Dow Jones isn’t just reporting on the data revolution, it’s leading one. With powerhouse brands like Wall Street Journal, Barron’s, MarketWatch, and Investor’s Business Daily, the company has long set the standard for trusted journalism. But behind the headlines, a new story is unfolding, one powered by data science, GenAI, and a bold push toward smarter, faster, and more personalized audience engagement.

A growing team of data scientists, technologists, and marketing leaders are working to turn insights into action. One of the key architects helping shape this transformation is Bhaskar Borah, SVP and Head of Marketing Data Science & Technology. Brought in to build the function from the ground up, Borah isn’t just running models, he’s rewriting how Dow Jones uses data to grow subscriptions, optimize campaigns, and make business decisions that move the needle.

He’s also a key voice on Dow Jones’s AI Steering Committee, helping shape how the company adopts and governs AI. From real-time pricing engines and GenAI-powered creative testing to AI-driven content summaries and customer insights, Borah and his team are turning data into a competitive advantage.

In this no-hype, insight-packed conversation, Borah shares how Dow Jones is scaling smarter, where GenAI is showing real ROI, and why the media industry’s next big leap won’t come from headlines, but from the data behind them.

Edited Excerpts 

Q: Can you walk us through your role and background and how you contribute as a member of the AI Steering Committee?

I joined Dow Jones more than a year ago to head Marketing Data Science & Technology, reporting to our Chief Marketing Officer who spearheads our subscription strategy. I came on board to set up this capability from the ground up, focusing on organizational marketing functions and our subscription business. Prior to this, I had spent close to two decades in financial services, leading global data science teams across different consumer businesses and channels.

I represent marketing and subscriptions in Dow Jones’s AI Steer Committee. The role enables us to formalize AI governance policies and review and approve AI use cases across the organization. Our goal is also to advance AI capabilities across the business, with governance in mind for relevant use cases.

Q: What are some emerging trends in marketing analytics that you are particularly excited about?

I continue to be excited and optimistic about the possibilities and advancements in this field. A few of them in particular are:

• Real-time decisioning using live user data to make granular decisions in pricing, ad placement, and customer interaction in real time.
• Integration of traditional machine learning approach with GenAI.
• Natural language querying and AI-driven insights that help in better storytelling easily, a much needed upgrade from the traditional dashboard approach.
• Causal inference models that help move beyond correlation based analytics.
• AI agents to help manage campaigns and test different strategies faster.
• Use of GenAI to test and optimize large variations of creatives and messages in real time.

Q: Could you share specific examples of how you have been able to improve decision-making, or enhance customer experiences, and business growth, using data science?

We have been able to stand up a great team quickly. My team is currently focused on standing up both foundational and transformational data science capabilities for the business. Foundational capabilities include AI/ML governance processes, streamlining executive dashboards, streamlining data infrastructure and third-party data enrichment.

I am also very proud of the team for building critical transformational capabilities for the business that will act as growth engines and help make data science drive granular decision-making in all key strategic decision areas – whether it is pricing, media optimization, product design, personalization, acquisition, engagement, or retention strategies.

A few examples include the dynamic pricing, forecasting and scenario planning engine for finance and marketing, media optimization tools like MMM & MTA, next best recommendation engine, Market 360, NPV based decisioning, and Customer 360.

We are also taking a data science approach to identify key product features and journeys that have high causal relationships with better engagement and retention, which is going to inform our product design and content strategy.

Some of these capabilities are in production now and some are work in progress. We are just getting started here and we are going to build on this great start across different areas in the organization.

Q: Can you highlight any key pain points that you or other data leaders in the industry are facing as AI becomes increasingly prevalent?

There is still a big gap in access to clean data, unified across platforms and channels. Stitching them together in a clean way for the models to consume takes quite a bit of effort and time. Innovators in this field should solve this problem better.

With the field evolving rapidly, there is a big talent and skill gap in all areas of AI/ML and related fields. Also, AI governance is new and evolving and uncertainties around this make it hard to operate.

Lastly, measurement and attribution is increasingly becoming challenging, with less dependencies on cookies and more on first-party data.

Q: What were some of the early challenges you encountered, and how did you approach driving change in the face of those obstacles?

I was lucky for a few things to fall in place nicely. We were able to assemble a great team through our networks quickly. We also onboarded a few analytics vendors to help us get started and act as our thought partners as we were ramping up internally.

Reporting directly into the business also helps in terms of getting the much needed alignments and support. We were also able to establish key connections with our cross-functional partners in finance, tech, product, our newsrooms and the GMs.

The culture at Dow Jones is very collaborative which has been helpful as we look to  upgrade data infrastructure and data science tools to run the business more effectively. We have a large base of subscribers interacting with our content and products on a daily basis and if we are not able to utilize that data to curate better content, product and experiences for them, we will be missing out on a big growth lever.

In terms of work, my preference has been a custom build approach, where our internal teams work closely with external vendors to build these custom solutions. We work with a variety of vendors depending on use case and business needs on a project-to-project basis. That has enabled us to build high-quality solutions that work on the ground while providing learning opportunities and meaningful challenges for the team. I am a big proponent of this approach, rather than buying black-box solutions from vendors.

Q: As AI continues to reshape industries, how has Dow Jones’s data strategy evolved — particularly in the age of GenAI? How are you exploring the use of Large Language Models (LLMs)?

GenAI is poised to profoundly impact the media industry and more broadly how we live and work. It’s an important breakthrough and opens up a world of opportunities, but these tools can introduce new risks. That’s why we are taking an active and considered approach to how we adopt and implement emerging GenAI technologies.

We are actively exploring how we can harness the power of AI in service of our readers and customers. Our philosophy is to view AI as “Authentic Intelligence” — cutting-edge technology built on a foundation of trusted journalism, data, and analysis to help people make decisions. Trust and transparency are our guiding principles as we deploy AI across our solutions and internal operations.

From the innovation perspective, we have integrated GenAI throughout some products for use cases around translations into local languages, article summarizations, GenAI search capabilities and compliance offerings. Specifically for marketing, we are exploring a shortlist of use cases to drive efficiency and productivity in areas like customer service, marketing operations and creative production.

Q: How do you see the future of AI and data science shaping the media and publishing industry?

AI will continue to transform the media and publishing industry at a rapid pace. A few examples where I am already starting to see some green shoots are:

• Content summaries and headlines: AI-assisted content including summaries and headlines will be widely used in newsrooms and marketing use cases.
• Content localization: AI will be used to translate and culturally adapt content for global audiences at scale, improving reach with much lower cost.
• Hyper personalization: Recommendation engines for content will continue to improve, increasing user engagement.
• Intelligent search: AI-enabled tagging and semantic search will help make content libraries more searchable and usable.
• Monetization: AI will continue to improve significantly to determine the optimal moment and medium to present ads and paywalls.
• Fact checking: AI will help detect misinformation more accurately.
• Workflow automation: AI will drive enterprise wide efficiencies to streamline processes and serve as assistants in various departments.

Q: What’s Dow Jones’ view on AI models being trained on proprietary content, an issue that’s sparked legal pushback from some publishers? And do you think clearer guardrails are needed around data use in AI?

Dow Jones’s approach to AI and working with AI companies is to embrace the opportunities that it can unlock for our industry and our business while also protecting the value of our IP and our archives.

Our preference is for commercial agreements that safeguard the value of our news and information like the deal News Corp brokered with OpenAI – but we will also fiercely defend our IP via legal means when necessary.

Q: You have witnessed firsthand the data and analytics landscape both in India and in the U.S. Can you highlight any key observations?

India has a huge talent pool in this field. I have worked with so many talented and hard-working colleagues and teammates from India, both when I started my career there and later when I moved to the U.S. I have learned a ton from them over the years.

The U.S. does not have the same scale of talent in data and analytics, but I have come across quite a few bright peers and teammates who have deep research mindsets and have solved complex problems.

In the U.S., you also get the advantage of being embedded into the business and getting that much-needed context, which is critical when it comes to decision-making and harnessing the true power of analytics.

Q: What advice would you give to data professionals working in this field?

These are exciting times to be in this field of data science and AI. This space is evolving rapidly. My advice will be to embrace change and continually upskill yourself.

It is also very important to align closely with the business, solve real business problems and drive real quantifiable business value. More importantly, have fun and buckle up for the joy ride!