Business analytics and data science can play critical roles when facing a crisis and for getting a business back on its feet after a disaster.
From re-allocating resources to identifying customers most at risk via modelling of recovery scenarios, there are many ways for analytics professionals and teams to add value to the business and help minimise the cost of disruption.
Below are some actions we would recommend taking.
From re-allocating resources to identifying customers most at risk via modelling of recovery scenarios, there are many ways for analytics professionals and teams to add value to the business and help minimise the cost of disruption.
Below are some actions we would recommend taking.
1. Understand where the risks lie
The first step to take is to quickly gain clarity on the different risks the business faces.
By leveraging analytics and data experts along with cross-functional expertise, businesses can assess the threat to the different areas of the business.
Depending on the type of crisis/disaster, businesses can use existing or new datasets to analyse where the biggest risks lie, e.g:
By leveraging analytics and data experts along with cross-functional expertise, businesses can assess the threat to the different areas of the business.
Depending on the type of crisis/disaster, businesses can use existing or new datasets to analyse where the biggest risks lie, e.g:
- Demand: customers, policies, product categories, etc.
- Supply chain: locations, suppliers, costs, etc.
2. Re-prioritise analytics resources and investment
During a crisis, resources are likely to be stretched to their limit. It is therefore key to ensure their allocation is optimal.
Businesses first need to get a clear overview of their current data and analytics capabilities (datasets, KPIs, reports, tools, skillsets, etc.), along with their in-progress projects.
If data and analytics teams exist within the business, they can use this information to analyse where and how to reduce, redirect or increase resources (e.g freeing up analytics team members to support some key functions with analysis).
If there is no existing data and analytics team, the information can still be leveraged to temporarily revise reporting KPIs, broadcast audiences or access to tools and technology (e.g temporarily allowing access to the analytics technology stack to external support).
(We have software available to small and medium business that support the collection and maintenance of all this information, get in touch for more information).
Businesses first need to get a clear overview of their current data and analytics capabilities (datasets, KPIs, reports, tools, skillsets, etc.), along with their in-progress projects.
If data and analytics teams exist within the business, they can use this information to analyse where and how to reduce, redirect or increase resources (e.g freeing up analytics team members to support some key functions with analysis).
If there is no existing data and analytics team, the information can still be leveraged to temporarily revise reporting KPIs, broadcast audiences or access to tools and technology (e.g temporarily allowing access to the analytics technology stack to external support).
(We have software available to small and medium business that support the collection and maintenance of all this information, get in touch for more information).
3. Perform post-disruption scenario modelling
Once the immediate threat and risk mitigation plan is in place, businesses can start looking beyond the crisis and modelling different recovery scenarios.
Post-disruption insights can be collated and turned into assumptions for use in scenario models.
Example analysis and models can include:
Post-disruption insights can be collated and turned into assumptions for use in scenario models.
Example analysis and models can include:
- Scenario modelling drops in demand and its impact back down the supply chain
- Scenario modelling cost increases, disruptions to the supply chain and the commercial and financial impact
- Creating cost analysis plans, prioritised by potential risks / reward areas
- Reworking strategic pricing models using new market insights and trends.
- Optimising commercial and trade investment by focusing on most profitable and resilient partners
4. Focus on data integrity and new datasets
If resources are available (see 2.), this may be a good opportunity to focus on maintenance and perform the data cleaning tasks businesses never have a chance to do.
The objective is to make sure businesses are in the starting blocks with clean and integrated data when the recovery beings and that they have high confidence in the data available to them.
Businesses should also consider collecting and purchasing new datasets.
E.g Consumer trends and buying behaviour may drastically change during the crisis. If businesses do not integrate new data feeds to monitor these, they still need to make sure that they can switch them on as soon as needed.
The objective is to make sure businesses are in the starting blocks with clean and integrated data when the recovery beings and that they have high confidence in the data available to them.
Businesses should also consider collecting and purchasing new datasets.
E.g Consumer trends and buying behaviour may drastically change during the crisis. If businesses do not integrate new data feeds to monitor these, they still need to make sure that they can switch them on as soon as needed.
5. Create flexible frameworks and process
This may be a luxury during a crisis, but once recovered, businesses should work to make their analytics and data capabilities become overall more flexible, by learning and improving on their response to the crisis.
This may involve gaining a better oversight of datasets, KPIs and analytics capabilities, but also of existing analytics assets, and resources. Written training, handover and general documentation may also need to be improved.
Finally, businesses should deep dive into analytics assets and assess potential areas where automation and modularisation is possible. This will help to build needed resilience if and when future crisis arise.
This may involve gaining a better oversight of datasets, KPIs and analytics capabilities, but also of existing analytics assets, and resources. Written training, handover and general documentation may also need to be improved.
Finally, businesses should deep dive into analytics assets and assess potential areas where automation and modularisation is possible. This will help to build needed resilience if and when future crisis arise.