This post is part of our 8-part series exploring the role of data in business recovery and planning in the era of COVID-19. Read the rest of the articles here.
The ongoing COVID-19 pandemic is testing businesses severely, not just in managing contingencies, but also in terms of implementing the business continuity planning steps designed for such a crisis. However, even if your forecasting models did not entirely prepare you for a situation as prolonged and as widely impactful as the pandemic, you may be able to dissect current business circumstances and utilize the takeaways to fine-tune your scenario planning. You may find it useful to analyze not just the impact of the pandemic on various areas of your business but also the response and resiliency of those business units.
If you are already using data-driven tools to track performance, you may be able to repurpose or refocus them for this analysis. Based on the volume and granularity of the business performance data available to you, you can gauge which areas are most critically impacted and which are responding well. This can help you effectively marshal your resources. You can also use this information to see if you need to be more targeted with your business continuity planning steps in those business areas.
Modeling and testing scenarios to avoid unwanted surprises
The business continuity planning steps you take during a crisis are usually aimed at ensuring a quick turnaround in business performance, at the lowest cost to your organization where possible. For you to do this successfully, you should be able to forecast with reasonable accuracy the different risks your business can face during various crises. In addition, you also need to assess the fallout from these risks on performance and organization. The challenge for any business is to plan for an exhaustive list of scenarios in a detailed manner while keeping track of the business continuity plan at an enterprise level.
Even if you have leveraged data intelligence earlier when designing your business continuity planning steps, you may now need to rely on it even more to minimize the risk of business disruption. In a crisis, business continuity becomes even more dependent on a highly responsive organization. You will need the most efficient parts of your business to be most flexible in responding to the crisis. The use of decision-support tools, based on artificial intelligence algorithms, can reduce the guesswork involved in such scenario planning by using automated insights to identify business levers capable of delivering the necessary impact, within defined resource parameters.
Some of the critical business questions you can answer by leveraging data intelligence include:
- What alternatives can you adopt if there are pandemic-related supply chain bottlenecks?
- To what extent can you absorb the financial impact of factory or outlet closures?
- Is there likely to be a significant shift in customer behavior that requires adapting brand messaging?
- How can you plan lay-offs while ensuring the availability of necessary staff in critical functions?
You can take your business’s crisis readiness a step further by testing out some of the scenarios, such as simulating an outlet closure to verify the financial impact predicted by data analysis. Since these tests will also involve the participation of different teams in your organization, they can help lay down a template for cross-functional communication during a crisis. You may also be able to test the responsiveness or the ability to recover from a crisis-like situation and identify the measures needed to bolster the same.
Making your business continuity plan visible using indicators and dashboards
The success of both forecasting and scenario planning can depend heavily on the metrics you track to gauge your business’s performance across business functions and areas. During a crisis, you may need to fine-tune your business’s key performance indicators (KPIs) or add KPIs more relevant to the crisis at hand and better tailored for your organization. You should also consider the ease of tracking these KPIs and the likelihood of tracking disruption during a crisis, as this can affect your ability to execute business continuity planning steps in a timely manner.
If you are using AI-based decision-support tools, such as InsightOut, the KPIs you select can be used as a performance monitoring feature. Data accuracy is critical for the efficient functioning of predictive algorithms – if your KPIs are not well defined, you may not be able to set their target values precisely, which can reduce the utility of the decision-support tool. If you are using KPIs to monitor specific business areas, you may need to involve the relevant management personnel in this process and allow them to write back or update the measured and target values in the KPI database.
This monitoring process can be made easy by using data-powered dashboards which can be programmed to offer the level of clarity and collaboration you require. You can easily visualize information relating to any of your business areas or to your organization. Note that, when using data-powered techniques in an environment with multiple stakeholders, you may need to establish some ground rules regarding how changes need to be managed. A dashboard that also maintains an accessible version history can enable reporting as necessary.
Data platforms are a meeting ground for collaboration and communication during a crisis
Data-driven business intelligence tools can sometimes seem daunting, but using a dashboard to visualize and track business performance makes the data more comprehensible and accessible to a wider audience. You can then broaden your organization’s decision-making and get a wider buy-in from various stakeholders like your crisis management team when laying out or implementing business continuity planning steps. A data platform that allows collaboration between stakeholders can tend to become a “shared fabric” which is at once seen as trustworthy, given the decentralized ownership, while enabling more direct communication regarding business processes and performance.
Having a culture of data-driven decision making established in your organization makes it easier to ensure the flow of actionable intelligence across business areas in real-time and speed up response times during a crisis. Whether conducting impact analysis or risk assessment and mitigation, your crisis management team can interact through the data platform. They can also add the necessary human element to ensuring that the tool’s automated insights are not tainted by data inaccuracies but continue to be most relevant to your business continuity plan. Combining human and machine intelligence can be particularly necessary during a crisis as business priorities may keep changing rapidly.
Seeing the big picture with data
A common critique of global businesses is that business areas tend to get divided into opaque silos that rarely share necessary information. The use of data platforms is seen as one way of breaking through the silos, but participation is critical to ensure that the data visualized offers all stakeholders a comprehensive view of the situation, including both organizational data and globally relevant public data from other unimpeachable sources. For instance, when dealing with the spread of the COVID-19, data about containment zones and border closures may be sourced from the World Health Organization and may be correlated with your organization’s supply chain data to help plan the most effective workarounds.
While gathering data from different sources and ensuring its usability can be challenging, InsightOut and other business intelligence providers offer decision-support tools which automate both data consolidation, via data connectors, and data validation. Data preparation has been traditionally seen as a major bottleneck in data analytics and is also significantly expensive. Automating this critical part of the data intelligence process gives your business the capability to process higher volumes of data in shorter time spans, which can be a huge advantage during a crisis. Having data available in a pre-configured template can enable teams trained to access the platform also import data specific to their business processes and customize automated insights as relevant.
As an example, your logistics team can leverage a blended data pool with third-party public health data to:
- Model alternative routes less affected by travel restrictions.
- Consider whether to switch mode of transports for time-sensitive shipments.
- Gauge how these affect other business continuity planning steps.
With other business areas also contributing to modeling responses to various crises scenarios, you will be able to add nuance to your business continuity planning steps.
Further, these can be continually refined using the automated insights gleaned through data intelligence. With the stakeholders in your organization’s business continuity planning buying into the advantages of contributing to and leveraging data platforms, your decision-support tools become even more central to tailoring business continuity planning steps.