What is Data Mining?

Every business needs a wealth of data to be successful in the modern market. They need to collect, analyze, and understand data about their target audiences, the broader economic market, and even their performance to make wise decisions, avoid pitfalls, and bring in more revenue.

But collecting raw data, even in staggeringly large amounts, is not enough. Instead, that data has to be transformed into useful information through a process called data mining.

How does data mining work?

Data mining is when data analysts or scientists:Collect data,Compile that data into a large data set, then Run different analyses or use different algorithms to extract important information from the data set, which can be difficult from just looking at the data points “raw.”

Depending on the needs of a business or client, data scientists may perform data mining using different modeling techniques, such as:

Descriptive modeling that can help to uncover similarities or groupings and historical data to explain failures or successes.Predictive modeling that helps classify or predict events in the future or estimate outcomes.
Prescriptive modeling that helps organizations filter and transform unstructured data and use it for predictive models. This modeling can help to improve forecasting accuracy and make wise decisions for the future.

The data mining process:

The data mining process runs the length of data collection and analysis. It includes initial data harvesting and then proceeds to data visualization. In the visualization step, data analysts extract information from big data sets. They may use different techniques to generate predictions, descriptions, or other information about a targeted data set.

Furthermore, data scientists can describe the data they collect and mine using observations of correlations, associations, or patterns. They may also classify or cluster data through different regression or classification methods.

Data scientists can use a variety of data mining techniques, as well as algorithms, to mine large quantities of data and extract useful information. A few of the most common data mining techniques are:

Association rules, which use different rules to find relationships between data points in a data set. Association rules are often used for “market basket analysis” so companies can understand the relationships between the different products, consumption habits of consumers, etc.
Neural networks, which are used for deep learning algorithms. These process training data and mimic how the human brain works using different layers of digital nodes.
Decision tree analysis. This technique uses regression methods or classification to predict outcomes based on predetermined decisions. It provides its conclusions with a treelike visualization so laypeople can understand the outcomes of different decisions.
K-nearest neighbor or KNN algorithms. These are algorithms that classify data points based on proximity and association to other relevant and available data points. They can be useful for calculating the distance or difference between data points (such as Euclidean distance).