Post COVID-19 Business Model For Data Science Companies
The COVID-19 pandemic negatively affected millions of professionals working in the data and analytics fields – everything from consumer behaviour to supply chains was disrupted, and the economic fallout is furthering the damage. However, this crisis has also exposed technology’s Achilles’ heel. After the vaccines for COVID-19 were developed, the next normal 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’s critical for business leaders to understand that large-scale shifts are changing how people work and how business gets done,” says Brian Kropp, Distinguished Vice President, Gartner. “Leaders who respond effectively to these HR trends can ensure their organizations stand out from competitors,” he added. Currently, these companies are starting to structure their analytics so organizations won’t face the same model challenges as they saw during the pandemic. A company should incorporate strategic, durable execution in this period. These are the key activities: Discover new, repeatable, scalable processes and workflows for managing operations.Use the lessons learned and patterns from prior phases to formulate a new foundation and path forward. Source: Gartner Some basic business model customizations include: 1. Deploy a digital nerve centre Digital nerve centres that act as a critical link between digitalized operations, processes and assets, short term operational efficiency and long term strategy have become a key capability during COVID-19. They allow companies to mobilize resources, such as new data sources and analytics systems, to enable business teams to analyze emerging trends more quickly, shorten feedback cycles, and gain more insight into possible outcomes. For example, An international retailer with grocery stores in 15 countries uses a digital nerve centre to provide key business functions – supply chain, employee protection, finance, customer and store operations, and digital channel operations – with rapid access to data about the business, customers, and suppliers. As a result, supply-chain leaders can keep store shelves stocked, even with high-demand items. 2. Embrace real-time data Monitoring real-time data from websites, social media, clickstreams, 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 stream-processing capabilities, enable real-time data processing and analysis; use of the hybrid cloud allows decision-makers to respond in hours instead of days or weeks. 3. Prioritize cultural shifts The pandemic taught many leaders that their organizations could be more agile than they realized they were during a crisis. A growing number of interdisciplinary teams, agile working methods, and data-driven mindsets have sprouted overnight, creating highly targeted and fruitful analytics capabilities. Keeping the momentum going will require cultivating these shifts — such as reskilling workers. Such work is still possible while employees work remotely. As part of its preparation for the future, one financial-services company used Zoom video training to teach senior executives about AI concepts, ways to use the technology, and tips for implementing change. Organizations can be more accurate and faster at predicting the changing needs of their customer communities by having a diverse workforce. 4. Adopt a compliant design Analytical development teams can enhance risk management and detection with various activities and tools, allowing them to build critical oversight into the process. For example, documented guidelines, checklists, and training materials are available to set up diverse teams, use risk metrics, and stay on top of changes, such as changes in policies, laws, and regulations. Activities include putting in place methods and data tools for detecting and mitigating risk in data and monitoring models. There is no time for complacency or nostalgia in this new world. What was once normal cannot be restored; neither risk nor opportunity is small in this new era. In order to deal with constant uncertainty, disruption, and ever-changing environments, leaders must prepare organizations to thrive in this new environment.
The COVID-19 pandemic negatively affected millions of professionals working in the data and analytics fields – everything from consumer behaviour to supply chains was disrupted, and the economic fallout is furthering the damage. However, this crisis has also exposed technology’s Achilles’ heel. After the vaccines for COVID-19 were developed, the next normal emerged, allowing leaders […]
The COVID-19 pandemic negatively affected millions of professionals working in the data and analytics fields - everything from consumer behaviour to
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["data science", "Go", "API", "covid-19", "AI", "AWS", "Machine Learning", "Scala", "Git", "analytics", "GAN", "Data Science", "Data Scientist", "R", "AI (Artificial Intelligence)"]
["AI", "data science", "analytics", "AWS", "R", "Go", "Scala", "Git", "API", "GAN"]
Sohini graduated from the University of Kalyani with a master's degree in nanosciences and nanotechnology. She hopes to become a tech journalist one day. Her work focuses on digital transformation, geopolitics, and emerging technologies.