Breaking up with spreadsheets: why they’re bad for business and how to replace them

Thanks Jonny, and hi there everyone. Firstly, I'd like to thank you all for attending today; I know everyone is really busy right now. So my name's Alastair Semple, I'm from the Scottish Manufacturing Advisory Service, and in a moment, I'm going to hand you over to the Data Lab, so don't worry, you don't have to listen to me for too long. I am. But prior to doing that, I just wanted to give a bit of background and a short introduction as to why Scottish Enterprise and SMAS were keen to hold today's event and promote the TORCH programme.

So for the last couple of years, SMAS have conducted their own industry 4.0 review, helping businesses to generate an industry 4.0 improvement plan, designed to help companies understand how they can take advantage of new technologies, and better use their systems and the data that they generate.

During the course of conducting these over 180 reviews, 88% of companies identified the need to carry out a project, post the review, to help improve and optimise their data visualisation and analysis; so really make better use of the data they’re generating. And another key finding was that the majority of support available to companies, particularly small ones, tended to focus on helping them understand where they are currently in their journey, and where they would like to get to in their journey – if you like, the A to Z.

However, the most important support is to help companies take the next step or their first step in this journey. And that's why support such as the Data Lab’s TORCH programme is so invaluable, because it concentrates on that early stage of the journey and helping them make that most important step – the first step. So the A to B, if you like. And it took me a while to come up with A to Z and the A to B, so I'm happy with that one.

And so we're going to talk about that programme, but also, perhaps with a Valentine’s theme, if you need to start breaking up with the spreadsheet in your life.

So if you want to know more about the industry 4.0 review, please indicate in your feedback and SMAS would be more than happy to follow up. But hopefully, I've just given a bit of background as to why we see the TORCH programme as a key one.

And without rabbiting on any longer, I'll hand you over to Rhona at the Data Lab to kick off today's session.

Excellent. Thank you very much, Jonny. My name is Rhona MacLennan, and I lead the Torch Business Advisory Service for the Data Lab. I’d just like to echo Jonny's words there and say thank you all very much for taking the time to join us. Hopefully, to be an enjoyable and interesting session for you all.

I'm going to give you a brief introduction to the Data Lab itself, and then a bit of an intro into the TORCH service and what it's all about. And then we'll move on to look at the topic for today and the joy and troubles that can come with spreadsheets.

I'm just going to move on. So, the Data Lab is one of Scotland's Innovation Centres, and our focus is on data and AI. And, really, we strongly believe that Scotland can be part of a future where data powers scientific progress, economic prosperity and social good. And we try to support that by accelerating the journey for individuals and organisations, fuelling innovation through collaboration, building skills, and growing talent, and strengthening Scotland's thriving data community.

Really, our role is to act as a catalyst within that, using our expertise and our network and the funding that we have available to us to change how Scotland innovates with data. And we do that through three main pillars: our support to innovate, our skills and talent, and our community and events. It's all about, for us, the collaboration; building the skills that are needed for businesses to grow and become more data driven; and strengthening that community that we have and the collaboration that exists within all sorts of parts of the Scottish industry and academic communities.

In terms of the Data Lab services within those three pillars, Support to Innovate, is where the TORCH service we're going to talk a little bit about today sits. We also provide funding to support collaborative innovation between industry and academia and we provide larger scale funding for transformational projects where there are consortia of academic and industry bodies who will come together to look at something specific – usually in an industry sector – about using data more innovatively. We work with external funders like Innovate UK and support organisations to make submissions for external funds, and of which there have been quite a few successes in the last year as those funds switched with both Brexit and Covid changing the focus of them. And we have a number of resources and case studies and so forth, available on our website, which we will have a link to later on. 

Our skills and talent pillar. That's got two main focuses: both developing skills and capabilities in industry through leadership training, online learning, data education for work, focusing on people who are already in the workplace; and also building the data capability through our students, our masters students, and our industrial doctorates and PHD students. Our master's programme has been running for about five years now and we have alumni of over 500 students who've been funded to study data science or data engineering placements in Scottish Universities. And this year’s cohort we have 160 students who are studying in there just now. And we offer them industrial placements as part of their course, and that enables a lot of businesses to have their first touch of having a data science or a date engineering student, working with them on a specific project over the summer period.

And our final pillar is a community and events. And this is certainly one that's had to change a lot with all the working from home over the last year, so we haven't had our face-to-face events like our big Data Fest festival or our meet ups that we may have had previously. But we’ve pivoted a lot of those to be online. Our Data Fest for 2021 is going to launch on the 25th of March this year, and it'd be great if some of you kept an eye out for that and looked to join some of those events. 

So moving on to the TORCH service. We started up in March last year, funded through Scottish Enterprise, so perfect timing for all our plans to be changed with lockdown. So as I say I’ve introduced myself and I lead the service. I joined the Datalab a couple of years ago to help set up TORCH, and get it up and running. Prior to working on TORCH I've worked for about 30 years in largely larger industries – organisations focusing on IT data and technology in the last 15, 20 years as an Enterprise Architect in that role. And I'll let my colleagues introduce themselves if they want to just go down the line in line with the pictures. So Iain.

Iain: Hiya, Iain Henderson and I'm one of the Senior Solutions Architects in the team. Long term CRM and analytics and artificial intelligence specialist with experience across a range of sectors. I’ve spent most of the last 10 years in financial services.

Rhona: Margery. You're on mute.

Margery: Apologies, guys – somebody had to do it, might as well be me. Hi I’m Margery, I joined the TORCH project about three months ago, I've got 35 years’ experience doing data-centred roles working for companies, start ups right up to Oracle Corporation, which I'm sure people will come across in the manufacturing world. That's me.

Rhona: Thanks. And Lauren.

Lauren: Hi, thanks Rhona, I’m Lauren Spark, I joined the Data Lab in April last year as a business development executive for the TORCH team. Prior to that I spent the last 15 years in sales, but five years working with one of Scotland’s independent data analytics practises working on projects quite similar to what we do in TORCH. I’m normally the first point of contact so if you do have any questions following today's webinar, feel free to get in touch, the contact details are below and shared after the webinar, thanks very much.

Lauren’s contact details are lauren.spark@thedatalab.com.

Thanks, Lauren. So just moving on in terms of the TORCH service. What we try and do with TORCH is provide a tailored service to customer needs that targets early intervention, so taking those first steps. And by doing that, we try and get the foundation's correct for organisations. We accelerate the projects and allow companies to start their journey to data innovation and start delivering value much work more quickly. Because we're applying proven techniques and using best practice, we can reduce the risk to your organisation of taking some of those first steps.

We always operate from a vendor neutral perspective. What we do is we look across the spectrum and try and pragmatically find the best fit for you in terms of data, process, skills and people capabilities, and technology, and we do that with working through a network of suppliers. We have nearly 50 suppliers signed up to our TORCH supplier network, from very big tech and cloud providers, down to individual consultants. So, what we aim to do is find a partner that meets your needs and fits with the way that you want to work. And we try to do that, as I say, using proven methods and techniques.

In terms of the TORCH services – I'll just build this slide very, very quickly – we kind of have three main offerings, but largely what we do is we tailor it to the requirements. Most of the work that we've done in the year so far has been in the in the ‘validate data opportunity & identify solution’. So essentially, help companies work out what they want to do, and help develop a roadmap as to how they're going to go about and do that, looking across the domains of people, process and your data and technology.

It’s key to say at this point that the TORCH services are focused on private sector organisations at this point in time, and what we do is we’re a paid for service, but our funding from Scottish Enterprise allows us to offer discounts on the day rate that we charge for the work that we do, based on the age and stage of the organisation – so start ups, smaller organisations depending on their status. And obviously, the current situation with Covid, if it's within industries that have been particularly badly hit, then we've got some flexibility for looking at the discounts around that as well.

And, for some organisations, if they still want us to continue and work with them as they start their projects with their tools and partner, then we do sometimes provide some support through implementation with a small number of days added on, so that we can be called upon to provide oversight as the project goes through. In terms of the solution evaluation, we’ve not done too many of those yet it, but we see in the future there may well be larger projects that require that involvement, and we would be able to pick that up as well.

So, just a very, very quick look to finalise on TORCH. This is just an overview, I’ve pulled this together, right at the end of December, and actually it’s moved on quite a lot. The focus for TORCH was to reach out to companies that maybe had never worked with or heard of the Data Lab previously. The Data Lab’s been in existence for it for seven years now, so it had been going for about 5.5, 6 years before TORCH got going. A number of those organisations had never engaged with the Data Lab and that was one of the key measures that we were asked to meet from that from Scottish Enterprise and our other partners.

We've had a really broad range of industries and projects, and key to that has been introductions out to our supplier network and other members of the Data Lab ecosystem to support that. And even just in the last couple of months or six weeks, since the turn of the year, we started a further four projects across now 13 industries, and we’ve had 27 introductions out into the supply chain. So we're really moving quite quickly on that. And just to give you an idea of the breadth of industry, so we’ve had food and drink, wellbeing, tourism, pet food manufacturer, the built environment, communications, and PR. So our focus has been working with SMEs within TORCH and we've had a broad range of those right across a real spectrum of different industry organisations. 

So, thank you for your time for that. I just wanted to give you a brief overview of what TORCH is about and where we are, but there's much more available on our website. On there’ll be links to that in the presentation at the end, which will be shared with you after today's webinar.

So, to move on to the topic of today: why are spreadsheets challenging? And I think, you know, everybody's worked with spreadsheets; this is a quote from an article by Hjalmar Gislason, (I should have maybe picked somebody with an easier name to say). 

The quote referred to by Rhona, shown on the webinar slide, is “Spreadsheets changed the world. They’ve revolutionised how business is done. Industries everywhere run on them.”

There's hundreds of articles out there about the challenges of using spreadsheets. They changed the world, they’ve revolutionised how business is done, lots and lots of companies run on them. But there's a bit of a downside. They’re definitely rife with errors. And even just probably a fairly rough estimate is that 88% of spreadsheets include mistakes and half of those have material defects. And all of those errors combined cost billions and billions every year. There's a number of absolute classics out there, and in a very quick internet search I'm sure you would come across them. So one being JP Morgan – which is known as the London Whale – it cost them $6.5 billion alone through having formula errors in a risk modelling spreadsheet and copy and paste. So they made a six billion loss and got a further £500 million fines because of that one incident. And these incidents that are known about, are the ones that are found – I'm sure that are many others that are never actually discovered.

Another recent example with the Edinburgh Children’s Hospital and the issues with the air flow in there, that was traced back to a value being wrongly entered in a spreadsheet many years before even the first bit of ground was cut on that building project. Recent reporting issues with Covid test numbers where a spreadsheet was used to gather test results, and it run out of capacity, and thousands of test results were not recorded as a consequence. I'm sure you've all come across some of those before.

We’re going to just do a quick poll, now. Jonny's going to drop up a poll just to get an idea of how many of you are using spreadsheets in your businesses just now.

Jonny: Hi folks, it's Jonny here. As Rhona said, the poll’s now on your screen, so please just take a minute or so just to cast your vote, and we will close the poll up in just a second. I can see around 80% of our audience today have voted, so thank you very much. Just a few more seconds, then we'll close things up. Just while I'm here as well, just a quick reminder, if you do have questions, you can click the raise hand button, if you'd like to come on over the mic. Or you can type your questions into the questions box, and Rhona and the team will be pausing periodically throughout the session today to take some questions. So please continue to raise hands and submit questions. But, we'll close this pull up now. And, Rhona, I'll pop the results on the screen for you.

Rhona: Thanks, Jonny. Wow, that's even higher than I expected so I’ll be interested to hear from the 10% if they use something, what they use as an alternative at some point. But that’s great, thanks. Thanks for that Jonny.

So, just to summarise, and Jonny’s going to drop a link to this article. This is a blog from the Data Lab website that was written for TORCH about excel spreadsheets, and we’ll drop that into the chat. You can have a look at that, and the link’s also here in this space.

But in terms of some of the disadvantages, you know, they are very vulnerable to fraud and changes, human errors, data quality. It's very, very hard in a spreadsheet to actually see what's changed, when a value has been modified, particularly if it's a value within a formula, or if something's just moved, and often these things can happen without you really realising it. You click into a cell in error and clear it just by hitting a key on your keyboard, and you don’t spot it happening at the time, and suddenly a calculation might be impacted further down the line. Sometimes the checking of the information that's required prevents you from making those quick decisions with real accuracy that you want to be able to make. 

The scenario that I'm sure is common to a lot of you: somebody creates a spreadsheet to send out by email, and suddenly there's multiple versions of that file, and people start looking at maybe making some changes. So which is the master one? Which is the one that you're going to trust going forward? There are multiple versions of that out there. 

And that ties into the next point which is that it doesn't really support collaboration all that well. It’s then extremely hard to consolidate and bring in – if you're using spreadsheets for data gathering – and consolidate that back together, unless the formatting is extremely well enforced within the setup of the spreadsheet. And it can be a lot of manual effort to try and consolidate information back together when you've got multiple versions. And then, I alluded to in the example with the Edinburgh Children's Hospital, there’s size limitations. Although they're significantly more than they maybe wait a few years ago, within Excel there's still a size limitation that as you collect more and more data, it can be very easy to catch up to it.

I'm going to hand over to my colleagues Margery and Iain who are going to take you through some common data challenges that we've found and discussed across the board, and we can dig into a little bit more about where spreadsheets can have issues around that. So we'll move on to the first one, which Iain’s going to talk through, which is about operations.

Yeah, thanks Rhona. Yeah let's just drill in a bit in the operational side of the business and focus in on some specific things that we’ve encountered, and it would be good also to get your feedback or more questions at the end of each section.

So, clearly, 90% of people are using spreadsheets. There’s tons of spreadsheets around, I mean it’s easy to be running 10, 20, 30 different tabs of spreadsheets everyday, that would be fairly typical. But there's no means to keep track of them. And version control doesn't really work very well because it's all dependent on each individual can choose to name or update things as and when they wish to. Data gathering into spreadsheets is typically manual, and there's no real means to operate or automate that.

How up to date is the data? There's nothing on the sheet that's going to tell you when it was updated, and no real means to validate that that is actually the case. Do you trust the data in the spreadsheet? Maybe if I built the whole thing myself, I trust it, but then do other people trust that same spreadsheet, because they don't know the processes that I’ve followed in order to build it. So there’s kind of a general trust type issue. Is it available where, when and to whom needs access to the spreadsheet? Is it 24 hour a day access, or, indeed, are you all editing it at the same time? Because that's often some of the specific problems that have happened, multiple editors at the same time.

All of which leads to a kind of – does this spreadsheet actually fulfil its purpose, which is to give you the data to drive the operation? My experience is that many people have the spreadsheet that they know they're supposed to use, but the reality is they don't trust it that much. Therefore, their decisions are often based on gut feel and not data driven.

So shall we pause for a minute and see if there's any questions or thoughts on that from the audience on that one?

Jonny: Yep, thanks, Iain. I can see there's some questions coming through just now. Joe has said: could I ask how the 88% is calculated or known? I'm not sure what 88% that is.

Rhona: I think that 88% is probably from the from the quote. I think, if you look at the link for the article, it does give some explanation as to how they calculated. That was a quote from the author of that particular article, but I've seen similar numbers in a number of different. It's usually varies between sort of 75 to 88% but I don't know off the top of my head what the specifics are.

Jonny: Super. Thanks for that. And another one just come through here from Alastair, not Alastair Semple, a different Alastair. Does Iain think that 365 (and I'm assuming that's Office 365) addresses a lot of the problems associated with standalone spreadsheets? So, I guess, Alastair’s asking about the kind of collaboration features that Office 365 maybe offers. What's your thoughts, Iain?

Iain: Yeah, I think that's a big leap forward in terms of that collaboration and version control type thing. But I also know lots of businesses that use 365 that didn’t really change the process from what it was before. So people can still download and do their own thing. So, I guess I would say yes, if you set up your Office 365 very robustly with SharePoint, yes, it would certainly help, but certainly my practical experience would be it doesn't really fix the whole problem.

Jonny: Super. Thanks for that and Alastair’s just followed up to say thanks, Ian. I think that’s us on questions just now, but again, folks, you can submit questions anytime. You can type them in or you can raise your hand. But for now, I'll let you crack on, OK?

Iain: Margery, do you want to take the next one?

Yes great, thanks, if you could move the slides please. Thank you, Rhona. So just picking up on those points that Iain was making – the thing about the supply chain, which is certainly one of the areas that a lot of businesses will see challenges in, is that as soon as you move across to look at your supply chain, by definition, you're talking about an area where as well as multiple parties wanting to share facts in your own organisation, you're obviously going beyond the organisation. So that increases that challenge and adds an extra dimension to it. And that is where you tend to see people sending spreadsheets backwards and forwards between organisations, which is just adding a whole new dimension to that unpredictability of some of that data.

A supply chain’s dynamic by its very nature, whereas a spreadsheet’s a static thing. And sometimes your supply chain spans more than one geography or you've got potentially things like different currencies in it and even data formatting can be error prone if you think about things like the dates that people record, and even extending as far as language challenges, which people then have to know what the fields are entitled to be able to use them properly. 

So a shared real-time record is much better than people needing to check and follow up progress, reaching out to one another to provide updates and see what's happening. Particularly when you look at changes that might need to made on the move – people using mobile phones or using handheld devices – spreadsheets aren't really a natural medium for them to be handling that kind of information and passing it around and keying it back in.

So any process that spans multiple locations, you'd be hoping to have much more automation and much less manual handling of information that passes around. You might want to know, where is your product in the process? And obviously, when you're in manufacturing processes, you quite often have that kind of ‘just in time’ ambition, so you don't have time to wait for spreadsheets to be updated. And there's a lot of the technologies that are now becoming much more prevalent, where you've got RSID tags, or logs, or barcodes, so the kind of data that doesn't naturally sit in a spreadsheet and wouldn’t naturally be captured by it.

So that's when you start to see a desire to be much more integrated, and much more automated in the way data is moving around your business. Really the hidden logistics that would be concealed in the spreadsheet if you use that mechanism to record them.

So if you're looking to get that trust and traceability and accountability, perhaps you want to part-pay contracts for part-completion, your really need a much more resilient and a much more auditable format than you're going to get from spreadsheets. And when you look across your wider business, you start to see a lot of integration in the other systems that you'll be using. So, for example, your finance systems, stock systems, that can now integrate with each other and talk to each other. So taking information out and putting it into spreadsheet and passing it around manually is not really taking advantage of some of those developments moving forward. And there's a lot of potential in moving towards cost savings and much more predictability in your business if you start to move beyond spreadsheet world for managing those types of aspects.

Any thoughts or questions on that one?

Jonny: Thanks, Margery. Let us quickly see what's coming through. There's quite a few coming in and one here from Alex that says: having robust processes and procedures, protection and accountability can all contribute to positive attributes for using Excel. Controlling the environment for editing can help negate much of the negativity highlighted thus far. I am, however, interested in what you’ll be demonstrating as positive alternatives to some of these kind of data challenges. I suppose that's more of a statement, Margery – anything to add to that?

Margery: I think it's perhaps just that word ‘editing’, and this is my interpretation of that, implies that somebody's having to do something manual, which is where that perhaps efficiency, is not going to be the same. You're going to have those dependencies on people, making the changes, and updating to the data. That would just be a thought on that one.

Rhona:  I was just going to add to what Margery has said, there’s a quality perspective, as well – as soon as there's a manual entry, there is a risk of error. And depending on re-typing information and so forth. I hope we're not coming across too negative – we saw with the first pool that are many, many people use Spreadsheets, so what we’re trying to say with TORCH is that there’s now viable alternatives out there, and consideration should be given to it. But we're not expecting that everyone will be chucking their spreadsheets away after they come off this call today. It's going to be a long journey, but there is strong reasoning why you should certainly be considering it.

Margery: Yeah, if I could just pick up another example. I think, that the data types is quite important – if your spreadsheets are including quite a lot of numbers, data, calculations, then that might serve the purpose adequately for what you're trying to do. But we're seeing so many more types of data now in manufacturing. Even just thinking of it was a simple supply chain – something's been delivered, a photograph’s been taken or a barcode has been coded. And typically, you're not storing that kind of data in the spreadsheet. So, if it's been stored somewhere, then you start to need to build correlations between those sources and your spreadsheets, and spreadsheets typically don’t have these other data types in there.

Jonny: Absolutely. And just some more general sentiment coming through. Someone just touching on what Iain had mentioned earlier about people still use an old processes, which is maybe still part of the problem. And another one coming through here, saying that their experience is often that the design of the actual spreadsheet is what's the problem, rather than the data entry. It maybe causes people to enter the data incorrectly if the design isn't there. So just some of the comments coming through just now. I think we've managed to get through them all. So again, folks, if you want to keep those questions coming, you can type them into the questions box, or if you want to come in over the mic, you can raise your hand. But I'll pass back to you for now.

Margery: OK, thank you, over to Iain then. 

Yeah, just one further point on that previous slide. Data volumes are growing exponentially and have been for 10, 15 years. So that's the other thing to bear in mind that spreadsheets are built for a certain volume, which is not necessarily the volumes that we have at the moment.

Anyway, so, yeah. Let's touch on some issues that have emerged in the customer area, so this is my bread and butter for most of my career. So when it comes to looking at customer life cycle – so acquisition of customer, retention, development, customer engagement – the metrics that you need for analysis are typically metrics over time. And static spreadsheets are really just not built sophisticated enough for the kind of metrics that you need. You might do your analysis somewhere else and log it back into a spreadsheet. But the spreadsheet itself is not sophisticated enough. And I think that's a common theme in the customer dimension. So when it comes to knowing, and understanding your customers – and that typically means data mining, pattern analysis, that kind of skill set, that kind functionality – again, the spreadsheet is not built for that. It's just not sophisticated enough.

Likewise, when you need to understand ‘what the customer product, relationship what’s the customer loyalty, what’s that?’ ‘What's driving retention, what's driving lifetime value?’ - again, these are typically more complex calculations with many more inputs than a spreadsheet can handle. Likewise, when it comes to customer feedback, customer research, the problem tends to be in a spreadsheet, the feedback is no actionable. And I like to tell me the answer to some feedback, give me some feedback, and let me action that feedback within 1 or 2 clicks. If that feedback is coming in a spreadsheet, that's typically 5, 10 clicks rather than 1 or 2. So that's the issues I see in the customer dimension.

So are there any questions or comments on that side of the house?

Jonny: Let's have a quick look through. Serena says: do you think spreadsheets can still have a place for data, or will they be obsolete in future?

Iain: I don't think they'll be obsolete, I just think they're morphs into horses for courses. They are very good for some things; quick calculations and summaries and reporting. I don't think they should be run as a database, essentially, because as we'll come on to shortly, the alternatives are becoming much better, much cheaper. So I think they will find the natural home over time.

Jonny: Super. And Andy and a few others in the chatbox, are quite keen to hear about some of those alternatives. I assume they're going to be coming very shortly, so hang fire guys, and I'm sure we'll get onto them shortly. But that seems to be it for now questions wise.

So this is picking up on a theme that is probably very topical within the manufacturing sector because it really picks up on that whole theme of the industry 4.0 that Alastair mentioned at the outset. And I think this is perhaps one of the key things to bear in mind as we have this conversation, because the kind of ambitions that these industry directions are very much about having all that data and having collected it, and having been collecting it for some time. Obviously, if you want to start thinking about having actions and activities informed by that data, and increasing automation within your business, then that's exactly where you start, having to collect the data almost sooner, rather than later. So the data that's in your spreadsheets will have value. But it could be the foundational value for some of these things that you're going to want to start doing moving forward. And we'll be talking about that.

So that journey towards the Industry 4.0 kind of model is very much reflecting that need to do things now and plan ahead for when technologies and other capabilities are going to do all of that work for you. And then, the comment you made about how spreadsheets will fit into that world, I think you'll see that there are things you start to do with spreadsheets that start to tell you that maybe you want to think about taking some of that work and putting it into other tools. So, for example, you start to a lot of pivot tables, and you've got lots of different versions, and 1 or 2 people in your organisation who really understands the spreadsheet, and nobody else does. Then you kind of start to see a risk associated with that, from a business point of view, that you might want to look to maybe be reflecting some of those activities in a different way.

But when it comes to the predictive maintenance side of things, so the whole model here is about starting to capture enough information to be able to predict what's going to happen, so that you can plan your maintenance schedules around anything, whether it's part of your manufacturing process itself, or whether it's assets that you've already sent off to customers and you're taking care of in the medium term for them. So, instead of turning up when something breaks, you're looking to actually predict those things and the model there is to start to learn from the data that's being generated and start to see patterns in the data, which tell you ‘ah, it's going to be good to be able to do that on a particular schedule, we're going to get less breakdowns and more responsive systems and potentially we're going to be able to have more efficient running of our machinery when it's out there, if we put this work upfront’. So you're looking to schedule and plan things in, by taking all the data you can, understanding what's happening and seeing the patterns and failures around the environment that you've got.

So, adopting some of these approaches actually takes you even further down the line of thinking about a pay as you use model. So if you've put machinery out onto your customer’s sites, you can say, well, you will pay as you use that asset. And a company like Rolls Royce, for example, they now actually have customers who pay as the fly for their engines, and they never buy them as an asset. So it's quite a radical way of looking at the way that the business operates and runs, but it will all depend on collecting that data and having it and being able to inform your learnings as you go along.

On the Data Lab resources that we've got on our website, you'll see there's a number of case studies that go into quite a lot of detail about this, because obviously it's a topic that you really need the use cases to be able to see how it might apply in your business. And there's a number of top names on the website, we've got case studies with Queensferry Crossing, so the link will be put into the chat area, and you can have a good look and see some of this coming to life with organisations with quite high names, people you'll recognise. And that is really the fruition of gathering much more data, integrating things together, tracking it with sensors, and the kind of world beyond spreadsheets, for some of the processes that you might be doing today.

Any questions on that? Or thoughts?

Jonny: Let’s have a quick look. Nothing seems to be coming through at the moment. Let's just see if anyone's got their hand raised. No hands raised at the moment, so I think we're good for now, Margery.

Margery: OK, so over to Iain then.

Yeah, so this slide touches on insights and reporting, but maybe I'll just skim this quickly because I think there's an appetite to talk about the solutions. So insight and reporting, the issues tend to be if I'm trying to articulate what happened, why did it happen? What might happen? They’re just difficult to do in a spreadsheet, because the sheet itself is not sophisticated enough to deal with that type of data. Likewise, predictive analytics is better done elsewhere.

Why don't we just jump to the next slide, which is the solutions. So when it comes to solutions, how does one move beyond spreadsheets? Well, as Rhona mentioned, no one’s going to throw out all their spreadsheets instantly after this call. But the general themes that are emerging are upskill, move beyond just that one person in the business that understands pivot tables. There’s many hundreds of courses out there and the Data Lab runs several which we’ll point to in the slides that we send out. So build up the skill set so that there's greater levels of confidence around data and what you can do with data and how you manage data. This will help people move beyond that which they feel comfortable with, which is the spreadsheet.

The key dimension that's really happening is the migration to the Cloud. So migration to cloud started, say, 10 years back. It was scary, complicated, not that robust – but over time, the costs just keep coming down and down. And what's available to migrate to is going through the roof, essentially. So a migration to the cloud is now much less complex, and much less expensive than even two, three years back. There's more or less an application for everything. If you just do a search on an app for whatever – expenses, asset tracking – you're going to find options that you just wouldn't have found 2 or 3 years back. And these applications that you either buy or build yourself (we'll come back to that in a minute) will typically have an API that allows you to automate data getting pushed into the analytics repository, or pulled from it. So much more automated in the standalone system would be.

Just to give you one example of that, then. We have one client at present that had a big spreadsheet of all their products and all their components that fitted it into different things that they sold. And that spreadsheet had become unwieldy over time to the point where they literally did not use it. And then it just sat there, without being updated. We're working with them at the moment to migrate that into their CRM system, which, in that case, happens to be Salesforce. It doesn't have the be Salesforce, they all work in roughly the same way. So, essentially, they have a table within Salesforce for each of the tabs that would have been in there prior component spreadsheet.

So it can be Salesforce. It can be Microsoft. It can be Dataverse, Power App, Power Platform – multiple platforms that are emerging. Or, indeed, you can build your own on Amazon Web Services, or Microsoft, or Google Cloud. So those are just hundreds of options emerging now then. Or ultimately, if things become bigger and bigger for you, build your own data platform.

So again, that's a much more valid option than it would have been even 2, 3 years back. You’re unlikely to go straight there from a basic spreadsheet, but over time, dedicated data platforms, again, are running on AWS, Microsoft.

So well, that's the kind of solutions that we're seeing at the moment. Was there any questions or thoughts or feedback on that?

Jonny: Some interesting points, being raise there, Iain. Just a question that's come through from, and I hope I'm pronouncing this right, Justinus. It says: what do you think about Odoo? Now I hadn’t heard of Odoo before, maybe you have, Iain.

Iain: It’s a good open source CRM, ERP type thing. That's one of several very good options.

Jonny: But like you say, most of them tend to work in the same way anyway. So I suppose there's many answers and solutions to moving beyond the spreadsheets, as you say. No more questions coming through at the moment so we’re just going to pause. If you do have a question, folks, remember, you can type it in or you can raise your hand. Nothing else coming through just now. We will gather any final questions as well just before we finish the session. So maybe we can do a quick sweep up of any final or missed questions, but I think for now that’s us.

Thanks, Jonny. I just wanted add to what Iain’s said in terms of the options out there. There's an option for everyone. There's an option that will meet a price point and will meet a level of capability. There's a big move towards, as Ian mentioned, some of the tools, like a Power Apps or Power Platform, what they call low code. So much more you’re plugging different components together, different applications together, and they integrate far more easily than they ever have and that allows you to have your data much more united and linked together to enable you to work with, so that you don't have separate pots in different spreadsheets or different systems across your business. And it's in bringing that data together we see the greatest value can be realized. And we're definitely seeing that with the companies we're working with so far with TORCH and with the one example that Iain gave there – translating that spreadsheet is now going to be much more accessible and much easier to keep accurate and up to date, and used to support the business going forward in terms of those information sets. 

That's great. Thanks so much, Jonny. We've got a final poll, and then we'll move on to wrapping up. So if you want to share the final poll.

Jonny: There we go then, folks, the final poll on your screen just now, hopefully. Has what you've heard on this morning’s session changed your thoughts about spreadsheets? So, nice and easy, yes or no. And we'll just hold that pull out for a few seconds, just to let you cast your vote. I can see that 70% of you have voted already, so thank you so much. A few more seconds, and then we'll share the results.

OK, so just short of 90% of you have voted now, so we'll close this one up, and let's share those results then. So Rhona, over to you.

Rhona: Thank you, Johnny. So, a few folk have changed, more folk haven't changed their thoughts. Maybe they were aware of some of these things. We probably haven't mentioned anything that's completely groundbreaking today, this is something that's been talked about for quite a period of time. I think what has changed is it's now much more realistic to use alternatives to spreadsheets that perhaps weren’t there, as Iain and Margery talked about in their specific examples. But that's great, thanks very much, Jonny.

Just to finish up, and again, we'll share these slides with you after the event, all the contact details. Lauren’s, our first point of contact for TORCH enquiries, so if you do have an interest in finding out more about TORCH, please do get in touch. And there's lots of stuff on the website – we've now got some case studies up there from the projects that we've already done, and there’ll be more coming in the next wee while.

But I would just like to thank you all very much for attending today. It's really been great opportunity for us to talk to you this morning. I'll hand back over to Alastair now to close off. Thank you.

Thank you very much, Rhona. I thought it was a great talk and thank you for all those points. It's funny when you were mentioning all those people that within a business are the 1 or 2 that know what to do with a spreadsheet. And I've previously been one of those people myself in a project doing the V lookups and the H lookups and your pivot tables. And I've previously had experience of one of the low code and no code applications, and I just found it amazing. You could always do it in Excel, but you would be the only person who could do it in Excel – and as soon as you gave it to someone else, it would break some of the things that you were able to do.

And it's just making, I think, some of those tools – and also Power App, which is in Office 365, there is an alternative there. And perhaps we spent so long, I know in my case, figuring out how to use Excel to the best ability, it can be sometimes a bit difficult to look beyond that. So I know, personally, I was there, but thank you ever so much for the great topics and great content today. The Data Lab have shared their slides with you, and if you have any specific questions that you haven't been able to put down, as of yet, you can put that into the feedback forms that will be sent out after the event, and we'll make sure that those questions get back to the Data Lab.

So, again, just final point to say thank you very much, everyone, for attending.

Jonny: Thanks Alastair. Just to re-iterate on Alastair’s thanks – a big thanks to all of our speakers and our audience for joining today. We will be sending out copies of the recording as well. So, as I said, the entire session is being recorded, if you want to come back and rewatch the session in your own time then please feel free to do so. A link will be sent to all of you who have registered for this session and feel free to pass that on to any friends or colleagues that might benefit from seeing it as well.

As I also said, feedback surveys will be sent out and launched after the session. So please just hang on the line for an extra few minutes and give us some feedback and help shape the sessions as we move forward. And you can find more webinars upcoming on the webinar page of Scottish Enterprise. And hopefully we can have you on another session and the near future. But until then, stay safe, and we’ll see you very soon. Thanks very much.