You've probably heard or used the term "analytics" in the past, most likely when talking about some level of deep information analysis. The problem is it's fairly nondescript. If you're at a fancy cocktail party and ask someone what they do for a living, and they respond "I do analytics," what are you imagining? Do you think they look at numbers all day? Do you think they're certified in that Big Data thing you read about once? Do you think they just cleverly deflected your question? The term has slowly died over time in favor of "Data Science", and for good reason. What people slowly began to realize was that working with data requires a scientific view. Its purpose is not to go out and confirm the existing ideas/opinions of management or executives. The purpose is for the analyst to observe the data, report the answers it provides, and potentially model the current state to predict the future.
It's science because its central concern is finding the truth. What is our true rate of customer turnover? What is our true market share? What is our true return on R&D? Finding this truth takes extensive work and expertise, and one of the best descriptions of what Data Science encompasses is Drew Conway's Data Science Venn Diagram:
"Math & Statistics" is an obvious requirement when dealing with numbers of any kind and "Substantive Expertise" is a requirement for ANY business role, so let's particularly zone in on the 3rd (and arguably most difficult) "Hacking Skills". In today's world you don't need to hack The Matrix in order to get started with data analysis. Hacking implies a black box of knowledge that you're secretly applying in a dark room alone, but a more accurate description of "Hacking" would be:
Knowing How and When to Use the Right Tool.
For data scientists this comes with experience and familiarity, as you eventually develop a toolbox from exposure to many varieties of problems. However, even with so much knowledge it's never effective to go into a problem blind. The single best way to gain vision and insight before you begin? The beauty that is data visualization. We all know that visualization is used to report end results, but even experienced analysts don’t consider the potential in using visual tools for every step of the data process. Once the "science" has been performed at any point--gathering, cleansing, reporting, modeling--visualization is the story. If you find yourself performing Sentiment Analysis on Twitter, visualizing your data work in Tableau during the cleansing process could discover statements that were categorized incorrectly. If you find yourself trying to predictively model how States will vote in the upcoming presidential election, you can carefully retune the model if you've predicted a drastic 100% swing. Data is too prevalent and comes from too many sources to be able to glean information with the naked eye (the days of only having to deal with 1.1 million rows in Excel are over), and what better way to express that information than with a picture even a layman can understand?
Tableau Conference 2016 is approaching (come visit ProKarma at Booth 612!), and Tableau in particular provides major advantages in capabilities and ease of use. Your only limitation is your own understanding of the problem, what business leaders need to know, and your ability to tell the story. Similar to a news reporter your job is to be objective while exposing the most interesting and important facets of the story; you frame the data in the most transparent way possible while also spurring the cognitive juices of whoever's looking at it. Combine this with showing yourself a clear path for where to go next, and it becomes invaluable. It's an iterative process where the data visualization deepens your insights and those insights point you in the direction you want/need to go.
Anyone working in Data Science needs to have a focus on mastering all three of its major areas to perform effectively: quantitative skills, a wide variety of industry experience that provide substantive expertise, and proven mastery of the most powerful Data Science tools. This is enhanced to the "Nth" degree with visualizations painting clear pictures of data that would normally be impossible to understand. From overall data discovery, to developing mobile dashboards, to real-time performance monitoring of any KPIs, tools like Tableau can give business leaders access to what was seemingly a black box before.
This work goes beyond just "hacking". There's legitimate artistry in being able to explore the world in way that would not have been possible even 20 years ago. When you've mastered all the tools and have the benefit of experience, the real creativity begins.