Data Mining


Data mining is the process of extracting relevant patterns, deviations and relationships within large data sets to make accurate predictions and gain knowledge. Through it, companies convert big data into actionable information, relying upon statistical analysis, machine learning and computer science.

Data mining involves the computer science needed to process big data and unearth anomalies. It strips out the noise and brings forward the most pertinent information quickly.


Through data warehousing, sifting through data to discover hidden connections has become faster and more precise. Data mining is leveraged in a variety of businesses. Examples include consumer analysis by retailers regarding how, for instance, age and gender correlates to shopping behavior.

It also empowers credit card companies to strengthen anti-fraud measures through the identification of purchasing patterns. Law enforcement officials rely upon it to identify patterns among individuals and neighborhoods.

Difference between Data Mining and Data Analysis

Data mining falls under the broader scope of data analysis. It seeks patterns within a data set, whereas data analysis yields insights from a specific data set. Some people will use the two terms interchangeably, but in an ever-growing specialized world, the differences are becoming more pronounced. Data mining strictly seeks to identify patterns or irregularities in data sets.

The more complex the data set, the more potential there is to uncover relevant insights. Initiatives at times can be limited by traditional data warehousing structure and data applications. Snowflake’s unique cloud data platform architecture supports modern data warehouse application development and data analysis at almost any scale.

Additionally, Snowflake’s analytics platform partnerships enable significant development at the intersection of machine learning, data mining and data science. Snowflake’s advanced capabilities foster an environment in which systems driven by artificial intelligence and machine learning can improve rapidly. There’s no limit to the amount of data that can be stored and shared. In turn, there’s no limit to feeding models with real-time data.