Researches has done numerous things for companies no matter what industries they are into. Primarily, research gives companies a chance on how to improve their services and/or products, and how to approach their customers efficiently and effectively. This is because they will know where their products may have some problems or if their services are not reaching the standards set by the industry or even just within the company.
Since it is research, data mining and data analytics will do their roles in helping the company be at their best. Coming from the term itself, data mining would mean that important data would be drawn out and used for the company to study. These are raw data that may be results from a series of research studies done in a company.
Data Mining
A field in computer science, a process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of statistics, artificial intelligence, machine learning, and database systems.The overall goal of the data mining process is to extract knowledge from an existing data set, and transform it into a human-understandable structure for further purpose. Besides the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, complexity considerations, post-processing of found structures, visualization, and online updating.
Data Analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting information, suggesting conclusions, and supporting decision making. Data analysis has multiple approaches, encompassing many techniques.
Data mining is a data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that focuses on business information that relies on aggregation. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
The Process
After data extraction process of data mining, the next step is the data analytics where conclusions are made out of the data collected. These conclusions will be beneficial to make the ways of operational procedures within the industry and the production of products, and the delivery of service more efficient. The great news that these two bring is that they utterly accurate.
It is very important that any data be as accurate as possible. Once the product and/or are being tested to pass their standards, then the company can precede manufacturing the product and delivering the service. If it doesn't pass the standard, then it must go through another set of improvement processes before being launched to public. If a test product does not reach a certain standard for quality, then it can be concluded that it won’t be able to serve the customer properly.
Testing with Data Mining and Data Analysis
Testings also helps the company determine whether they must produce more of a certain product or if they have to stop on producing more of its kind. This will help the company steer away from potential quality issues especially if their offerings have not been proven to serve the customer well. Testing can also ensure that the services a company provide really do solve the customers' problem without giving them necessary problems.
Data mining and data analytics are not only helpful in testing services and products. These two procedures will be helpful in equipment checks, maintenance and managing the resources to run the industry.
In conclusion, doing data mining and data analytics is certainly helpful for company improvements. This is a way of knowing if they maintain the important factors that will contribute to their success. As long as they do a number of testing, they will have enough information to draw out accurate conclusions in the process.