Shine a Light on Your Data | Transcript | Episode 003

[Adam] Hello and welcome to tech for finance, where we help finance teams, leverage technology to support their ambitions as businesses and as individuals, I'm your host, Adam Shilton and in this episode, we're going to be chatting with Simon Divine owner of Hopton Analytics.

Simon is a business intelligence specialist and has years of experience in the technology space, working with ERP applications, such as SAP and Microsoft dynamics and business intelligence applications, such as QlikView and Microsoft Power BI.

It is Simon's belief that data lies at the core of decisions that drive businesses forwards and is currently pioneering the BI as a service concept, which gives businesses powerful insight into their data without the need to hire skilled resource internally.

So thanks for joining me today, Simon.

[Simon] Thank you for inviting me.

[Adam] No worries. So can you tell us a little bit more about how Hopton analytics came to be?

[Simon] Yeah, absolutely. So it probably started, but didn't about 10 years ago when I guess at some point with working for an organization, you say, I could do that. I could do this. And I guess what happened is I then got a fantastic opportunity working for a Microsoft business partner, not doing BI at all, actually doing ops.

And I did that for seven, eight years. And last year, it was a bit of a turning point for me and I thought, you know what, maybe I should give it a go before I can't give it a go, because I’m a bit too old. So that was it for me. It was something that I'd always thought I'd like to try.

It's always interesting to me as, as BI, even within a Microsoft partner doing operations, in fact, probably more so because you can't get away from data in ops, so that helped there and then I'll just do it do it for myself really.

[Adam] Very good. But you had experience with QlikView who were, I mean, I won't say, I don't know whether they were the first to the marketplace, but they were some of the first to start really pushing business intelligence weren’t they?

[Simon] They were certainly in the time and you can argue still are, but there was certainly in the time the leaders in the technology and functionality that they had, you know, they were big on ‘BI tools should allow you to ask the last question first.’

So what is it you're actually driving at then drill in and drill in and see all the different components, which back then, so this is what 10 years ago, maybe a bit less, BI was nothing more than for, for 90% of organizations, it was nothing more than Excel, or, expensive XL. It's an application that does the same thing, you know, just a report, just a list.

And so Qlik, and then later the likes of Power BI, they really did say, well, hang on, we can take this day or an associative data engine and we can say, well, actually start your story, your conversation with the data, wherever you want to start over here, we’ll start it over there and still get to the answers that you're looking for.

[Adam] Very good. But you and for people listening, it's QlikView with a, with a Q, Q L I K isn't it?

[Simon] It is. Yeah. Just to make searching difficult.

[Adam] Yeah. Just to make things easy and then that's fine. But even in your, I mean, I suppose we can, we can go into real-world application.

So you're, you're experience with Qlik actually lends itself quite well to when you were doing the operations in the dynamics space, because you used BI when you were organizing workload, when you,

[Simon] For me it's... and I appreciate you don't necessarily need this.

But for me having a tool, which does that number crunching, which gives you that helicopter view that high-level here's the state of play is the first thing you should do because that saves time running reports.

It actually, you know, traditional businesses, especially if they want operational reports, will run them once a week, maybe even once a day, which is okay if that's the cadence or the speed with which your business operates.

But if it's super-fast moving, you need to know what the situation is now. You need information immediately at your fingertips.

And that is, I think, where my experience in BI helped and certainly something that I relied on, I couldn't have done the job that I had done in ops without a BI tool, be it Qlik, which we used to begin with. And then when the time was right, I moved that to Power BI. Yeah.

[Adam] And just appreciate it. This isn't a question that was on the list that I gave, but I'm, ad-libbing a bit here.

So in that context, working for a professional service organization and just to, to apply a little bit, a little bit more, that was a service organization that ran complex projects with skilled resource available at different times.

So if we could scratch a little bit further, what was the advantage of having a BI tool when it came to that scheduling and finding suitable resource at the right time compared to just using a project management tool or, you know, Excel, for example.

Okay. Yeah. Well, I can't answer that question cause it's no… No. I think the reality is there's a few ways of doing it.

So take project management software, the best ones out there now will give you up to date, accurate information upon resources, so you can make a decision on the fly.

And that's the important thing we're talking about decisions here. The software is almost the thing we use to do the real work.

So if you've got the tools that enable that, but do enable you to make those decisions and pick the right resource with the right skills at the right time. Fantastic.

If you haven't, if you don't have that one way of achieving it. And I, I believe this from 10 years ago and I believe it now. Is you can use BI to deliver similar sorts of results.

Now, BI, if you were to compare a project management app, which allowed you to schedule resources and make some of the decisions for you, and then came up with an answer, BI won't do the level of detail or integration that one of those tools would, because, guess what, it isn’t for that, it’s for a wider audience or a broader use.

But BI can sit alongside or on top of these sorts of tools. So when it comes to the likes of how I would best describe that, that I've just explained is ERP or large CRM projects, lots of moving parts, lots of components.

These are change management, fundamentally these change, how businesses work for the better. Sure. But they change and people don't like change.

They just don't know. And they say they do it. My CV says, I like change. I like implementing change on others is the reality, love that, can do that all day long

Change that happens to me. Isn't so good. And that's exactly the same with any form of ERP or CRM product.

So could do that. You could implement the best, most fabulous fanciest project management tool out there, or for probably a smaller, smaller cost, smaller outlay, less time virtually no change, you can implement a BI tool. So, whereas business system changes do change it. BI tools almost sit on the top.

It's like the warm fluffy duvet, that no one could really be offended by them. No, one's upset, especially now with they’re done well and what it does is only provide them with information and insight that they either didn't have before or had before, but maybe not in the same way.

So it's always a win-win. So that is where I think and that's not the question you asked and I appreciate I’m monologing now, but that's where the value gums of this sort of thing.

[Adam] Yeah, no. And, and, and I'd agree. Yeah. And, and it comes back to what you say earlier, you know, with all of these tools, it's, it's sometimes looking at what you, what you want as the metric, and then working back from there, as opposed to, you know, just trying to throw a load of data together and try and make informed decisions from stuff that may not make much sense in the first instance in its raw format.

Right. So, yeah, So that kind of leads me on and I think, I mean, I don't need to ask the question about how you summarize business intelligence.

Because I think you've, you've already done that by, by what you've just said, but especially with some of the businesses and this is, this is more, I guess, SME space rather than enterprise space.

Right.

Some businesses might come to you saying, oh, I don't think we're ready for, for BI yet, because we can't, you know, we can't really get the fundamentals.

Right. You know, so, so would you, that, would you consider that to be a bit of a myth? Do you think that that is correct?

Are there any other myths that you think that you'd like to dispel in the BI space?

[Simon] I think that is a, is a good one and absolutely is one that I hear. We're not ready because the data isn't ready.

Okay. How do you know the data's not good? Well, the reports aren't very good. Okay. So one of the ways to achieve better reports, one of the ways to achieve better data is to shine a light on it.

So if you use the tools and you don't have to start big right, this is, this is one of the benefits of tools such as Power BI or even Qlik in the initial footprint can be reasonably small or very small.

If you take the likes of Power BI, which is very much through Microsoft democratization of these sorts of software low-ish code, right? Build it yourself.

So we can start small, but by starting, even from a position where, you know, the data is not great, or, you know, you don't have the right day, or you're not sure what you do have, or even what you want.

You can start to build something using these tools and what will happen. When they're done well, is that adoption will increase and then users will make specific requests.

I say, users, et cetera. That's a traditional way of referring to people in ERP space. There's no other industry that talks about customers as users, other than ERP we call them users. Right. But let's not, let's call them people. So, you know, if you've got a system that you can roll out, but you, you do say, okay, so what we really ought to do is, so one from earlier today, what we really ought to do is wait until we have implemented our new CRM system. And then we'll implement business intelligence.

Okay. How are you going to pull the information out of CRM, your old CRM system? It's going to be a nightmare. Okay. Why don't you use a BI tool? The BI tool could be used to extract that data.

If indeed, if it's historical, you need to extract it. We’ll extract it and store it in BI, rather than push it into, you know, take terrible data from one system and push it into another. Let's not do that, but it's used the BI tool to store or the BI stack to store that data and then do all the new, sexy, shiny, lovely stuff in the new system without ruining it with all the old data.

So I think I get why people say it, you know, we should probably wait. And it's more likely down to a desire not to take on too many projects, get that completely. But I think the way that, the way that BI is, which is, or can be like touch small steps actually means you can do it at any point.

And the fact that data might not be great is absolutely not a reason is not a reason to do it.

It's a reason to do it. You should be looking at a BI tool in that scenario.

[Adam] I think that's fair. So going back to the point about data quality, and the fact that a lot of these BI tools now are becoming much more accessible, you know, they're not high investment, you know, something like a power BI, I mean, what premium version’s a tenner a month, something like that I can't, or pro version or whatever it's called.

Are we saying then that in theory a finance team or operations team or whatever team, if they've got big Excel spreadsheet or SQL database, they connect up power BI to start surfacing some of the information and actually the BI tool could be used as a way to identify what gaps they might have in the data, because when they start building, they then get a much better impression of, oh, I can see we're working towards this insight, but actually here's a gap because we haven't got that, and then maybe backfill.

[Simon] Absolutely. Yeah. So I'm working. So I try and build in actually to most clients that I work with a model or a data set, if you like a subset, an app, which says things that are missing.

So things that are obviously missing. So a project app that I did with an organization, a large organization recently includes a separate page tab or whatever you want to call it with things that are missing.

So, you know, you've got your, all these project lines on new projects for delivery. And these 15 don't have an owner. These 25 have a start date and not an end date so that you can then start to think about data quality, not as a, as something that's a given yeah, the data there is not very good, but you can start to use it to proactively improve it. The data isn't very good. We know where it is in very good, let’s fix it. And, you know, you'll know the work that you do and the people that you've interviewed as well.

A lot of what we talking about is software as an enabler. It's not, it's not software for the sake of software. It's software that enables individuals to do something it's part of a process.

It's part of an outcome. It gives the organizations something, and that isn’t more software, that’s information to make decisions.

You know, I do say from data to decision, because that's what it's, that's what it's about really.

Arguably that statement from data to decision can be used in a setting of ERP solutions.

You get the data in, you've got something, the decision is just, do we buy that new machine based upon our profitability,

CRM, same principle, data to decision. Do we hire more more people in the factory or the warehouse based upon the amount of leads that we've got coming in.

These are decisions that apply equally well to BI or to ERP or to CRM and fundamentally. And I know we've not really covered it. And to be fair, the sort of conversations are more over a beer than a, on a, on a, like this, but fundamentally what we're talking about that is just tools to provide information.

And in that instance, is that CRM tool, not a variant of it, the BI tool yeah. Is it's just tends to be designed for CRM or tends to be designed or ERP. And doesn't have necessarily that ability to say right, as BI would, that we'll get information from here for sales and here for resources and, you know, whatever it might be.

[Adam] Yeah. And going back, just to that second point that you made a little bit earlier about moving, not necessarily bad data, but data that isn't perfect into a shiny new system.

So you talked around the concept of, and I think you and I have had a couple of conversations around this as well, because I think a lot of businesses, especially if they are embarking on big software migration projects will not always know whether they need to keep their existing system running in tandem with the new system for a period. And if they do keep both running at the same time, is that going to have a, a cost cost associated with it?

Because of course they're paying for two systems at the same time. Never ideal really? Or do they do a hard go live when they say, right, well, this is the point where we are going to switch off the system, maybe because they will literally lose access to it at that at that time.

But I think what you've positioned though is, is maybe a middle ground where you can say, right, well, actually, even if your system is being switched off, that's not to say that we can't still house it in a format that's useful.

So dumping something to us, I mean, it might not appease the auditor's right, but in theory, dumping data to a spreadsheet, and then maybe, I dunno, I've heard of people building access databases off the back of it, which is, I suppose, just a way of creating a, a more interactive spreadsheet at the end of the day.

But they've used that to house previous data, but if you can then put a pretty front end on it, that then makes it a lot easier to digest that information, then to a business that's moving to a new system, they can say, right, well, are our go live on the new system is from this data with our new data, that's going to be all cleansed and accurate and moving forward, that's the lay of the land.

But actually if we do need to look back over history, whether it's a customer sales order from, you know, two years ago, for example, you can have a BI front end that allows them to drill that into that data without having to retain the old system.

[Simon] Exactly. Yeah. And that I think is so in, in ERP land, I think that's probably pretty common. Certainly most implementations you'd say, let's lose the old data because we don't want to bring it across.

We're not doing it. Okay. So if you don't use a tool, and BI of course is a, it's one of these terms it's slightly ERP, it actually covers a multitude of different elements, right?

So BI could be the data storage. So we got all this information from the old system. We're not going to lose it. We're going to put it in BI. And then what are we going to do is we're going to have some more user apps, power BI being one, and we're going to render both new sales information and old, new financial information and old, and if it's done well, it will satisfy the auditors.

So that's the way around it, right? So we've got that ability to to layer multiple versions of history and not lose it.

And with with, you know, changing from one system to another, in the ERP world, finance systems, whatever, often you lose that. You don't need to, if you use a tool such as Power BI, because we can still refer to the old data.

It’s cool.

[Adam] And that's, and that's, that's an interesting point as well, because again, mate, maybe I'm stuck in before and after mode, but actually if you can essentially link up however many data sets, you want to a single BI front end, then even if your data is being pulled from a spreadsheet or an access database for the past system, you're still then able to bring in data from the new system.

So, if you cut across halfway through the year, you still be able to use a power BI dashboard to say it's an amalgamation of our historic data and the data in the new system. To get a complete picture.

So the other piece that jumps in my mind is, is actually a thread that we picked up on a, on a previous interview as well, and it's once you have this data, how do you, how do you tell the story on top of that? Because as you alluded to, yes, having information is one thing, but then being able to present that in a format, that's understandable too, if we, if we're talking in the finance world, you know, if I speak in finance terminology, other members of the company and quite rightly so, you know, not every member of the company is trained on what, you know, a nominal code is or a P and L balance or sheet report or that sort of stuff.

So how do you see BI making that storytelling piece a bit easier? Is it, is it the visual element? Is it the, having everything in one place?

What, what are your, what your thoughts on that?

[Simon] So I think in actual fact from a consultative perspective and it's cheesy to say, but it's the people, so you touched upon it there, so, okay, a nominal, or a GL, so, okay. So we've got, we've got all these and what do they mean? So having that either experience to be able to have those conversations to say, okay, so we've got this information, what will we do with it?

Or the people with that experience to at least grasp what is being asked for makes a big difference. And then it's, it comes down to taking that, then, so we've got that, we understand the requirements and building just enough that it gives the users the information they want.

And I don't mean just enough in a negative, you know, well, they've asked for this or you just make sure we give them what they want.

I mean, you will see or have seen, in fact, some software vendors build into their products, something where what they've done is thought, okay, so we've got a landscape, this big by this big, we'll just throw everything at it until we fill it.

And actually in the same way that the modern ERP systems and CRM systems employ the UX user experience skill to say actually more is not always better. Let's consider what we're looking for and what the users, what people, not users, other people want out of the system and deliver them just that.

That's really the key. And I think that's the saying, if you're talking about finance or your talking about sales, or are you talking about warehousing or distribution, you give, you tell the right story and give the right information to the right users at the right time.

[Adam] And, you may not be able to answer this again. It's, you know, we sometimes need to place ourselves in the future, but, when we look at the makeup of a team, I mean, traditionally, whether it's finance, operations, fill in the gap. You'd have, I suppose, in sales terminology, it could be described as you know, above the line and below the line, you know, we've got senior individuals that will be presenting information, you know, providing strategic guidance to the business. And then you've got the sort of below the line individuals that are dealing with more of the operational elements, you know, getting stuck into the spreadsheets and doing the day-to-day processing.

Now, if we, if we put, put ourselves in a scenario where businesses are adopting more business intelligence types approach to just day to day, and we've not got people that need to spend a life in spreadsheets, because actually we're able to surface information a lot easier. Do we think we're moving to a scenario where our requirement for different skillsets will change?

So instead of for example, bringing on a financial analyst or a financial administrator, are we instead looking for individuals that have a grasp of industry and their interpretation skills are enough to the point where they can tell the story?

You know, are we looking at the shift of analysts rather than operational staff? I guess if I've worded that correctly, do you see where I'm going?

[Simon] Yeah. Yeah. So I think if we tackle that for actually thinking about the people at the moment, so I've got in my mind, I've got a good few examples where this suits really well, and you will as well, where you have individuals in the organization who number crunch. Program to produce a spreadsheet, or maybe they've got the free version of power BI or whatever it is they got, and they are producing this information, which means something to the organization.

So as your first example. In actual fact, if you look at those sort of people, and there are many within a business, regardless of what role they play, the sort of people who do that actually already have some form of industry knowledge because they understand their job and their business well.

They tend to be analytical because they need to be, to do the job currently. And they ask the right sorts of questions and understand it. And they focus upon taking the information that they've got, but often it takes them an age to extract from the system and double check it with what they know and they think because, you know, you could, you could probably ask them what do you reckon we did in terms of revenue yesterday, and they have a reasonably good idea.

But you can take their knowledge and their information, their understanding about the business, merge that with the manual work that they're doing, and actually all you end up really doing, and this is a positive not negative, is taking that manual drudgery out of it.

OK, so what I have to do is, is follow these 12 steps and then do this then do that, and then hope I haven't missed one out in order to get the information that I actually need to do my job.

And I think regardless of what level within an organization, it is for the most part, all information does is allow someone to make a decision. It allows the next action to happen within an organization. It’s down to the people and the processes if you like.

So I think in actual fact, all tools like this will do in the long term and it's all is a bit unfair, but all we're actually doing is getting rid of the drudgery at least to begin with.

And then, yeah, sure we start looking at machine learning and AI? So we actually use the information there to make predictions.

But for the most part, 90% of businesses, aren't even doing that in any form of system generated way. It's, it's again, going to Bill who runs the sales team and has done for the last 10 years and saying, right Bill, what are we going to do next year?

So, you know, actually we'll use those tools, those AI, those ML tools, we'll use them for sure, but that isn't even in most organizations replacing anybody. It’ll only really enhance and add to, to what's there is my guess.

[Adam] I'm going to give a shameless plug to power BI now. Only, only because I I'm probably ignorant of other solutions on the marketplace, but I do have experience power BI and you correct me if I'm wrong.

But I remember when I used to work with it, and this is on the AI and ML side, you could go into your data set, just click the button on the left.

I think it said it does that say data set or something like that or data, and there's a little light bulb isn't there.

If you click on the light bulb, it will automatically troll through your data and it will present you with a load of analytical or trend graphs, won't it.

And the machine learning algorithm, doesn't it doesn't know what that data means. It will just say, you know, Axis A, you know, or X, Y, but in doing that is automatically trying to spot outliers and plot trends for you.

[Simon] Yeah, absolutely.

[Adam] So actually, if you've got a reasonable data set and again, correct me if I'm wrong, even without knowing what the answer is, you could click the little light bulb and get some inspiration.

I'm guessing.

[Simon] Yeah, that's it. And it's a really good example. And I built it in to at least one app that we do for each client, because. So a good example of that is debtors. So we've got an aged debtors and actually what we want to do is obviously provide an Aged Debtors. Fantastic. We can do that.

Is that special? Yeah. Why? Because previously somebody in finance has spent two, three hours maybe more producing it and pulling together, and now it happens instantaneously. But it is, it's that it's that AI now element there, which actually adds to the use of it because then what you can do is say, okay, so what, what factors influence a debt to be over 30 days, over 60 days, over 90, over 120?

And your right as well. It does come up with some silly things. So one of the factors, again, using an example that we built just recently, one of the factors that influences a debt to be in the 120 day category is if the debt is over 120 days old, that's a surprise.

But equally then what it really does is say, well, actually one of the factors is, it's this customer or it's in this region, or this is the credit controller, or this is the account manager or whatever it might be.

And I've seen variants of all of those. And it's not so we can then go after them and beat up these individuals that it highlights with a big stick, because there'll be very good reasons I'm sure behind it, but it does begin to lift the veil, start to uncover potential stories, potential as to why certain things are happening.

And it's fantastic. And that's just one example, you know, you mentioned where can do it really, if you've got the data and if it makes sense to do it across the board, it's very clever.

[Adam] And that’s the thing, and you can really start getting into the weeds in it and I'm fascinated by this sort of stuff. But that example that you gave there is such a small portion of what makes up an organization. You're talking about one function, and then you apply that same principle to all elements of a business.

You could really start building some, some powerful insights couldn’t you.

[Simon] Yeah. So take ML. So another example, which is real world we use machine learning we'll crunch customer sales and we'll crunch customer sales by how often they buy, how many, how much do they pay?

You know, frequency, cadence, everything we’ll really start to get into. And then we'll push this through machine learning algorithms, using Azure as a, as a data source and using the Microsoft stack to really do the numbers.

So now what we can look at is, here’s all our customers and here are the ones that are about to leave us. There are the customers that are going to stop buying from us, and they're going to stop buying because historically they've bought loads, now they're not buying.

Equally, here’s the customers that if we sold them this product and this product, there's a 70% chance that they'll buy it because in our experience, all the others supports it.

And these are things that, yeah, with an army of people you could crunch through and work yourself. These are all so things that, you know, Bill that I mentioned in sales, that's been there for 10 years, intuitively knows, well, why don't we go and sell them that product?

Because most people buy it if we sell them this product, or do you know what I've not heard from Sally at that customer for a while, I reckon they're gonna leave us, they know that, but if you've got volume, yeah. Bill doesn't scale.

So if you've got volume and a lot of it, you can actually use these machine learning tools, which are built-in pretty much into definitely the work that we do with our clients and start to deliver it to, to your organization.

So again, you can start to make decisions off the back of it.

[Adam] It's interesting, actually, I've just. A company that I've been speaking to recently, a food manufacturer, and it'd be interesting to delve into what BI could mean for them, because a big part of what they do linking to sales is trying to get stuff out the door before the item expires.

So, so it would be interesting to combine those sales stats alongside average sales times, and then compare that to proportion of goods that have been written off because they weren't sold in time.

So makes for some interesting chains of thought, I guess,

But the, and we'll go through another couple of examples and again, this, this links to storytelling and, and you've, you've probably got a few more, but there's, there's two and one, but I mentioned in obviously the, the prep that I sent through about this, and it's, it's one that you and I have covered a couple of times.

And if I can describe what it looks like, I suppose the visual is a warehouse. So it's a wire frame. And within that, you've got your different boxes, which are essentially your shelf numbers in your locations, right?

And I remember seeing a dashboard that you did turn that wireframe with the shelf locations into a heat map whereby the darker regions were the ones that had the higher volume shifted and the aim of that dashboard or BI report, or how have you term it, was to allow a warehouse manager or an operations director to rearrange their warehouse so that the higher moving goods were more accessible.

So, you know, they'd be able to know for a fact that, you know, they weren't in awkward positions or they could get them out the door as quickly as possible based on, you know, what, what they could, what they could find there.

And I found that really interesting because to me that made it real, you know, it wasn't just lines on a spreadsheet. It wasn't just a pivot chart, you know. That was actually business intelligence embedded within a real world scenario.

And I always remember that because it's one of the best applications, and telling a slightly different story. And it was, it was a custom built BI tool actually off the back of a, an energy management system.

And the, the customer was a massive university, I think. And they wanted to map their energy consumption across the different regions and the, and the way that it worked is obviously each of the different areas of the campus, whether it was the sports ground or the, or the loos or the whatever they'd have their own electric and gas meters.

So we took all of that data and we put it in a dashboard and it wasn't a clever dashboard. It just plotted a trend line. I think it was just a line graph or something like that. But as soon as the data was in one place, they could see, at one specific time of day, a massive spike in energy consumption.

And of course they wouldn't, as you say, that could have probably worked it out if they delved into the detail, but it probably would have taken them months to surface that information of that anomaly that was that massive spike. But from that, they could then ask themselves the question, why is that happening? You know, and that's very important question.

So because they had that target, which was spike at this time of day, they were able to monitor that energy consumption. And it was in this example, it was the sports ground. It was the football field. So they paid attention at that, that time of evening. And they noticed at that time, all the floodlights, literally all of the floodlights in the pitch would light up and they couldn't work it out.

Was it, you know, was it on a timer and somebody accidentally, you know, set the timer up wrong or something like that.

And they had a particular groundskeeper that, when he was doing his rounds, shutting everything down in the evening, he'd go into the stadium outhouse, he’d switched on all the floodlights. And then at the end of his rounds, he turned off again and they said, why, why are you doing this?

Do you have any idea how much money it's costing us use to turn it on the floodlights?

And he said, oh, I've just always done it so I can see better.

[Simon] That's a great example of asking the last question first. So your tool that you mentioned allowed you to see that in a split second without actually being at the other end of it. And at some point, if you're lucky someone saying. Energy expenses are really high, why might that be? Dunno.

Okay. So, and you know, you eventually, and probably you'd get to the point of saying, oh, it's because the groundsman turns all the lights on so he can see when he's walking around or maybe not, you know, just don't know.

Cause what would you do? You'd check to make sure there wasn't any any timers on, brilliant we've done that. Then you might ask, or you might just say dunno, we'll call someone out, probably a dodgy meter.

I do think, I think for me, this is what BI is all about. Yes, you have solutions which cover a multitude of different areas, which allow information to the business that allows them to drive and run their business better. Tick, that should absolutely should be the driver, but if you can have point solutions, one thing to help one issue, why would you not?

You know, and it doesn't have to be right, we'll implement BI for everyone, you know, it can be something that's iterative.

We do something first. Then we do this. And there's a load of value in that. It's the thing that excites me most about it, is that it’s not a big bang in two years time when we switch it on, it's a, we'll do something that adds value, then we'll increment and do something else.

And there's a load of good stuff to be had. It's interesting as well.

[Adam] The other story that, and I won’t elaborate as much as the last story, but the other story was a local council. Again, coming back to energy, and one of the public toilets at a certain time of night, again, the energy consumption would go through the roof.

So they monitored it and it was a homeless person that broken in, and he’d used a broom to jam the the hand dryer on. So he could stay warm overnight. Yeah. So it's, it's it's, as I say, it's all about real world application isnt’ it. It becomes real. It becomes real when you can, when you can tell the story. So that was pretty good.

But I suppose, you know, more relevant to, to business as opposed to energy consumption, you know, you can distill that into any number of other stories.

I mean, we know at the moment that cost of everything is increasing. So a lot of organizations are becoming more ruthless with approvals around purchasing, you know, they really want to get into the nitty gritty, behind what they are spending their money on, you know, and, and applying those concepts of how do we find the outliers.

It could be if you're able to analyze expense by department that you've got a spike from, I don't know, let's talk about sales team, right. You've given a couple of sales examples.

For whatever reason, we have a spike in lodging at the end of every month, you know, we've got monthly sales meeting, somebody stays in the Marriott, you know what I mean?

And it's, it's seeing, it’s little bits like that. It’s course correcting? Isn't it? You know, as you say, they're not drastic changes, you know, so you don't, you don't need to implement a whole suite of business analytics tools to start seeing some of the value from it.

[Simon] And, you know, in actual fact, the example with the electricity, I'm working with an organization at the moment. I'm one of, it's a large estate, loads of properties on a, on a big place and they've got a grounds team.

And actually one of the examples from the finance team is actually, we'd really like to know and be able to see reasonably quickly if were using a load of water, if someone's just left a tap on or see about electricity use or see, and, you know, actually, you know, I don't wake up in the morning. Do you know what I'm going to do, I'm going to save someone some money, based upon their water usage, it doesn’t occur right. Nor should it.

But equally, I don't think that I don't sit there and think, oh, that's got nothing to do with BI cause actually that is, that is all, this is about you’re shining a light and giving visibility of something.

And if that, if that is useful, because it shows how outliers, it shows variation, it just whatever. Then it will be used, and if it's not useful, it shouldn't be developed in perspective.

But yeah, it's, I suppose, because there's so many different things to cover so many avenues that you can go down expenses, water usage, electricity usage, you wouldn't necessarily have thought of that, down to sales analysis or finance or you know, I don't want a 3d map of my warehouse I want 3d map of production line. And I want to see in real time items moving down. So when a customer rings and says, where's my delivery of this, when am I going to get it? You can have a look and say, right, well, it's currently a second stage production. I can see that. So, our new estimated delivery date is this. Brilliant, loads of value in that, value to the bottom line as well.

[Adam] So focusing on what you guys do there, are you okay to talk a little bit more about your approach? So somebody, somebody comes to you and says, we've got a load of data. You know, we, we know that we're not getting as much insight from it as we want to. Where do you start?

What recommendations do you make? What journey do you take them through, you know, to get there essentially.

[Simon] First to actually, I suppose back in the olden days, children would say 10 years ago you get a lot of that. We've got something we know we need something. We don't know what.

I think you get out a little less now. So more it's we know we, we know we want to do something. We have the Microsoft stack, we need help.

And that can be to work with data strategy. We need you to help us understand and align and come up with recommendations, or it can be a build. You know, we want solutions that enlighten us on what's happening in the organization. Fantastic.

If it's the latter, if it's the, we want solutions that… What our approaches is, the proof of value, or what used to be called a proof of concept.

So we'll deliver you something that's real and works. It's not a show and tell, look at this, I've shown it, put it away again, it's a finished solution, which, and that, that will, the reality is most organizations will say either sales or they'll say sales, or they'll say occasionally, they want to do that analysis against finance, but for the most part, it'll be sales.

And that works really well actually. I don't, I don't say that because it's easier. I think sales is often a good first step because that is seen as the, the life blood of most businesses, which it is. And if you can prove it and make it work for a sales team, not being derogatory of how the sales teams, you can do that, and actually it will probably work elsewhere in your organization. And so that is, is our approach.

We prove the concept and that's always the aim. It's not about going in and, you know, selling a hundred days that does this, that and the other and it’ll be amazing and you just have to wait two years for it.

We work in a very agile methodology of sorts approach. I don't literally mean agile, but I think gone are the days where you can say, especially the pandemic has taught us this, if nothing else. Gone are the days where you say right I'm going to be working with you for a full day and I'll start at nine and finish at five because people have day jobs and sitting with you on working through this isn't here anymore.

So what we will do is say, look, you know, we'll work around you. So if you can meet with us, great, we'll work with you. If you can't, don't worry, if we can work on our own we will do. If we can't, we'll do something else.

And that, that agility helps our customers save money. Which, you know, if they don't have to spend it, they shouldn't be spending it. And the aim is always prove the value. So can we prove value if we can prove the value, we'll move on to the next step. Can we prove value in your sales app? Yep. Brilliant. What's next? We want aged debtors. We want a finance app, we want a delivery app. We want a warehouse app. Brilliant. Do one at a time, do it and move on, move on.

And I think that buys and builds trust as well. Yeah. And that, that tends to be our approach. It's not necessarily, as it used to be done, but I think it certainly works for us.

[Adam] And what, what do you, what are your recommendations in terms of format? And I know these platforms are getting better and better with what they can and can't connect to, but could you start as simply as saying, right well, you know, if you've got loaded data in a spreadsheet, we can start there or is it better if you can get live data?

Are there any formats that are more difficult to, to work with BI in compared to others?

[Simon] I mean, they're, they've all got their special little surprises. I said, I'm working at the moment for an organization on telephone data. Call stats in essence. And it's interesting, but it's difficult because of the way the data is structured.

And you know, this, this project will save the organization a lot of money. It’ll give them insights that currently happen once a month, if they're lucky and I'll give them every day. It's a difficult one.

So that's actually a relatively simple data format in theory. But the way that the underlying app has been built is truly filthy. Then you can go the other end and have really complex data, you know, where you bring in via web apps and this, that, and the other. And we’re merging all sorts together, but because he's been written well, it's not so difficult.

I suppose, what, what it comes to isn't necessarily good data formats or bad data formats it’s good understanding of the data.

And sometimes that can take a while and sometimes with the help of our clients who might understand it, or with our experience with products in the past, that obviously helps.

But yeah, I, I wouldn't like to say this format's better than this because as I say, I'm working on one at the moment, it should be, should be super simple and it's truly awful.

[Adam] So I suppose what we're saying though, is that the medium isn't as important, you know, whether it's Excel or sequel, you know, fill in the gap, isn't as important as making sure that that data is formatted in a way that's accessible.

[Simon] Yeah. And you know, that isn't something that our clients have to do. So, you know, Excel, sure, it’s still out there, you're not going to get rid of it. It'd be better if it wasn't there for loads of good reasons. But if Excel’s the data source, fantastic. The data doesn't have to be perfectly formatted. So that example of their phone records is far from perfectly formatted, but we can do something with it.

And, you know, we talked about this right at the start when we said you know, don't wait, don't wait until we've got it in a perfect format to do something with it because you know what, if it's perfectly formatted, you don't need to do anything with it, right. Just look at it. And, and all your answers will pull up here. It's about saying what do we want to achieve? What's the outcome? Not in a technical sense. I want a, I want a report. I want decisions to appear from data or something that allows me to make those decisions, and this is the dataset. Brilliant. Let's talk about what it is you're trying to achieve. And as long as we can be clear on that, the outcome will be what the clients expect.

[Adam] Are you able to talk a bit more about what insight they're hoping to get from the call data? You don't have to, I’d be interested in what the business case is for that.

[Simon] Yeah. So I suppose, I suppose one of the things with the call data is it's, it's a ridiculous number of rows, let’s call it what it is, rows of data.

And you know, other than the obvious, how long does it take to answer to get a call answered? How many calls are answered in an hour, in a minute, in a day, in a week, whatever. We can start see how many are dropped, how many go to voicemail, how many go to voicemail and don't leave voicemails.

And then we can start to get on the the more touchy, feely stuff. So, okay. So we've got ring groups. Who's answering the most calls in a ring group? And it isn't, you know, I'm, I'm always talking here about who is, not who isn't, it's not so we can get our pitchforks, and go after the one who isn't picking up the calls, it's actually, so we can start to say, okay, so in that team, we've got a user who's picking up, a user, you see I said user again, the person who's picking up the call, 50% of the time out of a team of 10, but actually that person is the team lead or is really needed to focus on this or that or the other.

And actually the reason they pick up the call so often isn't because it rings their phone first, but just because they have a propensity to want to be helpful, right.

So it's all positive, there’s no negative in that. Apart from we can then start to say, well, actually we need to make sure I shared out more equally. So we're delivering a better service to our customers. And I think that's it. Yeah. You can easily go to the bottom and find the negative in these but actually what we're looking for ways to improve.

And that's what, that's what the work that I do with my clients does. It allows us to see ways that we can improve.

[Adam] And hopefully you’ve got time for a couple more quick questions.

[Simon] Yeah.

[Adam] So first one is, we've spoken a little bit about AI, machine learning, some of the stuff that is, I mean, it's readily available now, but maybe a little bit more future focused.

What, what do you think is the next level in BI? Shall we say? What, what do you think the future looks like?

[Simon] So predictive analytics I think is clearly going to become more and more important.

I think what will happen is the differentiation, the separation between what is traditionally BI and what is, if there is even such a concept of traditional BI and what is ERP or CRM will start to disappear.

And that won't necessarily be noticeable, you know, to be able to make the predictions. So let's take a, let's go back to debtors.

So a good example would be so that debt is due on the 30th of June. However we know through our predictive analytics, through ML, we know that actually they always pay two weeks late. So I know it's due on the 30th of June, but the realistic due date, is more like the 13th, 14th of July that is not seen as BI, right, and it’s sort of not, but it's using that data to make those predictions.

So I think the separation between what is traditionally ERP and CRM and BI, that will start to disappear.

I don't think it'll go forever. There are, as I'm sure you've come across all the time. Some customers, some companies out there, you just wouldn't expect to not yet be on the ladder of understanding data and working with it.

So there's a long way to go until the, you can't really separate the two. But I do think it's going to become more and more prevalent that the things that are the traditional spaces of BI become inbuilt into the ERP and CRM solutions or whatever other solutions it is that people use.

[Adam] And it's a question I ask most people and it's a bit of a tangent, but I'm a bit of a nerd when it comes to, is there an app for that? You know, what's the latest and greatest, you know software as a service I can sign up for or productivity tool or whatever.

Have you got an app that you use, a piece of software, you can't say power BI, sorry, that that you couldn't live without either when you're working or in your personal life.

[Simon] Yes, indeed. Yes. So I probably like you, I go through them all, I'll try and find the best ones to suit.

I use a tool called Motion. usemotion.com I think. And it is the best product ever.

So one of the, one of the reasons. It’s not cheap, but it is one of the best products.

One of the reasons I love it. And it touches upon what we've just talked about actually. It takes my diary from, or diaries, from outlook or whatever I'm using. And it then is a task management app. So I go in there and I say, right, I've got these things to do. I’ve got this call with Adam, that'll take an hour and I want to do half an hours prep. So I plug that all in and I say, when I want it to be done by.

So the call is firm, it's not going to shift, so that's done, but I've got half an hour prep I want to do by Tuesday evening. And I can say what times I work, what times I don’t, I can say I'm prepared to do out of hours or not. And it will work out, based upon my other priorities that I've got, the best time to schedule something. It'll go away and book it in my diary so that I have literally slot after slot after slot, to focus on the work that I need to do.

And just because it's a task management app, I can just click tick when I'm done. And it's done. The clever bit is the AI elements.

So I add a new task today, or I add a new task that must be completed by 12 tomorrow. And it's high priority, the highest priority to that deadline of 12 noon. It’ll go in my diary and say, okay, well that's not problem, but I can see you've already got this two hour block in this one hour block scheduled for this and scheduled for that, but they’re medium priority. And they’re not due until next Friday. So I tell you what, I'll just move them out. So it will adjust my diary for me.

Automatically.

[Adam] Amazing. Yeah. I have seen it come up and, and, and I've, I've, I've almost, I think I'm probably on Instagram. It just bombarded me with different productivity apps. So, so I think I've clicked on the learn more a couple of times, but I've not, I've not dug deep because I don't, I know if I go down that rabbit hole, you know, I’m in for a whole, whole new world of set up, but what you're essentially saying though, is if you've got a personal assistant, you know, in an app.

[Simon] That’s Exactly what it is.

Yeah. And I can send a meeting link and actually what that does is it's clever enough to say. He’d really prefer this time and this time, but you know what, pick any, so it's not like traditionally where it's, you know, here's my diary, fill your boots. It says this is preferred and that's preferred, but the rest were available too. It's fantastic.

[Adam] Okay. Really, really good. Cool.

All right. Well, where can people find out more about you, more about Hopton analytics?

So LinkedIn is always a good place, Simon Devine, or on the web Hoptonanalytics.com.

[Adam] Perfect. And I'll, I'll link to this in the show notes for anybody that's on audio. It’ll probably be adamshilton.com/bi is the link that we’ll use.

So I'll put links in there as well, because I think you've also got some example dashboards on your website as well, haven't you where people can go in and just see what the art of the possible could be,

[Simon] And they're fully interactive. So it's not just a, an image you can play with them and flex the data and see what's possible there as well yeh.

[Adam] Perfect. Excellent. So I'll put the links there, but Simon as always, a pleasure. Thanks for your time, mate.

[Simon] Thank you. Good to see you.