In the latest episode of the Digital Leaders Dialogue series by XPON, host Tim Lillyman sits down with Rahul Ashiya, Senior Data Product Manager at Nine, Australia’s largest locally owned media company.
If you’ve ever read the Sydney Morning Herald, watched the NRL, or indulged in some Married at First Sight, you’re familiar with Nine’s media empire.
But what happens behind the scenes with their data strategy? That’s what this fascinating conversation explores.
Want to hear the full conversation? Check out the Digital Leaders Dialogue podcast:
From Shopify Stores to Media Giant: Rahul’s Journey
Rahul brings a diverse background to his role at Nine. He began his career in India building Shopify stores as a consultant, gaining valuable experience in adtech, martech, and e-commerce. His journey took an interesting turn when he arrived in Australia in 2020 – just one week before COVID lockdowns began!
“I came here to Australia in 2020. I think a week before Covid, so obviously you can imagine new country and Covid lockdowns. So it was interesting time there,” Rahul shares.
After roles at companies like Shopin in recruitment and sports tech, Rahul moved to Network Ten as an audience and growth product manager before landing his current position at Nine.
Perhaps most fascinating is Rahul’s former life as a startup founder. In 2021, his love for reading fantasy novels sparked an innovative idea: “bringing books to life” by turning written content into visual experiences.
This venture had him experimenting with generative AI tools like DALL-E and Midjourney long before they became mainstream.
“We were technically dabbling in the world of Gen AI two years before it became popular via ChatGPT,” Rahul explains. While the technology wasn’t quite ready then, his early exploration of AI has proven invaluable for his current role.
What IS a Data Product Manager?
So what does a Senior Data Product Manager for Publishing actually do? As Rahul explains, his role focuses on turning raw data into usable products that help business stakeholders achieve their goals.
“We take data as the raw entity and try to create a product that fundamentally can be used by a business stakeholder to achieve something,” he explains. “It could mean campaign performance for someone. It could mean the ability to predict the performance of something else. It might mean building a personalisation engine.”
At Nine, Rahul serves an impressive range of stakeholders. While marketing is certainly part of the equation, his team also supports:
- Newsrooms with hundreds of journalists and editors who need real-time data to make quick decisions about article publishing and placement
- Strategic teams handling audience growth
- Product teams making decisions about what to build
- Adtech and commercial teams driving revenue
As Rahul puts it: “Never a dull day in publishing.”
But managing all these stakeholders and data sources isn’t easy. Rahul’s team acts as a bridge between raw data/engineering capabilities and business stakeholders.
“Unlike a normal product manager, we really need to know how data works,” he says. This understanding helps them determine what data sources are needed to answer specific questions – sometimes requiring ten different sources to solve a single problem!
Building a Data Culture Through Education
A key focus for Rahul’s team is educating stakeholders about data processes and tools. This involves running workshops on topics like how to use certain tools, data terminology, setting OKRs, and tracking KPIs.
“Not everyone is that involved in data or understands how it works,” Rahul notes. His team aims to help stakeholders appreciate the journey behind seemingly simple requests like “How many page views were there?”
They’re also working to create self-service capabilities so stakeholders can access the data they need without always requiring assistance.
“There are several different definitions of self-serve,” Rahul explains. “For a data analyst it might mean really different things compared to an editor.”
The team approaches this challenge by:
1. Providing good tools that make it easy for stakeholders to access data
2. Strategically designing these tools to answer 80% of high-level questions for specific business domains
3. Stepping in personally for more complex requests that require deeper analysis
Beyond the AI Hype: Finding Real Value
When discussing AI, Rahul takes a refreshingly grounded approach, looking beyond the current hype cycle. He’s skeptical of many current AI implementations:
“Right now, what I’m feeling is that a lot of the use cases have got that ‘snake oil merchant’ feel – it’s the basic thing that I could have done in two steps now able to do in a single step. And sometimes the value proposition is not even that good.”
He references a study suggesting that 80% of current Gen AI use cases are bound to fail, with only 20% truly solving customer problems in a sustainable way.
Rahul is also critical of approaches focused solely on staff reduction: “That’s not a good approach. I’m thinking of this in terms of time to value minimisation.”
Instead, he sees AI’s real value in reducing the time it takes to accomplish tasks – whether writing an email (from an hour to five minutes) or creating an image in Photoshop (from a day to an hour).
Rather than replacing humans, Rahul believes AI can create “super-powered” workers who can accomplish much more. He sees particularly exciting possibilities for product managers, who can use AI to:
- Quickly understand unfamiliar technical concepts
- Run artificial focus groups with multiple perspectives
- Provide more detailed requirements to engineers
- Easily measure business impacts
“What a team of ten people could do earlier, now they can probably do 100 people’s worth of work in the same timeframe,” he suggests.
The Future: Super-Marketers and Quality Data
Looking ahead, Rahul is excited about how AI is driving a renewed focus on data quality. “Everyone has realized the importance of high-quality data,” he notes. Companies are now more willing to invest in data cleanup as a foundation for AI initiatives.
For marketers specifically, he envisions AI creating “super-marketers” who can handle every aspect of marketing more effectively:
“You take every single aspect of being a marketer – from campaign creation to the creatives… you can just do it yourself now. You can use Gen AI to personalise the creatives as well.”
He predicts future email campaigns where all 100,000 emails have personalised content AND visuals tailored to each recipient. He also sees potential for improved attribution modeling using AI.
As Rahul puts it:
“Imagination is your limit. If you can think it, you want to try, maybe I can help you do it. Otherwise, that’s your next startup idea.”
Key Takeaways
This conversation offers valuable insights for anyone working with data and AI in marketing or media:
- Data quality is essential – “garbage in, garbage out” applies even more with AI
- Focus on using AI to enhance human capabilities rather than replace them
- Creating self-service data tools requires understanding different stakeholder needs
- The most valuable AI applications reduce time-to-value, not headcount
- Building a data culture requires ongoing education and stakeholder engagement
As organisations navigate the rapidly evolving landscape of data and AI, Rahul’s practical, value-focused approach offers a refreshing alternative to the hype cycle.
Listen to the full episode now
Want to hear the full conversation? Check out the Digital Leaders Dialogue podcast: