Business model

Post COVID-19 business model for data science companies

The COVID-19 pandemic has negatively affected millions of professionals working in data and analytics – everything from consumer behavior to supply chains has been disrupted, and the economic fallout is compounding the damage. However, this crisis has also exposed the Achilles heel of technology. After the development of COVID-19 vaccines, the next standard emerged, allowing leaders to move from survival mode to a more secure position. Now is the time to reimagine and reform the business model of data science companies.

“It is essential that business leaders understand that large-scale changes change the way people work and the way business is conducted,” says Brian kropp, Distinguished Vice President, Gartner. “Leaders who respond effectively to these HR trends can ensure that their organizations stand out from their competitors,” he added. Currently, these companies are starting to structure their analyzes so that organizations don’t face the same model challenges they saw during the pandemic.

A business must incorporate strategic and sustainable execution during this period. Here are the key activities:

  • Discover new, repeatable and scalable processes and workflows for operations management.
  • Use the lessons learned and models from previous phases to formulate a new foundation and a new way forward.

Source: Gartner

Some basic business model customizations include:

1. Deploy a digital nerve center

Digital nerve Centers that act as a vital link between digitized operations, processes and assets, short-term operational efficiency and long-term strategy have become a key capability during COVID-19. They enable companies to mobilize resources, such as new data sources and new analysis systems, to enable sales teams to analyze emerging trends more quickly, shorten feedback cycles and better understand results. possible.

For example, an international retailer with grocery stores in 15 countries uses a digital hub to deliver key business functions – supply chain, employee protection, finance, customer and store operations, and digital channel operations – with quick access to company, customer and supplier data. As a result, supply chain managers can keep store shelves well stocked, even with items in high demand.

2. Kiss real time data

Real-time data monitoring of websites, social media, navigation routes, and mobile apps has become increasingly important in recent months. A leader no longer has the luxury of waiting days and weeks for the latest information. Various technologies, including messaging platforms and flow processing capabilities, enable real-time data processing and analysis; using hybrid cloud enables decision makers to respond in hours instead of days or weeks.

3. Prioritize cultural changes

The pandemic has taught many leaders that their organizations can be more nimble than they realized during a crisis. A growing number of interdisciplinary teams, agile working methods and data-driven mindsets have sprouted overnight, creating highly focused and fruitful analytical capabilities. Keeping the momentum going will require cultivating these changes, such as retraining workers. Such work is still possible while employees are working remotely. As part of its preparation for the future, a financial services company used Zoom video training to teach executives about AI concepts, ways to use technology, and tips to implement change. . Organizations can be more accurate and faster at forecasting the changing needs of their customer communities by having a diverse workforce.

4. Adopt a compliant design

Analytics development teams can improve risk management and detection with a variety of activities and tools, enabling them to embed critical monitoring into the process. For example, documented guidelines, checklists, and training materials are available for setting up diverse teams, using risk metrics, and staying on top of changes, such as changes in policies, laws, and regulations. Activities include the establishment of data methods and tools to detect and mitigate risks in data and surveillance models.

There is no time for complacency or nostalgia in this new world. What was once normal cannot be restored; neither the risk nor the opportunity are small in this new era. In order to cope with constant uncertainty, disruption and ever-changing environments, leaders must prepare organizations to thrive in this new environment.