It opens opportunities for business growth since it helps stakeholders gain insights from their organizations and make informed decisions. Data mining is under the umbrella of Business Intelligence and consists of extracting data from a large set of raw data and analyzing them to find anomalies, patterns, trends, and correlations to predict outcomes. Businesses can use data analysis to increase sales and revenue, cut costs, reduce risks, etc.
Data Mining Techniques
- Association rules- (market basket analysis) search for relationships between variables. Historical sales databases are analyzed to discover which products are commonly purchased together.
- Classification- uses predefined classes to assign objects. Data is categorized and summarized across similar futures or product lines.
- Decision trees- used to categorize or make predictions based on a set list of criteria, and decisions previously made as well as how some specific questions were previously answered.
- K-Nearest neighbor (KNN)- classify data based on the proximity to other data. It is used to predict the features of a group based on individual data points.
- Neural Networks- process data by using nodes. Data is mapped through supervised learning, like how human brains are interconnected. One of the uses of Neural Networks is to determine model accuracy.
- Predictive analysis- leverage historical information to build graphical or mathematical models to forecast future outcomes.
Data Mining Process
Different data models, such as Cross-Industry Standard Process for Data Mining (CRISP-DM), Sample, Explore, Modify, Model, Assess (SEMMA), will have different steps; however, the general steps are similar.
Step 1- Understand the business.
What are the goals the company is trying to achieve by mining data? What is the current business situation? What are the findings of a SWOT analysis? What will define success at the end of the process?
Step 2- Think about data.
What data is available? How will it be secure and stored? How will the information be gathered? What may the outcome look like? How will data limits, storage, security, collection, and assessment be done? How will it affect the data mining process?
Step 3- Prepare the data.
The data should be collected, uploaded, extracted, or calculated. Then, it is cleaned, standardized, scrubbed for outliers, assessed for mistakes, and checked for reasonableness and size.
Step 4- Build the model.
Search for relationships, trends, associations, or sequential patterns, and make predictive models to forecast future outcomes.
Step 5- Evaluate results.
The outcomes from the data analysis may be aggregated, interpreted, and presented to decision-makers.
Step 6- Implement change and monitor.
At this stage, management evaluates the findings of the analysis. They can decide that the analysis results weren’t either strong or relevant, or the company might use them for business decision-making. Moreover, management reviews the impact of the analysis on the business and identifies new business problems or opportunities.
Data mining can be applied, for example, in sales- to craft its product line, marketing- to develop marketing campaigns, manufacturing- to find out bottlenecks, fraud detection- by discovering reoccurring transactions to an unknown account, human resources- to understand why employees leave, customer service to find out weak points and highlight what the company is doing right.
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