Although the answer to that question varies, analytics can be simply explained as a process by which information is turned into insights for decision making. As data is generated at an unprecedentedly rapid pace (an estimated 2.5 quintillion bytes of data per day!), the ability to interpret this data effectively and efficiently is more important than ever before. By using various statistical and computational tools we can find the patterns behind the data, ask the best possible questions, and come up with the best insights. While this vast quantity of data can be daunting, the capabilities to sift through it have never been as good as they are right now, which is where CARE comes in.
At CARE, our motto is "Analytics For Good." This means that our focus is on improving the world around us, whether through local efforts here in Boone or on projects that scale the entire globe. Social responsibility is an important part of the ASU culture, as is readily apparent in the research that the faculty and students in CARE choose to undertake.
Projects in CARE cover areas such as Sustainability, Education, Economic Development, Human Resources Science, and Health & Wellness, among many others. Whether it's an initiative to prepare high school students for college and beyond or a global partnership on bee data collection and sustainability, CARE has been able to generate not only actionable insights that consistently improve the community, but an educated and experienced generation of young professionals to carry the CARE values and skills into the future.
What is Big Data Analytics?
Big Data - Term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Three Components of Big Data:
- Volume - Enormous size of data sets. Specifically, this component of big data has to do with the amount of information or data that is collected.
- Variety- Information collected is now not just numeric but includes information such as text, video, and pictures posts, Facebook likes, Twitter re-tweets, and LinkedIn endorsements.
- Velocity- Information is continually being collected, and continuous collection has important implications for both the technology needed to store the data and the quantitative methods used to make sense of the data
Analytics - A collection of computer automated algorithms and methods that follow a set of parameters defined and monitored by the user that are used to interpret, describe, and identify patterns within and between structured and unstructured data.
Four Types of Analytics:
- Descriptive - The goal of this type of analytics is to understand what happened in the past (Cech et al., 2015). This is the simplest form of analytics and is the most commonly used by organizations,
- Diagnostic - Look at patterns between variables to determine the strength of the relationship between various concepts.
- Predictive - Builds upon the ability of diagnostic analytics to identify causes, and attempts to use that information to predict future outcomes.
- Prescriptive - Combines business rules with the results from the other three analytic types to compare the outcomes of various situations being encountered and proactively recommends or takes a course of action
Guilfoyle, S., Bergman, S. M., Hartwell, C., & Powers, J. (2016). Mo' Data, Mo' Problems? The potential of big data and analytics in employment decisions. In. R.N. Landers & G.B. Schmidt (Eds.), Using Social Media in Employee Selection: Theory, Practice, and Future Research. New York: Springer.