Business Intelligence and Data Warehouses

Introduction

Nowadays modern and progressive companies are highly interested in Data Warehousing for getting a better idea about their business and to get insights into the strategy of their competitors. When it comes to the current situation concerning data warehousing, the most common theme is the fact that volumes of data are expanding at uncontrollable rates. A perfect environment for making decisions includes all of the accurate information available. The majority of data warehouses use relational database technology due to its ability to offer a reliable approach to managing large amounts of data (Chaudhuri et al., 2001, p. 52).

Operational VS Decision-Support Systems

The fundamental aspect of data warehousing is the recognition of differentiation between decision support and operational data warehousing. An important differentiation is the mixing of the different types of processing environments. When mixing the DSS with operation environments, other processes in the system become mixed too. When the two environments are separated again, the processes occur in parallel within separate environments.

The second difference between the environments is the size of the transaction found in each mixed workload. When the two environments are separated, small-size transactions occur in the operational while larger transactions run in the data warehouse environment. While separating the two data warehousing environments, the types of workers are also separated. Clerics tend to work in the operational environments while the decision-makers lean toward the data warehouses (Inmon, n.d. para. 5).

The decisions that are made in the separated environments are also separated. Short-term decisions are commonly made by using data taken from the operational environment while long-term ones are made with the help of data stored in the data warehouse environment. As decisions are products of these environments, their separation can be considered natural.

Decision-Support System

If data cannot be used effectively, it is not relevant, so this is where the decision-support system comes into play. A successful decision support system is a complicated system that has a variety of different components. To illustrate this fact, the Footwear Sellers Company produces footwear and sells it through two different channels – online and through resellers. To get an idea that a selling platform can bring more sales, the company has to build a decision support system to answer their main concerns about the highest rates of sales, the customers, etc (Chaudhuri et al., 2001, p. 49). Problems faced by organizations are different in terms of their structure, namely the extent to which the process of solution can be started. Decision-supported systems are used to assist the maker of the decision in facing a problem that is not structured, very often by adding necessary factual data (Joshi, n.d., para. 2).

Examples of Databases Used for Making Decisions

In terms of commerce, data-driven decisions are the way to go. By analyzing the current situation on the market, evaluating the competitor’s strategy, researching whether the sold products fit the needs of the potential customers, a company can make decisions that give a start to some changes in the strategy. By applying analytics and getting valuable data from the research, large corporations like Nissan Motor Company or Puma were able to increase sales and make their products customer-oriented.

Education is also the one for applying databases for decision-making. For an educational institution to sustain its prosperity and make sure that the students are well educated, and making decisions towards improvements, getting to grips with all the available data about students as well as the school and staff is crucial (The Importance of Data-Based Decision Making, n.d., para. 3).

Politics are also closely coined with decisions made with the help of databases. Databases can become important tools in elections and other political issues that are connected with population and the effect the decision will have on the population.

Examples of Data Mining and Data Warehouses Used for Supporting Trend Analysis and Data Processing

Data mining and data warehouses are predominantly used in the sphere of retail. For instance, Blockbuster Entertainment was able to successfully recommend different kinds of videos for separate customers after mining through the history database of their rental videos. Another great example of data processing based on data mining and data warehouses is that American Express was able to suggest new products for their clients based on the analysis of their monthly spending. For using a data mining application in conjunction with games image recording, NBA used some data warehousing for data processing.

Conclusion

To conclude, data warehousing is the way to go when it comes to decision-making in a large organizational environment. A perfect environment for making decisions includes all of the accurate information available. Decision-support systems help make the acquired data effective; thus an organization has to build a decision-support system that will answer their main concerns in terms of decision-making. However, decision-making does not occur by itself, but in a large context of aims and goals set by the corporation.

References

Chaudhuri, S., Dayal, U., & Ganti, V. (2001). Database Technology for Decision Support Systems. In Berndt, D. J. (Ed.), Cover Feature (pp. 48-55). Tampa, FL: IEEE.

Joshi, K. (n.d.) Decision Support and Executive Information Systems. Web.

Inmon, W. H. (n.d.). Operational versus DSS. Web.

The Importance of Data-Based Decision Making. (n.d.). Web.

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