Management of data revolves around its processing and organization to meet a desired end. Data has to be controlled to ensure it is delivered only to the rightful end user (Laudon, & Laudon, 2011). Dell is a company based in the United States. The company offers technology solutions, services, and support. Information management can play a number of significant roles in the company. First, Dell is one of the largest companies in the industry. Thus, the company is likely to suffer from diseconomies of scale. In order to address this concern, the company can rely on information management. Information management can help it identify internal inefficiencies and take appropriate action to sustain its operating efficiency.

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Secondly, the company operates in a very competitive industry, with multiple small-scale players. Therefore, it has to maintain its grip on the market by providing customers with innovative products to meet their needs. To that end, information management can help the firm, trace consumption trends in the market, and make necessary changes to meet the ever-changing customers’ needs and demands. Moreover, it has to be noted that, the company has a strong brand which differentiates it from the competitors. Information management can help the company identify areas in which the brand is exposed and make informed decisions to protect it.

Lastly, information management will also be critical in ensuring that, the firm can track markets changes. Some of the changes include; changes in demand, cost, and competitors’ strategies. Based on this informed background, the decision on how to respond can be arrived at quickly and deployed to protect the company’s market share.

Fundamental Impact of I.T Architecture
The relationship between infrastructure and information is an important one. Each of the two concepts impacts on each other. For instance, within the information technology sector where Dell operates, Davenport (2012) notes data centers are positively impacted by improvements in microprocessors. Advances in microprocessors ensure that, the information can be collected, analyzed, stored and retrieved with ease. As a result, investments have to be made to improve the capability of data centers to operate with these microprocessors. Another area in which information technology relates to infrastructure is virtualization. Ohlhorst (2012) notes virtualization today touches on the entire process of information management and storage. A lot of applications have been developed to make the process even more seamless.

For efficient information management, infrastructure must be compatible with the needs of the information being processed. Otherwise, the information may not be accessed, or the pace at which the data is obtained or processed will primarily be affected. Therefore when looking at requirements to handle a given volume of data, Dell must ensure that the available information technology infrastructure is compatible with those requirements.

Data Storage
Data storage at Dell is unique. This is because there are a wide variety of data stored. This includes; user-based data, and store purchasers based data. Some of the data stored is accessed much frequently compared to others. One of the storage options available to the company is data warehouse. This is a federated repository, bringing together all the data that has been collected from various systems within the business (Schulz, 2011). The idea behind data warehousing is to ensure that, the data collected from diverse sources can be analyzed with ease and is available and accessible to the right people.

There are two approaches to building a data warehouses. Davenport (2012) notes under the top-down approach, a database warehouse is first created and then data collected from users. On the other hand, the bottom-up approach entails collections of the data and consolidation into a single central warehouse. The other alternative to data warehouse includes the use of data marts. Davenport (2012) notes while data warehouse relates to the organization as a whole, data mart entails the collection of data into a central repository. Data stored within the central repository is only accessible for specific function, for example the human resource function. Therefore under the data mart approach, data is only available to a specific end user.

When building a data mart, the first stage is to analyze the needs of the users. On the other hand, the first stage for data warehouse is to look at the available data and establish how it can be collected and managed for later use (Schulz, 2011).

Optimal Data Storage
Dell handles a lot of data. Traditional information storage tools such as local databases and warehouses have proven to be inadequate (Davenport, 2012). The content to be stored has increased exponentially overtime, beyond the capacity of the traditional systems. Given the large amount of data that the company has to store, it is advisable for the company to use cloud technology. Ohlhorst (2012) notes cloud system is a valuable resource because; the storage capacity can be increased to meet the user demand. Hence, it helps to address traditional structural challenges that the company may have faced.

Secondly, Ohlhorst (2012) notes the organization has to consider decentralizing its storage into multiple locations. The multiple locations can be spread out in a number of countries. This allows for the stored data to be accessed more easily. Having all materials in one place may compromise this capability of a storage system due to the massive data involved. Besides, decentralization of data can act as a security check. In the event of a security breach, only a specific volume of data under a given location may be affected Laudon, & Laudon, 2011). However, it is important to note that, this is only possible where the various data storage locations are not synchronized together.

  • Davenport, T. H. (2012). Competing on analytics. harvard business review, 84(1), 98- 104.
  • Laudon, K. C., & Laudon, J. P. (2011). Essentials of management information systems. Upper Saddle River: Pearson.
  • Ohlhorst, F. J. (2012). Big Data Analytics: Turning Big Data into Big Money. Chicago: Routledge.
  • Schulz, G. (2011). Cloud and Virtual Data Storage Networking. New York: CRC Press.