Predictive analytics is becoming a critical tool for businesses looking to determine the results of key decisions before committing to them. The benefits are evident but implementing the tech can be a challenge. With rising inflation impacting costs and fears of recession affecting demand for services, companies across the country are decreasing their spending and exploring new growth opportunities.

Identifying where, when and how much to invest is critical, but it’s vital during a challenging downturn when choices can have a profound impact on the growth prospects of your business once the economy recovers. In an attempt to adapt their investment plans, companies are increasingly applying predictive analytics to support them in assessing opportunities and risks. 

James Petter, VP of data storage business Pure Storage, believes risk management is a major discussion point among CFOs and regulatory teams, especially in the current economic climate. Senior leaders consider risk management a top priority, assessing their economics, financial structures and technologies. Businesses contain lots of data, and most are trying to determine how to use this information. Often, companies are focused on the current market conditions and responding to these but moving forward, there is likely to be more of a push on looking ahead, and predictive analytics will play a significant part in this. The rise of predictive analytics is no surprise to Shankar Balakrishnan, VP for Europe at Anaplan. Balakrishnan refers to businesses using historical data to navigate challenging times as someone driving by only using their rear-view mirror. Balakrishnan believes companies must utilise more data on potential outcomes and react smarter to possible disruption. Anaplan recently partnered with the South Central Ambulance Service Foundation NHS Trust to support its predictive potential. By applying machine learning and predictive insights to their data, Anaplan determined the number of emergency call the ambulance teams would receive at any given time. This process allowed the trust to deliver resources more efficiently. 

The challenge of implementing predictive analytics 

For finance leaders, the challenge is understanding what to focus on. One initial area to work on is automating functions in the back office. Applying technology, such as robotic process automation and AI-focused data analytics, improves the processes, tackles skills gaps and improves efficiency. It can also provide intelligence that can support forecasting and planning. Automation like this allows employees to focus more on value-added tasks.

Bearing in mind the potential risk and uncertainties, few leaders will want to make critical investments and resourcing plans on instinct. Risk management may be a priority in a crisis, but can business leaders avoid this crisis in the first place? Whether it’s a pandemic or cyber attack, making effective plans under pressure requires accurate and data-focused insights. Successful risk management needs data to deliver various scenarios and options. For example, in the travel and tourism industry, predictive analytics may prove critical to enable them to recover from significant disruption experienced after Covid. Aircraft manufacturers are using technology to find the best times to perform maintenance tasks, and airlines are using similar technologies to predict demand for flights and plan their staffing and fuelling requirements. Quality data and predictive analytics are critical to risk mitigation within the finance industry. They are vital for fraud detection, auditing and other types of advanced work.

The overall success of this technology depends on the quality of data fed into the system. Insights created on incomplete inputs could be misleading and potentially cause harm to a business. Implementing predictive analytics isn’t a one-time process. It takes time and effort to examine the findings, understand them and alter the system accordingly. It’s important to have clear goals when implementing analytics, adapt them when necessary and continuously revisit them to ensure the business is getting what it needs. Accuracy and compatibility are critical when measuring performance across various teams.

If leaders work with inaccurate data, they risk making inaccurate decisions. Similarly, if teams spend hours validating data, it makes the entire process impossible for decision-makers to react quickly. Despite the challenges, the benefits of predictive analytics are clear. With the insights it delivers, predictive analytics offers significant value for business leaders, converting data into critical information for a business.