Heigh-Ho, Heigh-Ho it’s off to work we go!
With the extensive growth of data, data mining has become an essential component of data analysis.
Data mining is the process of extracting valuable insights, patterns, and knowledge from large volumes of data by utilizing analytical techniques, machine learning algorithms, and statistical models to discover relationships, make informed predictions, and uncover business opportunities.
“The process of extracting valuable insights, patterns, and knowledge from large volumes of data.”
It can be used to learn valuable business intelligence, ranging from customer interests and segmentation, buying trends, social media commodification, fraud detection, spam filtering & more.
Data mining has come under harsh criticism over the years, as the lack of transparency with data mining has many users concerned about their privacy. With individuals oftentimes unaware of the extensive data mining happening on their personal data, and the influence it has on preferences, the mining of this information can pose a threat to organizational trust if not handled with care and consideration.
Some techniques used in mining include:
- Clustering
- Classification
- Association rules
- Regression
- Decision trees
- Predictive analysis
- Neural networks
How it works:
Step 1 – data is collected and stored in data warehouses (either on-site, or in the cloud).
Step 2 – analysts, managers, and IT professionals organize the data based on their needs.
Step 3 – The software then sorts the data to be used.
Step 4 – the end-user derives insights from the data, either through data storytelling, and presents these insights to inform decision-making.