Whether you know it or not, you’re in the data business. As our lives continue to migrate to the Internet – combined with 5 billion mobile phones by 2017 – we’re producing a constant stream of digital exhaust. In fact, 90% of the world’s stored data has been produced within the last two years, and this data footprint continues to double each year. The result is a rapid expansion of data, and we’re witnessing the “Big Bang of the dataverse,” which is truly the next new frontier. Your company is inevitably part of this data ecosystem and, without a method of harnessing the information, you cannot make smarter business decisions.
As the cliché goes, “data is the new gold.” The world generates exponential data and, at the same time, everyone wants an extra edge. Intuition, gut feeling or common sense rules are useful, but not enough. Data lets organizations understand clients, products and processes much better. For example, Rolls Royce has data scientists analyzing airplane engines data to determine when to schedule maintenance, and L’Oréal has data scientists studying the effect of several cosmetics on several types of skins.
No matter how data savvy you are, it can be a daunting task to deal with big data, which is why it’s important to employ staff that can specifically tackle this huge feat. Can your current data analysis team accomplish what you need?
The term “data science” has been thrown around industry news a lot lately, and for good reason; it’s one of the hottest new professions and academic disciplines. But, what does it really mean? More specifically, what does it mean for your company?
Data Science vs. Statistical Analysis
Data science differs from traditional statistical analysis and computer science in that scientific method is applied with data collected using scientific principles.
The reason for the growing need for this new approach is related to big data, which requires the use of a very different technology stack than statistical analysis. In other words, statisticians from 20 years ago would not be required to analyze massive data sets on the almost real-time scale that’s often required by today’s business applications.
Boiled down, it’s the difference between being able to explain what data means now and predicting what a data set could mean in the future.
Traditional data analysis in companies has typically been implemented to explain trends in data by extracting interesting patterns from individual data sets with well-formulated queries. However, data science is seeking to uncover actionable knowledge from large, unwieldy data sets that can be used to make decisions and predictions, not just interpret numbers.
If you want to start making more long-term decisions and predictions by exploring the big data your company is generating, then you might want to consider adding data science into the mix.
Do you need a data scientist?
While the career path of a data analyst and traditional statistician is well trodden, that of a data scientist is still unpaved. The profession is on the rise, so it’s important to know what to look for in hiring a data scientist.
While not all universities offer degrees specifically in data science yet, a common requisite background for a data scientist is engineering, and a Ph.D. is often required. At its core, data science is the intersection of computer science and statistics, so candidates should also have good computer science skills – including data structures, algorithms, systems and scripting languages – as well as a solid understanding of correlation, causation and related concepts which are central to modeling exercises involving data. Ultimately, it’s about understanding business challenges, so effective data scientists should also have business acumen, collaboration skills and creativity.
Similar to computing, data science can be applied to many domains of knowledge, and not restricted to one industry, as traditional analytics tend to be. While domain expertise is imperative to identify problems specific to your industry and company, the understanding and experience in extracting knowledge from many different domains often provides a much greater scope of insight.
As the dataverse continues to expand, data scientists must be increasingly familiar with using artificial intelligence, particularly machine learning, in order to do what humans cannot. The job typically requires five different types of tasks: data cleaning, asking questions, analyzing from data using statistics and machine learning models, visualizing results, and improving upon models and algorithms to yield better results and execution.
However, while big data is often too much for humans to analyze in a timely manner, computers still cannot provide the nuanced comprehension and analysis that a trained professional can. It’s important to hire a data scientist with domain expertise in your business who can effectively use machine learning to identify commonalities across diverse challenges.
This job cannot be done with strict human intelligence or machine work alone. The beauty of this role is that it needs both – and the person who can do that is who you’re looking for in a data scientist.
As our lives continue to go digital, it’s crucial for your organization to embrace a science-based philosophy for making data-driven decisions. As the dataverse continues to grow, you’re increasingly dealing in the business of data, whether you like it or not.