After a deep dive into the inner workings of one class of prescriptive analytics—recommender system (personalization engines), it’s time to step back and explore how we can leverage prescriptive analytics in general. Today, I am going to outline 3 relevant use cases in business.
Each of us is pretty unique. The reason that some of us may appear similar to a brand is because brands typically don’t have enough data to tell us apart. Big data changes that fact! With social and behavior data, we will have enough data to view a user in over hundreds of different dimensions. With so many dimensions, the chance of finding 2 matching individual along all these hundreds of different dimensions is highly improbable. We are truly unique!
As the Charles Dickens novel goes “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness”. This reminds me of the beginning of personalization and the challenges of the early approaches. Today, we live in a world where access to information is at our fingertip. In this world, relevance instead of raw impressions is more critical to the consumption of content, because an irrelevant impression is often ignored even if it’s right in front of us.
There is certainly a lot of exciting new developments around data science, especially with deep learning and artificial intelligence (AI). However, data science is actually not a new discipline in business. It was simply disguised under a different http://panelpicker.sxsw.com/vote/67189name—analytics. I want to revisit this topic of analytics because it is one of the most nebulous topics in the industry.
Before I continue to the next stage of my data science journey, I thought it would be nice to discuss “what is a data scientist?” This is very timely, because “Data Scientist” is a fairly new role, and it’s somewhat confusing in the industry. In fact, I’ve just participated in Experian Lab’s #DataTalk last week to discuss this very topic.