Tech Talk interview to Fabrizio Tocchini, Head of Innovation at CCH Tagetik, originally published in Utility Week
What is your focus as Head of Innovation?
Two main things: first, making sure that we’re using advanced technologies in our platform to give our clients a competitive advantage – right now and in the future. Second, looking at how we can react faster (and help our clients react faster) to new scenarios. That became more urgent when the pandemic hit.
How did you respond to last year’s unexpected turn of events?
Straight away we set up a dedicated team to help our clients work remotely. Obviously, one of the big things was moving on-premise customers to our cloud platform. Luckily, that’s pretty straightforward with CCH Tagetik.
Another thing we did to help both our clients and prospective clients, was to share our pre-built model of industry-specific KPIs (including utilities). This allowed their finance teams to quickly re-forecast using industry-standard benchmarks as events unfolded. I’m really proud of our team for getting these assets to the market within just a few weeks of the lockdown announcement.
How is CCH Tagetik helping utility companies run more efficiently?
Three ways – ease of use, combining operational and financial data, and ‘anywhere connectivity’.
There’s a lovely quote from one of our clients, Electricity North West: “CCH Tagetik lets us do things that were previously in the ‘too hard’ box, like monthly regulatory reporting”. That reflects the concept behind CCH Tagetik – to create a complete Corporate Performance Management (CPM) platform that’s simple and intuitive for finance professionals. That’s why we include pre-built logic so finance teams only have to make choices based on their knowledge of their business, not their knowledge of code or IT. This saves everyone time.
More broadly, I think the real game-changer for our utility clients is the way we’re connecting all their operational data to feed the financial analysis and regulatory reporting, through our Analytic Information Hub (AIH). It helps you model your business in all directions – not just account data but operational KPIs too. It allows you to see all that data in granular detail, so, for example, you can do an end-to-end analysis of cost unbundling across any business combination, in a simple cascade of steps. It’s great for running a C1 table.
Third, our clients are asking us more and more for ‘anywhere connectivity’ – pulling data from a huge range of sources. This goes beyond typical ETLs. We need less latency, and we need to make sure there’s no data redundancy when you ingest all these sources. We’ve already released a few APIs for various databases (on top of our full suite of ETL functionality), and more are coming. I’m particularly excited about the idea of the ‘virtual dataset’ that we have now. It lets you view the data in another dataset and use it to build a report without physically transferring it. This avoids data redundancy and ensures data security (because you don’t have to store it).
How is CCH Tagetik using AI to help clients outperform their competition?
We thought deeply about this, beyond just applying predictive analytics to time-series data for more accurate forecasting. We said: starting with data, I have a good first version of my forecast. But I need to know why I reached these results. If I know why, my scenario planning and what-if analysis will be more valuable, because I know where to act to change things. This explainability is now possible – to explain how a result has been reached.
So, to take an example use case, for each business combination (a given product sold in a given channel at a given time), I can understand which variables have the biggest impact, whether that’s the promotion or the channel, for example. That means that, for that specific business context, I can act specifically on the key drivers. I can do a conscious scenario analysis. We’re moving from what is likely to happen, to why it’s likely to happen, to how I can change what’s likely to happen. This explainability helps people trust the data, too. Our January 2021 release was the first with this functionality.
We’re also looking at how AI can help our clients with more extended modeling. For example, your business is affected by given metrics (regulations, taxes, costs of distribution) but they aren’t easy to predict. If you’re accurate on these metrics, then you’re doing well. But there are exogeneous factors as well, trends in the wider market such as employment levels, how behavior is being affected by the pandemic, the weather, electric vehicle usage and other decarbonization trends. Today, there are datasets beginning to explain these wider trends in a way that can be analyzed. We can take this data into account and include it as a variable in your modeling. It’s like ‘driver hunting’ – looking for other drivers in private or public datasets that allow you to be even more accurate in your predictions.
Take cashflow as an example. The problem normally is that you can set whichever terms you want for your credit payment, but the reality says that things don’t always work like that – delays are caused by claims, inefficient processing etc. This should be taken into account in the DSO/DPO calculations you do upfront. AI can help with this, by analyzing the trends of past transactions, so you can see what’s going on product by product, supplier by supplier, and you can infer the real DPO you can expect from a particular transaction as well as what kind of remedy you can use to mitigate the risk of delayed payment. On top of this, you can also calculate your provisions for losses more accurately.
Are there any other emerging technologies that finance leaders should be keeping an eye on?
Blockchain. This could change the way the transaction world works. If there is a chain that connects the sender and the receiver in intercompany matching, then there isn’t a need for the reconciliation. The effort will be spent on checking the chain itself, not the transaction. A few of our clients are trialing how they could use blockchain technology for a portion of the ledger, and we’re looking at how we might need to develop our solution to adapt to this, to check the chain not just the transaction.
Looking even further ahead, I’m excited to see how quantum computing might help us predict the future price you can apply to an asset even more accurately, taking into account vast quantities of variables and possibilities. That’s a problem I would love to solve brilliantly, but that’s working on a longer timeframe.
* Tech Talk interview was originally published in Utility Week: https://utilityweek.co.uk/how-ai-is-changing-finance-and-operations-in-the-utilities-sector/
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