5 ways data scientists can prepare now for genAI transformation

Until recently, data scientists and analysts’ primary deliverables were data visualizations, machine learning models, dashboards, reports, and analytical insights used for storytelling. Now, with genAI capabilities, data scientists are called to expand their analytics to include unstructured data sources, help business teams pivot to data-driven decision-making, consult on AI ethics and governance, and help establish guardrails for the growing ranks of citizen data scientists.

“GenAI accelerates time-to-insight, lowers technical skills barriers, and empowers teams to scale bandwidth for data-driven decision making,” says Anant Adya, EVP at Infosys Cobalt. “While human expertise remains crucial, genAI acts as a potent force multiplier, augmenting human capabilities and unlocking new data innovation opportunities.”

In a recent OpenText survey on AI and analytics conducted by Foundry, 75% of respondents said leveraging genAI for data visualization and reporting was important. However, only 27% of respondents in data architecture and analytics roles ranked it as critically important. AI is driving significant business expectations, and leaders expect data scientists and analysts to gain the knowledge and skills needed to deliver competitive advantages. Data science teams should review their goals and discuss their strategies for leveraging generative AI.

“Analytics, data visualization, and machine learning are rapidly advancing with generative AI capabilities, enabling more intuitive data interactions, automated insights, and sophisticated predictive models,” says Sreekanth Menon, global head of AI/ML at Genpact. “As these technologies evolve, generative AI enhances these fields by creating more accurate visualizations, simplifying complex data interpretation through natural language processing, and automated generation of analytic reports.”

I’ve recently covered how data governance, software development, low-code development, and devsecops are evolving in response to AI breakthroughs and new business drivers. This article looks at the evolution of data scientists’ and analysts’ roles and responsibilities and the tools and processes they use.

Target revenue and growth

Data scientists have always sought a portfolio of use cases to apply their skills to, including lead generating in marketing, pipeline optimization for sales, profitability analysis for finance, and skills development for human resources. Finding productivity improvements is important, but with genAI, data scientists should expect greater demand for their services, especially in revenue growth areas, as businesses seek new digital transformation opportunities leveraging AI.

“To go beyond mere productivity gains, it’s important to focus on accelerating long-tail revenue, which has already benefited from digital transformation but still relies on human analysis. AI can now enhance this area for greater topline growth,” says Sreedhar Kajeepeta, CTO at Innova Solutions. “Key areas include analyzing demand from long-tail customers to adjust products and services, optimizing pricing and promotions, creating targeted marketing content for niche segments, and identifying new customer segments beyond traditional sales strategies.”

Paul Boynton, co-founder and COO at Company Search Incorporated (CSI), adds these strategic analytics use cases. “Generative AI significantly enhances the user interface for analyzing market trends, predicting product demand, optimizing supply chain efficiency, and identifying compatible partnerships to drive sales and growth,” he says.

To meet these increased business needs, data scientists will need to increase their business acumen and find ways to discover and analyze new data sets targeting revenue growth.

Integrate with AI-generated dashboards

Data scientists have traditionally developed dashboards as quick and easy ways to learn about new data sets or to help business users answer questions about their data. While data visualization and analytics platforms have added natural language querying and machine learning algorithms over the last several years, data scientists should anticipate a new wave of genAI-driven innovations.

“Over the next two years, we expect a transition from static business intelligence dashboards to more dynamic, personalized analytics experiences,” says Alvin Francis, VP of product management for business analytics at IBM. “With generative AI, the reliance on traditional dashboards diminishes as users can remove the noise of the analytics and get to actionable insights conversationally. Freed from ad-hoc dashboard-generation, data analysts and data scientists will concentrate on documenting organizational knowledge into semantic layers and conducting strategic analytics, creating a virtuous cycle.”

Another prediction comes from Jerod Johnson, senior technology evangelist at CData, saying, “As genAI platforms become integrated into visualization tools, they enable more dynamic and interactive representations of data, allowing for real-time synthesis and scenario analysis. Over the next few years, data scientists can expect these tools to evolve to make visualizations more intuitive and insightful, even answering unasked questions for innovative discoveries.”

Data scientists should use this period to learn how to use genAI capabilities in their data visualization platforms. As visualization becomes easier, data scientists will need to be prepared to use the advanced analysis capabilities to deliver new types of insights.

Empower citizen data scientists

Many leaders expect an uptick in features targeting citizen data scientists and an increase in business people learning self-service business intelligence tools with genAI capabilities.

“GenAI is unlocking data’s full potential, enabling IT professionals to optimize planning and analytics capabilities through extended functionalities and automated workflows,” says Jared Coyle, head of AI at SAP North America. “This evolution streamlines complex tasks and makes advanced tools more accessible to non-technical users. In the coming years, increased automation of routine tasks will empower teams to focus on more strategic work, driving more efficient data-driven decisions across organizations.”

The growth is likely to come as data visualization tools enhance natural language capabilities and automate the application of machine learning models. These capabilities will simplify the work for citizen data scientists, who can query data, find outliers, identify trends, and create and maintain dashboards with less expertise and fewer clicks.

“GenAI-powered applications and platforms can generate dynamic visualizations, data storytelling narratives, and clear explanations for complex data insights,” says Sharmodeep Sarkar, enterprise AI architect at RR Donnelley. “This makes them easier to understand for non-technical audiences, helping to breed empowered ‘citizen data analysts’ across large organizations.”

The transition of data, analytics, visualization, and modeling skills from data science to business teams has been happening for over a decade, but genAI is likely to be an accelerant. What does this mean for data scientists and their work?

“As GenAI becomes more integrated in analytics, routine tasks like data prep and basic analysis will become more automated, freeing up time to dive deeper into insights,” says Jozef de Vries, chief product engineering officer at EDB. “Advanced AI tools will make data visualization and storytelling more intuitive, making it easier for data scientists to communicate complex findings to non-technical colleagues while also empowering these colleagues to use natural language to explore data. This will help bridge the gap between data teams and other departments, fostering a more collaborative environment.”

Julian LaNeve, CTO at Astronomer, says data science teams should expect increased stakeholder interest and participation because of generative AI capabilities. “The barrier to entry to interact with and extract insights from data will be significantly lower, so establishing a strong data culture and practices is extremely important,” he says.

LaNeve recommends developing a proper data platform based on data engineering best practices and well-cataloged data dictionaries for non-technical colleagues. Another role is consulting on proper governance and guardrails for end users.

Harness unstructured data sets

As analyzing rows and columns of data becomes easier for business users, data scientists should expand their skills and analytics efforts to investigate unstructured data sources. Many marketing, sales, and customer service data sets are unstructured, so analyzing them helps align with businesses that seek growth and competitive advantage.

“Generative AI is revolutionizing how customer-centric organizations synthesize and analyze large volumes of free-text conversations,” says Saeed Aminzadeh, CPO at mPulse. “By accurately categorizing consumer intent and needs at scale, these advanced tools provide richer, more actionable insights.”

One technology data scientists should learn is graph databases. Another, knowledge graphs, can be useful for developing RAGs that augment LLM models with domain intelligence.

“Organizing data as knowledge graphs instead of flat SQL tables gives a tremendous advantage in doing advanced analytics, but also running machine learning models,” says Nikolaos Vasiloglou, VP of research ML at RelationalAI. “The most frequent task is feature engineering, and as LLMs get embedded in knowledge graphs, data scientists should expect to get more meaningful generated features.”

Hema Raghavan, head of engineering and co-founder at Kumo AI, says data scientists should be familiar with graph neural networks (GNNs). “GNNs have the capability to look across tables and find the signal needed for predictive AI tasks, thus eliminating the need for a large number of feature engineering workflows. Data scientists can then focus on impact and identifying opportunities in their business where predictions can be plugged in.”

Leverage AI agents and models

Two emerging AI capabilities that should interest data scientists are industry-specific AI models and AI agents.

For example, Salesforce recently announced Industries AI, a set of pre-built customizable AI capabilities that address industry-specific challenges across 15 industries, including automotive, financial services, healthcare, manufacturing, and retail. One healthcare model provides benefits verification, and an automotive model provides vehicle telemetry summaries.

Regarding AI agents, Abhi Maheshwari, CEO of Aisera, says, “AI agents elevate LLMs by engaging in reasoning, planning, decision-making, and tool usage, handling tasks like CRM and ERP transactions autonomously. These agents simplify data tasks usually done by data analysts, including cleaning, exploratory data analysis, feature engineering, and forecasting.”

These two trends illustrate a secondary shift in the data science role—from wrangling data and developing machine learning models to focusing on leveraging AI agents, investigating third-party models, and collaborating with citizen data scientists on applying AI, machine learning, and other data science capabilities.

Another critical area for data scientists to become versed in is AI ethics and how that contributes to their organization’s AI governance. “As genAI embeds further in analytics, data science teams must adapt by acquiring new skills, focusing on strategic collaboration, and prioritizing AI ethics,” says Bogdan Raduta, head of AI at FlowX.AI.

Menon of Genpact says, “The use of genAI in data storytelling will need to address ongoing challenges such as mitigating biases and ensuring the accuracy of generated content through responsible AI to ensure ethical use, transparency, and fairness, enhancing trust and accuracy in data-driven decision-making.”

There is no doubt that AI is transforming how data scientists do their work and what tasks they focus on. The real opportunities lie in guiding the organization forward and delivering analytics-driven impacts in ethical ways.