacast. Join me next time where I’ll be talking to Alex Draper, Founder & President of Chicago-based [company], DX Learning about leadership in the marketing function and the great resignation.

Alex has also promised to shine a light on my own leadership style. What on Earth could possibly go wrong?