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.
Would you like an understandable definition as well?
Data science is making sense of raw data that can't talk on its own. Math and science systems extract valuable knowledge from stored, consumed, and managed data and make sense of it all. What were once huge, complicated troves of data are now clear and useful reports primed for decision-making, product development, trend analysis, and forecasting.
The Future of Data Science
For most industries, the future is data science.
Connected devices can impact $32.3 trillion over the next 15 years, according to Patil at a recent talk. (That's trillion with a "T.") He also shared a story about smart airplane engines that talk to pilots in real time using data science. If these smart engines can save 1% in fuel over the next 15 years, that 1% will result in $30 billion in saved fuel costs (and reduced pollution).
Patil's speech at Strata + Hadoop 2015 was titled "Data Science: Where Are We Going?" and delved even deeper into his projections. He focused on the addition of data science across educational systems to include many high school curriculums: "(People) are starting to be more focused in how they can actually use data and apply data to everyday life."
Patil then went on to highlight the new technology powering the future of data science. Innovative tech such as software libraries and Web data collection systems are "designed to power the next generation of technology," and at Iris Mobile, we know this firsthand. Our own data scientist, Adrian Esparza, is well-versed in his craft and skilled in applying his data science skills to the retail market.
We recently sat down with Adrian and picked his brain on several aspects of his exciting industry.
Thoughts From a Real-Life Data Scientist
What do you enjoy the most about being a data scientist?
Data provides the vital raw material to develop modern society, and strengthen and improve the machinery of decision-making. The tools of data science allow us to put together a rich and accurate model of the world.
I think of data as a microscope. One can see the inner workings of a company or economy in illuminating detail to inform understanding and guide actions. The ability to connect the dots with statistics and mathematics in ways that bring to light valuable insights or discoveries is what I enjoy most about being a data scientist.
Why are data science and retail a perfect match?
Because of cheap storage, proliferation of mobile technologies, improved processing power, and other innovations, retailers now have access to a treasure trove of information.
Insight derived from Big Data can be used to deliver personalized customer experiences, refine loyalty programs, and hone marketing campaigns. The majority of what companies do today is either created in digital form (like email or Web) or tracked digitally (like barcodes or sensors). This explosion of digitization is a revolution in measurement and a seismic change enhancing business decisions and innovation.
What are your predictions for the future of data science?
Data-fueled computer systems like IBM's Watson have proven immensely powerful for streamlining our understanding of how the world works and how we behave.
For example, Watson-like technologies will be part of the future of medicine, working as a kind of "over-the-shoulder" assistant to guide human decision-making. These intelligent helpers will be crunching data while humans do the higher-level thinking and management.
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What was once a relatively niche discipline is now changing how decisions are made across virtually every business sector in the world. Our industry research complemented by Patil and Esparza's professional commentary leave no doubt that data science is going to significantly influence our future in many ways.
And you don't have to be a data scientist to figure that out.
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