hearst publications
randy bean
How is legendary media and publishing company Hearst using data and AI to transform business in the 21st century?
That’s the question I posed to Jessica Hogue, Hearst’s chief data officer (CDO) for consumer media. She is responsible for developing integrated data strategies that enable Hearst companies to scale business initiatives faster through shared capabilities. Hogue brings deep expertise in leveraging data, analytics and technology in the media and advertising sectors to his role as Hearst’s first CDO. She previously held data, analytics and digital leadership roles at Inn0vid and Nielsen. Hogue commented, “This is a new role for Hearst’s Consumer Media division. Our mission is to make data usable, reliable, and durable for our teams. AI, and specifically generative AI, is accelerating this transformation by changing the way Hearst builds, tests, and operates its data systems.” She added: “Ultimately, it’s about using data and AI to enable new forms of growth.”
Today, Hearst is one of the nation’s largest global and diversified information, services and media companies. The company was founded in 1887 by William Randolph Hearst. By the 1920s, Hearst owned the world’s largest media conglomerate, including magazines, newspapers, and radio stations in major cities across the United States. The firm’s diverse portfolio includes Fitch Group, a global financial services leader. Hearst Health, a group of medical information and services businesses. Hearst Transportation; ownership of cable television networks including A&E, HISTORY, Lifetime, and ESPN; 35 TV stations. 30 daily newspapers and 50 weekly newspapers. Digital service business. Published in over 200 magazines worldwide. Hearst remains a private company.
Since our founding, Hearst has continued to innovate in the media and publishing industry. “Data underpins how we understand and serve our audiences at every touchpoint, including subscribers, general readers, and viewers,” said Hogue. She continues, “We use data across the lifecycle of our business, from measuring engagement and behavior to powering decision-making systems like next-best behavior models.” Hogue added: “Given the breadth and depth of Hearst’s media operations, spanning hundreds of digital properties globally and dozens of markets locally, our competitive advantage is our speed and agility to provide teams with trusted, decision-ready information.”
Managing data as an enterprise business asset at Hearst
For Hearst, the company’s enterprise data strategy is built on the premise that data is the company’s business asset. “To fully value an asset, it must be managed and measured, which is why we actively measure how our data capabilities translate into business outcomes, including revenue realization, efficiency, and accelerating innovation,” Hogue explains. “The use of our datasets evolves with the needs of our business. Our datasets inform our paywall strategies, subscription offers, retention efforts, newsletter programs, and how we evaluate the quality and addressability of our advertising inventory. “We are also innovating our revenue workflow end-to-end, from the first customer interaction to how we balance our product mix and run dynamic sales processes,” Hogue added.
For years, the media industry has focused on accumulation: more signals, more scale, more segments, Hogue explains. “We used to see documents touting petabytes of data. That no longer creates an advantage. Value now comes from how easy the data is to use, trust, and access,” Hogue says. She added, “This change has led to a renewed focus on quality, metadata, and governance, not just for traditional data custodians, but across all levels of the business.” Hogue added: “This includes building foundational systems that allow data to scale across a decentralized organization. It also means striking a balance between centralization and federation.” She summarizes: “We create consistency where it matters while enabling companies to move and innovate quickly.”
Hurst has been intentional about how the company built its chief data officer function. Hogue explains: “We are embedding data and machine learning capabilities within the enterprise to operate a collaborative federated model, while our enterprise teams are focused on generating robust data inputs with built-in governance and enabling data into disparate systems to power workflows and support model development.” “We are increasingly thinking about how data assets extend beyond our own ecosystem. The growing demand for high-quality, trusted content from platforms and hyperscalers creates opportunities to restructure data assets to create new forms of value.”
Data and AI Business Transformation at Hearst
At Hearst, we know that data is the foundation of AI. Hogue commented, “We are building a unified view of audiences across all touchpoints to better understand lifecycle behaviors, consumer macro trends, and long-term value. As media monetization continues to evolve, we need better tools to assess what’s working at scale.” “The power of data is determined by the ability of the systems to metabolize it. We are continually modernizing our data architecture to make it more composable, more accessible, and more AI-ready.”
Hogue explains that, like many organizations, Hearst’s data exists across multiple environments, including CRM systems, order management platforms, product catalogs, advertising systems, and user data layers. “Rather than trying to centralize everything, we are redesigning these datasets into machine-readable metadata and semantic layers. Vectorization, embedding, and knowledge graphs are becoming key components of this foundation. We are building these capabilities modularly,” she said. Hogue added, “This will enable our analytics and AI systems to make inferences about data, rather than just querying it.”
Data no longer just informs decisions at Hearst. This allows both human and machine systems to interact first and then act. This change has led to a renewed emphasis on the usefulness of data. “Data must be well-structured, contextualized, and trusted to drive outcomes at scale,” says Hogue. “The goal is not just to understand what happened, but to continually shape what happens next.” The next phase is less about adding data and more about making it programmatically and contextually accessible. This includes a semantic layer, standardized definitions, and richer metadata. “Given our broad content portfolio and the value of our journalism, we believe there is a significant opportunity to surface and amplify that value,” said Hogue.
AI is accelerating this transformation in many ways. The company has started using AI agents to offload repeatable analytical and operational tasks. “Functionally, this enables a wide range of use cases, from agent-driven analysis to faster experimentation and real-time decision-making. The goal is a system where insight generation and action are tightly coupled,” Hogue said. She added, “One of the most important changes we’re seeing is in what it means to be data-driven. Historically, being data-driven meant looking at dashboards to inform decisions. This model no longer works in the age of AI. We have to become intelligent enterprises.”
Build a data and AI business culture with Hearst
Hearst is focused on building an infrastructure that allows both employees and AI systems to interact with data more naturally. “AI has accelerated our cultural change,” Hogue explains. “All of our colleagues at Hearst are working directly with AI tools, and there is a growing awareness of the importance of data quality and metadata in producing reliable output.” She goes on to say, “The ability to transform between business and data is critical and often underestimated.”
“We focus first on understanding the business problem we are trying to solve and then translating that into a data solution,” Hogue says. “Early on, we recognized the need for a better way to understand consumers, partnering with a company that has been operating mass distribution for decades,” Hogue continued, “which allowed us to build audiences from fragmented signals across systems and experiences. “Understanding the business not only influences what we build, but also how we design solutions, communicate our priorities, and partner with the business.” She added, “We seek and hire for this ability across nearly every role because it is fundamental to success. We have been looking at greenfield opportunities. Our mission has always been to help businesses as they transform and grow.”
The business value of investments in data and AI is measured within Hearst in several ways. “One of the principles I’ve held onto since the beginning of my career is that usage is the clearest signal of value. It’s a form of market fit,” Hogue said. “We closely track adoption and are disciplined about stopping work when usage stagnates. This is where our operating model adopts a product mindset.” She continued, “Furthermore, we map our work to core business outcomes such as subscription growth, retention, ad performance, and revenue that is influenced by data-driven features.”
Hearst monitors and measures data enablement, specifically how teams can effectively access and use data. This can be seen in the speed of decision-making and experimentation, or the reduction of redundancy. “We measure durability, whether we are building assets that can be reused and scaled across the organization. We aim to compound investments, address multiple use cases, and drive long-term value,” adds Hogue.
Prepare for the future of AI with Hearst
Leadership from the top of the company is key to building a committed data and AI business culture across Hearst’s organization. “One of the strengths of our culture is that individuals across departments can develop these capabilities,” Hogue commented. “It’s powerful to see AI expertise coming from different parts of the organization, and the conversation is moving from ‘Do you have the data?'” she continues. “Can we leverage it effectively?” This reflects a more mature operating model. ”
“There is strong agreement across our leadership that data and AI are not side initiatives; they are core to how we operate our business,” Hogue concluded. “Hearst’s leadership, supported by the right tools, resources and expertise, has created the conditions for experimentation and skill development. In this age of AI and data, we feel we are not just looking forward, we are building forward.”
