Building Resilience with Predictive Analytics in the Cloud

Building Resilience with Predictive Analytics in the Cloud

September 17, 2020

Oracle Head of Apps Singapore Levent Tavsanci argues why building resilience is necessary for survival, and how using predictive analytics in the cloud will help businesses navigate uncertainty with confidence.

By Levent Tavsanci

Most businesses globally are treading in unchartered waters in 2020 – truly we have entered into a new age of uncertainty. Most recently, the Ministry of Trade and Industry announced that Singapore economy contracted by 13.2 per cent year on year, sharper than the 12.6 per cent plunge earlier estimated and the worst on record.

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 is constantly changing at an accelerated speed, and old models are thrown out more frequently than ever? While businesses strive to make use of every tool in their arsenal, scenario modelling, integrated data and automated business processes stand out among other tools that can help organisations ready themselves for the next obstacle.

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 and current 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 needed 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, however COVID-19 impact on the forecast models’ variables is affecting the predicted outcomes’ accuracy. 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 would 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.

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 the organisation, divided into numerous silos that prevent it from being analysed together. Planning in response to disruption needs a coordinated game plan, 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 artificial intelligence applications and solutions can greatly accelerate forecasting and prediction by automating manual data processing steps. 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.

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.

(Ed. Featured image by Pixabay,)

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