Data mining is a process of extracting meaningful information from large volumes of raw data. Data mining is a tool that can be applied to explore and analyze any type of big data but it tends to be focused on structured data. 

Data mining utilizes vast amounts of raw data from businesses to generate solutions to their business problems. It can identify patterns and connections in data based on certain criteria set by the business. The outcomes from data mining result in businesses becoming more efficient, productive, and profitable. In particular, data mining has several advantages for businesses such as 

• Extracting highly relevant and reliable data. 
• Rapidly analyzing and processing large volumes of raw data. 
• Detecting hard-to-perceive patterns, correlations, and trends in data. 
• Building models from data to generate predictions and forecasts. 
• Identifying valuable insights that would otherwise be missed. 
• Developing solutions that meet business specifications and requirements. 
• Enabling an evidence-based approach to decision-making. 

Data mining involves many different tools and techniques, each of which has a specific purpose and objective. Some of the most common techniques are as follows: 

• Clustering organizes data into groups, called clusters, based on similar characteristics. An example of this is customer segmentation.  Customers are grouped based on different characteristics (location, age, gender, etc.) into representative segments. Clustering can help identify common customer sentiments and behaviors associated with each segment. 
• Classification sorts data into distinct categories or classes. Classification can be used to identify a particular class of data by describing the attributes of that class. An example of this is loan application systems in which customer attributes, such as age, gender, income, and so on, are used to identify customers’ credit risk (high risk, low risk, etc.) to determine their loan terms and creditworthiness. 
• Regression uses data analysis and statistical methods to predict a range of numeric values for given variables. An example of this can be seen in real estate.  Estimated property value is calculated based on variables such as location, square feet, price when last sold, the price of similar homes, and other factors. 
• Association identifies relationships and connections between variables in data. An example of this is online video content streaming sites where suggestions for Movies, TV Shows, or videos are made based on a user’s content viewing history.  

Businesses can leverage data mining in a variety of ways. Social Media, for example, presents an immense opportunity for data mining. Social platforms generate vast amounts of data on users. Businesses can use this data to understand the preferences, interests, and behavioral patterns of potential customers. Data mining insights can help businesses optimize their products and services, improve customer experience, boost the success of marketing and sales campaigns through targeted advertising, predict customer response to new products, and much more. Data Mining can be similarly used in industries such as Banking and Financial Services, Retail, Manufacturing, Communications, Healthcare, Scientific Research, and so on. 

While Data Mining presents enormous potential, the success and efficacy of data mining are dependent on choosing the right algorithms, tools, and techniques. AIPI3 is built around a collection of artificial intelligence & machine learning experts with extensive experience across a wide range of industries, specializations, and applications. We equip businesses with the knowledge and expertise as well as optimal algorithms and data applications necessary to implement data mining.