By now, COVID-19 has been in the international spotlight for over three months. The actions of every country in the world are under intense scrutiny. Each country’s press is closely reporting every move of their government.
Organizations deal with unfavorable findings all the time – situations when there are audit findings in healthcare sectors or when the R values of heavily invested Data Analytics projects do not turn out in the way they are anticipated. We just don’t get to see these organizations handle these situations as transparently as we can see countries dealing with COVID-19.
If we are willing to look beyond some differences, countries and their leaders are not very different from organizations and their CEOs.
This is a rare opportunity for us to peek into the inner workings of these proxy organizations and learn how some approaches can help, how some hinder value delivery, and how leaders and processes can be more effective at delivering analytics value.
From the multiple data analytics projects that Gramener has seen and executed, some points are strongly applicable to analytics projects as well.
Leaders need data-backed decision-making skills
The U.S. had its first Coronavirus case on January 20th, while Italy had its first case on January 31st.
There were expert group warnings based on statistical models in both the countries on how quickly the situation could escalate, but the reaction has been more sluggish than expected. Let’s look at a possible reason.
Leaders tend to act in known ways or based on their gut feeling, especially during uncertain times. Being able to process information from multiple sources to make decisions is not an entirely new skill for a leader.
Data/outputs of ML models are objective inputs and are a more valuable source of factual information than a market poll or a unit head’s perception. Leaders have to give sufficient weightage and priority to this objectivity in their decision framework.
Leaders can leverage data to identify areas of business that can benefit most with a data-driven response. We are organizing a webinar to help business users fight the #covid19 recession. Register and join us live on 7th May 2020.
Implementation trumps everything
The U.S. ranks first in the Global Health Security Index (83.5), highest across 140+ countries. It has scenarios planned on how to respond to epidemics. The CIA even had the information about the disease spread since January. But the time it took – from the emergence of the first COVID-19 case on January 20th to implementing lockdown measures leaves a lot to be desired from an implementation standpoint.
Having frameworks, processes, and being ready is one thing. Using those when required is a whole different story altogether.
Many organizations are willing to invest in mature processes for analytics use case identification and implementation. But what really pays is having these well-integrated with existing organizational processes so the teams can use and benefit from them.
Having leadership forums review the Analytics KPIs and implementing a process to deal with red KPIs seems like much more work than hiring highly skilled Data Scientists to run an ML project.
And it definitely is a lot of work, but the pay-off makes it worth it, especially in a crisis situation when every miss gets magnified. The bright side is that there are data consulting organizations that can help leaders effectively implement these.
Agility in implementation is crucial
China took an approach towards COVID-19 that played to its strengths when it first detected a case. South Korea adopted a different approach that worked for them too. Switzerland and Italy took widely different strategies with varying results.
Though all of them took different approaches to tackle the pandemic, the common thread for them is how they started. They quickly tried options to find out what best worked for them.
Consider this usual scenario:
Mr. X has a need. Someone told him that Analytics techniques can solve it. So,
- A deck is created for management approval
- A rough solution is sketched out in consultation with a Data Scientist
- Use cases from other companies are added, and a use case is finalized
- The deck is presented
- Budget and resource approvals are secured, and a project starts.
Data almost always is a surprise package. I would practically guarantee that analytics projects can’t promise assured results until execution. And data in every company and every department is different.
An agile approach with a practical execution plan in data analytics projects is critical. After identifying the need, identify the best algorithm and start small, validate else course correct and go full-blown once proven.
Only culture can make things happen
If there is one thing that is very evident in how countries are navigating the Coronavirus crisis, it is the importance of culture in managing the situation.
From the governments’ side, we can observe the culture in how governments communicate about COVID-19, how they react to ease the concerns among their citizens, and how they approach the problem holistically.
From the people’s side, culture is visible in how they respond to the government’s communication, how they show solidarity in adherence, how they deal with discomfort to the benefit of the broader population.
Change management, internal stakeholder buy-in, communication, holistic and well-thought-out processes are necessary for general and more essential in analytics projects where the uncertainties are higher & cross-functional collaboration is more needed than in standard software development.
Culture is that binder that makes all the difference.
