Data Democratization With GenAI: Alteryx, BBDO and Light & Wonder
In a 2024 AI Connect webinar, analytics leaders from Alteryx, BBDO and Light & Wonder explained how GenAI is opening data to non-experts. They covered skills gaps, silos, governance and privacy, and how roles will shift.
By The AI Enterprise Magazine Team4 min read
Key takeaways
- Access, not just tools. Bhatia defined democratization as letting any employee access, understand and use data to make decisions.
- Natural language lowers the barrier. Martinez said chat-based interfaces let non-experts prepare and blend data without SQL.
- Start governance early. Panelists urged role-based access, stewards in each function, cross-functional policies and training.
- Assistants over chatbots. Venkataraman described orchestrated assistants with defined roles, such as data cleaning and exploratory analysis.
- Work changes, not just headcount. Less time on tool configuration, more on reviewing outputs and explaining models.
In a 2024 AI Connect webinar, brought to you by Alteryx and Astronomer, a panel of analytics leaders discussed how generative AI is changing who inside a company can work with data. Peter Martinez, Product Marketing Director at Alteryx, was joined by Supriya Bhatia, VP and Head of Data Science and Advanced Analytics at BBDO, and Murali Venkataraman, Executive Director at Light & Wonder. Dr. Frances Boykin, Director of Advanced Analytics at AT&T, was also listed as a panelist. A AI Connect co-founder moderated.
The discussion covered what data democratization means, what blocks it, where GenAI helps and how to keep it safe.
What is data democratization and why does it matter now?
Bhatia defined it as making data accessible to everyone in an organization, not just IT or data specialists, so any employee can access, understand and use data to make informed decisions. Speed is the reason it matters now, she said, because decisions are made faster than ever.
Martinez said it does not mean everyone needs a PhD in data science. It is about empowering knowledge workers to do analytical tasks "without having to submit a ticket or wait for a data science team that might be at capacity."
Venkataraman compared it to the arrival of the internet in the 1990s. Giving everyone access sparked innovation, he said, and data should be treated as a utility available across the organization, supported by a culture of curiosity.
What are the biggest obstacles?
Martinez pointed to skills gaps. Many knowledge workers lack the time to learn coding, he said, and low-code and no-code tools, plus AI that explains insights in plain language, can help.
Bhatia said the challenge depends on where a company is in its data life cycle. Early on, technical infrastructure is the problem, she said. Later, data silos are, and the answer is consolidating data from marketing, supply chain and media into one accessible repository such as a data lake or warehouse.
Venkataraman called democratization "a journey." He advised first finding the organization's maturity level, then centralizing data, defining governance frameworks and building data literacy. He also cited global privacy rules such as GDPR as a challenge in the gaming industry.
How is GenAI making data more accessible?
Martinez said natural language interfaces are among generative AI's biggest value propositions. He described a use case assistant called Playbooks in an Alteryx product, which, he said, generates use cases and builds a proof of concept from a user's role, function and industry. He also said Alteryx was building a copilot to help non-technical users prepare and blend data, replacing the SQL join that once required expertise with a chat request.
Venkataraman said asking a company's data a question should feel like chatting with a colleague. He said his team is moving from chatbots toward orchestrated assistants, each with a defined role such as data cleaning or exploratory analysis, passing results to the next. Bhatia noted GenAI tools are also helping with code generation, QA and testing and customer support.
Martinez said AI lowers the barrier to adoption, and the best people to do analytics are often those who know their functional area best. There will always be experts, he said, but "there will never be enough of them to service all of the needs of a data driven business."
How do you balance access with privacy, security and quality?
All three speakers said governance has to come first. Bhatia recommended starting governance policies even if imperfect, then using role-based access control, mandatory training and data stewards in each function who own compliance.
Martinez said policies should be cross-functional, involving legal, business leads and analytics professionals, and that data quality work like preparation and blending becomes more important as AI is used more. Venkataraman described detailed standards for collecting, securing and using data, group-based access policies, continuous automated validation checks and ongoing employee training. He said his team also uses an automated machine learning platform.
Will democratized data change jobs?
Bhatia said her data scientists asked whether they were becoming redundant, and her answer was "we are not becoming redundant." She compared the shift to cloud computing, where workers upskilled. Venkataraman said AI will not replace people so much as change how they work, comparing it to the introduction of calculators, and expected new roles to emerge.
Martinez expected the nature of work to change rather than total headcount. Analysts might spend less time configuring tools and writing queries, he said, and more time reviewing outputs, evaluating models and explaining them to business colleagues.
Where can you hear more from data and AI leaders?
AI Connect, formerly AI Connect, hosts 24 free live webinars on its 2027 schedule, including the AI × Enterprise Panel Series and AI Demo Days. Registration is coming soon. See /webinars/.
Frequently asked questions
What is data democratization?
Panelist Supriya Bhatia of BBDO defined it as making data accessible to everyone in an organization, not just IT or data specialists.
How does generative AI help democratize data?
Panelists said natural language interfaces and assistants let non-technical users prepare data, generate use cases and ask questions without coding.
How do you keep democratized data secure?
They recommended governance policies, role-based access control, data stewards in each function, continuous validation checks and ongoing training.
Will data democratization replace data scientists?
Panelists said no. They expected roles to shift toward reviewing, evaluating and explaining results, with upskilling as in earlier technology waves.
This article is based on remarks by Peter Martinez, Product Marketing Director, Alteryx; Supriya Bhatia, VP, Head of Data Science and Advanced Analytics, BBDO; Murali Venkataraman, Executive Director, Light & Wonder; Dr. Frances Boykin, Director, Advanced Analytics, AT&T at AI Connect webinar (Analytics for All) on July 17, 2024. More coverage: Webinars.
Filed under: People, Culture & Leadership, AI Governance & Trust, AI Use Cases & Applications
