CFOs should take the lead in harnessing corporate data for not only their own strategic decision-making but to expand their influence outside core finance functions, according to recent research by Deloitte.
This means CFOs can use Big Data for not only financial functions like planning, budgeting and forecasting, but also to expand their reach into areas ranging from supply chain management to production and even managing customer interactions and setting pricing.
The Deloitte survey, nevertheless, revealed who is typically in charge of data analytics in different companies. In 23 percent of cases, the business unit or division head was the most frequent leader of analytics and often had significant budgetary responsibility.
However, CFOs came in a close second at 18 percent of the businesses surveyed. Finance itself was also recognised as the area most likely to invest in analytics, and this was the case at 79 percent of companies.
At digital infrastructure company Equinix, CFO Keith Taylor points out that the pandemic has meant that financial directors must now play a wider role in moving their companies away from traditional operating models to ones that can give access to real-time insights, enabling better, faster business decision-making.
“During the pandemic, CFOs were forced to take immediate and decisive action to insulate their businesses, such as setting new payment terms with suppliers and implementing revised expenditure protocols. They must now look to the future and consider how best to deliver an environment to promote a fast and effective recovery,” he says.
“A critical element will be access to real-time information about key performance indications to support clear-sighted decision making,” he adds. “However, data delivers insights, as well as unwanted background noise, and business insights can potentially be misleading if we fail to address that noise.
“CFOs must invest in data curation initiatives to better manage, maintain, and validate data to ensure a laser focus on quality.”
Meanwhile, international recruitment consultancy Hays launched a major project to centralise its global data three years ago. Paul Venables, CFO at Hays, says that Big Data now comes under the remit of the finance function with board level members such as the chief technology officer all actively involved.
“Today, more complex, global businesses are multi-locational and operate different business units, but it is important to have one version of the truth,” he says, pointing out that historically many companies managed data at a local level or business unit level.
“At Hays, we wanted to ensure rapid insight into key operational data to underpin decision-making at global level, country level, office level, and then also at desk level,” he adds.
Download our Whitepapers
“A CFO needs to understand data on a global basis to challenge the business units.”
Hays’ Big Data project involved setting parameters for the key operational data that should be collected globally and centralising in one data warehouse, from where users can access it via a common set of interactive dashboards. This centralised data can then be drilled into at a global level by the CFO to understand each country’s performance, and by country managers to understand individual office performance, in relation to a number of criteria such as jobs posted, clients attracted and the fees charged, as well as the job candidates attracted in a given time period. Team leaders and consultants can even drill into the same single common data platform to access the data most relevant to them.
“We are also now in a good position to conduct predictive analytics into a number of areas such as what our revenue might be, where we need to allocate additional resources and where we need to attract more job candidates,” says Venables. “We also have complete visibility into our customers across the world. This means, for example, that if one of our offices is pitching to a company in a particular country, it can quickly drill into global data to find out what other business we have with that company or its subsidiaries in other parts of the world and gather as much information as possible on the customer prior to the pitch.”
Predictive analytics will be vital
Taylor agrees that companies must now introduce more analytical processes and use raw data to deliver business-critical information.
“Big data is only going to get more important as we move forward and it will not just be empirical data – but also predictive data,” he says. “It is important to proactively look at the top line, bottom line and governance opportunities by leveraging intelligence from data analytics.
“Data science initiatives can predict critical business events in advance in order to take preemptive action to deliver that all important competitive edge. However, the specialist tools and technologies employed must be able to exploit the data efficiently,” he says.
Taylor acknowledges that some progress has been made towards this, but many businesses need to continue to invest in data science initiatives. “For instance, smarter analytics tools could predict customer churn inside a company and how that might impact the business to help capacity investment and planning decisions,” he adds.
“The tools would also ensure that CFOs invest in the right infrastructure, in the right part of the business, to deliver the best return on investment.”
Equinix itself leveraged spend analytics data to predict business risks posed by suppliers, which were impacted by the recent pandemic. This helped to avoid disruption for its customers.
It also took a different approach to bookings and fulfillment data to predict customer interactions so that it could optimise workforce planning.
“In addition, we leveraged data science to predict power usage and data center capacity utilisation. We have used this data to proactively build data center capacity where demand is predicted to increase,” adds Taylor.
He also recognises that artificial intelligence (AI) tools can be used to manage certain operational tasks more effectively than human intervention. Employing an AI-based auditor, for example, enables finance teams to eliminate manual approvals for transactions below a certain threshold amount.
“Automating this process for us led to the elimination of manual approvals for 67 percent of transactions saving 2,000 plus hours for the finance organisation,” he says. “Equinix is looking at adding more intelligence layers to the model which will allow us to create further efficiencies and accuracy in the system.”