Big Data / Small Data are two terms that are commonly used interchangeably in the data analysis and data management field. Even thought they sound similar, these two terms are actually very different types of data. There are a handful of key differences between big data and small data regarding the challenges and opportunities that each present.
What is Big Data?
Big data is the large and complex datasets that are typically too big and complex for traditional data processing software to handle (i.e. spreadsheets). These datasets come from a variety of sources: i.e. social media, triggers, phones, smart TV’s and other sources. Big data is characterized by its high volume, velocity, and variety. This means that this data is not only large-scale, but it’s generated and collected in high volume, at high speeds and comes packaged in different formats.
What is Small Data?
Small data refers to smaller and more manageable datasets which can usually be contained inside of a standard spreadsheet (i.e. <1million rows). These datasets are typically collected by a single data source. Small data is recognized by its lower volume, velocity, and variety. These types of datasets are identified by their smaller size, as well as their slower collection speeds and limited formats.
What is the Difference Between Big Data and Small Data?
The primary difference between big data and small data is the way the data is generated and categorized. Big data sets can be generated automatically, without any human intervention, and can be acquired in real-time. This allows big data sets to be very content rich and detailed, but can also make it more difficult to clean, optimize and prepare for analysis.
Small data sets, on the other hand are generally hand-crafted by humans, and are collected for a sole purpose (i.e. customer purchasing patters or transactional data). This makes small data sets simple and relatively east to clean for data analysis, but they don’t allow as much comprehensiveness as larger data sets.
Big Data and Small Data Applications
Another important difference is in the ways that big data and small data are utilized. Big data is typically used by large organizations to make strategic decisions and improve their operational efficiency. Retailers may leverage big data in order to identify customer behaviors and trends in order to adjust their product mix to better suit their needs. Small data is often used by individuals and small businesses to make operational decisions based on transactional data. A small business owner may leverage small data transactions in order to track their expenses and make budgeting forecasts.
Can Big Data and Small Data be used Together?
Even though they share a few differences, big data and small data can actually be used together in several ways to provide a more well rounded evaluation of business needs.
Using data to identify trends and patterns is necessary in order to validate and refine decision making. One use case would be for a retailer to use big data to identify which products are customer favorites, and then use small data gained from customer feedback in order to better understand the logic behind the purchase decision as well as how to improve the overall customer experience.
Another way to combine big data and small data would be to combine them to create a more comprehensive picture. For example, a healthcare provider could use big data from electronic health records (EHS) to identify patterns in groups of patients, and then use small data based on individual patient reactions in order to identify the route causes and correlations.
By leveraging big data and small data together, it provides a more holistic approach in taking raw information and transforming it into something more digestible, aesthetically pleasing and easier to understand.
