Data: Big, Fast, and Smart
Today’s information economy requires financial managers to think strategically about data and how it can improve efficiencies for individual analyst or clerk, to improving performance for entire organisation. In a 2015 Wired article by Alissa Lorentz, “Big Data needs to be Smart and Fast….’Smart Data’ means information that actually makes sense. It is the difference between seeing a long list of numbers referring to weekly sales vs. identifying the peaks and troughs in sales volume over time. Algorithms turn meaningless numbers into actionable insights….By ‘Fast Data’ we’re talking about as-it-happens information enabling real-time decision-making.” AP and AR departments are transactional, but offer a substantial amount of data to support operational and strategic decision making. Firms that fail to leverage AP and AR data miss opportunities to improve efficiencies and improve their bottom line. Finance managers need to be able to offer actionable decision support to senior management. Managers who adopt real-time reporting and analysis, and machine learning based applications will enable performance enhancement writ large.
A simple model
Let’s begin with first principles. Without scaling the esoteric ivory towers of philosophy and computer science departments, the concept of thinking about Data, Information, Knowledge, Wisdom and Action is not new. A quick desktop investigation will find hundreds of information models, for simplicity’s sake, we’ll use the following model:
cartoon by David Somerville, based on a two pane version by Hugh McLeod
The image conveys a basic message: data becomes more useful by identifying patterns. To gain knowledge, each successive step needs more data. In the above example, additional data is essentially meta data , in other words, data about data. Other opportunities may exist by combining different data sets to find new patterns, which would otherwise be counterintuitive. In either scenario, with ‘wisdom’ the decision maker can make better actions.
Financial managers can usually reach the stage of ‘knowledge’ from their data, but never truly capture insights or wisdom. In a typical finance department, would likely see a ERP or accounting system to provide some level of reporting to enable KPI management. Standard solutions offer ‘information’ level reporting, where data is not random but at least categized in a structured way. The standard solutions will support internal management reporting, and inform KPIs. Common KPI management will include (not exhaustive, with some variation):
In this simple model, the above metrics could be classified as ‘knowledge’ as they give additional knowledge beyond standard reports from accounting or ERP systems, but they come at a cost.
For many firms, the reporting process is highly manual. ERP and Accounting systems fail to provide suitable dashboard functionality or flexibility. As a result, firms will devote time and energy to exporting data, re-analysing with Excel, then updating the monthly or quarterly reports in PowerPoint. Financial management reporting can cost SMEs anywhere from £100-£1000 in labour, and can be in the tens of thousands for larger firms. The challenges are usually two-fold: connectivity among different systems, and personnel skills. Technology solutions are rapidly addressing the first problem: the ‘API economy‘ whereby open-architectures enable 3rd party value added services are now ubiquitous. Data visualisation services are readily available, and training is not difficult to procure. Firms that fail to adopt real-time data solutions will die a quick death.
In addition to real-time reporting, new technology solutions expand the insight and wisdom opportunities. 3rd party infrastructure and platform providers have democratized statistical methods and machine learning offering non-technical or non-academic experts to explore data and find new patterns. Those with technical backgrounds are supercharged. As the cost of 3rd party infrastructure and machine learning services drastically reduce, enterprises need to be prepared to innovate. The following are just a few examples how modern analytics can elevate operational finance teams:
- AR data can help predict DSO extensions based on customer characteristics, not already factored into short term cashflow forecasts
- AR communication data can be analysed to improve or degrade CEI
- AP data can reveal insights in unexpected price increases if payment behaviour is seen as a risk to suppliers
Moreover, opportunities to combine traditional metrics and insights are now available. The following examples show ways to leverage natural language processing for AP and AR teams:
- An analysis of customer sentiment when conducting collections combined with other AR metrics to discover optimal opportunity selling times
- Sentiment analysis of vendor communication combined with transaction data to find superior buying strategies for ad-hoc purchases or supply chain finance
- Analysis of digital corporate reputation and its correlation with operational metrics can uncover new ways to enhance brand value
Big data to smart, fast data
By combining real-time reporting with new knowledge discovery systems, finance departments can truly convert their Big Data into Smart, Fast Data. By moving into the ‘Insight’ and ‘Wisdom’ categories operational finance becomes front and centre for senior management decision support.
At solv.ai, we believe in real-time data analysis by providing flexible, web-based dashboards, training. We also provide bespoke analytics services to enhance AP and AR performance and CFO decision support. To find out more email us at firstname.lastname@example.org or click here.