How data science delivers value in a post-pandemic world

With businesses from various industries tightening their belt due to pandemic-induced economic challenges, investing in data science applications and building out their teams may be taking a backseat. While the primary focus must be on preserving cash flow, what many companies don’t realize is the power evolving data science applications have on business continuity and growth during these uncertain times, and the importance of shifting data science roles in implementing effective solutions.

Applying data science to help companies achieve their business objectives during this time should no longer be considered a luxury, but a necessity to help organizations stabilize and enter a phase of strategic growth. Here are the top areas where data science is delivering value for businesses post-pandemic, and how the roles within data science teams are shifting to facilitate this.

Refine customer targeting

As consumer preferences continue to shift in ways that would have been unimaginable pre-crisis, companies can no longer go off what they have always known to be true. Whatever their preferences, customers only want to be targeted with the most accurate product recommendations and content for them. Achieving this during these economically tumultuous times requires a constant finger on the pulse of what targeted messaging will be most relevant.

For example, an e-commerce business may discover that its customers that were previously most interested in travel products are now investing in gardening tools as they gear up for a summer at home. Action: show products from gardening section. The same applies to B2B companies too: A software-as-a-service (SaaS) company might uncover insights into the different features of its product that have become more popular across certain user segments and use this data to upsell or cross sell relevant packages.

Data science powers forecasting and simulations

With the potential for a second wave of the virus still a reality, businesses can use historical data from the first wave of the pandemic to anticipate how they can best react to future events. Now, with three months of customer behavior data, you can simulate various business outcomes during the second wave.

For example, those in the consumer packaged goods (CPG) industry were hit hard by the pandemic, with big disruptions to supply chain, impacting the entire operation. Now, knowing how the middle nodes of the supply chain have the potential to break down due to quarantine and changing demands, CPG producers could seek to open up direct-to-consumer channels to reduce their dependence on wholesalers and retailers.

Here, the company could use data science to create a simulation model of working directly with consumers and integrate this into its business continuity planning going forward. AI-powered modeling can help companies not only stabilize for the near-future, but also drive them to simulate other dramatic changes such as those that may come from the climate crisis.

Data science empowers workforces

The automation of manual tasks and use of AI chatbots is not new to many teams, but with the advent of the crisis, these technologies became more valuable than ever. While teams are squeezed on time and resources, AI-powered automation allows them to channel their efforts to the business activities that require human intelligence. One example of a sector leveraging AI to keep itself afloat during this time is air travel, as airlines have been overloaded with customer service queries related to cancelled travel plans, many are using AI chatbots to provide this information to customers.

Data science also shows value within workforces by providing insights to managers on areas where employees might need more support or resources. For example, company leaders can gather data on cloud infrastructure use by certain employees and determine whether or not they need more bandwidth or access to different features.

For those businesses that understand the value of data science but don’t have the in-house expertise that is necessary to execute it, low-code and no-code development platforms are allowing them to create analytics solutions – without a data science team. These platforms include Alteryx, Google’s CloudAutoML, Amazon SageMaker and Azure AutoML. They provide an environment for developing AI or ML applications without the need for extensive programming experience.

Data science team roles are shifting

The uptick in the need for data science, across industries, comes with the need for data science teams. While hiring may have slowed down in the tech sector – Google slowed its hiring efforts during the pandemic – data scientists professionals are still in high demand. However, it’s important to keep a close eye on how these teams continue to evolve.

One position which is increasingly in-demand as businesses become more data-driven is the role of the Algorithm Translator. This person is responsible for translating business problems into data problems and, once the data answer is found, articulating this back into an actionable solution for business leaders to apply.

The Algorithm Translator must first break down the problem statement into use cases, connect these use cases with the appropriate data set, and understand any limitations on the data sources so the problem is ready to be solved with data analytics. Then, in order to translate the data answer into a business solution, the Algorithm Translator must stitch the insights from the individual use cases together to create a digestible data story that non-technical team members can put into action.

Data Engineers are also growing in importance as the amount of data that businesses routinely collect continues to grow exponentially. While data gathering is an important initial step in an organization’s data journey, the majority of this data goes into databases and stays in storage without ever being mined.

Here’s where the Data Engineer comes in. Data Engineers exist to stop this data from sitting idle and make it accessible, and hence actionable. This role is vital at the moment as companies may be missing important data insights from the last few months that are sitting unaddressed.
Data science is no longer something that selected departments of organizations within certain industries deal with, as teams from across sectors and departments realize its value during uncertain times. As industry applications grow and data science and business teams become more analogous, organizations will discover that the only true way to operate – in the good times and the bad – is by being data-driven.