Analytics tools analyze large datasets to determine patterns and trends, assisting in decision making. They can be divided into interest indicators, evaluation of activities, and selection of data. Analytics tools are not new, but the internet has fueled their growth.
There is a wide variety of analytics tools that can be used to conduct analysis of large datasets and transactional activities. Analytics has grown in popularity over the past few years and is expected to continue to experience above-average growth well into the next decade. As the technology has improved and received greater acceptance, organizations have collected and stored large amounts of transactional data. The purpose of analytics tools is to use this data to determine patterns and trends. This information can be used to assist in decision making.
Analysis tools can be divided into three categories: indicators of interest, evaluation of activities and selection of data. While many people assume that analytics tools are a new development, they actually represent some of the oldest concepts in statistics and data management. The advent of the Internet and the desire of companies to track how well this tool is reaching customers has fueled the rapid growth of analytics tools. To enable any organization to determine how much resources to allocate to the Internet, metrics are needed to determine the return on investment and relative utility of this tool.
Interest indicators are the most common of all web-based analytics tools. A small program or script is added to the website to track user activity. The most basic tools can provide a summary of the user’s country of origin, access time, browser used, total amount of time spent on the website, and referral source. More complex commercial products can provide the exact Internet Protocol (IP) address, the number of times the same person has visited the site in a given time interval, where they went and how much time they spent on each page.
Business evaluation tools can range from simple data collection to business process evaluation. For example, a web-based tool can provide a summary of the most common access paths, time spent in each stage, and who accessed each table of data. For a transactional system, the same type of analysis can be completed using a combination of information from multiple tables and databases. The tools used for this type of analysis are typically quite resource intensive and require significant hardware and storage space to function.
The data selection or data extraction toolkit is used to move specifically identified data from the transactional database into the warehouse or data analysis cube. The specifications must be completely exact in order to create the appropriate data set for use with the analysis tool. Too much data is expensive and not enough will not provide accurate results.
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