Can we think of analytics without data?
Analytics cannot be performed without data as it is the raw material that is necessary for analytics. We can’t think of analytics without data. Data mining is about finding patterns that are hidden in data; hence, without data, there is no data mining.
Many types of data are associated with the input of data analysis in this modern era, such as social media data, Internet of Things (IoT), and business process data. Many types of data are also associated with outputs to the analytic continuum, such as data in the form of dashboards, reports, and knowledge applications. Business analytics data comes from diverse sources, such as social media, the Internet, IoT, and business process information.
Several primary metrics are developed for analytics-ready data, such as data content accuracy, data source reliability, data accessibility, data security, data privacy, data richness, data currency/data timeliness, data consistency, data granularity, data validity, and data relevancy.
(Sharda et al., 2020)
Why are there many names and definitions for data mining?
Data mining has many definitions one such definition. Data mining is the method employed to find unknown patterns in data. Data mining can also be defined as a process that employs mathematics, statistics, and artificial intelligence approaches to obtain and discover information followed by knowledge from massive data sets, which include most automated data. Finding mathematical patterns in huge data sets, such as affinities, correlations, trends, rules, or predictions, can also be defined as data mining. Because data mining is broad and extended outside the boundaries of software vendors, it takes on several definitions. This includes data mining that involves typical data analysis utilized to boost sales.
Why are there many names and definitions for data mining?
The primary reasons for data popularity include (Sharda et al., 2020):
- Fierce global competition fueled by customers’ continuously changing wants and needs in a growing saturated marketplace.
- Shifting in the direction of de-massification of business practices.
- Data processing and storage technology is on an exponential rise.
- Hardware and software for data processing and storage dramatic reduction.
- Database consolidation and other repositories in a central data warehouse location.
- Integration and consolidation of database records that allow a single view of vendors, transactions, customers, etc.
- Common acknowledgment of unknown data values in massive data sources that are not exploited.
What an organization should consider before making a decision to purchase data mining software.
The standard criteria, such as cost/benefit analysis, historical data availability, talent to do data analysis, talent to use the software, and business requirements, must be considered by the organization to justify the use and investment in any major data mining software. Hence, organizations buying data mining software should consider these standard criteria before buying any data mining software.
References
Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson.