SMU M.S. in Data Science / The DataScience@SMU Blog
In a world where “big data” is more than a buzzword, the demand for data mining analysts is on the rise. But what exactly does “data mining” mean? What do data mining analysts do, and what role do they play in a business or organization? What is the best way to prepare for a career in the field? Let’s take a closer look at this fairly new, but steadily growing, role.
What is data mining?
Data mining involves analyzing data in order to identify hidden patterns and systemic relationships that can be used to predict future behaviors. It is the process of transforming information into insights that help businesses make more meaningful, fact-based decisions.
Companies and organizations of all shapes and sizes employ predictive analytics and data mining to:
- Minimize risks and detect fraud
- Better understand customer behaviors and needs
- Make more informed decisions faster
- Tap into new markets
- Improve branding and marketing initiatives
- Increase revenue
While data mining is a relatively new term, the practice itself is not. From supermarket scanners that track consumer purchases to database management systems that capture customer demographics, companies have used data mining tactics for years. However, thanks to the rising popularity of big data and advancements in technology, data mining has become more sophisticated, feasible and prevalent than ever before.
What do data mining analysts do?
Data mining analysts turn data into information, information into insight and insight into business decisions. They use statistical software to analyze, identify and assess data attributes in order to develop recommendations and creative solutions that improve operations and support their organization’s business objectives.
Data mining analysts are generally considered experts in business intelligence. Not only do they understand data, but they are also capable of translating the results of data mining into actionable ideas.
Typical responsibilities of a data mining analyst may include:
- Performing strategic data analysis and research
- Identifying opportunities to improve productivity via sophisticated statistical modeling
- Looking at user behaviors to find common patterns or trends that may be used to improve a product’s performance
- Interpreting results and identifying opportunities for improvements
- Communicating recommendations to stakeholders and/or clients
Where do data mining analysts work?
From preventing fraud to identifying adverse drug effects during clinical trials, data mining can be applied — and is used — in practically every industry. For example:
- In banking, data mining is frequently used to model and predict credit card fraud and analyze risk.
- In financial markets, data mining is employed in areas that include stock price forecasting, portfolio management, bond rating and more.
- In retail, data mining is used to analyze shopping patterns, evaluate advertising and direct mail campaigns, co-locate products in display areas and better understand customer preferences and behaviors.
- In health care, data mining can help identify best practices and inefficiencies, evaluate new treatments, and detect fraud or abuse.
- In telecommunications, data mining is used to handle “churn management” of customers and determine which promotions will resonate with which target audiences.
These are just a few of the ways that data mining is used to help companies and organizations solve real world problems and gain a competitive edge. Other key industries that employ data mining include energy and utilities, education, transportation, criminal justice and public service.
How much do data mining analysts earn?
The national average for data mining analyst salaries is about $65,000 a year. At some companies — such as Microsoft — an experienced data mining analyst can earn as much as $110,000 a year.
According to the U.S. Bureau of Labor, the demand for individuals with proven statistical competencies is expected to grow 22 percent within the next decade.1 As more organizations elect to couple the power of big data with predictive analytics that improve decision making, the more the demand for experienced data mining professionals will grow.
What do data mining analysts need to know?
Mathematics and algorithms play a big role in data mining. Data mining analysts need to understand statistical concepts and basic principles of knowledge induction. Depending on the particular job, data mining analysts may need to be familiar with common data analysis tools and programming software that include SQL, T-SQL, PL/SQL (SQL Server or Oracle), NoSQL and Hadoop. Many employers also prefer programming experience with VBA, Java, Python and Perl languages, as well as experience operating in a Linux environment.
In addition to strong analytical and programming skills, data mining specialists also need to have a flair for communication and strong presentation skills. They should feel equally comfortable in a boardroom as they do behind a computer, as they are often required to present their findings in a clear and simple manner in the appropriate business context.
A smart step in preparing for a career in data mining is to gain in-depth knowledge about data science. This area of study provides an opportunity to explore the statistical methods used to extract meaning from large datasets, as well as an explanation of how to develop predictive models and applications that support optimized decision making and are used to address important issues in business. Learn more by visiting ourhomepage.
1https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htmarrow_upwardReturn to footnote reference
Last updated April 2015
