Data mining analysts use data analysis software to perform tasks such as data analysis, research, data mining, computational analysis and relationship modeling. They report findings to their internal or external clients, including by using bar charts, histograms, scatterplots and other visual tools. These analysts work with operational data (such as sales, cost and inventory), non-operational data (such as industry sales) and/or metadata (such as logical database design and relationships). When they work for the retail industry, data mining analysts help interpret historical patterns and future trends relevant to consumer buying behavior; their findings enable their companies to determine relationships among internal factors (such as price, staff skills, promotional types and product positioning) and external factors (such as competition, customer demographics and economic conditions). Data mining analysts must keep their skills up to date by reading professional journals and attending relevant classes; they may be required to train new analysts as well.
Employers generally require that candidates have a bachelor’s degree in information technology, computer science, mathematics or another relevant field. They must have strong computer skills and thorough knowledge of data analysis software. Some employers require candidates have work experience in data science and data analysis as well. Data mining analysts must possess excellent oral and written communication skills, as well as strong analytical skills, the ability to work independently with minimal or no supervision and excellent presentation skills. Some companies require data mining analysts to pass a security clearance and background check.
Data Mining Analyst Tasks
Identify, collect, analyze, and present trends, visualizations, and summaries of data.
Create, update, and maintain data dictionaries, including data integrity problems.
Collaborate with business owners to define metrics and performance indicators and produce analyses.
Create algorithms and predictive models using statistical techniques.
Produce presentations and reports for multiple audiences.