How much data should companies have - Essay Example

Is it better for companies to have too much data or too little data? Why? Data in business organizations comes from both internal and external sources and is employed for data exploration, data mining, predictive modelling and several other such business intelligence (B!) applications. According to Lucker 2005, “common reality is that most enterprise data is quite useable, since BI data typically doesnt need to be used in the same way accounting data is used (i.e., it doesnt necessarily have to balance to the penny or provide a perfect view of things)”.

Thus it is better for companies to have too much data then too little data. Credibility of data is an important factor for BI applications. Most of the internal data that a business organization possesses is credible in that it has been derived from the past experiences in running and managing various business activities. Therefore the data is credible. This data irrespective of the volume and imperfections that may be present is a valuable asset from the perspective of the insights it can provide in the running and managing of several business activities.

The imperfections do not matter much as in business intelligence applications the focus is on reading patterns and signs in the available data, and so the data does not have to be precise as may be required in the case of accounting or auditing (Lucker, 2005 ). Let us take the case of insurance companies. Several years of data with regards to policies, claims, premium bill payments, agent/producer sales, and so on are available with the insurance company. This may be considered as too much data by some companies.

Yet, irrespective of the volume of the data, intelligent use of the data will help the company to be better at precision pricing, customer retention, claims fraud detection, product cross-selling and similar activities to overcome competition and prosper in a competitive environment (Lucker, 2005). It is not too much data that is need be the worry. Rather, it is the intelligent use of the data that is available and such intelligent use is not possible in the absence of data. Intelligent use of data is based on an enterprise information system that focuses on simplicity, practicality and usability and not on any other parameters.

Such an approach will allow the business enterprise to appreciate the worth of internal and external data and remove the misconception of too much of data causing the organization to drown in it (Lucker, 2005). At Calpine Corporation, a business organization into the business activity of natural gas based and geo-thermal based power generation, the concentration was on security in the logging in of data and access to data, given the sensitive nature of their operations. This made it difficult for data logging in and data access, which was compounded by poor knowledge on availability and usage of the available data.

By simplifying the logging in of data and access to data and improving the awareness of data availability, the operational and business activities at Calpine Corporation were improved (Higgins, 2005). Another example of the intelligent use of voluminous data is at Master Card. Master Card provides its clients with the facility for Online access to Master card for a variety of customer reports. This leads to a vast volume of data that provide information on customer needs and behaviour trends.

Master Card has inbuilt technology and intelligence of the company to sift out this important information from the large volume of data. The technological tools report, extract and summarizes the data received, while in house analysts evaluate the information provided in this exercise of data mining to improve the competitive ness of Master Card (Higgins, 2005). Any quantum of data is an asset to an organization, when the focus is on locating the data, managing it and employing it in such a manner as to make the investment in data mining provide more than return on investment through the enhanced success attained in business projects and activities (Lucker, 2005).

Thus, in conclusion it is better for companies to have more data than too little data, as more data can make the business organization more efficient and competitive. Literary References Higgins, K. J. (2005). Calpine Corp. Powers Up Data Logging. Retrieved March 16, 2010, from, Network Computing Web Site: http://www.networkcomputing.com/data-protection/calpine-corp-powers-up-data-logging.php. Higgins, K. J. (2005). Warehouse Data Earns Its Keep. Retrieved March 16, 2010, from, Network Computing Web Site: http://www.

networkcomputing.com/wan-optimization-and-application-acceleration/warehouse-data-earns-its-keep.php Lucker, J. (2005). Sometimes Good is Good Enough. Retrieved March 16, 2010, from, Insurance & Technology, Information Week Media Network Web Site: http://www.rmislab.com/docs/I&T-Sometimes%20Good%20Enough%20Is%20Good%20Enough.pdf Lucker, J. (2005). Swimming-Not Drowning-In Enterprise Data: Step 1 – An Enterprise-Wide Information Inventory.