What is Data Science? The jury is still out on a precise definition. This probably has to do with the reality that the field is constantly evolving as the types of data and the tools we have to extract value from data also evolve. A Booz Allen Hamilton guide says that data science is about turning data into action and delivering this actionable intelligence in an understandable way to business end users. Data science pioneers Thomas H. Davenport and D.J. Patil aptly described the iterative nature of data science in a Harvard Business Review blog post, stating “data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.” Others specify that unlike business intelligence, data science uses complex algorithms and machine-learning/predictive analytics to not only look for answers, but discover new questions to ask.
At Zaloni, we see data science as an umbrella term encompassing many data-related activities that have been going on for some time. The distinction is that true data science brings formerly siloed disciplines (and people) together. Data science teams have to not only understand data and develop technology and algorithms, they need to effectively collaborate with business partners and ultimately solve problems that create value for enterprises. They must have the ability to put those problems into an analytics framework, apply mathematical techniques, and then translate them back into business results. Today, many successful data scientists come from fields with a strong data, statistical and computational focus – anything from astrophysics to systems biology.
Data science is what helps us find new ways to discover, combine and manipulate Big Data to make analysis possible as the volume of data balloons and the variety of data becomes more complex. The data science process involves four basic steps: 1) data discovery and acquisition, 2) data preparation, 3) data analytics and modeling, and 4) delivering actionable insights or product deployments that solve real business problems. Although analytics, modeling and business insights are the more exciting parts of this process, it’s important to note that they are unachievable without a solid investment in steps one and two.