Scattered data, extensive manual work, speed of process, and inconsistencies were some of the key challenges the Google treasury department faced when it came to build a cash flow forecast model. The data the department needed was scattered across database warehouses with few formatting consistencies from one to another. In addition, since Google actively pursues different business and investment opportunities, variables continue to multiply. Each variable is associated with a different spend or collections team and as a consequence there were inconsistencies in the way each group operated.
Photo of Ronni Horrillo, Google.
Founded in 1996 as a research project, it is now estimated that Google runs over one million servers in data centres around the world and is recognised as the world’s leading search engine. Headquartered in the US, Google has more than 20,000 employees and a reported turnover of €23.6 billion in 2009.
Google’s solution to these problems is innovative and efficient. The company’s treasury workstation automatically aggregates a combination of financial data warehouses and user inputs which populates into the appropriate fields of the forecast model. It supports cash forecasting for both the short term and long term, ranging from one month to three years. The solution eliminates many of the manual processes required to gather the data and input the data into a single consistent format. Reports, charts, and graphs become readily accessible. Variance analysis of different time ranges can also be done quickly.
Because cash flow forecasting requires a collaborative effort from many groups within an organisation, Google’s treasury department had gone to great lengths to train and educate other teams within the organisation on basic cash management and treasury strategy/goals. Other groups within the organisation became more willing to help and provide precise numbers once they understood the importance of an accurate cast forecast – even at a ‘cash rich’ company such as Google.
The cash forecasting solution has allowed Google to cut down on manual work, increase accuracy, maximise investment opportunities and will provide up-to-date forecast and actual data at any point in time. By automating the ERP inputs to the cash forecast model, a significant amount of manual work has been eliminated, which also reduces human error risk and improves accuracy.
The process which uses the treasury workstation’s cash forecasting solution is innovative because it automatically links various financial data warehouses and user inputs into one consolidated forecast. Through this automated solution, upper management can log into the treasury workstation and view the data at any point in time, which expedites decision making.
Armed with an accurate cash forecast, the treasury team is able to determine when operating cash balances will be high and low, which enables the treasury team to earn higher yields with maximised investment opportunities. Furthermore, by understanding all the cash inflows and outflows, the treasury department can enhance the company’s days’ sales outstanding (DSO) and days’ payments outstanding (DPO) analysis. Updating the cash forecast will be instantaneously aligned with automated inputs and various reports can be generated at ease. Accurate and up to date information is critical in making upper management business decisions. More relevant information and faster access to cash data leads to a more streamlined decision making process.
“Our treasury workstation automatically aggregates a combination of financial data warehouses and user inputs which populate the appropriate fields of the forecast model. It supports cash forecasting for both the short term and long term, ranging from one month to three years. The solution eliminates many of the manual processes required to gather the data and input the data into a single consistent format. Reports, charts, and graphs become readily accessible,” says Ronni Horrillo, Assistant Treasurer.