Data science. Data analytics. Big data. There are a buzz of terms around data and their usage today and they touch upon every industry. The truth is, data – in all forms – make our lives better every day in very tangible ways. And the best is yet to come.
However, with all these advancements and opportunities, we also have confusion. What are the differences among the different data disciplines? How are they related? What does it all mean?
To answer those questions, let’s first look at the different roles involved in gathering, processing, and interpreting the mountains of available data. Then we can begin to understand how to harness the power – and potential. This is, after all, one of the main goals of Claremont Graduate University’s IS&T program: “…to explore how massive volumes of data can be used to solve business and societal problems through data mining, specialized programming, and other computational skills.”
Data Science vs. Data Analytics vs. Big Data
Data science encompasses a vast array of techniques used to process and extract information from data in various forms. It combines statistics, mathematics, programming, and other methods of capturing data, then cleanses and prepares the data for practical use. Data scientists are high-tech detectives, seeking to discover new patterns and insights into data trends, which become especially valuable for businesses.
Data analytics is more specific and concentrated than data science. While data science finds points of connection among data, data analytics sorts though data with a specific goal in mind. Through automated systems, data analytics checks hypotheses and focuses on connecting trends and patterns that align with goals. It’s also worth noting that there is a difference between analysis and analytics. Analysis looks back in time, providing a historical view of what has happened, whereas analytics looks forward to model or predict a result. While analytics tends to be a better predictor of behaviors, analysis is the process required to answer key strategic questions.
Another big difference among data science, big data, and data analytics is how and where the resulting information is utilized. Common applications include:
- Internet Search Engines – Algorithms used to deliver instantaneous search results
- Digital Ads - Algorithms applied to achieve higher CTR (click-through rate)
- Search Recommender Systems – Product promotion determined by consumer search results
- Financial Services – Operational, customer, compliance, and fraud analytics
- Retail – Interpretation of data to better understand the customer
- Communications – Data applied to retain and gain new customers
- Healthcare – Tracking and improvement of patient flow, treatment, and equipment
- Gaming – Analysis of what the customer does and doesn’t like
- Travel – Understanding the customer to provide travel recommendations and improve sales
- Energy Management – Monitor and improvement of utility networks performance.
What Does this Mean For You?
In a data world moving faster than the speed of light, it’s imperative to have enough educated, informed data management experts to meet the needs of the exponentially growing volume of world data. CGU has carefully crafted our Online Master’s in Information Systems & Technology program to meet these needs and build the next generation of IT leaders.
In an expanding world of possibilities, what is your niche – data science, big data, or data analytics? We can help you with all three. Contact us today to learn more now.