Data mining is the process of analyzing vast amounts of data to find patterns, trends, or useful insights, which is essential in turning raw data into meaningful information. In the context of data mining, a key framework often referenced is the DIKW pyramid: Data, Information, Knowledge, and Wisdom.
Data
This is raw, unprocessed, and unstructured material. Data includes facts, figures, and observations that don’t yet have context or meaning on their own. In data mining, this could be things like transaction records, customer interactions, or sensor outputs, gathered from various sources.
Information
When data is processed or organized to add context, it becomes information. For instance, if you organize customer transaction data by time or product category, it provides a clearer picture of what is happening. Information provides answers to basic questions like who, what, when, and where.
Knowledge
Knowledge comes from analyzing information to understand the how and why. It’s about identifying relationships and drawing conclusions that can inform decisions. In data mining, knowledge might involve identifying patterns, like which products are frequently bought together, which helps in understanding customer behavior or optimizing sales.
Wisdom
Wisdom is the final level, where knowledge is applied to make informed, impactful decisions, often in a strategic context. It answers the when and why questions, helping to determine the best actions based on insight. For instance, a retail company might use its knowledge of customer purchasing patterns (gathered through data mining) to make decisions on inventory management or personalized marketing.