While business leaders are still lauded for exercising their keen instincts and good judgment, more of them are relying on the more quantitatively based results of data mining efforts to guide their decision making processes.
Data mining, or pattern mining, involves identifying data sets for analysis and determining patterns within the data that can be used to make predictions through the use of statistical models. Pattern mining is conducted as part of the information technology (IT) career field, and the main challenge of the practice is the large amounts of structured and unstructured data that must be assessed during pattern mining projects.
Here are some typical business problems that are solved by pattern mining, popular jobs in the career field and suggestions for gaining pattern mining employment.
Pattern Mining Activities in Business Problem Solving
Pattern mining activities involve creating models that predict trends about revenue, customer attitudes towards brands as well as customer buying habits. Simple pattern mining activities can generate various customer profiles that allow marketing professionals to employ customized strategies to earn their customers’ business. Trends can be detected that allow companies to extend their products and services to wider audiences by using pattern mining techniques. For instance, a company that has customers whose profiles identify them as natural product consumers and pet owners may be candidates for the company’s new line of organic dog treats. Companies can also use pattern mining to improve customer relationship management activities and increase loyalty for their brands.
Types of Pattern Mining Careers
Careers in pattern mining typically take the job titles of data scientist, data analyst or data engineer. These jobs require solid quantitative and research skills to be successful. Data scientists and analysts must sift through huge amounts of data and know how to form relevant groupings that are unique to the projects on which they work. These scientists also develop models and algorithms that help them to streamline their analysis. Besides employing their extensive quantitative skills, data engineers must have deep knowledge of the industry in which their company operates. Successful data engineers are able to refine their analytical efforts and determine subtle trends by exploring and appropriately applying data from external sources. Successful pattern mining professionals also need great communication skills to verbally and graphically convey the results of their work to all types of stakeholders.
Tips for Gaining Employment in Pattern Mining
Although data science and engineering jobs are said to be some of the most popular in the IT career field, there are very high barriers to entry for those seeking employment in pattern mining. It used to be that companies were unsure of what they could get out of available data, and job seekers were gaining employment by strategically using a common set of big data related buzz words. As the pattern mining field matured, the expectations of employers have become more structured, and their hiring practices for data scientists reflect this. Viable candidates usually have technical degrees in mathematics, physics or computer science to gain employment, and labor surveys show that 88 percent of those holding data scientist jobs have advanced degrees in technical disciplines. Other pattern mining workers have technical undergraduate degrees, but they gain additional training through certificate programs and online courses. Those who want to prepare early for pattern mining careers should take as many mathematics, statistics and programming courses as possible.
Technological advancements have made it possible for all types of businesses to collect and store large amounts of data items that relate to their customers, suppliers and company performance. The principles and methods that are employed within data mining allow businesses to use their data and publicly available industry information to avoid high risk situations and create future opportunities.
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