Data Science vs. Big Data vs. Data Analytics - what’s the difference?
(and does it matter?)
As we live and work in an ever increasing digital space, it’s not surprising that we’re all influenced by the information we consume on a daily basis. In fact, at the beginning of 2020, the amount of data in the world was estimated to be 44 zettabytes (or 1,000 bytes to the seventh power). Simply put, it’s an astronomical figure. With such a significant amount of data being generated and consumed daily on a global scale, it’s crucial for businesses to learn how to leverage it for their benefit. However, data cannot simply be categorized into a one size fits all grouping. Rather, it’s broken down into three core technologies - big data, data science, and data analytics - and when used properly, can empower businesses with invaluable insights.
Although these terms are often used interchangeably, there are significant differences between the trio and the functions they perform. As such, it’s imperative that we understand the differences between data science, big data, and data analytics, in order to best utilize and leverage them to their full potential.
Data science is a combination of techniques that help in extracting insights and information from both unstructured and structured data. It comprises everything related to data; from data cleansing to data preparation to data analysis. Data science intelligently combines mathematics, statistics and programming to not only capture data, but also give a diverse perspective or insights for problem-solving. Because of this, it comes in handy in various fields including:
Internet searches: data science algorithms help search engines in delivering the best results for search queries quickly and effectively.
Digital ads: whether it’s display banners or digital billboards, data science is at the heart of digital advertisements and is the reason behind higher CTR of digital ads than traditional advertisements.
Gaming: empowers leading games companies to deliver top notch gaming experiences with the algorithms self-improving or self-upgrading as the players progress to higher level. Similarly, in motion gaming, the computer analyzes the players’ previous moves and respectively molds the game.
Airline route planning: from predicting flight delays to forecasting plane requirements to helping airline companies determine the fleet of airplanes to buy to scheduling flights, data science plays a key role in guiding business decisions for airline companies.
Healthcare: data science algorithms significantly influence different areas of the healthcare industry, namely medical image analysis, genetics & genomics, and drug development. Through this, healthcare providers are able to optimize and personalize individual treatment.
Recommender system: enables companies to analyze a user’s search history to better understand their demands and display suggestions accordingly, resulting in more effective product promotion while positively augmenting the user experience.
Big data is collecting or bringing together immense volumes of data from diverse resources that cannot be processed effectively using traditional applications. You can leverage big data to process:
- Unstructured data such as emails, blogs, tweets, mobile data and web pages
- Structured data such as transaction data, Relational Database Management Systems and Online Processing
- Semi-structured data such as text files, system log files and XML files
Collating data from multiple sources makes big data highly useful for several industries including:
Fintech and Financial Services: from retail banks and credit card providers to insurance firms and private wealth management advisories, big data enables everyone to gather the massive volumes of multi-structured data stored in different systems for customer, compliance, operational, and fraud analytics.
Information and Communication Technology (ICT): whether it’s gaining new subscribers or retaining customers, ICT players leave no stone unturned to expand their subscriber base. This is only possible by gathering and analyzing the colossal volumes of the customer- and machine-generated data that’s created and stored in their repositories every day. ICT players are thus able to better understand the customer needs and align the offerings accordingly.
Retail: the key to remaining competitive in the retail industry is to understand your customer. Whether you’re a brick and mortar or an online retailer, big data enables you to analyze the data collated from social media, order history, customer transactions and loyalty programs to make informed, customer-centric decisions.
Healthcare: the increasing adoption of mHealth, eHealth, and wearable technologies means that the healthcare industry has amassed huge volumes of data from multiple sources. Researchers mine all the data - electronic health records, medical imaging, and patient- and sensor-generated data - to develop effective treatments for particular conditions, identify possible side effects of a drug and uncover other information that can help patients and lower care costs.
Data analytics is the science of inspecting raw data to draw inferences. It involves applying algorithmic or mechanical processes over the raw data to derive insights. Various industries leverage data analytics to examine their huge number of data sets to draw conclusions and ensure the attributes are correlated. These include:
Energy management: data analytics helps in areas such as energy distribution and optimization and grid management. It combines hundreds of millions of data points in the network performance, enabling the engineers to leverage analytics to control and monitor network devices, manage service outages and dispatch crews for optimum results.
Security: data analytics or predictive analysis helps in dropping crime rates or keeping crime in check. A few cities globally have used it in isolated pockets to increase police patrolling where they witnessed or were expecting a surge in crime rates.
Healthcare: healthcare centers globally strive to ensure efficient treatment of patients while focusing on improving the quality of care. Typically, the instruments and machines that are used in healthcare centers generate a huge volume of data, which can then be leveraged by data analytics to optimize the patient flow and the treatment provided in the healthcare facility.
Travel: data analytics plays a vital role in optimizing the customer experience throughout the customer journey. With travel portals storing significant amounts of customer information through signup forms, inquiries and social channels, analyzing this data can help offer customized packages and deliver personalized travel recommendations.
Delivery: the ever-increasing dependency on online shopping has prompted many businesses from logistics companies to e-commerce giants to leverage data analysis to improve operational efficiency. Data analytics applications can help identify the most economical mode of transportation, best shipping routes and delivery times, ensuring prompt service.
So in summary:
Living in a data-driven world
Today, the vital role that data plays in helping businesses make key decisions cannot be overlooked. Data science, big data, and data analytics all play a major role in enabling businesses in all industries to shift to a data-focused mindset. The advent of these technologies has shown how even the smallest piece of information holds value and can help in deriving useful information to elevate the customer experience and maximize business potential. The key is to understand the nuances of each area of data specialization to help you extract the information needed to get the business results you want.
As a leading provider of customer experience and digital IT innovation, learn more about TELUS International’s data analytics solutions to support your customer journey.