Building resilience: your fortune-teller in the Cloud

In the age of AI and ML, companies have entered a new paradigm of forecasting and modelling. Then the pandemic hit and the data that had fuelled these models became outdated and insufficient. How then, can businesses make informed decisions with confidence?


This is a contributed article by Angela Mazza Teufer, Senior Vice President ERPM WE, Oracle.

Little in business is certain, but organisations truly have entered into a new age of uncertainty. Resilience in the face of adversity is critical, and a key part of resilience is foresight. Business leaders need the capabilities and systems to look ahead, future-gaze and predict future disruption, helping them to adapt accordingly.  

Yet how can businesses forecast accurately when everything has changed and old models have been thrown out? Scenario modelling, integrated data and automated business processes in the cloud can help organisations ready themselves for the next obstacle.  

Planning for uncertainty 

Businesses should make use of every tool in their arsenal. It’s important to distinguish between forecasting, scenario modelling, and planning. Forecasting is the act of predicting where your business or the market will be at a point in the future based on relevant historical data. Scenario modelling is when analysts create a range of likely scenarios by looking at possible key turning points. Planning represents the measures and decisions you make based on those insights.  

Forecasting and scenario analysis feed the planning process, making them crucial early stages in adapting to disruption. Yet forecasting has become extremely challenging in the current environment.  

Forecasting depends on massive amounts of first-party and public data, but COVID-19 has brought businesses into unknown territory. McKinsey data shows that businesses remain divided on the shape of the pandemic recovery, with outlooks shifting towards a muted, slow recovery. We lack the historical data we’d normally depend on to analyse a crisis, and we also lack the trends that would help forecast what conditions will be like once disruption has passed. 

This is where scenario modelling comes into its own. Scenario modelling helps businesses visualise a wide range of possible futures, plan for multiple scenarios and assess how to respond to each one. While the process still depends on data, it doesn’t require historical data relevant to a particular scenario. Instead it presents a range of likely outcomes that businesses can prepare for. It’s the ideal antidote for a future where little in certain. 

However, recent experiences suggest the need for a more mature approach to scenario modelling. Despite many organisations actively modelling future scenarios before the crisis, few foresaw or were able to plan for the pandemic. You can’t plan for every outcome, but businesses should start investing into a wider range of possible scenarios going forward. Setting up dedicated analysis teams in each department can help bake scenario modelling into business processes.          

A platform for resilience 

More regular, comprehensive scenario modelling won’t be enough by itself to guarantee resilience. As time passes, organisations will collect more and more data that facilitates traditional forecasting. Both methods of prediction are necessary to help businesses plan for the future. Yet both can also be easily undermined by the quality of data and systems in an organisation.

Massive amounts of data concerning customers, employees and competitors can be difficult to manage. Often it will be segmented across an organisation, divided into numerous silos that prevent it from being analysed together. Planning for disruption needs a coordinated response, consultation and collaboration, but it’s difficult to achieve when plagued with silos.   

Speed is another issue. The time it takes to perform manual and unnecessary tasks – including data cleansing or entry for analysis – is precious time wasted. It becomes a case of wasted resources, but it also means the organisation may be too slow to respond to rapidly emerging trends, challenges or opportunities. 

Companies can make the task easier by leveraging cloud tools and applications. Many businesses are doing this already – Gartner expects cloud spending to increase by 19% in 2020, a rate of growth it hadn’t expected until 2023. Centralising your data estate in the cloud encourages collaboration because workflows and data reside on one rather than multiple systems. 

Consolidating forecasting activities in the Cloud also instils more confidence in the process as everyone is using the same methods and tools. Cloud applications can be updated to the latest best practices regularly, so processes are always up-to-date for all groups. 

Embedded AI apps and solutions can greatly accelerate forecasting and prediction by automating manual data processes. This contributes to agility because people spend less time gathering and verifying data and more time planning for disruption. With the right information sooner, executives and lines of business can make decisions faster and with more confidence. 

Foresight is resilience 

Modernising with a combined planning and forecasting cloud solution can elevate both scenario modelling and forecasting capabilities, better aligning them with requirements to be more agile and transformative. When a company can anticipate an upcoming trend or challenge, it gains an invaluable head-start on the competition, and the space needed to adapt and capitalise.     

Angela Mazza Teufer is Senior Vice President of ERPM (ERP, EPM, and SCM) for Oracle Western Europe. She is responsible for growing the ERPM market share in the region; this includes raising the awareness of Oracle Cloud’s leading position in the Application segment of the business. Mazza Teufer joined Oracle in 2018, after 14 years at SAP. Throughout her career she has gained deep experience of the technology and industries as well as the impact of innovations like Cloud, IOT, Block chain and AI in addressing business challenges.