Big Data has been a marketing buzzword for years. Cheap storage, proliferation of mobile technologies, improved processing power, and a host of other innovations have provided tons of data for brands and retailers. However, possessing Big Data and knowing what to do with it are two completely different things.

In a perfect world, Big Data would magically arrange itself into useful reporting. In reality, you need a process to study, categorize and implement this data for you.

Say Hello to Data Science

Data science uses raw data and algorithms to predict customer behavior and improve user experience. The ability to predict buying behavior and fine-tune personalization is every marketer's dream. The more effective and targeted your messaging is, the more likely you have a customer and advocate for life.

In fact, the value of data science is so well-known the government recently appointed its first US chief data scientist, DJ Patil. Patil, who is co-credited with coining the term "data scientist," is a pioneer and influential presence in the field. He has spoken at various global tech events over the years and even delivered a speech at Strata + Hadoop San Jose 2015 on short notice the same week his new title was announced.

Data Science Definitions

A formal definition of data science might sound like this: Data science integrates techniques from statistics, computer science, business strategy, and other fields to gain deep insight from troves of data. Mathematic and algorithmic techniques are deployed to decipher this hidden insight buried within the data to forecast opportunities. Tactical optimization, predictive analytics, nuanced learning, and automated decision engines represent typical data science projects.

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Matt Diehl is digital content writer for Persio, a multichannel marketing and decisioning platform for retailers. Persio helps marketers send data-driven and personalized campaigns across Web, email, SMS, mobile app, and other channels.

LinkedIn: Matt Diehl