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Investment – a perspective from Deloitte

Andrew Berry, Director specialising in data and AI at Deloitte, gives us an overview of his data journey and experiences.
It’s been an interesting, last 20 years, I guess, since I’ve been in the data industry. So I’m currently a director at Deloitte. And you know, we specialise in providing data solutions and helping clients improve the way they manage and use their data. But that’s just been pretty, pretty recent before that I was working in industry, I worked for a bank and ran a data management function and then went into data architecture.
And then in between, I decided to go and actually see how how it could be done with a tech tech firm specialising in data and analytics and how to work with clients globally, and how they can improve the use case effectiveness of how to deal with data and the exploitation of that. So yeah, it’s been an interesting journey I’d say over the last 20 years or so.
Getting investment for data projects, analytics projects is is is sometimes a bit of a hurdle for organisations, particularly if they can’t communicate it well, and I’ll find the first and the most important step is making sure there’s a clear linkage between the business outcomes that you want to deliver on, and particularly the alignment to the business strategy. That’s really important because that brings it to life for the exec team, but also makes it tangible as to the the value that can be delivered through, through data. And typically, what I always recommend is that when you start a data project, take a step back and say, Well, what are the outcomes?
And what are the business benefits that I think we can we can deliver by, for example, if we improve the way we collect and manage data, will that reduce our risk? And will that make us more compliant? Because that’s, that’s a good benefit. Or if we if we improve the way we we analyse our data, and will that help us actually the way we deal with customers? Or will that help us convert prospects to becoming customers quicker and easier? Guess what that’s going to drive increased revenue and help with the benefits case. And then the other area we often look at is, most organisations struggle with too much data and trying to integrate that data and they living off spreadsheets.
But if we can do that in a better way, will that actually make our people more efficient and effective? So does that free them up to do things that are more value added, and that’s, that ultimately leads to cost reduction, and efficiency. So that’s the three paradigms you would look at when you look at a business cases, risk, revenue and cost. And you can absolutely link most data projects to one of those. So that’s, that’s how I would tackle it. I mean we’re working with one client at the moment, helping them on on their data journey. And you know that they’ve been explicit, though, that they don’t want to be, it’s not about cost reduction.
This is about improving the customer journey. So we’ve been able to help them articulate that and it’s, you know, the the business case, actually, once it’s once you can articulate it and link it to the KPIs of the business that becomes quite easy to, to justify. The challenge we often find working with clients is, is getting that first step, getting started on our data project. And recognising that people don’t have unending budgets, you know, to do this. And the way we we recommend people start is having a clear view of what’s their ambition when it comes to data. So actually starting with a clear data strategy, and that’s important because they need to know.
Are they wanting to use data to improve the way they they deal with the outside world, with their clients, with their stakeholders? Or do they want to use data more effectively, internally, to provide better information to their staff and to their, to their management. So it’s, you know, understanding, what their data strategy is from that perspective. And then looking at the different parts of the, the layer, I like to call it the different lenses on their data strategy. So have they got the right people in place? Have they got the right processes? Do they have the right technology to do it?
So once you have that view, and actually that’s not a, it doesn’t have to take long to build up that that strategy and operating model, or and that targets state, you can then say, Okay, how do I meet that and let’s come up with a number of small bite sized steps to meet that. So we recommend actually, that people have a process of experimentation. That they that they explore, experiment and then execute. And that’s actually that’s worked very well with a number of our clients. So you start small, you do that initial exploration, you do some experimentation on your highest priority challenges, and then you recognise that not all of them are going to go through to fruition.
Some of them you’re going to have to park or cancel, so, you know, failing fast, but others you’re going to be able to execute. And that can actually not, it doesn’t have to be that expensive. You can, you can run those sorts of proof of concepts or prototypes, in cloud based environments that are quite easy to stand up, quite low cost. But it what it does is it starts to visualise it for the executive for your senior stakeholders, who can then start seeing something tangible with real data that can bring it to life. And that that’s often then what allows you to to justify the business case, and move that into sort of more production, scalable type activities.
So that’s what we would recommend. So first, start with a data strategy and then once you’ve got that start small and build up through, what we, you know it’s an innovation process, that that we’re we’d recommend there.
I spoke to Andrew Berry, a Director specialising in data and AI at Deloitte. I asked Andrew if he could give us an overview of his data journey and experiences. I also asked him how people justify the investment in data, especially those first steps.
Andrew shared some amazing insights, the points that resonated most with me were:
  • A strong link between data and business strategy helps communicate the value of the data investments and makes the conversation more tangible for investors.
  • 3 hooks for investments are Risk removal, Revenue increases and Cost reduction.
  • Reviewing your data ambition through different lenses including People, Process and Technology (a topic we will return to in the Ability To Execute section).
  • The 3 step process to get started: Explore, Experience, Execute.
Have a look in the “See Also” section below for Andrew’s Deloitte profile.
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