Using Data Science & Artificial Intelligence In Digital Publishing

Data Science & Artificial Intelligence In Digital Publishing

What are artificial intelligence and machine learning?

What’s the difference to digital publishers?

Putting artificial intelligence into practice

Why testing may be the greatest application of A.I. for publishers

> “At any given point in time, there isn’t just one version of Facebook running, there are probably 10,000. Any engineer at the company can basically decide that they want to test something. There are some rules on sensitive things, but in general, an engineer can test something… And then, they get a readout of how that affected all of the different metrics, and things that we care about. How were people connecting? How were people sharing? Do people have more friends in this version? Of course, business metrics, like how does this cost the efficiency of running the service, how much revenue are we making?” – **_Reid Hoffman interview with Mark Zuckerberg – ‘Perfect is Imperfect’ – Masters of Scale Podcast_**

I think this is a good indication that the testing ethos is expanding. Google also said on the AdMonsters panel that their search algorithm (for search results) is now largely driven by A.I. and not individual updates (e.g. t[he days of one big update e.g. Panda — are over](https://blog.ezoic.com/reasons-google-traffic-digital-revenue-drops-common-causes/)).
This quote also brings to light something obvious. Most digital publishers, even the smartest ones, aren’t doing the kind of testing that platforms like Facebook and Google are doing. This is one of the biggest untapped opportunities for most publishers; as testing that affects UX has more potential than just about [any other strategy to increase user experiences and revenue](https://blog.ezoic.com/user-engagement-imapcts-ad-revenue/).

Where does data science fit into all of this?

A definition of data science I heard recently from a digital publisher was the best I’ve heard. They said that data science is a combination of statistics, scientific method, and computer science — a mix able to make intelligent predictions using data to maximize the desired outcome.
The evolution of digital publishing will see more companies embracing data science because of it’s obvious connection to technologies like _true_ A.I. I think this will mean more personalized experiences for users, more testing, and more data-driven decisions that don’t rely on things like surveys, user feedback, and personal preferences (as publishers know these things can be hit and miss).
Ultimately, these changes for digital publishers can have an intimidating — and frankly insincere — feel to them; however, being exposed to what these technologies can really do and the problems they can solve will ultimately prove to be empowering to digital publishers.
What do you think?