In this sense, apart from the analysis phase itself, data mining covers aspects of data management and preprocessing, modelling, identification of metrics of interest, visualisation. In short, Big Data is the resource while data mining is the 'manager' that is used to provide beneficial results .
To carry out data mining there are some organizational and preparatory phases:
Understand the problem, or at least the area of investigation . The business decision maker needs a general understanding of the domain in which he or she will be working, i.e. the types of internal and external data that need to be part of this exploration. He or she is presumed to have a deep understanding of the business and the functional areas involved.
Data collection . Start with your own internal systems and databases. Connect them through your own data models and various relational tools or collect the data in a data warehouse . This includes all data from external sources that are part of your operations, such as field sales and/or service data, IoT or social media data. Examine and acquire rights to external data, including demographic, economic and market data, such as industry trends and financial benchmarks from category associations and governments. Incorporate it into your toolset (bring it into your own data warehouse or connect it to the data mining environment).
Data preparation and understanding . Use business experts to romania whatsapp data define, categorize, and organize data. This part of the process is sometimes called data wrangling or munging . Some data may need cleaning or 'scrubbing' to remove duplications, inconsistencies, incomplete records, or outdated formats. Data preparation and cleaning can be an ongoing activity as new projects or data from new fields of research become of interest.
To be successful in these operations, academic training and continuous practice supported by specialized studies such as those that can be acquired with a Global Master in Business Analytics and Data Strategy or a Master in Big Data & Analytics are necessary to master the most advanced techniques.
Data Mining Examples
Data mining is essential for sentiment analysis, price optimization, database marketing, credit risk management, training and support, fraud detection, health and medical diagnostics, risk assessment, recommendation systems, and much more. It can be an effective tool in any industry , including retail, wholesale distribution, service sectors, telecommunications, communications, insurance, education, manufacturing, healthcare, banking, science, engineering, and online marketing or social media.
Some concrete examples of data mining .
Product development : Companies that design, manufacture, or distribute physical products can identify opportunities to better target their products by analyzing purchasing patterns combined with economic and demographic data. Designers and engineers can also cross-reference customer and user feedback, repair records, and other data to identify opportunities for product improvement.
Production – Manufacturing companies can monitor quality trends, repair data, production rates, and product performance data in the field to identify production issues. They can also recognize potential process upgrades that would improve quality, save time and costs, enhance product performance, and/or indicate the need for new or better factory equipment.
Service Industries : In services, users can find similar opportunities for product improvement by cross-referencing customer feedback (direct or from social media or other sources) with comparable level services, channels, performance data, region, pricing, demographics, economic data, and more.