If we’ve learned anything this year, it’s the degree to which a crisis can hit with little warning. Case in point: We’ve collectively watched as the spread of COVID-19 around the globe majorly affected supply chains, workforces, and consumer demand in a matter of weeks.
Of course, the fallout from a worldwide pandemic is far from the only source of disruption businesses today may face — and safeguard against. The experts at Deloitte point out, as business models become increasingly distributed around the globe and dependent on international players, businesses find themselves open to more geopolitical risk — like changing regulations and governmental conflicts.
Organizations can also face disruptions from new start-ups entering the market, turning previously held convictions and predictions on their heads, as everyone must scramble to keep pace and retain customers. It is clear business ecosystems are changing faster than ever before, and any company incapable of keeping up will fall behind the pack.
Crises can emerge from any and all sides, which is why it’s imperative to prepare for the worst before it actually hits. As a result, many enterprises have found themselves relying more heavily on business intelligence and data analytics before, during, and after a crisis. As TechHQ cites, a special report found nearly half (49 percent) are using analytics more than before the coronavirus pandemic emerged.
So, let’s examine the roles of BI and analytics for driving decision-making in a crisis.
Using Data to Optimize Short- and Longer-Term Operations
Over half of those companies surveyed in the aforementioned study (55 percent) are specifically using BI and analytics to improve efficiency.
While operating efficiently is always a goal, it takes on a new level of importance during a crisis when revenue drops, and every expense truly matters. After all, offsetting a dip in revenue by reducing expenses and “trimming the fat” can help enterprises avoid hemorrhaging money when they’re already vulnerable. But companies must first be able to identify those opportunities ripe for optimizing, which is where analytics play a key role.
Reducing operating costs is an initial reaction to a crisis worth taking with a grain of salt. Companies also must make sure their focus on cost-cutting is not diminishing the safety or quality of their products/services. Some strides toward efficiency look good in the short-term while opening the company to more risk and potential expense down the line.
As Power Mag outlines, financial performance for energy companies is about more than just operating costs; it’s important to factor in efficiency and reliability too. Data analytics can help companies account for all these different variables — making smart choices in the short and long terms to mitigate crises and stay afloat in competitive markets.
Use cases for crisis analytics include not only delivering critical efficiency but also:
- Driving necessary adaptations with agility.
- Operating reliably despite changing external conditions.
- Protecting margins as much as possible.
Prerequisites for Data-Driven Decision-Making
It’s worth considering what companies need to have in place to facilitate data-driven decision-making before disaster strikes.
One hurdle many organizations using legacy BI and analytics platforms have traditionally faced is reporting lag. In other words, decision-makers would have to wait for IT teams to create, format, and deliver reports — which can allow a backlog to flourish. Every second count during a crisis, so enterprises are doing themselves a favor by facilitating ad hoc reporting. A search-driven platform allows users on the front line to query data, create charts, and share their findings with other teams in minutes, which can be a game-changer in the wake of a catastrophe.
Besides being able to access information quickly, decision-making models must be in place to allow employees to act expediently and decisively on their data findings. This depends in large part on the data-driven culture — or lack thereof — an organization has fostered. Leadership investment, job-specific data literacy, and fluency training and accessible tools all go hand in hand to build a culture resilient enough to utilize data when it matters most.
It makes sense for companies to turn toward business intelligence and analytics during times of crisis. After all, good decisions can mean the difference between sinking and swimming. Enterprises must have systems in place to support effective analytics before a crisis emerges.
