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Regular planning and analysis is in demand at the moment, but finance’s data gap shows that they need more than a planning solution

Thanks to the pandemic, there is a greater demand for scenario planning and forecasting. It’s a problem that needs to be fixed now, and finance teams are rushing to fix it. But trying to plug that gap with some software leaves you liable to leaving bigger holes in your systems and processes.

It’s a bit like saying: “we need an e-commerce solution for the Christmas period” when what you really need to do is sort out your e-commerce platform full stop. You need to look at everything holistically if you’re going to really add value through transformation.

Forecasting and planning software is a part of it, but it won’t be much good if you don’t know the value of the data and infrastructure that you’re trying to access. Any transformation project needs to be pegged on business priorities rather than cost savings.

If you really need to ramp up your planning and forecasting, first and foremost, you need to understand the problems the business is facing. How can you support and partner with the rest of the business to tackle these problems?

That could be all sorts of things, from revenue growth to customer behaviour, which then opens up the conversation about what you actually need for your digital finance function. Intelligent automation could be a part of that; improving processes so that as the business gets better at delivering, the customer experience gets better. You might get more revenue that way, but it’s very process-led.

You can do more planning, sure. But you can do so much more than that. What about understanding the customer better? Think about the qualitative as well as the quantitative work that you might be able to deliver after you’ve completed your transformation project.

There’s been a lot of talk around the potential for finance to use data from broader sources, pulling in profit, people and planet into their work. It’s a conversation we’ve been having for years without taking much action. That’s starting to change.

The newest development in this field is xP&A, which is another way of positioning the same argument. It’s all about looking at all of the data within the business for our plans and analysis. We need to think much more wisely about the information and insight that we provide. It needs to be focused on clear communication of the steps needed to improve the business.

This, obviously, is a massive departure from traditional FP&A, such as variance analysis. In a data science project, you set a hypothesis and try to prove or disprove it. That’s a completely different approach to comparing your budget with your actual.

Say you’re looking at data trends for a particular line of business. What’s the relationship between customer behaviour and what the business is doing? What can we do to influence that behaviour? Will revenue go up if we create a new product? Or change our pricing? You need to test this idea, then turn it into a series of points of action, communicated clearly with visualisation. 

But, and here’s where we come back to our original point: you need to extract that data from your processes. And if your process is rubbish, you don’t get the data you need and you can’t run the project.

Coming back to data literacy and skills, the first step is to understand that we need to move into this space. Then we need to start thinking differently about what we do, because it’s not just the tools we use, it’s the mindset.