As financial professionals, we hear regularly about how big data and analytics will change our world and that we must harness its power to transform our businesses. As finance functions, looking into new ways to analyse our business and optimise our processes, aren’t we tantalised by the opportunities? We lean in and listen closely – only to hear business to consumer scenarios that just don’t apply to us or to our businesses. Does this mean that the promise of analytics is just a pipe dream for operational finance? Can analytics really help financial professionals transform their business? What skills should they develop to function efficiently for a world being driven by analytics?
Analytics opportunities for operational finance
For the past two decades, the focus has been on optimising the physical supply chain and the sales process. Large business warehouses were built to extract and transform data for optimisation and reporting. While they provided function-specific reporting needs, these projects didn’t offer opportunities for operational finance improvements. Process transparency, or an ability to identify operational finance bottlenecks, was not possible in the traditional data warehouse implementation. It didn’t bring the end-to-end view of the cash-to-cash cycle, nor was it organised to account for finance indicators such as cash tied-up throughout the process.
We know that there are still many untapped opportunities locked up in the financial supply chain. However, we have lacked the technology and applications to take advantage of the data we are collecting in our systems to identify and harvest them.
Let’s take working capital optimisation (WCO) as an example. Advanced analytics can really be a game-changer here. Many of us look at days sales outstanding (DSO), days payable outstanding (DPO) or days inventory outstanding (DIO) from an aggregated-company average angle. At that level of reporting, it is extremely difficult to determine where to start your improvements process.
What needs to be done must be measured at a granularity that allows you to turn insights into an action plan. Significantly, more granularity of reporting is needed to assess this ratio at any level of the organisation. For example, a 22.5-day DSO can conceal a 55.1-day DSO for a given plant, a 62-day for a sales division or 12.7 for a market. Also, DSO only measures order-to-cash cycle average duration. How do we know where the actual operational bottlenecks are occurring?
We can only detect any internal blocking point if we can measure days spent at each step of the process. So knowing that we spend 7.2 days for ‘order received to order fulfilled’ or 5.7 days for ‘goods shipped to invoice issued’ will allow us pinpoint problems and then take appropriate actions. Finally, leaving any suspicion about numbers validity by calculating these ratios from the transaction documents themselves, removes the classic excuse of “my data doesn’t match yours” and will empower finance team to move faster.
Measuring working capital with the highest level of detail, identifying what is invested at each stage of the process and relying on undisputable data is key to supporting fast, focused and impactful actions. This is where analytics changes the game.
Supporting this opportunity, are the in-memory databases, which are transforming our ability to analyse millions of transactions quickly. The barriers of processing time and volume of data are disappearing. When equipped with descriptive and predictive analytics capabilities, these technology advances allow for new and innovative approaches to solve the old, classic problems.
The power of analytics
Descriptive analytics seeks to describe data in order to create meaningful nuggets of information. It is typically backward looking-in nature. It addresses questions such as ‘How much cash is tied up by this product’s sales?’; ‘What DPO do I get for what level of rebate?’; ‘When do I really get paid by customers from this region?’. Descriptive analytics is critical in order to understand the current state and be relevant to the business; it can’t remain high-level or too aggregated, or else it is meaningless. For many companies, even describing what they have today in a systematic and reliable fashion would be a large improvement on the one-off reporting requests and spreadsheet army knives used to support operational reporting.
Predictive analytics builds from historical data to propose what might happen in the future, using statistics and algorithms. As with weather, traffic, sport or machine failure predictions, predictive analytics leverage millions – if not billions – of data elements to detect the repeating patterns of events and measures their likelihood to occur. We are no longer backing our decisions with hunches or perceptions of the past, but on an unbiased prediction.
It is not a guarantee that the prediction is perfect all the time; instead it is the best representation of your business behaviour as well as a systematic and sustainable method to drive optimal business decisions. Imagine your ability to make project decisions about where to invest your valuable time and efforts based on predictive data. Or better still, to reject projects because they don’t provide enough predicted return.
For example, predictive analytics could be used to analyse the time between when an order is received and when we issue the goods. We could see the cash tied up when there are longer delays, and predict the cash saving associated with minimizing the time between receiving the order and shipping the goods. Shrinking this timeline will allow us to ultimately collect cash faster.
Yet identifying the problematic orders and opportunities is only possible with applications and technologies that can analyse large volumes of data to find the proverbial ‘needles in the haystack’. Once these are identified, root causes can be analysed such as inappropriate inventory levels, inadequate credit processes resulting in order holds, or training problems leading to increased system errors and processing time.
With a shift to focus in analytics comes the need for finance professionals to develop new skills. How do individuals develop talent in this new space? There are three basic things to consider when preparing for the future shift to analytics:
1. Begin utilising analytics in areas where you are already a subject matter expert. Understanding and applying analytics concepts will be significantly easier when you have a background and baseline experiences in a particular function.
2. Be curious and systems-aware. Understand the tools you have today and the underlying constraints before attempting process optimisation. It’s easier to come up with solutions without considering their feasibility within your systems or processes. This will often lead to additional risk, complexity, and customisation during implementation.
3. Raise your process integration awareness level. In order to truly harness capabilities in a predictive analytics space, understanding of the underlying processes is necessary. Genuine process solutions emerge during collaboration across the business process.
A fantastic journey lies ahead of us all. Technology has laid the path: easy data access, fast processing, advanced analytics are now accessible to everyone. Solutions are built to get you started in weeks, and a systematic thought process to leverage the data you have already been collecting lays the groundwork for operational finance improvements. Finance is being given an opportunity to reconquer its supply chain. So what are we waiting for?
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