subscribe: Daily Newsletter

 

Data quality helps to optimise cash flow

0 comments

Effective cash flow is essential for business survival, as it promotes liquidity and enables an organisation to meet its short-term debt or credit demands. Although it is often perceived to be a finance issue, the reality is that cash flow optimisation is an operational issue, since it requires coordination across the organisation, writes Gary Allemann, MD of Master Data Management.
Cash flow optimisation relies on the ability of an organisation to bring in monies owing, a process that begins with invoicing and incorporates areas such as credit terms. Technology has often been viewed as the silver bullet to fixing many of the problems that arise around cash management, however, the reality is that technology alone is not enough.
Technology is simply an enabler, and in many instances the main challenge around cash flow optimisation is the quality of available data. Addressing this issue through data quality and appropriate data governance is essential.
The process of cash flow optimisation, also known as cash management or working capital management, incorporates three components, namely collecting, managing and short-term investment of cash resources.
Successful cash management processes avoid insolvency while reducing cash receivables, increasing collection rates, and ensuring effective short-term investing while maintaining appropriate liquidity levels. Many organisations have significant amounts of cash tied up in working capital, and it is therefore essential for organisations to encourage financial and cash flow discipline.
There are various methods for improving cash management, including the use of technology to shorten the cash conversion cycle. This may be achieved using electronic invoicing, for example, which automates the invoicing process and removes challenges such as the postal service from the equation.
Regardless of the technology used, however, its effectiveness is entirely dependent on the sales and billing staff capturing the correct payment for each customer individual.
Data quality is therefore critical, since the negative impact of incorrect billing on a business’ ability to bring in outstanding cash can be significant. Errors such as the incorrect email address or lack of correlated data can result in unpaid invoices and poor cash flow.
Other areas affected by poor quality data may include matching funding to cash flow obligations, automated forecasting of working capital metrics, and optimising financial functions through a variety of methods.
While these techniques may assist with improving and expanding the available capital, a critical analysis of potential causes for unrealised and unconvertible cash assets reveals data quality as the core of the problem. Common issues include inconsistent categorisation of data as a best case, and worst case a complete failure to populate entire data categories. Incorrect or missing data frequently results in less than optimal cash flow despite investment into technology and solutions to address this.
One example of this can be seen within the business environment with regard to ‘Days Sales Outstanding’. Businesses offering credit facilities with various payment terms may experience delays based on poorly defined payment terms. Where a business may typically expect payment within 30 days of invoice, some customers may be eligible for longer credit terms.
The inconsistent use of payment terms hinders ability to collect, requiring more stringent collection processes to be activated. With the automated solutions that monitor collections, a single misallocation of a payment term or the lack thereof may give rise to inconsistencies and delays in collections.
Data is a critical enabler, and it is therefore essential to focus on both policy – under which conditions certain customers may be offered unusual payment terms – and quality of data as the basis for achieving optimisation.
A focus on data quality through formalised data governance will assist organisations to achieve consistency in data categorisation across the business. Data governance creates an environment for defining policies on credit facilities and associated payment terms approved for use.
This will eliminate any misalignment across business units and ensure that consistent payment terms are in use. Data profiling exposes invoices and client terms that do not comply with these policies and helps prevent possible cash flow delays.