I was the kid who couldn’t stop asking “why?” ad nauseam. Why does a cat have whiskers? Why does candy taste so good? Why does that lady have a mustache? I was experiencing things for the first time, trying to make sense of the world – and my curiosity helped me learn (although sometimes at the expense of my parents’ sanity).
Basically, I was a little data scientist. Mini-me used information to understand situations and draw learnings and conclusions about life around me. Similarly, data science uses data to understand complex trends and draw correlations and conclusions for the objective at hand.
Why do marketers care about data science? Let’s dig in…
Like all scientific fields, data science is a broad practice that covers a variety of activities, like:
This list is just the tip of the iceberg (AI-ceberg?); we could go on to talk about data governance, data visualization, and more. But these are the four key areas of data science that any marketer should be aware of.
Artificial Intelligence is completely dependent on data science. Without data science, you don’t have data to fuel the AI. We could drop the mic here, but that hardly seems helpful.
The more tangible dependency between data science and AI is rooted in analytics. The hot-button example is predictive analytics – i.e. a data science method that uses data and mathematical models to calculate likely outcomes, such as a person’s likelihood to click, visit your site, or convert.
When paired with AI, predictive analytics become much more powerful. Artificial intelligence boosts predictive analytics with (1) the ability to account for massive amounts of real-time data, and (2) the addition of “intelligent” processes (e.g. decision making, problem solving, learning). As a result, the dynamic duo can analyze billions of variables in real-time, make simultaneous decisions, and take real-time actions (like serving an ad to the person who is most likely to convert in the next hour).
Even if you have good data, you can’t do much without good data science.
Although analysis and application get the most glory, the steps leading up to them are arguably the most important. Data scientists need a thorough understanding of the data available (what it means, how it can be used) to use it properly and produce meaningful, accurate, and actionable results.
Otherwise put (in acronym-marketing-speak): GIGO, or “garbage in, garbage out.” If your initial data science processes are flawed, your results (and how you apply them) will also be flawed.
For example, Magnetic continuously collects a huge breadth and depth of consumer intent, behavior, interaction data. If our data science stopped there, the party would be over. Fortunately, we have incredible data scientists on board, who know exactly how to extract the most value from our data.
Thanks to our data science, Magnetic’s new AI platform can:
Not to toot our own horn, but it works pretty darn well.
At the end of the day, the first step to achieving “good” data science is to accept the advancements in data science and AI. If you’re still waiting for the AI fad to fizzle, you’ll be sorely disappointed. It’s here to stay, and those who don’t get on board will most definitely be left behind.
Craving more AI content? Check out our previous posts “Explaining AI: Machine Learning vs. Deep Learning” and “Explaining AI: AI vs. Predictive Analytics” – and stay tuned for our next topic in our Explaining AI series as we dive deeper into the rapidly growing world of AI.
Magnetic is an artificial intelligence company that uses machine learning to deliver smarter, faster, and more effective advertising. Our powerful AI platform continuously analyzes the attributes of over 350 million live user profiles alongside real-time inventory supply and bid opportunities to deliver highly performant audiences and profitable campaigns for our clients.