Data Mining: Why is it Important for Data Analytics?
Data mining is the process of classifying raw dataset into patterns based on trends or irregularities. Companies use multiple tools and strategies for data mining to acquire information useful in data analytics for deeper business insights.
Data is the most precious asset for modern businesses. Like mining gold, extracting relevant information from an unorganized data set is a difficult task. You need to use tools for data patterns or trends. Unlike mining minerals, data is not wholly removed from a data set. This process involves identifying a data set’s structure, relationships between the various data; and determining what data to extract for data analysis.
The data mining process
Data mining operations can be simply represented by the following diagram:
- Business Acumen: Businesses managers need to have the understanding to identify relevant data sets for deeper business insights.
- Data Knowledge: Data engineers classify the data and their sources.
- Data preparation: A data engineer will use an ETL system to feed data from varied sources to a data warehouse for analysis. A data scientist is responsible for selecting the relevant data from the warehouse, which will be useful for a specific use case. There will be data cleansing, structuring and organizing to fit analysis and business intelligence requirements. Data transformation is the process of altering the format, structure, or values of data.
- Data modelling: Data modelling is the process of organizing the structure, associations, and constraints of the prepared data.
- Evaluation: Data scientists analyze the data using machine learning tools to identify patterns or trends in the data.
- Deployment: Here, data analysts will do the reporting in the form of data visualizations.
Data mining business applications
It brings out the true value of data by unravelling latent information from complex data sets. We can list three major business applications of data mining.
You can use it to forecast purchase trends or customers’ behaviour. Retailers can analyze links with data such as customers’ age, gender, and previous purchases to project their future behaviour. Educational institutions can predict the number of successes and dropouts.
Deliver personalized services
It has applications in health care services too. It will help to predict risks or illnesses in various segments of the population. Doctors can prescribe treatments effectively with data like medical records, physical examinations, and treatment patterns. Retailers can run customized marketing campaigns or loyalty programs by mining customer data.
A large organization faces difficulty determining the profitability of any business decision with multiple products and sales processes. Taking an informed decision will require filtering various data like investments in customer support, the time of product development and marketing.
Data mining challenges
Let us check out the common challenges which hinder the desired results:
- Incomplete data: It is a usual observation that data sets are incomplete. For instance, sales data for the entire business lack information from several departments. This can minimize the impact on the reports and data trends.
- Noisy data: A corrupt or poorly structured data set with irrelevant information is said to be “noisy”. So, a data analyst has to extract relevant data from the data set or find ways of removing noisy data before mining.
- Scalability: Larger data sets demand more resources for data mining. Organizations using on-premise data warehouses with fixed hardware configurations face a lot of difficulties in scaling. Businesses hosting their data infrastructure on a cloud platform do not face problems with scalability.
Data mining best practices
Businesses should employ the following best practices to obtain the better insights and avoid hindrance:
- Data Preservation: For effective data mining, all raw data should be preserved in a data lake or warehouse.
- Business Understanding: You need to have a thorough knowledge of important insights relevant to your business.
- Data quality: Data quality issues can be avoided by eliminating duplicate or inaccurate data entries. Otherwise, these issues might hamper a smooth data mining operation.
- Identify outliers: Outliers are a vital source of insight. Design a data mining process that reports on the most common features within a data set, and also identifies anomalies related to the business goals.
Start Data Mining With Us!
Data mining operations can easily be simplified by using an ETL solution and a cloud-based data warehouse which will extract data from more than 100 data sources to your data warehouse. Daton is a simple data pipeline that can populate popular data warehouses like Snowflake, Google BigQuery, Amazon Redshift and acts as a bridge to data mining, data analytics, and business intelligence.