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high quality (error-Free) data entry.
Data entry is a vital part of a number of businesses. As such, it has to be of the highest quality and error-free. While there is generally no guarantee that data can be 100% error-free all the time, the more any data avoids errors the more likely the company will be to provide its customer with a good experience (Kos, Kosar, & Mernik, 2012). Additionally, companies that have data errors can end up in trouble because they are using the wrong details to make decisions about what they can and should be doing to move their business forward. If a company is not focused on entering data in a way that is free from errors (and omissions), that company can get a reputation as being sloppy and not well-prepared to handle its business and its customers (Kos, Kosar, & Mernik, 2012). Consumers who provide data to a company want to know that the company is being careful with that data, and not abusing or misusing it in any way.
When data contains errors, it can be more difficult to determine what the company has done with the proper data and where that data ended up (Kos, Kosar, & Mernik, 2012). Some companies also have confusing or convoluted ways of correcting data errors, and when there is an error in one piece of data it can quickly lead to errors being made in other data (Kos, Kosar, & Mernik, 2012). By the time an error is caught, there can be other issues because much more than the original error has to be corrected. Everything that has taken place since the time of the first incorrect entry and everything that was done based on that incorrect data must be located and corrected, as well (Kos, Kosar, & Mernik, 2012). That can be extremely time-consuming for a business, and can lead to serious and significant mistakes being made.
Kos, T., Kosar, T., & Mernik, M. (2012). Development of data acquisition systems by using a domain-specific modeling language. Computers in Industry, 63(3):181 -- 192.
2. What competitive advantages does agility provide to a manufacturing company?
Having a competitive advantage is a good thing for any company, and when a company works to manufacture goods it must stay ahead of its competitors or it can quickly lose ground, profit, and market share. By the time a company realizes that it is in a losing position, recovery can be difficult and the company is left scrambling (Porter, 1998). The goal, therefore, is to make sure the company never gets to that position, and the key to that is agility (Porter, 1998). People think of manufacturing companies as large, unwieldy businesses that cannot move fast or make rapid changes. They have huge buildings and giant pieces of equipment that produce a particular product. In that context, they do not sound very agile at all. However, manufacturing companies are realizing the value of becoming more agile, and they are beginning to make that part of their design from the beginning (Porter, 1998).
When a manufacturing company is agile, it is able to move with the times and adjust to changing market conditions and consumer attitudes (Porter, 1998). This can involve making more items or fewer items, depending on consumer demand, but it can also involve equipment that can be adapted to make more than one item or to change the size or style of an item easily. If a company wants to remain competitive in the goods it manufactures, it has to be prepared for changing conditions on a nearly daily basis (Porter, 1998). Getting a product to market after a consumer expresses a desire for it is often a competition between manufacturing companies, and the company that provides the item first will get the largest part of the market share (Porter, 1998). Without agility, the ability to do that is lost or greatly hindered, rendering the company's efforts ineffective.
Porter, M.E. (1998). Competitive advantage: Creating and sustaining superior performance. NY: Free Press.
3. What is the role of data mining?
Data mining plays a significant role in the operation of many companies. Many people think of data mining as collecting information, but there is much more to it than that. Once the information has been collected, it also has to be analyzed and the meaningful information extracted from it (Witten, Eibe, & Hall, 2011). In other words, the data itself is just a random collection of numbers, names, and other bits and pieces of information. Companies take that information and turn it into something they can use to decide which direction to take next or determine their target market (Witten, Eibe, & Hall, 2011). Until and unless a company does that, however, the data that is collected (mined) is not going to provide the company with anything of real value. Just having the information does not help the company unless that company knows what to do with the information -- and that is where a number of companies run into difficulties.
The role of data mining in any company should be one of collection, but also of analysis. When one mines something from the ground, he or she pulls up a lot of things that are not needed, and sifts through that to find the things that are desired. Gold, diamonds, and a number of other precious things are mined this way. It can be helpful for companies to see data mining in the same context. Much of what they collect may not even be valuable to them, but they can sift through all of the data they have acquired and find the parts of it that provide them with value they can use (Witten, Eibe, & Hall, 2011). Then they can take that data and work with it in a way that will benefit the company moving forward. The other data is not needed, and over time companies will develop a better sense of what they really need to collect.
Witten, I.H., Eibe, F., & Hall, M.A. (2011). Data mining: Practical machine learning tools and techniques (3rd. ed.). NY: Elsevier.
4. What are two challenges of legacy systems?
Legacy systems are those that are old or outdated. Because they are not the most up-to-date choices for companies, they can have problems that would not be seen with newer systems. One of those problems, or challenges, is getting new hardware and software -- including parts and updates -- for these systems (Bisbal, et al., 1999). As long as the system continues to work well, the company can keep using it. The cost of changing it may be high, so the company would prefer to avoid upgrading it to something else. In the meantime, though, the system can (and often will) break down in some way or need various types of repair (Bisbal, et al., 1999). When an older, legacy system needs to be repaired or the software needs to be updated, finding what is needed can be difficult and expensive. Eventually, the company will generally find that the cost to maintain the legacy system is actually more than it would cost them to replace it with a newer system (Bisbal, et al., 1999).
Another challenge with legacy systems is the speed at which they operate (Bisbal, et al., 1999). When they were first created they were likely the fastest things on the market, but over a period of time many new options have appeared, and those options are faster and more reliable than older system. Companies that do not rely heavily on their legacy system will not notice this as much as companies that do almost everything through their system, but any company can be slowed by these older systems (Bisbal, et al., 1999). This can serve as a source…[continue]
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