DATA ANALYTICS AND DATA MINING
Data analytics
The world we live in is as a result of data availability that has simplified the lives of many people. Data is simply the clustering of figures, numbers, and symbols which when used in computer-usable information is developed. Analytics the discipline of analyzing raw facts to make inferences about that set of data (Delen, Eryarsoy, and Şeker, 2017). Data analytics cannot exist without data since analyzing is all about data and its evaluation to give a more elaborate meaning.
Business analytics
BA involves the examination of the past and incumbent situations in the business and acquires knowledge and skills that are reliable and adequately applicable for a successful journey. The BA has various inputs and outputs that are associated with business analytics (Tan, Steinbach, and Kumar, 2016). Inputs include the mining tools, videos, and visual graphics and having output as decision making.
Source of business data
the availability of data shows that there is a source that produces the very products. The BA has sourced its data from wide range laces that include. Market research, the Amazon Web Service, fig share. These are some of the platforms that avail information to businesses through online demand (Delen, Eryarsoy, and Şeker, 2017). The nature of the data being availed is always published and are reproduced after a compilation of figure so to give the meaning of the words and data being availed.
Metrics for data analyzed
The ability to source out data is a crucial metric that has to be considered all through. The other metric is that there is a need for the data to be continuous, this helps the implementation and use of the data more easily. Data accuracy is another factor that has to be addressed (Choi, Wallace, and Wang, 2018). the data content must be correct to avoid producing wrong output mainly in decision making. Data inclusivity is another factor that has to be addressed very significantly. The data has to be fully loaded with every fact and content that is crucial to make references.
Part 2
Data mining
This is the process of retrieving meaningful data from very big data such as the database. Data mining has been a buzzword that has acquired many names that describe more about it. These names include data retrieval, data extracting, analysis, data exploration, and many more. These names describe simply the act of digging out the useful content from a very large piece of data which is simply data mining.
The popularity of data mining
There have been various factors that have resulted in the popularity of data mining in recent years. They include the increasing number of people who use cloud computing in their dealings. Data mining is largely connected with cloud computing since it may offer on-demand service online (Tan, Steinbach, and Kumar, 2016). There has been a competition between companies to maintain their market share all through and this has resulted in organizations seeking information to counter any competition. The urge of sustaining successful operations has resulted in many to seek information through data mining.
Considerations.
For a successful purchase, there is always a need for persons to have a wide knowledge of the things they are pursuing. First, they need to consider if the system is cloud-based or not. They also need to consider the most trending and fashionable features that the system may offer (Choi, Wallace, and Wang, 2018). They need to understand if their employees have the skills to use the systems or not.
Data mining and analytical tools.
Data mining is unique from analytical tools since it recognizes the patterns in large volumes of data. Data mining is worked by mathematical points while data analysis is based on business intelligence (Delen, Eryarsoy, and Şeker, 2017). Data mining has no virtual tools while data analysis involves virtualization.
Data mining methods
Clustering analysis works by grouping items into clusters depending on the similarity and their similar differences. pattern tracking this method is founded on identifying frequent patterns in a given period (Tan, Steinbach, and Kumar, 2016). Predictive analytics gives prediction concerning the future based on the current and the past events on occurrences.
References
Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India.
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883.
Delen, D., Eryarsoy, E., & Şeker, Ş. (2017, January). Introduction to data, text, and web mining for business analytics minitrack. In Proceedings of the 50th Hawaii International Conference on System Sciences.
Ghasempouri, T., Azad, S. P., Niazmand, B., & Raik, J. (2018, August). An Automatic Approach to Evaluate Assertions’ Quality Based on Data-Mining Metrics. In 2018 IEEE International Test Conference in Asia (ITC-Asia) (pp. 61-66). IEEE.