Bhavesh Vaghela: Hello and welcome to Ops Game Changers, the podcast that shines a light on the crucial role that operations teams have in driving business performance and improving staff and customer experience. My name is Bhavesh Vaghela. I’m the Chief Marketing Officer here at Active Ops, and I’m delighted to be your host. Now, in each episode I hone in on a KPI or a challenge that a business has solved. We delve into how they went about doing this and we explore the outcomes achieved. Now, today I’m joined by Ben McManus, who’s the Associate Director of Performance and Planning over at Fidelity International, to really learn about how Fidelity embarked on a strategy to become more data centric. Now, Ben’s going to tell us about how they went about building a central data environment and the impact this is now having on operational decision making. But before we jump into topic. Let’s welcome Ben to the podcast. So, Ben, welcome to Ops Game Changers.
Ben McManus: Hi Bhavesh, thank you for having me.
Bhavesh: So, before we jump into the topic, then it’s probably worthwhile just giving yourself, giving our listeners a little bit of an introduction to yourself and, more importantly, how you actually ended up in Ops.
Ben: Sure. Yeah. So, I spent 13 years. I joined the Air Force when I was 19, spent 13 years in the Air Force and once having children, I quickly decided that I probably should reset my life. So, I looked at leaving the Air Force and I had really good opportunity at Legal in General and I joined the training team and ran the training team, at Legal and General and started to really kind of make some improvements in there and got an opportunity to go and work in the change function.
Bhavesh: Okay.
Ben: So moved into their change function, and which was in the insurance division and then, after some time, had the opportunity to come work at Fidelity, and that’s where I’ve landed now.
Bhavesh: That’s fantastic, I mean, I guess there’s probably a different podcast. In the synergies between the air force and operations. I’m sure there’s probably loads of synergies in the sort of things that you were doing back then. But that’s for, I guess, for another topic, for another day. So, I guess, for Fidelity, like most organisations, they’re trying to become more data centric. But before we delve into that element of it for our listeners who perhaps don’t know enough about or don’t know about Fidelity International, perhaps just tell us a little bit more about Fidelity International and what you guys do.
Ben: Yeah, sure. So, we’re a privately owned firm and we give investment and retirement advice to individual investors and their advisors, and also institutional clients as well. We’ve got about 2.8 million customers. I think we look after something like 586 billion pounds worth of their assets. So, it’s my job to make sure that we take care of that.
Bhavesh: And from an Ops perspective. What’s your team and your role in delivering that end customer experience?
Ben: So, the operational teams have kind of really gone for a big transformation at the moment they used to be kind of two separate divisions. We’ve recently merged into something called GPS (Global Platform Solutions), where we brought the workplace investing side in with the personal investing side and we’re all looking for synergies and working together and now my role is in a central team in the CAI function and we look after the entire group. So that’s about 2,500 people.
Bhavesh: Wow! Fantastic. So, listen, this is such a timely topic. I’ve had the privilege of talking to lots of Ops leaders like yourself all over the world. the idea of, or the topic of data and becoming more data centric is such an important pillar and from an operations perspective, especially, when you start thinking about what’s coming in terms of AI and you can’t really do a lot of this stuff without having a data environment, data infrastructure. we’ve just done some research in the market and, some of the numbers are quite scary. I mean, the headline numbers are 95% of Ops leaders are really not using data in real time, which is quite crazy when you think about an operational environment. I’m certainly thinking one of the stats in the UK is something like 90% of ops leaders struggle to and recognise that it takes a lot of effort to get any insights out of that data. So, it feels like Operational organisations are probably a little bit further behind in using data more effectively. So, when you think about the journey that you’ve been on in Fidelity. If I kind of wind the clock back, what does kind of being data centric mean for you and operations?
Ben: So, I guess for me is that we use it in every single facet that we can. When I joined Fidelity. One of the things that I know straight away is that they were very customer centric. What that actually led to is that they were focused on the kind of the retrospective things that have already happened and we all used to joke that the data that came out was a lot like driving the car facing backwards. You know, some of the some of the data was 45 days old, and it was really hard for senior leaders to make priority calls. The operational guys on the ground were spending a lot of time trying to manipulate the data to make sure this it was a true and accurate reflection of what happened. To me that was kind of one of the biggest problems that we wanted to solve straight away and that, and that was where we that’s where we embarked on our journey.
Bhavesh: And was it, was it at that point were you thinking? Okay, I’ve got to make sure this data is available for my Ops leaders and the customer facing folks to make better decisions. Or is it just it was a bit of a mess you just need to get a central view of understanding what you had? Perhaps, kind of paint us a picture of what it was like.
Ben: So, when I joined, I started having lots of different conversations with all the leaders. So, I was just acclimatising to the company, and one of the things that I guess, shone through was that in the individual pockets and individual teams they had a really good understanding of their, their backlogs, the work that was coming in, how they’re getting through that, and they had their own measures.
But then, when you start to look at that aggregated across the company. They were using different standards, they were using different tools when that then came up to the senior leaders, they were having to do it a lot of analysis themselves. that’s why we were creating a lot of presentation packs and things like that to really try and put all this together into tell one story. They weren’t talking a common language. So that was the big problem that I was trying to solve for, I guess.
Bhavesh: And what was it, you know I was at this view and impression of speaking to many customers and many Ops leaders, and it’s everything, from lots of spreadsheets to post-it notes and different ways of just tallying stuff up is that does that sound familiar in terms of things that you guys had?
Ben: It absolutely does, and it was. Everyone is doing their absolute best to try and get the outcome that everyone wants, but it was just inefficient, essentially. It was not a good use of everyone’s time to have to track it separately to interpret the data so that the senior leaders can make their decisions off of it. They needed something that was that was live and, in real time to just avoid that whole concept of driving the car facing backwards.
Bhavesh: And I’m just curious in terms of internally did the organisation recognise that they had that challenge? Or were they just because I get the feeling that when you talk to Ops leaders, there’s a there’s a there’s 2 groups, one where there’s generally a lack of investment in across ops generally. But there are some organisations that recognise that, and others just carry on the way they’re going. Was there an internal recognition that this needed to change and be and be solved?
Ben: Yeah, there definitely was and I think one of the things that Fidelity likes to build its own tools and its own systems. So, there was a whole culture around what we build stuff ourselves so to try and solve, for that was almost, one of the it was a huge task, and it was quite daunting for a lot of the leaders. So, when I came and tried to take in that problem statement and do something about it, you know, there was quite a lot to be able to take on, and every single one of them signed up to it because they said, you know, if you can solve for that problem then count me in.
Bhavesh: Yeah, totally. I think, yeah, as you say, once, you can demonstrate what good looks like and what the power of better data management looks like then, hopefully, you get people coming on the journey with you. So how did you start? You obviously created a problem statement. You started to go in front of different stakeholders. Just kind of talk us through how you got I guess the organisation aligned to want to do something like this.
Ben: So, we understood the problem statement and as with all change is best to start with someone with an open door, someone who’s willing to jump and be first. So that’s what we did. We had some teams that were based in the UK and India. The senior leaders were really bought into the idea in the concept, and they were the ones that actually had some of the self-built tools already. So, we had a really good baseline to be able to compare our information with them. But then, also, we purposefully chose a couple of teams that didn’t have any kind of solution in there who were just tracking things on emails or spreadsheets because we wanted to see if we could prove both cases out and we did. We rolled out for a kind of proof concept. I think it was around 150 people to begin with, just to really see if it would work and within 12 weeks, we had really usable data. We had data that we could trust that was validated all the way across the way. We had a whole planning suite that we were able to use to make better decisions, forecasts that we could trust. So yeah, it was it was a really successful proof concept and from that we were got all the funding approved, and we were able to progress for the rest of the journey.
Bhavesh: It’s a really, it’s really interesting. A few comments you made that was at a conference just actually yesterday. You know, a business transformation and a banking transformation summit and the commentary yesterday was all about, unsurprising, AI and data. But also trust. So, the idea of, can you trust the data that you’re generating? So, it seems like that within a short period of time you were able to prove within the organisation that you can trust that information. Did you ever find that a hurdle or was it because they were so far behind in the data, they had that once it got into the systems and it was more automated, it became more trustworthy? What was that journey like?
Ben: Yes, so trust with trust with all data, especially in the in the kind of planning teams that that we’ve set up in any planning team. I’d say if your data isn’t accurate that instantly you’ll lose the trust of the people that’s trying to use that data. So for me, that was like number one, we had to be able to trust it and one of the really important things is that the people who are validating the data, making sure that it’s accurate and a true reflection of the work that they’re doing, they take ownership of that because ultimately t’s not going to be for me or any of the senior leaders to go into the team level and try and quiz or question that data on a day to day basis. Yeah, so any discrepancies just have to be ironed out
at the ground level. They need to own it and they did. They were fantastic and the guys really took responsibility for it. They understood the big picture of why we needed this information, how it can help them on a day-to-day basis, but also how it can help senior leaders planning and prioritising for further horizons.
Bhavesh: And I guess, Ben, in some ways what you’re describing there is, if you can demonstrate to the individuals the importance of creating the accuracy and the data that’s going to help them in their roles moving forward, then I think you’re right. People take ownership of that right? Because sometimes it becomes a chore, doesn’t it? To make sure the data is correct, and you type in it into your systems and all the rest of it at that front end. Was there kind of penny drop moment, or was there something that you did that you can look back at and say, well, that’s the thing that tipped it over the edge?
Ben: I think it’s making it useful. If we’re creating data for data sake, that’s when it’s not useful. They won’t want to do it. They won’t buy into the idea and, things will just peter out whereas actually, if they’re using it on a day-to-day basis, they can see the value that it brings, and it’s and it’s turning the dials. That’s probably the biggest thing. Let’s be honest, they’re making decisions, and they can see how those decisions play out good or bad, and they can adjust. That’s the bit where it becomes valuable for them, and you’ve got them from there.
Bhavesh: Yeah, I think I think you’re absolutely spot on. It’s probably one of the biggest challenges with going down a data driven culture is convincing everybody the importance of it and I think you’re right. Once you have some quick wins, and people understand that it actually moves the needle, whatever that needle is for that organisation. Then all of a sudden it becomes part of the culture, doesn’t it? Becomes part of the DNA to become very, very data centric. So going back to the kind of journey you guys went on, so you’ve done the POC, and you’ve kind of proven it out, and you’ve got the funding. But technically, you’ve got, I would imagine you can describe it better than I would, imagine you had silos of data your data in different formats, different ways of collecting it. Different ways of understanding measurements. How’d you go about consolidating all that stuff and kind of journey look like?
Ben: So, we’ve got data in in all kinds of conditions. You know, we’ve got
teams that are taking their workflow in from emails which then transform onto our workflow tools we’ve got workflow tools that are directly controlling the processes. We’ve got multiple workflow tools. So, you’re right, all of that kind of stuff has to come together again to talk a common language, and that was one of the biggest challenges that we wanted to face, and I get on top of all of that. We didn’t want to impact the guys too much. So, they’ve got enough on their plate that they’ve got work to be getting on with. The last thing we want to be doing is adding additional touch points. So, for me right from the outset talked about at the conference last year, integration was like number one for us. We had to take all the bits of information that we had across all of our workflow tools and get it into one single source. So, we were fortunate enough to have a data lake which most of this kind of information comes into. So that’s anything from our process data to our workforce, their holidays, their leave, their sickness things like that all into one into one place. and we had another tool that was able to transform that and be able to pass it back. So, all the different systems are all talking together and where possible we had, if you click for a piece of work, that it’s being done. We know that it’s being done. We don’t need somebody to tell any kind of systems that it’s being done additionally removing touch points from people where possible trying to optimise the whole process and to be fair it was a challenge. There was lots of hurdles along the way, and I think one of the things that we learned from the outset was yeah, and it kind of came naturally. We couldn’t get hold of some of the some of the transformation tool developers for a period of time. So, we had to go live manually. So, we went live manually and then after those guys freed up we were able to then talk about the integration and the automation of that things. But what we kind of discovered really quickly on was that that was a benefit. Being able to have something that was recorded as individuals have recorded it, and then have something to be able to compare against how our integration and our automated solution was then working was really valuable. Goes back to some of this stuff that, we would teach guys in the military if you’re doing map reading, yes, you’ve got GPS, but learn how to do a compass. Because if the batteries yeah.
Bhavesh: It’s trouble, right?
Ben: You need to know where you’re going. So, what we’ve actually employed is that all the way along the way is, if we’re going to use and the integration we go live manually first, turn on the integration, later, the guys feel the difference as well. So again, that’s another kind of plus for them. But we have 2 different data sources that we can compare. Yeah.
Bhavesh: So, I mean, there’s a couple of things that that that kind of strike me in terms of as you’re as you’re describing it. In in a normal operational environment, you’ve obviously got the Ops function and then you’ve got the tech IT function within most organisations, and I know you’re kind of more in the CIO group.
So, a lot of Ops leaders’ kind of struggle to get airtime with their IT folks to kind of invest and do things differently within operations. So, they kind of struggle with what they’ve already got. In this case of Fidelity, was this thing driven out of
the CIO organisation, or was it driven out of Ops? How did that kind of politics work for yourselves?
Ben: So, for us, this was an operational problem and at the time it was driven from operations which wasn’t a traditional route. Usually, these kind of these kind of projects come from a technology team that kind of sponsored in from a wider improvement team. So, for us it was around gaining the sponsorship at the top level so they could have the conversations and, personally, we have to have lots of different conversations around because it was completely new to me. Onboarding a tool I had never had to do before, but I had to learn all the different processes that people would have normally gone through. So, it was a huge learning journey for me. But the guys, the guys at Fidelity and all the different teams they were great. They helped me through it. I had lots of people that I could lean on around the organisation to help me guide, guide me through that process. So, it wasn’t completely painless, but the due diligence and the right things that we had to do to get it over the line. You know we did that.
Bhavesh: Yeah, I don’t think it’s, it’s never as easy as it sounds right. These things are always quite a journey, particularly when you’re talking about data and merging data together and giving it a single view. We talk about single view of operational data. So now effectively, you’ve created that single view of operational data that allows you to then base decisions off the back of that right? So, if we if we kind of fast forward to what’s happened since. So, you’ve got that single view. So, what’s been the impact internally, what are the things if you take a bit from a point of view of your colleagues and operations. How has that impacted them?
Ben: So, the things that we’ve kind of seen across the board we’ve got the day to day running of the operational teams is now kind of forward-looking. It was always retrospectively. You know, we’re always looking back, and we were saying, you know, what did we do? That whole conversation is now completely shifted into, you know these are the things that we can see coming over the horison. How do we best plan for that? So, it’s a really nice dynamic and shift in in that conversation, because you’ve seen the things, trying to pre-empt, the things that are going to come and give us a challenge. But having all the information in one in one in one solution is fantastic. You can scroll up all the way through the organisation, aggregate it to an individual level all the way up to the top of the organisation, having that single view, that single language, and what we found is the teams, you know, they’ve been absolutely fantastic. I think about a third of our Operational teams now are able to flex resources between each other to optimise. So, we’ve been really sensible about how we do that. It’s also been a great learning opportunity for a lot of people, because they’re not just in one team. They’re able to kind of look out and support. So, it’s created kind of alternate career paths for people. So yeah, it’s been fantastic and for the senior leaders. Again, we’ve started to integrate that into as many decision-making processes as we can. You know the headcount planning, the budget process, and pro project sign off. So again, that’s a whole other angle that we struggled with a lot was that an initiative was put in place. It had a business case, we’ve delivered that solution and then we would find it really difficult to prove the benefits case of that of that project and there was always this kind of operations don’t feel the impact, technology say it’s been delivered and, there was that that kind of conversation going all the time. But that’s now completely gone. We’re able to say we did this process improvement. We think it was going to save X amount directly, and we can tell them the exact date, or we can. You know, we can say it didn’t do 5 FT, but it did 3. That’s how we’re feeling it. So, we can have that conversation. They’re doing that all levels now as well.
Bhavesh: That’s amazing and I guess one of the things that seems really basic, Ben, but having a common language or common lexicon across Ops must make a massive difference, right? It sounds obvious but, as you said before, there was different views and different ways measuring stuff and it’s just a simple thing like that just transforms how everybody engages. Is that a fair statement, I mean, do you see that?
Ben: Yeah, absolutely. You know, we have productivity, utilisation, effectiveness. We had all these different kinds of conversations going on, and different interpretations of the same measures. So yeah, being able to just talk one language, simplify things and make the decisions required at both of those KPIs.
Bhavesh: Do you think that or are you still? I’ve assumed this is still an ongoing journey, anyway, Ben. But there’s almost a feeling that what you’re saying is you went from kind of this view of people making decisions from gut feel and experience, and just what they knew at the time into now being, making decisions based upon the reality, in fact, of data and what’s actually happening in the business, and that’s a massive transition from where you were to where you are now. So has that had an impact so obviously had an impact internally from an operations point of view, and the savings and all the things that you’ve talked about have you seen a knock-on impact on the engagement with a customer?
Ben: So, what we’ve seen is because we’ve created. Now, we’ve been able to demonstrate and create more operational control, the kind of the ebb and the flow of seasonality has just become a little bit more plateaued. So, our service levels is service levels for us is always one of the key focuses because it just creates if we dip on our service levels that just creates problem downstream and complaints and things of that is the last thing we want. So, for us, it’s around that operational control. What we’ve then seen and what I guess the customers might may not have directly felt, is that because we’ve got that broader spectrum of skill set, we’re able to control the backlogs more. We’re able to control our overtime and things like that. So, for customers is probably not a direct impact that they would necessarily be able to pinpoint. But we can see a broader level that it’s improved things drastically.
Bhavesh: Yeah, that’s a that’s a big, big kind of step forward, I guess. So yeah, when you kind of look back. Now, I think I think you’ve been on this journey for a couple of years, maybe, and if I remember rightly, if you when you look back at this journey things that you’ve implemented. What would you say if you’re going to give advice to somebody that’s going down this path that needs to become more data centric from an Ops point of view. What are the sort of things that you would recommend? What things would you point to other things that you must do; I mean in in order to get to where you are today?
Ben: Probably I would focus on that integration point because you kind of don’t realise the benefits. You know, some of the some of the teams won’t necessarily know how lucky they are to have that I know I was speaking to different customers
at the Conference last year, where they haven’t integrated, something so something simple, like having to update your holiday. You know, we have tools, most organisations have tools to track that kind of stuff, and just having to do that twice is just let’s be honest, a waste of everyone’s time. So, knowing that stuff, knowing it in advance as well is super helpful. So, I’d definitely say, focus on the integration because it just saves so much time and it just creates a much more scalable environment, for when things change over time. Then probably just make sure that you’ve got the senior sponsorship is any kind of any kind of change. The senior sponsorship is really helpful, because you will face challenges within teams. I think every single team that we’ve gone into has told us how unique they are and how challenging this will be. You know, you guys probably find this all the time, how unique they are, and how impossible this task is going to be but just giving them that space to almost vent and then being able to take them on the journey to be able to say, you know, you are special. You are unique but rolled up ultimately, it’s all numbers, and it all averages, and would having more accurate, effective planning, be beneficial to you, the answer is going to be yes across the board. So just taking people on that. And then, like we’ve done, don’t just focus on operational teams. Kind of look wider. Be really experimental with it. You know, we’ve put it into our budget process, we’ve put it into our headcount tracking process. You know, the top of the organisational level. We’ve doubled down on some of the kind of metrics that we can get. We’ve kind of started to make our own. So, we’ve got a real focus on wellbeing, we want to make sure that we’re focused on our people’s wellbeing that we, you know, we’re allowing we’re pushing them enough that it’s challenging environment and but also, we want to know that we’re not pushing people too far because we care about our staff. So, we came up with a measure that’s a wellbeing score, and that’s taking things like the overtime Flexi time. You know, is in is the overuse of any of that over time, creating any kind of sickness? And are people taking their own holiday to kind of counteract that. So are people being responsible with when they take time off and what that’s been able to do is across the organisation, we’ve got that as a rolled-up score and we’re able to kind of then look to see if there’s any early warning signs that. For example, now we’ve got quite high usage of holiday. So, people are front loading a lot of their holiday that’s going to create as a problem later on. So, we were able to have that conversation at all levels and kind of get ahead of things. So yeah, the last one’s probably focus on the other opportunities that you can put this information out wider.
Bhavesh: Yeah, that’s a that’s a really good tip and I’m curious about the wellness score. So how is that kind of has it since you’ve been measuring it, have you seen any kind of trends in it? And are those markers true? If you put people through. I mean, it’s obvious. If you put people through too much overtime, they’ll burn themselves out. But are you starting to see those trends?
Ben: Yeah. So, I mean, like, from an operational planning perspective over time is fantastic. Because you, you’re being able to get experienced stuff and there isn’t any kind of shrinkage involved. So, you’re able to get that kind of optimum level of time. Overtime is paid, whereas we’ve got the measure of flexi time, which is time that isn’t paid for. So, it’s almost a goodwill of the teams, and we’re really conscious that we’re not putting too much goodwill. People aren’t waking up at 6 in the morning and logging in and then finishing at 6 at that night, because it’s just not sustainable. It’s not good for their wellbeing. So, we put those metrics across the organisation. So that we understand when they might be used but we’re aware of the impacts of that and that’s why we’re looking at if there’s any correlation between the usage of flexi time and overtime? Does it have any impact on sickness? And our people be in responsible the holiday taken? And like, I say, we’re able to have those conversations now where we can start saying, look, we are front loading holiday. Are we creating a problem? That’s going to jump up and bite us later on in the year and then also for the overtime and flex time. It’s really keeping a keen eye on that because it’s the indicator to. Does that team actually need more resource?
Bhavesh: Of course. Yeah, and I think it’s. You know, it’s really interesting how once you go get your data in a in an environment where you can leverage it, it opens up conversations around things that you assume. But now you’ve got data to back it up, or data to support that. So, when you, when you, when you think about the journey you’ve been on yourself personally, Ben, I mean, what’s your kind of proudest moment in the lifetime of this project? What do you kind of look back at?
Ben: I think it’s I think it’s probably taking something that is in your head and turn it into reality. I think that’s what what’s always tricky about these things is the senior leaders within the organisation are essentially buying you. You know you, I me working with, other people around the organisation, Laura, Jonathan. We were all able to come together pitch an idea to a problem and, essentially, we have to sell it to the business so that they’re and they buy into it and ultimately, they’re buying you. But also, you’re putting your head on the on the block here, because you, taking something that might work, might not work. So it was, it was really being able to say, I think I’ve got an idea, coming up with the solution and actually seeing it through to the end. I think that’s now like any project we’re in a nice delivery window, where you’re actually still going to be here to see the results. So yeah.
Bhavesh: That’s amazing. That’s amazing. So, I guess the converse question then, is, if you look back at it, what would you kind of do differently? What are the things that are your gotchas, think oh, man, I should have done it that way? Perhaps I should have done site different ways. Any tips like that for anybody else going through this kind of similar process?
Ben: Well, yeah, probably so I always quite like to push the boundaries and we were quite experimental in some of the teams that we put the solution in, and I’ll probably say, save that a little bit for later because we had enough on our plate to be able to keep us busy on just core operational teams. But I really did want to try and solve as many problems as I could, and we put it into some of the teams where it may not naturally fit. Now we’ve come through the other end of that, and it’s adding extreme value for all of those guys. But I guess just during a really busy window, that’s probably one of the things I would say. Just be mindful of the people that you’re delivering this to and come to that later on maybe.
Bhavesh: Yeah, cause, there’s a lot of change involved. Right? It’s not just a simple matter of getting a data environment. There’s a lot of change in the way you do things in order to move forward. So that’s a really good practical piece of advice. So, you’ve talked to a little bit about some of the stuff that you’re doing now with the Wellness score and those kinds of things. But what’s next for you in this data journey? Because now you’ve got the obstructed, the environment in place. You’re well suited, and you’re well placed to kind of drive further improvements within your organisation. What’s next for you in in that journey?
Ben: So, I guess key focus for me right now is to sustain what we’ve got already. At the moment. You know, it’s across a lot of people, and for me I don’t want any of that to fall all over and go back. So, it’s making sure that we’ve got all the things in place that we can sustain. You know the good weather that we’ve already delivered, and maintain that data integrity as we, as we spoke about. One of the things I’m, just said about don’t be too experimental. So, I’m going to contradict myself. We’ve got we’ve got an opportunity in some of our technology teams. So as GPS come together, you talked about the technology teams and operational teams traditionally being separate, in our new operating model that we put in, we’re actually trying to combine both. We’re having those teams put together to create capabilities that are focused on customer outcomes within that they’ll have that their own change and technology teams that will be able to deliver the value straight to the capability they’re involved with, and hopefully that will speed up change. And one of the things that that, that agile within those teams as they deliver is the organisation’s kind of got this this picture now, where we’ve got absolutely fantastic data on the on the run the business side and they’ve got these metrics that aren’t supporting longer term planning. So, hot off the press, we’re trying to do a proof of concept in a couple of the squads within the agile teams to see if we can support the quantification of a story point.
Bhavesh: Oh wow!
Ben: So, my thinking is that an individual’s interpretation of their time or their relative estimation is very similar to an operational task at the beginning of your journey. You know, if you think that if we could guess and put a standard time in. Say that task will take 10 minutes, if we can apply the same logic over a longer time for a story point I’m hoping that what that will show is the individual the individuals that what their estimation of that story point might equal over time, and then for the for the product owner, they’ll be able to then see what the team’s relative estimation of a story point is, and how many story points they think they’re going to get through. Then we just get back to the same conversation as we have normally, where you plan to get through this many story points in a sprint. You know, how many did you get through? If there was any blockers or barriers, what were the challenges. What did you learn from that? What were the things that were distracting you from delivering the outcome? And what can we do differently next time? So that’s the latest thing that we’re going to trial. Like I say, it’s going to be a proof of concept. I might be proven completely wrong and it’s not going to work. But the guys in the agile teams are they’re experimental in themselves. They’re up for the challenge and we’ll see where we get to.
Bhavesh: Sounds like a really interesting POC, or pilot you’re going to run through. Now, look it will be wrong of me not to bring up the AI question when we’re talking about data. It is the hottest topic out there at the moment in well, in all businesses and all parts of businesses, but particularly in operations, right? The whole idea of AI coming into Ops. You know things that Ops leaders have never been able to do in theory they should be able to do more automation, less people. All that kind of stuff is really central to all conversations. I mean I normally talk to 2 groups of people, either ones that are super excited about what AI is going to bring and then the other half of them, which just fed up of, when is AI actually, and deliver something of real value for an operations perspective. My view on it is always you can’t do any of it without data. You know, you can’t do really drive the things you want to do with our data environment. Now, you guys have already built that data environment and you’re well placed. So, are you thinking about AI, you thinking about how you leverage the technology within the work that you’ve already done? What’s your thoughts around that.
Ben: Yeah. So, we have heavily invested in a team to look at solutions within our organisation, around delivering AI. Some of the work that the operational teams complete requires them to consume an awful lot of information in summaries and legal documentation. So, I think that’s where kind of the focus is going to be is how they would they summarise some of that information presented back to a human being to help make the decision quicker. That that will evolve over time. I think, for me it probably goes back to some of the journey that was on with Legal and General, when they were pushing out automation, and as a as a change, lead the hardest thing when you put automation into an individual team is that the human beings in that process will always doubt the solution. They’ll always try and challenge it.
Bhavesh: Yes, of course.
Ben: And it goes back to that kind of trust within the data. So, it’s the responsibility of people who are delivering those solutions and the kind of the guys in the operational teams to make sure that it is trustworthy showing the right information and then get behind it and I think it’s exciting, because the guys on the ground if they don’t have to trawl through hours of information or legal documentation, that’s only a good thing as long as it gets the same outcome. There’s a load of other stuff for them to be focusing that time on rather than reading through. So, if it helps us in that angle, then then brilliant. I think for the data, the analytics side of it, I think, is going to be is going to be really key. So, you’ve got the amount of data that we now have, and all the other kind of information that we have across the board. You know, how can the predictive AI start to support better decision making for operational planning? That would be really exciting for me. I always like when there’s a human interaction point as well, because I like that you get presented with the information that the AI might interpret. I do like it when there’s a human intervention point when you can start to say, okay, but you don’t know that X is going to happen. So you can kind of manipulate it and add that human element. So yeah, there’s some stuff going on across the board. I think it’s I think it’s exciting and I think people that don’t just get behind it and invest in it are going to and are going to fall by the wayside. So, I think it’s basically on all of us to embrace it.
Bhavesh: Yeah.
Ben: But be cautious, I’d say.
Bhavesh: Yeah, I think I think that’s where we are isn’t in terms of the evolution, I think. Certainly, my personal view is that Ops in the future has to be data centric and that is the only way you can go in order to drive the improvements that Ops needs to make across. You know, the different businesses they operate within. So, being data centric allows you to capitalise on AI and other technologies that will come in the future. So hence why your story is such a fascinating journey that you’ve been on, because, in some ways you’re probably ahead of the curve in some organisations that we talk to, and I’ve had that privilege to speak to and there’s different pockets of it. But this whole idea of getting that central data environment becoming a data led culture sets you up very well for what the future is like it to be. So, I appreciate you taking the time to share your story. It’s been a really interesting exploration in terms of the journey that you went on and the outcomes you’ve achieved, and I think any of our listeners to the podcast, there’s a lot of really great insights there in terms of how they can take that and take it into their own organisations and start transforming their organisations to become much more data centric. So, Ben, thank you so much for taking your time.
Ben: And thanks for having me.
Bhavesh: You know and being an Ops game changer on the podcast thank you.
Ben: Yeah. Thanks.
Bhavesh: And thank you very much for our listeners for joining us and please do check in to catch up more episodes of Ops. Game changes to more, to hear more fascinating insights from Ops. Game changes and leaders in the field of operations as they go to transform and overcome challenges. So, thanks for listening and bye. For now.
