Data seems to be on everyone's mind these days from retail giants to healthcare professionals and travel agencies, organizations are looking for ways to collect and analyze data and apply insights to build more customer-responsive brands. It's not hard to believe that there is such a strong focus on data considering that 90% of the world's data was produced within the last 2 years1, from 2011-2013, so just imagine how much more data was produced in the 5 years since then.
With that huge influx of data, and the growing desire of companies to capture and analyze it to help drive business decisions, more and more companies are looking to hire data scientists, data analysts, or data managers. This is especially true given that over 80% of the data produced is unstructured, meaning finding someone who knows their way around data collection and analysis is crucial.
Yet, it is becoming increasingly evident that a lot of companies and recruiters are unaware that each role dedicated to working with data is very distinct and has very different objectives. Understanding the difference between data science and data analytics can help businesses drive data collection, storage, and usage strategies, especially if an organization wants to make the most of the available data. Hiring for the right skill set is also incredibly important within these roles and will save all involved time and money if you can get it right the first time around.
"The short version is that data science includes and goes beyond data analysis. If you contrast data scientists with data analysts, the data scientists' goals are deeper and their area of concern is typically larger."
Data science is multidisciplinary and covers the study of where information comes from, and how to get the right data. For instance, a data scientist is going to be looking at how to collect, and process data that connects and furthers the organization's goals on a broader level. They want to know the questions we need to be asking to get the answers we need. Data scientists use many different approaches to arrive at various insights, like predicting trends, exploring structured and unstructured datasets, applying statistics and mathematical approaches, and more.
On the other hand, data analytics is increasingly concerned with discovering insights to predetermined questions or objectives. A data analyst spends her day working in databases, manipulating data. For example, a data analyst is going to be performing statistical analysis and creating charts, graphs and other ways to represent the data in order to help businesses make strategic decisions.
If you need to hire a data scientist or data analyst, considering hiring for the skills below and base your needs off of the information above, this will help you get the most from your data and build a team that is capable of achieving what you want.
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SINTEF. "Big Data, for better or worse: 90% of world's data generated over last two years." ScienceDaily. ScienceDaily, 22 May 2013.