Data Mining the Amount of Knowledge Available

Data Mining

The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this, it is necessary to contrast the positive benefits of data mining and contrast those ideas with the negative connotations associated with the similar processes.

Data mining, according to Thearling (2009) is "the automated extraction of hidden predictive information from large databases. " Additionally, data mining is a proactive and aggressive tactic that can serve the overall business strategy when properly aligned. Statistical analysis is inherent within any type of data mining technique and is expressed in these terms. The reasons why data mining or data warehousing is important is because of the potential to simplify all types of processes within a complicated data field.

It is also important to understand the complex issues involved in gaining a total understanding of what is at stake. Greenfield (2005) examined the case for data warehousing and the case against data warehousing. There's several reasons why a company would want to implement these types of systems, these include: to perform server/disk bound tasks associated with querying and reporting not used by transaction processing systems, to use data models that speed the querying process, and to simple fly the query process for multiple transaction processes to one that is streamlined to a single or report. What's most important to understand is that whatever data mining technique is used, and for whatever purpose, it must be aligned with the greater organizational goals in order for it to contain any advantage.

Greenfield (2005) also stated a strong case against data warehousing and data mining processes. Data warehousing systems do not seem to capture a total organizational view. The complication that data mining often initiates can often distract the organization from its larger mission. Technology also has the tendency to move at a very quick pace. Consistently upgrading and overhauling data mining systems may not be worth accomplishing for some companies. The overall costs of data mining systems can be expensive and not cost-effective for some companies to implement. Once again it is important to make sure that whatever data mining system is implemented or not implemented that this maneuver is associated with the overall principles of the strategic goals of the business.

Currently on today's market there is a range of analytical tools that can help extract meaning within the large pools of data. Bardoliwalla (2009) predicted how some of these tools may be used and interpreted in the market. She claimed "the largest Web players such as Google, Yahoo, Amazon, Facebook have built their own solutions to handle their own incredible data volumes, with the open source Hadoop ecosystem and commercial offerings like CloudEra leading the charge in broad awareness." What's most important is the variety and flexibility of the market," there is a plethora of choices available as new entrants the market seem to crop up weekly. Visionary applications of this technology in areas like meteorological forecasting and genomic sequencing with massive data volumes will become possible at hitherto unimaginable price points."

Data mining requires the careful extraction of important details within the large pool or field of data within any informational system. Extracting this information requires a specific purpose and goal in mind before delving into such complexities. The ability to increase computing power and predict customer or supplier behavior within the business can be a tool of immeasurable benefit when properly applied. Overuse of a model that can distract from the overall benefit is significant when more time is spent fixing data mining systems and applying effort towards these problems when other more pertinent problems are ignored.