Data Mining: Why is it Important?

Data Mining

Data mining is the process of extracting useful information from an accumulation of data, often from a data warehouse or collection of linked data sets. Its tools include powerful statistical, mathematical, and analytics capabilities whose primary purpose is to sift through large sets of data to identify trends, patterns, and relationships to support informed decision-making and planning.

Why is data mining important?

Data mining helps spark ideas, thoughts, and opinions you haven’t thought of before, especially because a lot of teams are still not inclusive or diverse. It gives you an outside perspective on the world and helps you make informed decisions for your business.

Data mining consists of five major elements:

1) Extract, transform, and load transaction data onto the data warehouse system.

2) Store and manage the data in a multidimensional database system.

3) Provide data access to business analysts and information technology professionals.

4) Analyze the data by application software.

5) Present the data in a useful format, such as a graph or table.

How Data Mining Works

Data mining works through the concept of predictive modeling. Suppose an organization wants to achieve a particular result. By analyzing a dataset where that result is known, data mining techniques can, for example, build a software model that analyzes new data to predict the likelihood of similar results. Here’s an overview:

Start with historical data

Let’s say a company wants to know the best customer prospects in a new marketing database. It starts by examining its own customers.

Analyze the historical data

Software scans the collected data using a combination of algorithms from statistics, artificial intelligence and machine learning, looking for patterns and relationships in the data.

Write rules

Once the patterns and relationships are uncovered, the software expresses them as rules. A rule might be that most customers ages 51 to 65 shop twice a week and fill their baskets with fresh foods, while customers ages 21 to 50 tend to shop once a week and buy more packaged food.

Apply the rules

Here, the data mining model is applied to a new marketing database. If the company is a packaged food provider, it will be looking for 21- to 50-year-olds.

Key Data Mining Concepts

• Data cleansing: Also called data scrubbing. The process of correcting errors and omissions in data before analyzing it.
• Model: The knowledge discovery of relationships among data, often expressed as rules.
• Target: The goal of data mining, for example, identifying high-value customers.
• Predictors: The related data that leads to the target.
• Case: A specific instance of data, such as a particular customer’s information, that is plugged into the model to determine its relationship with the target. For example, is this customer likely to return for repeat sales?
• Market basket analysis: Discovering buying behaviors of customers based on past buying patterns, often using data collected from company loyalty marketing programs.
• Machine learning: Algorithms that use known cases to discover other similar or identical cases in large data sets.

Advantages of Data Mining

• Optimal product/service pricing: Using data mining to analyze the interplay of pricing variables, such as demand, elasticity, distribution and brand perception, can help a marketing business set prices that maximize profit.
• Better marketing: Data mining can help a company get more value out of their marketing campaigns. By segmenting customers with different behaviors, optimizing engagement by segment or providing insight to aid development of personalized ad creative. The results of ad campaigns can often demonstrated in sales dashboards.
• Heightened employee productivity: Analyzing employee behavior patterns and viewing KPIs in HR dashboards can lead to digital strategies for boosting employee engagement and productivity.
• Improved customer retention: Understanding customer behavior can improve customer relations, reducing churn.
• Increased cost efficiency: Manufacturing costs, for example, could lowered through many different data mining analyses, from insights into supplier pricing behavior to better understanding customer buying patterns.
• Higher product/service quality: Finding and fixing areas where quality falters can decrease marketing product returns.