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Up until recently, dashboards, reports, machine learning models, data visualizations, and analytical insights for narrative purposes were the main outputs of data scientists and analysts. With the advent of genAI, data scientists are now expected to broaden the scope of their analytics to incorporate unstructured data sources, assist business teams in making the shift to data-driven decision-making, offer guidance on AI ethics and governance, and assist in putting regulations in place for the increasing number of citizen data scientists.
According to 75% of participants in a recent Foundry study on AI and analytics for OpenText, using genAI for data reporting and visualization is crucial. However, only 27% of respondents who work in analytics and data architecture thought it was extremely important.
This article examines how data scientists' and analysts' duties and responsibilities have evolved, as well as the instruments and procedures they employ.
Data scientists have long sought various use cases in which their expertise can be used, such as lead generation in marketing, sales pipeline optimization, profitability analysis in finance, and human resource skill development. While efficiency gains are crucial, data scientists should anticipate higher demand for their services due to genAI, particularly in revenue growth sectors where businesses seek new opportunities to leverage AI for digital transformation. The market for generative AI is expected to reach USD 967.65 billion by 2032, from USD 67.18 billion in 2024, valued at USD 43.87 billion in 2023.
Traditionally, data scientists have created dashboards to provide quick and simple ways for business users to find answers to issues regarding their data or to learn about new data sets. However, data scientists should be prepared for a new wave of genAI-driven breakthroughs, even though natural language querying and machine learning algorithms have been incorporated into data visualization and analytics platforms recently.
Many executives anticipate seeing more features aimed at citizen data scientists and a rise in business professionals learning how to use self-service business intelligence solutions incorporating genAI. According to Jared Coyle, head of AI at SAP North America, "GenAI is unlocking the full potential of data, enabling IT professionals to optimize planning and analytics capabilities through extended functionalities and automated workflows."
Data scientists should be interested in two new AI capabilities: AI agents and industry-specific AI models. Salesforce, for instance, just unveiled Industries AI, a collection of pre-built, adaptable AI features that tackle industry-specific problems in fifteen different sectors, including manufacturing, retail, financial services, healthcare, and automotive. One healthcare model verifies benefits, while an automotive model provides vehicle telemetry summaries.
Without question, AI is changing the jobs that data scientists focus on and how they execute their work. The true prospects are in leading the organization forward and producing ethically driven analytics-driven impacts.
