A Deloitte research states that 60% of life sciences and healthcare organizations already run more than half of their applications in the cloud (1). 9 of the top 10 global healthcare organizations use AWS, with 90% potential savings on computing resources. In 2017, Gartner predicted that by 2019, more than 75% of life sciences organizations would have adopted cloud-first application deployment strategies. As of today, the forecast figure of 75% would have surpassed significantly.
Do these figures ring a bell?
As the pandemic has unfolded, healthcare organizations have further increased their cloud investments. They not only aim to streamline businesses or lower costs but also for a higher purpose of unlocking data, collaborating better across the ecosystem, creating meaningful engagements, thus delivering effective treatments to patients across the world. With spiraling costs and the need to bring drugs faster to the market, there is an urgency to embark on a cloud-native approach to solution design. Organizations are evolving in adopting cloud for efficiencies to cloud for growth across the value chain, and one such niche and important application of cloud technologies is Forecasting.
Here are the top 5 reasons why healthcare organizations are forecasting in the cloud:
Fast and superior performance in real-time
A typical forecaster invests significant time and effort in setting up forecast models, especially in complex disease areas like oncology. Models built on spreadsheets are heavy and pose challenges to forecasters in managing their day-to-day activities. The files can get so bulky that they are difficult to transfer among teams and, at times, take minutes to even open and make modifications. Forecasters also spend time building presentations from spreadsheet outputs, resulting in redundancy. Cloud-based forecasting platforms make the creation and modification of forecast models extremely intuitive and fast, with built-in reporting frameworks. They also easily scale in performance as they are cloud-based.
Deliver data and methodological consistencies across teams
With forecasters in an organization working on their spreadsheets to model the brands they are responsible for, inconsistencies in methodology often creep in. Different forecasters may have different methods in deriving a persistency curve from a median DoT value for example. Even data sources could be inconsistent across forecasters. The only cumbersome way to avoid such inconsistencies is to do a thorough review of the spreadsheets, which is time-consuming and may lead to errors again. Cloud-native solutions offer a single source of truth to drive consistencies across teams working together, seamlessly.
Minimizes errors and maximizes accuracy
A typical forecaster would manually build their forecast models typing in formulae in spreadsheets. They manually input varied assumptions data in spreadsheets, which may result in a high likelihood of errors and glaring inaccuracies. Cloud-native solutions with automation minimize errors and maximize accuracy in forecasting processes and building assumptions.
Multiple versions of forecast spreadsheets floating across the organization is a perfect recipe for a forecasting fiasco. They make it difficult for the forecasting organization to ensure real-time simultaneous collaboration. Centralized cloud-based forecasting enables forecasters to store all forecasts in a single place and collaboratively work on them, enabling seamless role and workflow-driven collaboration in real-time.
Secure institutional memory
Decentralized spreadsheet-based forecasting means the files and critical forecast information captured in them are often lost as forecasters may be involved in multiple roles and change teams across the organization or may move out of the organization. Centralized forecasting keeps your teams organized and also helps access forecasts across teams spanning over time, thus preserving institutional memory and minimizing dependencies on people.
With such tremendous potential of well-designed cloud-native solutions to effectively solve these glaring forecasting challenges, it is time for healthcare organizations to switch to cloud-native solutions. This will allow teams to invest their valuable time and efforts in driving strategic initiatives and actionable insights instead of day-to-day forecasting mechanics.
Many #FutureReadyHealthcare organizations are already forecasting on the cloud. Which are the best examples you’ve come across? Please do share them here.