Cash & Liquidity ManagementCash ManagementPracticeHow data analytics drives efficiency gains

How data analytics drives efficiency gains

An organisation’s core treasury functions can benefit significantly by using analytics to sift through the ever-growing volumes of data to extract key insights.

Cash is the life blood of an organisation – as conventional wisdom goes – and must therefore be managed properly. Many organisations are realising that truism, which has led to the corporate treasury function continuing to grow and evolve within an organisation. Put simply, the decisions and everyday actions of a treasurer directly impact on the cash flow within an organisation. Corporate treasury is thus increasingly becoming critical for the financial health, growth and successes of all organisations.

Most corporate treasury groups rely on multiple data sources and solutions to address business needs. This approach only brings increased operational difficulties and introduces further risk, rather than addressing actual challenges.

At a time when many companies are looking to reduce their IT expenditure, data analytics can offer clear benefits in terms of reduced costs, better forecasts and improved decision-making for critical treasury functions.

Why focus on cash flow forecasting?

Against a backdrop of low and volatile commodity prices, increased banking regulation, a weakening of the global economic outlook and the likelihood that the US will lift interest rates, businesses should focus on efficient cash management to ensure there is enough liquidity to run the business.

Since it is important to have available cash to pay short-term expenses, an accurate forecast is a critical task in corporate treasury. A robust cash flow forecast measures the organisation’s ability to meet liquidity needs. It enables companies – both small and large – to identify future gaps, manage liquidity and funding risks and prepare ahead for difficult situations. This allows spending patterns to be coordinated to mitigate potential shortfalls and balance the flow of funds.

For a myriad of reasons, many companies struggle to obtain the necessary figures for accurate cash flow management. Some of them can be related to the siloed business process, procedures and policies, a lack of required talent in the organisation or ineffective information systems.

Companies can also face cash management inefficiencies due to a lack of fit-for-purpose systems. Corporations with such limitations will find it difficult to capture the information needed to populate cash flow forecasts, especially for longer periods.

In some cases, cash flow forecast is impacted by business complexity, which brings additional variables that influence the cash flow and must be predicted. Cash flow forecasting is a fluid process, which must be continuously reviewed and updated to account for shifting business and market conditions. Embedding data analytics into your organisation can thus help optimise your cash management.

The need for data analytics

Data analytics platforms are enabling companies to uncover insights from the multitude of data sources at their disposal, internally and externally.

The annual Gartner Report on Business Intelligence and Analytics Platforms compares the different data analytics platforms in the market, and rates each for “ability to execute” and “completeness of vision”. Those scoring high on both criteria are placed in the so-called “Magic Quadrant” and therefore considered the market leaders in this independent study. In 2016 this comprised three vendors: Microsoft (Power BI), Qlik (Sense) and Tableau.

The key features which these market-leading companies offer include highly intuitive and quick-to-deploy data visualisation tools and powerful business intelligence (BI) analytics solutions, which are well integrated with other existing solutions or that have leading features such as an associative calculation engine, enabling business users to uncover causality in their data quickly.

KPMG has noted considerable interest in these leading three platforms in the market across different industries and business units, from finance to HR. The rate of adoption of these technologies is increasing exponentially in line with the explosion in the volume of data available to process and the ever increasing appetite for business insights.

Using cloud-based BI in supply chains to drive greater compliance, quality and improve forecasting accuracy is what many companies are looking for today. In particular, we are seeing an increasing trend towards organisations not just collecting large data sets, but using them to inform business decisions.

Paradoxically, data analytics platforms are becoming ever more sophisticated, in terms of their capabilities and functionality, yet easier to navigate for the business-user and quicker to implement.

A great example is the recent phenomenon of natural language querying, which makes it possible for end-users to ask questions such as “which business units are using the most working capital?” rather than writing comparatively complicated logical expressions in Structured Query Language (SQL) or similar syntax.

The impact of this is that the future treasury workforce will be a smaller core of people, who will need analytical skills and business understanding but not necessarily database querying skills.

The wider view

It is a common practice within treasury department to spend considerable resources on developing and maintaining cash flow forecasts.

Ideally, these resources should be focused on analytical activities, rather than operational/data entry/data acquisition activities. However, in the absence of effective data analytical systems teams typically end up spending the majority of their time with manual work – which is often susceptible to inaccuracy. It also contributes to business operation inefficiencies as the raw data must be manipulated in disperse systems.

Implemented correctly in the treasury, data analytics can realise the following benefits for organisations:
• Real-time management information with increased visibility and insights for management.
• Deeper insights from processing an entire population (large data sets) and hierarchical data models to enable drilldown (and closer examination of outliers and exceptions).
• Real-time positions and predictive statistical models to inform more timely and effective decision making, leading to optimal cash management, reduced working capital requirements and higher turnover of inventory/lower inventory days.

However, it is important to highlight that data analytics will not magically solve all problems. It is imperative for organisations to ensure that the right processes are in place to ensure timely and accurate information is reported.

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