Because I’d like to convince you to join our excellent programme in New York next week at the INMA World Congress of News Media, I thought I’d share some of the thinking behind the programming.

Now, most of this programming came together early in the year, but some folks had been “on my list” for quite some time. 

There were three different areas I wanted for us to tackle: data and its application in supporting performance; data and its application in how we manage our business; and generative AI. 

The programme balances somewhat equally among these three topics. 

Data and its application in supporting performance

The bleeding age of the conversation in data may be generative AI, but I don’t want us to lose track of the fact that we’ve been using data for years in increasingly useful and concrete ways to support revenue and performance. This, until proven otherwise, is where we have value in data today.

• In this respect, using data in how we understand and improve paywall systems is definitely one of the top-of-mind applications. But, of course, it’s not so simple. So having Rohit Supekar, a senior data scientist at The New York Times, to share with us some of the work he and the team have done to make their paywall more efficient was just perfect for this programme.

• Also under performance, I wanted us to look at how we improve our understanding of performance. In analytics, there are many types of ways our numbers get muddied up by the rather complex user journeys in play in our properties. Christian Leschinski, the data science lead at Axel Springer National Media and Tech in Germany, will share with us how his team clarifies some of these issues to get to a purer “truth” of what performance looks like.

Data and its application in how we manage our business

The judicious use of data in the organisation says something about the culture of these organisations and our ability to understand and analyse the picture that data paints for us. But even when a healthy culture exists around data, we still have to make data available to the various teams that want to use it. This one is an engineering challenge, and we’ll talk about this too.

• Somewhere, there are still folks who look at the data team and say, “Well, they don’t build the product, they don’t make money, and they don’t make journalism” — and are not so sure that the data team is worth the expense. Hopefully, there are fewer such folks than there used to be, but you can also argue that it’s up to analytics experts to make numbers useful and the value of having these numbers inarguable. June Dershewitz, a top data strategist and board member at the Digital Analytics Association, will focus on the essential question of connecting analytics with improved outcomes. 

• As the need and use of data grows around the whole organisation, so do the feeds of data that pour into our data infrastructure. But to make this data truly useful, and fully leverage it, means being able to surface it to many different parts of the business. It means that data about content performance is available in relation to a user’s lifecycle or page performance is available to serve segmented advertising. The business we manage is very dependent on our engineering infrastructure and having a vision for how to build it — and being able to manage it and afford it over time — is a long-term effort and commitment. Evan Sandhaus, the VP of engineering at The Atlantic in the U.S., will present on the work to bring a unified view of the user’s lifecycle, so every team in the organisation is able to benefit from a multifaceted understanding of the user.

Applications of generative AI

There’s no shortage of headlines (or, in fact, a whole report I wrote for you,) but I wanted us to focus on cases where generative AI was actually being implemented in our organisations. I also wanted to specifically focus on cases that took generative AI from small experiments to being scalable for large parts of the organisation. There are many projects of the former kind in flight — understandably, we should all be reasonable in how we roll out this young technology — and fewer of the latter kind. But we will have two speakers who can tell us more about larger generative AI projects in their organisations.

• Covering small communities is a challenge in finding scale in what is inherently small-scale. At Gannett, whose large regional network of local organisations covers communities coast-to-coast, the team is exploring how to give weather stories their due while leveraging different kinds of automation. Jessica Davis, the senior director for News Automation and AI Product, will share with us some of the insights from building these new capabilities.

• When we talk about generative AI, we quickly head to very abstract issues around accuracy or usefulness. But the real scale of generative AI is also in how we may integrate it in  our set of daily tools. So I was very curious to hear about cases that focused on the toolbox angle. Alessandro Alvani, the product lead for natural language processing at Ippen in Germany, will tell us about their work to bring generative AI tools to the fingertips of their newsroom, in their CMS. 

There’s still time to catch that plane to New York. You’ll have access to the presentations/material online shortly after the conference, so you could also join virtually that way. Though, well, that would be less fun. And so for folks joining us, I can’t wait to see you soon! 

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