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Hearst Publications<\/p>\n
Randy Bean<\/p>\n
How is the legendary media and publishing company Hearst using data and AI to transform its businesses in the 21st Century?<\/p>\n
This is the question that I posed to Jessica Hogue, Chief Data Officer (CDO) for Hearst’s consumer media divisions, where she is responsible for developing a unified data strategy that allows Hearst\u2019s businesses to scale their business initiatives faster through shared capabilities. Hogue brings deep expertise in leveraging data, analytics, and technology in the media and advertising sector to her role as the first CDO at Hearst. She previously held data, analytics, and digital leadership roles at Inn0vid and Nielsen. Hogue comments, \u201cThis is a new role for Hearst\u2019s consumer media sector. Our mandate is to make data usable, trusted and durable for our teams. AI, especially Generative AI, is accelerating this transformation by changing how Hearst builds, tests and operates data systems.\u201d She adds, \u201cUltimately, it is about using data and AI to unlock new forms of growth.\u201d<\/p>\n
Today, Hearst is one of the nation\u2019s largest global, diversified information, services and media companies. The company was founded in 1887 by William Randolph Hearst. By the 1920s, Hearst owned the biggest media conglomerate in the world, which included magazines, newspapers, and radio stations in major cities across the United States. The company\u2019s diverse portfolio includes global financial services leader Fitch Group; Hearst Health, a group of medical information and services businesses; Hearst Transportation; and ownership in cable television networks such as A&E, HISTORY, Lifetime and ESPN; 35 television stations; 30 daily and 50 weekly newspapers; digital services businesses; and more than 200 magazine editions around the world. Hearst continues to be a private corporation.<\/p>\n
Hearst has been innovating in the media and publishing industry since its founding. Hogue comments, \u201cData underpins how we understand and serve audiences across every touchpoint, whether that\u2019s a subscriber, a casual reader or a viewer.\u201d She continues, \u201cWe use data across the full lifecycle of the business, from measuring engagement and behavior to powering decisioning systems like next-best-action models.\u201d Hogue adds, \u201cGiven the breadth and depth of Hearst\u2019s media operations, both globally across hundreds of digital properties and locally in dozens of markets, the speed and agility to surface trusted, decision-ready information for our teams has become a competitive edge.\u201d<\/p>\n
Managing Data as an Enterprise Business Asset at Hearst<\/p>\n
For Hearst, the company\u2019s corporate data strategy is built on the premise that data is an enterprise business asset. \u201cAn asset must be managed and measured to be fully valued, so we actively measure how our data capabilities translate into business outcomes, whether that\u2019s revenue enablement, efficiency, or accelerating innovation,\u201d explains Hogue. She elaborates, \u201cOur use of data sets evolves with the needs of the business. Our data sets inform our paywall strategies, subscription offers, retention efforts, newsletter programs and how we evaluate the quality and addressability of our ad inventory. Hogue adds, \u201cWe\u2019re also renovating revenue workflows end to end, from the first customer interaction to how we balance product mix and execute a dynamic sales process.\u201d<\/p>\n
For years, the media industry focused on accumulation — more signals, more scale, more segments, explains Hogue. \u201cIt was common to see documents touting petabytes of data. That no longer creates advantage. Value now comes from how usable, trusted, and accessible that data is,\u201d says Hogue. She adds, \u201cThis shift is driving a renewed focus on quality, metadata, and governance, not just from traditional data stewards, but across all levels of the business.\u201d Hogue continues, \u201cThis includes building foundational systems that allow data to scale across a decentralized organization. It also means balancing centralization and federation.\u201d She summarizes, \u201cWe create consistency where it matters while enabling the businesses to move quickly and innovate.\u201d<\/p>\n
Hearst has been intentional about how the company has structured the Chief Data Officer function. Hogue explains, \u201cWe operate a collaborative, federated model, embedding data and machine learning capabilities within the businesses, while our corporate team focuses on producing robust data inputs with governance built in, and activating data into various systems, to power workflows and support model development.\u201d She adds, \u201cIncreasingly, we are thinking about how our data assets extend beyond our own ecosystem. As demand grows from AI platforms and hyper-scalers for high-quality, trusted content, there is an opportunity to rearchitect our data assets to create new forms of value.\u201d<\/p>\n
Data and AI Business Transformation at Hearst<\/p>\n
Hearst recognizes that data provides the foundation for AI. Hogue comments, \u201cWe are building an integrated view of our audiences across all touchpoints to better understand lifecycle behavior, macro consumer trends, and long-term value. As the monetization of media continues to evolve, we need better tools to evaluate what is working at-scale.\u201d She continues, \u201cData is only as powerful as the systems that metabolize it. We are continuously modernizing our data architecture to make it more composable, accessible, and AI-ready.\u201d<\/p>\n
Hogue explains that like many organizations, data at Hearst exists across multiple environments, including CRM systems, order management platforms, product catalogs, advertising systems, and user data layers. She notes, \u201cRather than attempting to centralize everything, we\u2019re redesigning these data sets into machine-readable metadata and semantic layers. Vectorization, embeddings and knowledge graphs are becoming critical components of this foundation. We are building these capabilities in a modular way.\u201d Hogue adds, \u201cThis allows our analytics and AI systems to reason over data, not just query it.\u201d<\/p>\n
Data is no longer just informing decisions at Hearst. It is enabling systems, both human and machine, to first interact, then act. This shift is driving a renewed focus on data usability. \u201cData must be well-structured, contextualized, and trusted so it can drive outcomes at-scale,\u201d says Hogue. \u201cThe goal is not just to understand what happened, but to continuously shape what happens next.\u201d The next phase is less about adding more data and more about making our data programmatically and contextually accessible. This includes semantic layers, standardized definitions, and richer metadata. Hogue comments, \u201cGiven the breadth of our content portfolio and the value of our journalism, we see significant opportunity to surface and extend that value.\u201d<\/p>\n
AI is accelerating this transformation on multiple fronts. The company is beginning to use AI agents to offload repeatable analytical and operational tasks. \u201cFunctionally, this enables a wide range of use cases, from agent-driven analytics to faster experimentation and real-time decisioning. The goal is a system where insight generation and action are tightly connected,\u201d notes Hogue. She adds, \u201cOne of the most significant transformations we\u2019re seeing is what it means to be data driven. Historically, being data-driven meant that we were referring to dashboards to inform decisions. This model no longer holds in the age of AI. We must become an Intelligent Enterprise.\u201d<\/p>\n
Building a Data and AI Business Culture at Hearst<\/p>\n
Hearst is focused on building infrastructure that allows both its people and AI systems to interact with data more naturally. Hogue explains, \u201cAI has accelerated our cultural shift. As all colleagues across Hearst engage directly with AI tools, there is a growing appreciation for the importance of data quality and metadata in producing reliable outputs.\u201d She continues, \u201cThe ability to translate between business and data is critical and often underestimated.\u201d<\/p>\n
\u201cWe focus first on understanding the business problems we\u2019re trying to solve and then translating those into data solutions,\u201d notes Hogue. \u201cEarly on, we partnered with our businesses, who have operated for decades with mass distribution, and recognized the need for better ways to understand consumers.\u201d Hogue continues, \u201cThis led us to build audiences from fragmented signals across systems and experiences. \u201cUnderstanding the business also influences not just what we build but how we design solutions, communicate priorities, and partner with the business.\u201d She adds, \u201cWe recruit for this capability across nearly every role because it is foundational to success. We have leaned into greenfield opportunities. Our mandate has always been to be useful to the business as it transforms and grows.\u201d<\/p>\n
The business value of data and AI investments in measured in several ways within Hearst. \u201cOne principle I\u2019ve carried from earlier in my career is that usage is the clearest signal of value. It is a form of market fit,\u201d notes Hogue. \u201cWe track adoption closely and are disciplined about stopping work where usage stalls. This is where our operating model takes on a product mindset.\u201d She continues, \u201cBeyond that, we map our work to core business outcomes such as subscription growth, retention, advertising performance, and revenue influenced by data-driven capabilities.\u201d<\/p>\n
Hearst monitors and measures data enablement, specifically how effectively teams can access and use data. This shows up in the speed of decision-making or experimentation or reducing redundancies. \u201cWe measure durability, whether we are building assets that can be reused and scaled across the organization. We aim for our investments to compound, serving multiple use cases and driving long-term value,\u201d Hogue adds.<\/p>\n
Preparing for an AI Future at Hearst<\/p>\n
Leadership from the top of the company is key to building a committed data and AI business culture across the Hearst organization. Hogue comments, \u201cOne of the strengths of our culture is that it enables individuals across functions to develop these capabilities.\u201d She continues, \u201cIt is powerful to see AI expertise emerge from many parts of the organization. The conversation is also shifting from \u2018do we have the data?\u2019 to \u2018can we use it effectively?\u2019 That reflects a much more mature operating model.\u201d<\/p>\n
Hogue concludes, \u201cThere is strong alignment across leadership that data and AI are not side initiatives. They are core to how our business operates.\u201d Noting the company\u2019s century plus history of innovation, she adds, \u201cHearst\u2019s leadership has created the conditions for experimentation and skill development, supported by the right tools, resources, and expertise. In this era of AI and data, it feels to me we are not just forward looking, but we are forward building.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Hearst Publications Randy Bean How is the legendary media and publishing company Hearst using data and AI to…\n","protected":false},"author":2,"featured_media":17667,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,203,12680,340],"class_list":{"0":"post-17666","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-data","11":"tag-hearst","12":"tag-media"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/17666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=17666"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/17666\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/17667"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=17666"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=17666"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=17666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}