What is Data Mining?
Data mining, also known as knowledge discovery in data (KDD) is the automatic or semi-automated process of finding anomalies, patterns and correlations within large data sets to predict particular outcomes. Depending on the industry involved, a wide range of techniques can be used in conjunction with the compiled recovered data, you can use this information to increase revenue, reduce costs, improve customer relationships, reduction of risk and more.
History of Data Mining
The concept behind the filtering through data to discover hidden patterns or connections and predict future trends has a long history spanning several decades, though the term “data mining” wasn’t coined until the 1990s.
Over the past decades, the rapid pace of advancement in processing power have enabled us to move beyond manual, repetitive and time consuming practices towards quick, efficient and automated data analysis mechanisms. There are many markets where data mining can be of great benefit, to discover relationships among everything from price optimization, promotions and demographics purposes to how the economy, risk, competition and social media are affecting their business models, revenue and customer relationships.
Why is Data Mining Important?
It is no secret that the amount of data generated each day is staggering, and the volume of data doubles every year. It is calculated that 90% of all digital information is unstructured data. However, more information does not necessarily equate to more usable knowledge.
Data mining has the ability to sift through vast amounts of unstructured and disorganized data and uses patterns to understand what is relevant. With good correlations between data established, the ability to make informed decisions is greatly enhanced.
Who Makes Use of Data Mining?
Data Mining is used in a vast array of industries, but some of the most commonly used are:
- Telecommunications, Media & Technology – In an already saturated market where competition is fierce, analyzing consumer data can give companies a leading edge. Telecommunications, media and technology companies can use analytic models to make sense of vast quantities of customer data, in turn helping to predict customer behavior and offer highly targeted, relevant campaigns.
Banking – Automated algorithms assist banks in understanding their customer base as well as track the billions of transactions which occur within the financial system. Data mining helps financial services companies get a more structured view of market risks, assist in detecting fraud, manage regulatory compliance obligations and achieve optimal marketing returns on investments.
Retail – Analyzing customer databases can uncover customer insight which can be used to improve relationships, optimize marketing campaigns, and calculate offers which will have the biggest impact on potential customers.
Education – With the use of structured data-driven views of student progress, educators can predict student performance before the next school year even begins, and develop intervention strategies to keep the students on course.
Insurance – With solid analytical data at their disposal, insurance companies can solve complex problems concerning fraud, compliance, risk management and customer attrition.
How Data Mining Works
Data mining usually consists of four steps: setting up the objectives, data gathering and preparation, applying data mining algorithms, and finally evaluating the results.
- Set the business objectives – This is probably the most important step within data mining: to clearly define what the end goal is for a given project. Data scientists and business stakeholders need to work together to define the business problem, which helps identify the data questions and search parameters. Data analysts may also need to do additional research to understand the business context appropriately.
- Data preparation – Once the scope of the problem is clearly defined, it is easier for data scientists to identify which datasets will help answer the pertinent questions to the business. Once they collect the relevant data, the information will be ‘cleaned’ to remove any duplicates or missing values. It is important to filter out as much unnecessary data as possible, as this will reduce the sheer amount of data to be processed.
- Model building and pattern mining: Depending on the type of analysis required, data scientists may investigate any data relationships, such as sequential patterns, association rules, or correlations.
- Evaluation of results and implementation of knowledge: Once the data is compiled, the results need to be evaluated and interpreted. If the data mining procedure has been implemented successfully, the data results can be used by the organizations to implement new strategies and achieve their intended objectives.
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