If we take a look at the past and make a comparison between the commercial electronic media that existed and were used almost 100 years ago and the media that exist and are used today, we will see that in general, the nature of electronic media has changed dramatically over the years. Various kinds of media that did not exist in the past are now holding the reins in today’s world, in the electronic age.
As reported by the Henry J. Kaiser Family Foundation (2005), “In today’s society, electronic media are thoroughly integrated into the fabric of life, with television, movies, videos, music, video games, and computers central to both work and play” (p.1). The media scene has been developing at an incredible pace in recent years, but there has been both convergence and divergence (Pleitgen, 2007). In this era of information technology, the old methods of providing information such as the radio, television and newspapers are gradually losing their importance in contrast to the computers and the Internet which have also come to be included in the electronic media (Alamgir, 2002).
Today, people listen to digital radio via their television, watch TV on mobile phones, listen to radio on iPods while also, newspapers have started turning into broadcasters by offering audio and video on websites of their own while also, there has been the arrival and increasing influence of citizen journalism through the use of blogs, vlogs or video blogs, and video clips taken on mobile phones (Pleitgen, 2007).
“The term electronic media… refers to both electronic mass media (e.g. broadcasting) and electronic personal media (e.g. cell phones)” (Medoff & Kaye, 2005, p.5). In addition, “Information and communications technology (ICT) is defined as computers, software, telecoms, such as mobile and fixed phones, the Internet and satellite technologies (United Nations org., n.d).
Before a discussion is provided on the effects that the changes in electronic media have on education as well as a discussion on their advantages and disadvantages, let us take a look at the past and see how the nature of commercial electronic media has changed and evolved over the years.
The start of broadcasting and commercial electronic media was signalled by the invention of radio telegraphy while in the late 1920s, Americans were fascinated with radio which enjoyed its place as the only instantaneous and electronic medium for over 30 years until the World War II, when television broadcasting started to become popular while in fact, television in its many forms such as broadcast, cable, satellite, video cassette, and DVD, was the center point of American media for over 50 years (Medoff & Kaye, 2005).
Afterwards, computers emerged as the new popular medium while also, the Internet followed, that emerged as a new mass medium at an unprecedented speed (Medoff & Kaye, 2005). “The Internet was the result of some visionary thinking by people in the early 1960s that saw great potential value in allowing computers to share information on research and development in scientific and military fields (Howe, 2009, ¶1).
As Medoff and Kaye (2005) outlined, the electronic media industry has changed dramatically over the past 10 years, most notably since 1996, when the Telecommunications Act was signed into law while also, satellite direct digital radio service began in 2002. In addition, wireless has grown rapidly in the past few years while also, a next big growth area is the surge towards universal wireless access (Howe, 2009).
Moreover, another trend is “the growth of smaller devices to connect to the Internet. Small tablets, pocket PCs, smart phones, eBooks, game machines, and even GPS devices are now capable of tapping into the web on the go” (Howe, 2009, ¶35).
Apart from these, some recent innovations such as e-mail, instant messaging and chat rooms, allow us to talk to others who are connected in real time while also, the use of the Internet led to the development of online journalism that is similar to a print diary and weblogs or blogs which are web pages posted by individuals who want to express themselves on various topics (Medoff & Kaye, 2005).
Going further, it is worth mentioning the following three important issues as reported by the United Nations org. (n.d): (a) it took radio broadcasters 38 years to reach an audience of 50 million, television 13 years, and the Internet just four; (b) there were 50 pages on the World Wide Web in 1993 and today there are more than 50 million pages; and (c) it is estimated that in just over five years some 900 million electronic devices could be connected to the Internet, equalling the number of telephones in the world.
Undoubtedly, the nature of commercial electronic media has changed dramatically over the years. As a result, this change has affected and impacted all sectors of society whether that is business, industry, medicine or education.
Taking as an example the field of education, it can be denoted that the changes in electronic media over the years has significantly impacted education and specifically, the integration and use of computers and the Internet in schools has impacted education and brought some remarkable changes to the teaching and learning processes.
As Rojem (2002) supported, the revolution of the Internet has had important implications for the educational system and consequently, the world of education is experiencing some major changes, not least of which is the use of the Internet in teaching and learning.
Access to world class broadband revolutionised education and provided students with the opportunity to engage more effectively with the resources from around the world as well as to engage in active learning (Rudd, Smith & Conroy, 2007). Past technologies have been capable of disseminating information but they did nothing to encourage student interaction but in contrast, the Internet, supports active, hands-on learning which can provide students with practical, real-life experiences while it can also lead to better retention and understanding of a given topic (Rojem, 2002).
Apart from these, in the past, students’ choice of resources was limited to resources that were available in the school library but now, with the revolution and integration of the Internet in schools, students are offered large amounts of information and resources through the Internet and the World Wide Web, which can access quite quickly and easily and therefore, learning in school now in no longer confined by the walls of the classroom (Rojem, 2002). As Medoff and Kaye (2005) stated, “The Web’s big advantage over traditional media is its lack of constraints in terms of space and time” (p.9).
Moreover, compared to the traditional reading habits of individuals, electronic media tend to be an attractive way of representing information and therefore, children can learn difficult concepts better when such concepts are demonstrated to them through the use of various electronic media (Poorani, 2006).
In addition to these, the Internet’s impact on education includes more student-centred learning as well as a shift from the traditional structure of students as passive learners toward that of active participants (Rojem, 2002).
Going further, various trends in electronic media such as convergence, consolidation, commercialism as well as the process of actually making programs that leads to the need for greater media literacy, have affected and impacted the way education is delivered (Medoff & Kaye, 2005). As Medoff and Kaye stated with media literacy, “the audience has knowledge and understanding not only of the meaning of the content of the media but also of the power of the media, the intent of the media, and the influence of the media” (p.13).
Convergence refers to “the blurring of the boundaries between the different types of electronic communication media” (Medoff & Kaye, 2005, p.10). As Murphy (n.d) outlined, convergence is the melding of previously segregated fields of computing, telecommunications, and broadcasting but this is not something entirely new since, there has long been a convergence of sorts as technological infrastructure has been unified and shared among seemingly unrelated fields. As reported by the United Nations org. (n.d), “The convergence of information technology and the Internet may well become as transformative as the industrial revolution” (p.2).
Except for these, the changes in commercial electronic media have affected in general, the lives of the people in respect of their cognitive, emotional, and behavioural levels. Specifically, as Medoff and Kaye (2005) outlined, electronic media: help people become more knowledgeable about the world, give people information that help them form attitudes towards things and ideas while they also make people feel; and they have the possibility to persuade people to change their behaviour while they can also change how people use their time since, people seem to spend a lot of time each day with electronic media.
Going further, the changes in electronic media have their advantages and disadvantages as well. Apart from the positive effects, benefits and advantages of the use of the Internet that were mentioned and discussed above, another important advantage is the easy and fast access to information that consequently overcomes time and distance constraints. As Poorani stated, “Today’s world… provides easiest and fastest access to knowledge overcoming time and distance constraints through the electronic media. The electronic media in the form of Internet, television etc. prove to be not only the easiest means of access to information but also provides access to immense amount of knowledge” (¶7).
On the other side of the coin, the changes in electronic media seem to have some disadvantages as well. “In this fast moving world, technology has brought about immense changes in a man’s life. [But] with each change [there] is a host of advantages and disadvantages” (Poorani, 2006, ¶2). For instance, cost constitutes an important disadvantage that relates to the changes in electronic media. Particularly, in the field of education, school districts are spending tremendous amounts of money in order to connect schools to the Internet and this is an issue of concern since, there is no money left for the professional development needed to assist teachers in learning how to include the Internet in their teaching while also, the money spent on technology is at the expense of books and other resources needed by teachers (Rojem, 2002).
Moreover, television and electronic media displace social interaction since, time spent on television viewing is a passive experience that replaces a creative activity or interacting with other people and therefore, spending too much time with electronic media can be an isolating experience that can negatively impact the development of social skills (Walsh, n.d).
Apart from these, the Internet contains unreliable information. Unlike the materials found in a library, the information found on the Internet is not proofread or edited for content and hence, children can be uncritical in evaluating information while also, due to the fact that there are many resources to choose from, children may even become overwhelmed and unable to judge the quality of information and materials included in a site (Rojem, 2002). In addition to these, as Rojem supported, some of these materials and resources can be unsuitable and inappropriate especially for students such as sexually explicit materials abound on the Internet as well as sites that publish destructive information such as how to make a bomb or how to grow narcotic drugs.
Plagiarism is also another disadvantage of the Internet that despite the fact that it is not a new problem, it has become easier to do and more difficult to detect due to he introduction of the Internet and specifically the World Wide Web (Rojem, 2002).
Furthermore, a more general disadvantage of Information and communications technology involves the digital divide. As reported by the United Nations org. (n.d), there is still a gap between those who have electronic access and those who have not or in other words, there is a gap between information-haves and information-have nots that exists between communities and between countries.
In conclusion, computers and the Internet are influencing the way we learn while also, radio is still a dominant medium with wide access while in general, all these media are very powerful to reach, teach and enrich (Arulchelvan & Viswanathan, 2006).
Television and radio are crucial for ensuring social cohesion and development in the digital world while also in general, the electronic media have a vital role to play in the information society (Pleitgen, 2007).
The Internet brought, brings, and “will inevitably bring about major changes in our educational system. As more and more schools gain access to the Internet, teachers will be confronted with the challenge of how best to incorporate this valuable tool into their classrooms. Of course there will not be a complete move away from the conventional teaching methods, but the Internet will definitely alter classroom routines in one way or another” (Rojem, 2002, p.7).
As Rojem (2002) stated, “For children growing up in the information Age, it is hard to imagine a world without computers and the Internet… Just as the generation before them would gather to report the arrival of a new colour television, children today are busy debating who has the fastest modem and biggest hard drive” (p.1).
Closing up, we must keep in mind that: “Communications technology is not an end in itself; it is a vehicle for the provision of information and content” (Pleitgen, 2007, p.13).
References
Alamgir, K. (2002) Debate: Advantages and Disadvantages of the Electronic Media. Retrieved November 4, 2009, from http://www.dawn.com/weekly/yworld/archive/021019/yworld4.htm
Arulchelvan, S. & Viswanathan, D. (2006) Role and Effectiveness of Electronic Media in Higher Education-With Special Reference to Tamilnadu. Turkish Online Journal of Distance Education , 7(4). Retrieved November 1, 2009, from http://tojde.anadolu.edu.tr/tojde24/pdf/article_2.pdf
Howe, W. (2009) A Brief History of the Internet. Retrieved November 1, 2009, from
http://www.walthowe.com/navnet/history.html
Medoff, N. & Kaye, B. (2005) Electronic media: Then, now, and later. Pearson Education, Inc.
Murphy, M.J. (n.d) Convergence, Interactive Media, and Innovation. Retrieved November 2, 2009, from http://www.innovation.ca/innovation2/essay_murphy.html
Pleitgen, F. (2007) A New Vision of Broadcasting in the Information Society. Retrieved November 5, 2009, from http://www.ebu.ch/CMSimages/en/Pleitgenfinal_tcm6-56034.pdf
Poorani (2006) On Electronic Media Vs Reading Habits. Retrieved November 2, 2009, from http://www.urch.com/forums/gre-analysis-issue/47225-electronic-media-vs-reading-habits.html
Rojem, K. (2002) The Impact of the Internet on Education. Retrieved November 3, 2009, from http://www.slais.ubc.ca/COURSES/libr500/01-02-wt2/www/K_Rojem/
Rudd, K.MP, Smith, S. & Conroy, S. (2007) A Digital Education Revolution. Retrieved November 4, 2009, from http://www.alp.org.au/download/now/labors_digital_education_revolution_campaign_launch.pdf
The Henry J. Kaiser Family Foundation (2005) The Effects of Electronic Media on Children Ages Zero to Six: A History of Research. Retrieved November 6, 2009, from http://www.kff.org/entmedia/upload/The-Effects-of-Electronic-Media-on-Children-Ages-Zero-to-Six-A-History-of-Research-Issue-Brief.pdf
United Nations Org. (n.d) Information and Communication Technology (ICT). Retrieved November 4, 2009, from http://www.un.org/cyberschoolbus/briefing/technology/tech.pdf
Walsh, E. (n.d) Electronic Media and Young Children. Retrieved November 4, 2009, from http://www.education.com/reference/article/Ref_Electronic_Media/
Wednesday, March 17, 2010
Utility Computing
Although computers have offered people an easy way to complete their tasks, the machines themselves can be challenging to maintain and repair and consequently, many companies are forced to spend millions of dollars on IT support in order to keep computers and applications running properly (Strickland, 2008). One potential solution in dealing with this kind of problems is utility computing. Utility computing is one of the many developing technologies and services emerging in the IT world and along with other technologies such as autonomic computing, grids and on-demand enterprise, utility computing gives IT management a new way of managing future workloads and applications (Murch, 2004).
“Utility computing is a usage model in which customers pay for computational resources through an established fee-per-time schedule, as if they were utilities. These fees can cover hardware, software, or storage usage, and fees for associated services rendered…Utility computing as a service has existed for many years, but with the increased demand for high-performance computing (HPC) resources, it is emerging as a World Wide trend” (Willard, Joseph & Lamy, 2007, p.1). Utility computing is based on the following principle: one company pays another company for computing services that may include hardware rental, use of specific computer applications, access to computer processing power, data storage space, etc., depending on what the client wants (Strickland, 2008). “The word utility is used to make an analogy to other services, such as electrical power, that seek to meet fluctuating customer needs, and charge for the resources based on usage rather than on a flat-rate basis. This approach, sometimes known as pay-per-use or metered services is becoming increasingly common in enterprise computing and is sometimes used for the consumer market as well, for Internet service, Website access, file sharing, and other applications” (SearchDataCenter.com, 2007, ¶2).
Utility computing consists of a virtualized pool of IT resources that can be provisioned to ensure that these resources are easily and continually reallocated in a way that addresses the organization’s service needs while also, these resources can be located anywhere and managed by anyone (Murch, 2004). “The vision behind utility computing is to have computing resources available on demand from virtual utilities around the globe-always on and highly available, secure, efficiently metered, priced on a pay-as-you-use basis, dynamically scaled, self-healing, and easy to manage” (Turban, Leidner, Mclean & Wetherbe, 2008, p.59).
As reported by the Saugatuck Technology Inc. (2004), utility computing can be separated into the following two kinds: (a) internal utility computing, where the IS organization acts as an information utility that delivers and charges on a pay as you go basis for the use of shared resources; and (b) external utility computing, where one or more Service Providers charge for the on-demand delivery of IT functionality on a pay as you go basis, using resources shared by multiple clients, each of which manages its own Information Utility. As stated by the Saugatuck Technology Inc., “In both cases, the delivered services are enabled by a combined platform of shared IT infrastructure, applications, and business processes” (p.3).
As Willard et al. (2007) noted, utility computing is coming to the fore due to the lowering of the utility computing price point and due to the increasing demand for computing power that cannot easily be fulfilled by capital expenditure alone. The goal of utility computing is to synchronize IT resources to match fluctuating service requirements in order to enable businesses to flexibly provide optimal service levels at a justifiable cost (Wagner, 2006). As Turban et al. (2008) supported, utility computing will change the way software is sold, delivered, and used while it is also estimated that all software will become a service and be sold as a utility one day.
It is the author’s belief and point of view that utility computing will be the dominating option for almost all companies, businesses, corporations, and institutions, including education due to many reasons that basically refer to the advantages of the utility computing model and the benefits it can offer to companies and institutions. Some of those advantages that can possibly contribute to making utility computing the dominate option for the future are next mentioned and discussed. Utility computing provides a cost-effective solution to companies and institutions. The use of utility computing can be less expensive compared to the cost of running computer operations in-house and in fact, most of the cost for maintenance becomes the responsibility of the provider and not the client (Strickland, 2008). With utility computing, companies can create cost-effective virtualized IT infrastructure for flexible workload consolidation while they can also offer hardware and software as a service (3PAR Inc., 2010). With the use of utility computing, clients only pay for the compute capacity they require while simultaneously, they gain access to the capacity of state-of-the-art supercomputers they may not be able to access otherwise (Willard et al., 2007).
As Barmijo (2005) supported, the cost-effectiveness of utility computing can also be explained from the following three aspects: (a) the infrastructure for the application can be defined easily online, one time, and then be reused again, reducing by this way the administration time spent in order to provision and configure servers, switches and volumes; (b) there is no need for spare resources to be retained for staging, testing, support or education because they can be deployed and used only while actually needed; and (c) the design of the applications can be done in such a way that takes advantage of the ability of the utility system to scale the resources they operate on and avoid over-provision.
An important advantage of utility computing involves convenience. In specific, the client does not have to buy all the software, hardware and licenses that are necessary for doing business since the client can rely on another party to provide these services and therefore, the client does not have to worry about and get involve with the burden of maintaining and administering the system since the utility computing company will take care of this and therefore, the client can concentrate on other tasks (Strickland, 2008). As Willard et al. (2007) supported, utility computing allows companies to focus on their core competencies and be more effective within their organization rather than dealing with IT issues.
Utility computing provides flexibility in implementation. As Murch (2004) stated, utility computing “provides total flexibility in implementation, from in-house and self-managed to fully outsourced, with everything in-between -including a hybrid deployment model in which in-house capacity can be supplemented by third-party resources to handle peak needs” (¶8). In addition, utility computing enables clients to be flexible and react to market changes rapidly while it can also avoid all the delays associated with acquiring and implementing the required IT infrastructure and therefore, it can cut the time to market of a new product dramatically (Willard et al., 2007).
Utility computing can reduce deployment time. In particular, utility computing has the ability to deploy resources faster, more effectively and efficiently than a user firm’s internal resources while also, flexible hardware, software and networking platform enable this type of adaptability in a more cost-effective way (Saugatuck Technology Inc., 2004). Furthermore, the utility computing model provides extensibility. It provides clients an extension of their IT infrastructure without the need to own, maintain, manage, and upgrade the technology (Willard et al., 2007).
Availability is also an important advantage of the utility computing model. In specific, the model is always available and hence, the customer can rely on the utility provider to deliver the required compute capacity in order to run business-critical compute-intensive workloads (Willard et al., 2007). Apart from these, compatibility constitutes an advantage of the utility computing model. Due to the fact that the files used by employees in one department of a company might be incompatible with the software used by employees in another department, utility computing offers companies the option to subscribe to a single service and use the same suite of software throughout the entire organization (Strickland, 2008). As reported by the Saugatuck Technology Inc. (2004), “Utility computing can deliver more efficient business operations, improve the responsiveness of IT to changing business needs, and reduce the complexities of managing IT, from sourcing to integration to upgrades and maintenance” (p.2).
As mentioned previously, the author supports the idea that utility computing will be the dominating option of the future. Utility computing will become the option of choice for almost all companies in various sectors of society. Consequently, utility computing will also become the dominate option in the education sector in the future that will also have the opportunity to gain the various benefits and advantages of utility computing that were mentioned and discussed above. In addition to these benefits and advantages, the field of education can gain and some other unique and important benefits from utility computing and some of these are next mentioned and discussed. According to McCrea (2009), “As technology proliferates…, a number of schools are looking beyond the traditional purchase and install software options and tapping the trend known as on-demand, software as a service (SaaS), hosted, or cloud computing” (¶1).
Cost-effectiveness is an advantage of utility computing that attracts not only companies and organizations but also the educational institutions. As Subramanian (2009) supported, every country can save millions by using utility computing in education while also, the model of utility computing can improve and increase the efficiency and productivity of teachers and students as well. Additionally, “SaaS enables all institutions-regardless of size-to purchase a software solution for a few dollars per student per year” (SchoolDude.com, 2010, ¶4).
Utility computing can greatly benefit schools in respect of software licensing. Schools do not want to spend more money on licenses but rather they need a simpler licensing model while they also want to invest minimal staff in managing their computer requirements, and hence, utility computing or SaaS can offer them an easy way to get over the clutches and problems associated with the traditional licensing models (Subramanian, 2009). As DeCoufle (2009) noted, utility computing offers schools a reduced and simplified expenditure on software licensing.
Furthermore, with utility computing, reliance on school-based IT staff is decreased. SaaS applications are delivered via the Internet and therefore, they do not require IT resources to configure, install, maintain, or monitor (SchoolDude.com, 2010). Also, with utility computing, fewer applications are hosted locally and therefore, the school-based technical staff has less to do while also, the particular skills and abilities of the staff are further reduced due to the provision of on-demand online help desks and remote access support (DeCoufle, 2009).
Another advantage of utility computing that attracts schools refers to faster implementation. In contrast with traditional purchase-and-install applications that might take months to install and implement, utility computing options can often be up and running within a few weeks or even sooner and as a result, schools can meet students’ needs much faster and therefore, they can focus more on educating students rather than running complex IT configurations and software programs (McCrea, 2009). As reported in SchoolDude.com (2010), “With SaaS, an educational institution can be up and running in days or weeks, not the 18-24 months that is typical with traditional client-server solutions” (¶10). Moreover, utility computing frees students from the boundaries of time and location and in fact, every student with access to a utility version of a database or web server will have the luxury of 24/7 availability of resources from any location with web access (Anderson, Wiles & Young, 2008). Also, utility computing provides teachers and students with greater ubiquity of access to their files, applications and social networks, anytime, anyplace, any device (DeCoufle, 2009).
Utility computing enables schools to experiment. In particular, under the traditional software system it is expensive and difficult for schools to experiment with different kinds of applications and platforms but on the contrary, utility computing offers schools greater flexibility to experiment with newer apps and platforms (Subramanian, 2009). Furthermore, an also important benefit that schools can gain from utility computing is that it will make the curriculum more agile. Due to the fact that with utility computing the current fixed cost is changed to a variable cost based on use it will be easy to add students to a class that requires server services while also, this will make teachers not to worry about reaching classes with infrastructure intensive hands-on components such as database and web development (Anderson et al., 2008). Moreover, utility computing reduces or even eliminates problems regarding software updates and therefore, schools do not have to worry about issues of software updates since they will happen automatically (DeCoufle, 2009). In addition to these, as Anderson et al. supported, utility computing offers students increased opportunities in providing instructional environments which would be impossible or extremely expensive otherwise.
On the other side of the coin, utility computing has disadvantages as well and some of these disadvantages are next mentioned and discussed. An important disadvantage of utility computing involves reliability. If a utility computing company faces financial problems or has frequent equipment problems, it is possible that clients could get cut off from the services for which they are paying (Strickland, 2008). Also, the use of utility computing raises issues of concern regarding security. Due to the fact that industries are extremely competitive, security of the data is priceless to the corporation concerned (Willard et al., 2007). Moreover, cost can be sometimes regarded as a disadvantage of utility computing. Although utility computing deals include declining costs per unit of usage over time, IT depreciation needs to be considered into the pricing and therefore, usage over time often increases as business needs increase and consequently, pricing contracts result in higher usage costs over time (Saugatuck Technology Inc., 2004).
Another disadvantage of utility computing refers to hackers. Utility computing can be an attractive target for hackers who might want to access services without paying for them or who may even want to investigate client files (Strickland, 2008). As Strickland noted, much of the responsibility of keeping the system safe falls to the provider, but some of it also relies on the client’s practices and therefore, if a company does not educate its workforce about proper access procedures, then it will not be hard for an intruder to find ways to invade the utility computing system of a company. Furthermore, there are instances where utility computing might not be an efficient model to use. In specific, “some workloads simply are not conducive to being run over the Internet. This can be due to bandwidth limitations or the amount of interactivity required. If the customer needs to continually evaluate results of many short runs with large data sets that need to be upload at each iteration, utility computing might not be an efficient model” (Willard et al., 2007, p.8).
Comparing the benefits and advantages of utility computing it can be denoted that the advantages outweigh the disadvantages and therefore, the author’s belief and point of view supports that utility computing will be the dominating option of the future as it will also dominate in education in the future. Closing up, “Our consumer utilities such as gas, water, and electricity all arrive on demand and independent of the uses to which they are put. This makes for a relatively easy billing structure-consistent infrastructure (pipe, wire) whose capital costs and maintenance are embedded in the usage rate. Exchange is simple: product in via infrastructure, invoice and payment on separate channels. Computing can be bought the same way. This is the basic premise of utility computing, which promises processing power when you need it, where you need it, at the cost of how much you use” (Murch, 2004).
References
Anderson, J.E., Wiles, F.A. & Young, K.P. (2008) The Impact of Cloud Computing on IS/IT Academics. Retrieved January 8, 2010, from http://www.iacis.org/iis/2008_iis/pdf/S2008_1086.pdf
Barmijo (2005) How Does Utility Computing Reduce Cost? Retrieved January 9, 2010, from http://blog.3tera.com/computing/how-does-utility-computing-reduce-cost/
DeCoufle, B. (2009) The Impact of Cloud Computing on Schools. Retrieved January 9, 2010, from http://datacenterjournal.com/content/view/3032/40/
McCrea, B. (2009) IT on Demand: The Pros and Cons of Cloud Computing in Higher Education. Retrieved January 5, 2010, from http://campustechnology.com/Articles/2009/08/20/IT-on-Demand-The-Pros-and-Cons-of-Cloud-Computing-in-Higher-Education.aspx
Murch, R. (2004) Introduction to Utility Computing: How It Can Improve TCO. Retrieved January 7 2010, from http://www.ibmpressbooks.com/articles/article.asp?p=344241
Saugatuck Technology Inc. (2004) Utility Computing: The Current and Future Business Reality. Retrieved January 10, 2009, from http://www.saugatech.com/reports/UC-ACS%2001Oct04.pdf
SchoolDude.com (2010) Why SaaS for Education? Retrieved January 11, 2010, from http://www.schooldude.com/solutions/saas-for-education/
SearchDataCenter.com (2007) Utility Computing. Retrieved January 6, 2010, from http://searchdatacenter.techtarget.com/sDefinition/0,,sid80_gci904539,00.html
Strickland, J. (2008) How Utility Computing Works. Retrieved January 6, 2010, from http://communication.howstuffworks.com/utility-computing.htm
Subramanian, K. (2009) How Cloud Computing Can Help School Education? Retrieved January 7, 2010, from http://www.cloudave.com/link/how-cloud-computing-can-help-school-education
Turban, E., Leidner, D., Mclean, E., & Wetherbe, J. (2008) Information technology for management: Transforming organizations in the digital economy (6th ed.). New York, NY: John Wiley & Sons.
Wagner, D. (2006) Delivering the Advantages of Utility Computing Today Through Proactive Resource Management. Retrieved January 7, 2010, from http://www.enterprisenetworksandservers.com/monthly/art.php?2082
Willard, C.G., Joseph, E. & Lamy, L. (2007) An Overview of Compute-Intensive Utility Computing. Retrieved January 10, 2010, from http://www-03.ibm.com/systems/resources/systems_deepcomputing_cod_pdf_idcutilitycomputingwhitepaper.pdf
3PAR Inc. (2010) Utility Computing. Retrieved January 5, 2010, from http://www.3par.com/solutions/utility_computing.html
“Utility computing is a usage model in which customers pay for computational resources through an established fee-per-time schedule, as if they were utilities. These fees can cover hardware, software, or storage usage, and fees for associated services rendered…Utility computing as a service has existed for many years, but with the increased demand for high-performance computing (HPC) resources, it is emerging as a World Wide trend” (Willard, Joseph & Lamy, 2007, p.1). Utility computing is based on the following principle: one company pays another company for computing services that may include hardware rental, use of specific computer applications, access to computer processing power, data storage space, etc., depending on what the client wants (Strickland, 2008). “The word utility is used to make an analogy to other services, such as electrical power, that seek to meet fluctuating customer needs, and charge for the resources based on usage rather than on a flat-rate basis. This approach, sometimes known as pay-per-use or metered services is becoming increasingly common in enterprise computing and is sometimes used for the consumer market as well, for Internet service, Website access, file sharing, and other applications” (SearchDataCenter.com, 2007, ¶2).
Utility computing consists of a virtualized pool of IT resources that can be provisioned to ensure that these resources are easily and continually reallocated in a way that addresses the organization’s service needs while also, these resources can be located anywhere and managed by anyone (Murch, 2004). “The vision behind utility computing is to have computing resources available on demand from virtual utilities around the globe-always on and highly available, secure, efficiently metered, priced on a pay-as-you-use basis, dynamically scaled, self-healing, and easy to manage” (Turban, Leidner, Mclean & Wetherbe, 2008, p.59).
As reported by the Saugatuck Technology Inc. (2004), utility computing can be separated into the following two kinds: (a) internal utility computing, where the IS organization acts as an information utility that delivers and charges on a pay as you go basis for the use of shared resources; and (b) external utility computing, where one or more Service Providers charge for the on-demand delivery of IT functionality on a pay as you go basis, using resources shared by multiple clients, each of which manages its own Information Utility. As stated by the Saugatuck Technology Inc., “In both cases, the delivered services are enabled by a combined platform of shared IT infrastructure, applications, and business processes” (p.3).
As Willard et al. (2007) noted, utility computing is coming to the fore due to the lowering of the utility computing price point and due to the increasing demand for computing power that cannot easily be fulfilled by capital expenditure alone. The goal of utility computing is to synchronize IT resources to match fluctuating service requirements in order to enable businesses to flexibly provide optimal service levels at a justifiable cost (Wagner, 2006). As Turban et al. (2008) supported, utility computing will change the way software is sold, delivered, and used while it is also estimated that all software will become a service and be sold as a utility one day.
It is the author’s belief and point of view that utility computing will be the dominating option for almost all companies, businesses, corporations, and institutions, including education due to many reasons that basically refer to the advantages of the utility computing model and the benefits it can offer to companies and institutions. Some of those advantages that can possibly contribute to making utility computing the dominate option for the future are next mentioned and discussed. Utility computing provides a cost-effective solution to companies and institutions. The use of utility computing can be less expensive compared to the cost of running computer operations in-house and in fact, most of the cost for maintenance becomes the responsibility of the provider and not the client (Strickland, 2008). With utility computing, companies can create cost-effective virtualized IT infrastructure for flexible workload consolidation while they can also offer hardware and software as a service (3PAR Inc., 2010). With the use of utility computing, clients only pay for the compute capacity they require while simultaneously, they gain access to the capacity of state-of-the-art supercomputers they may not be able to access otherwise (Willard et al., 2007).
As Barmijo (2005) supported, the cost-effectiveness of utility computing can also be explained from the following three aspects: (a) the infrastructure for the application can be defined easily online, one time, and then be reused again, reducing by this way the administration time spent in order to provision and configure servers, switches and volumes; (b) there is no need for spare resources to be retained for staging, testing, support or education because they can be deployed and used only while actually needed; and (c) the design of the applications can be done in such a way that takes advantage of the ability of the utility system to scale the resources they operate on and avoid over-provision.
An important advantage of utility computing involves convenience. In specific, the client does not have to buy all the software, hardware and licenses that are necessary for doing business since the client can rely on another party to provide these services and therefore, the client does not have to worry about and get involve with the burden of maintaining and administering the system since the utility computing company will take care of this and therefore, the client can concentrate on other tasks (Strickland, 2008). As Willard et al. (2007) supported, utility computing allows companies to focus on their core competencies and be more effective within their organization rather than dealing with IT issues.
Utility computing provides flexibility in implementation. As Murch (2004) stated, utility computing “provides total flexibility in implementation, from in-house and self-managed to fully outsourced, with everything in-between -including a hybrid deployment model in which in-house capacity can be supplemented by third-party resources to handle peak needs” (¶8). In addition, utility computing enables clients to be flexible and react to market changes rapidly while it can also avoid all the delays associated with acquiring and implementing the required IT infrastructure and therefore, it can cut the time to market of a new product dramatically (Willard et al., 2007).
Utility computing can reduce deployment time. In particular, utility computing has the ability to deploy resources faster, more effectively and efficiently than a user firm’s internal resources while also, flexible hardware, software and networking platform enable this type of adaptability in a more cost-effective way (Saugatuck Technology Inc., 2004). Furthermore, the utility computing model provides extensibility. It provides clients an extension of their IT infrastructure without the need to own, maintain, manage, and upgrade the technology (Willard et al., 2007).
Availability is also an important advantage of the utility computing model. In specific, the model is always available and hence, the customer can rely on the utility provider to deliver the required compute capacity in order to run business-critical compute-intensive workloads (Willard et al., 2007). Apart from these, compatibility constitutes an advantage of the utility computing model. Due to the fact that the files used by employees in one department of a company might be incompatible with the software used by employees in another department, utility computing offers companies the option to subscribe to a single service and use the same suite of software throughout the entire organization (Strickland, 2008). As reported by the Saugatuck Technology Inc. (2004), “Utility computing can deliver more efficient business operations, improve the responsiveness of IT to changing business needs, and reduce the complexities of managing IT, from sourcing to integration to upgrades and maintenance” (p.2).
As mentioned previously, the author supports the idea that utility computing will be the dominating option of the future. Utility computing will become the option of choice for almost all companies in various sectors of society. Consequently, utility computing will also become the dominate option in the education sector in the future that will also have the opportunity to gain the various benefits and advantages of utility computing that were mentioned and discussed above. In addition to these benefits and advantages, the field of education can gain and some other unique and important benefits from utility computing and some of these are next mentioned and discussed. According to McCrea (2009), “As technology proliferates…, a number of schools are looking beyond the traditional purchase and install software options and tapping the trend known as on-demand, software as a service (SaaS), hosted, or cloud computing” (¶1).
Cost-effectiveness is an advantage of utility computing that attracts not only companies and organizations but also the educational institutions. As Subramanian (2009) supported, every country can save millions by using utility computing in education while also, the model of utility computing can improve and increase the efficiency and productivity of teachers and students as well. Additionally, “SaaS enables all institutions-regardless of size-to purchase a software solution for a few dollars per student per year” (SchoolDude.com, 2010, ¶4).
Utility computing can greatly benefit schools in respect of software licensing. Schools do not want to spend more money on licenses but rather they need a simpler licensing model while they also want to invest minimal staff in managing their computer requirements, and hence, utility computing or SaaS can offer them an easy way to get over the clutches and problems associated with the traditional licensing models (Subramanian, 2009). As DeCoufle (2009) noted, utility computing offers schools a reduced and simplified expenditure on software licensing.
Furthermore, with utility computing, reliance on school-based IT staff is decreased. SaaS applications are delivered via the Internet and therefore, they do not require IT resources to configure, install, maintain, or monitor (SchoolDude.com, 2010). Also, with utility computing, fewer applications are hosted locally and therefore, the school-based technical staff has less to do while also, the particular skills and abilities of the staff are further reduced due to the provision of on-demand online help desks and remote access support (DeCoufle, 2009).
Another advantage of utility computing that attracts schools refers to faster implementation. In contrast with traditional purchase-and-install applications that might take months to install and implement, utility computing options can often be up and running within a few weeks or even sooner and as a result, schools can meet students’ needs much faster and therefore, they can focus more on educating students rather than running complex IT configurations and software programs (McCrea, 2009). As reported in SchoolDude.com (2010), “With SaaS, an educational institution can be up and running in days or weeks, not the 18-24 months that is typical with traditional client-server solutions” (¶10). Moreover, utility computing frees students from the boundaries of time and location and in fact, every student with access to a utility version of a database or web server will have the luxury of 24/7 availability of resources from any location with web access (Anderson, Wiles & Young, 2008). Also, utility computing provides teachers and students with greater ubiquity of access to their files, applications and social networks, anytime, anyplace, any device (DeCoufle, 2009).
Utility computing enables schools to experiment. In particular, under the traditional software system it is expensive and difficult for schools to experiment with different kinds of applications and platforms but on the contrary, utility computing offers schools greater flexibility to experiment with newer apps and platforms (Subramanian, 2009). Furthermore, an also important benefit that schools can gain from utility computing is that it will make the curriculum more agile. Due to the fact that with utility computing the current fixed cost is changed to a variable cost based on use it will be easy to add students to a class that requires server services while also, this will make teachers not to worry about reaching classes with infrastructure intensive hands-on components such as database and web development (Anderson et al., 2008). Moreover, utility computing reduces or even eliminates problems regarding software updates and therefore, schools do not have to worry about issues of software updates since they will happen automatically (DeCoufle, 2009). In addition to these, as Anderson et al. supported, utility computing offers students increased opportunities in providing instructional environments which would be impossible or extremely expensive otherwise.
On the other side of the coin, utility computing has disadvantages as well and some of these disadvantages are next mentioned and discussed. An important disadvantage of utility computing involves reliability. If a utility computing company faces financial problems or has frequent equipment problems, it is possible that clients could get cut off from the services for which they are paying (Strickland, 2008). Also, the use of utility computing raises issues of concern regarding security. Due to the fact that industries are extremely competitive, security of the data is priceless to the corporation concerned (Willard et al., 2007). Moreover, cost can be sometimes regarded as a disadvantage of utility computing. Although utility computing deals include declining costs per unit of usage over time, IT depreciation needs to be considered into the pricing and therefore, usage over time often increases as business needs increase and consequently, pricing contracts result in higher usage costs over time (Saugatuck Technology Inc., 2004).
Another disadvantage of utility computing refers to hackers. Utility computing can be an attractive target for hackers who might want to access services without paying for them or who may even want to investigate client files (Strickland, 2008). As Strickland noted, much of the responsibility of keeping the system safe falls to the provider, but some of it also relies on the client’s practices and therefore, if a company does not educate its workforce about proper access procedures, then it will not be hard for an intruder to find ways to invade the utility computing system of a company. Furthermore, there are instances where utility computing might not be an efficient model to use. In specific, “some workloads simply are not conducive to being run over the Internet. This can be due to bandwidth limitations or the amount of interactivity required. If the customer needs to continually evaluate results of many short runs with large data sets that need to be upload at each iteration, utility computing might not be an efficient model” (Willard et al., 2007, p.8).
Comparing the benefits and advantages of utility computing it can be denoted that the advantages outweigh the disadvantages and therefore, the author’s belief and point of view supports that utility computing will be the dominating option of the future as it will also dominate in education in the future. Closing up, “Our consumer utilities such as gas, water, and electricity all arrive on demand and independent of the uses to which they are put. This makes for a relatively easy billing structure-consistent infrastructure (pipe, wire) whose capital costs and maintenance are embedded in the usage rate. Exchange is simple: product in via infrastructure, invoice and payment on separate channels. Computing can be bought the same way. This is the basic premise of utility computing, which promises processing power when you need it, where you need it, at the cost of how much you use” (Murch, 2004).
References
Anderson, J.E., Wiles, F.A. & Young, K.P. (2008) The Impact of Cloud Computing on IS/IT Academics. Retrieved January 8, 2010, from http://www.iacis.org/iis/2008_iis/pdf/S2008_1086.pdf
Barmijo (2005) How Does Utility Computing Reduce Cost? Retrieved January 9, 2010, from http://blog.3tera.com/computing/how-does-utility-computing-reduce-cost/
DeCoufle, B. (2009) The Impact of Cloud Computing on Schools. Retrieved January 9, 2010, from http://datacenterjournal.com/content/view/3032/40/
McCrea, B. (2009) IT on Demand: The Pros and Cons of Cloud Computing in Higher Education. Retrieved January 5, 2010, from http://campustechnology.com/Articles/2009/08/20/IT-on-Demand-The-Pros-and-Cons-of-Cloud-Computing-in-Higher-Education.aspx
Murch, R. (2004) Introduction to Utility Computing: How It Can Improve TCO. Retrieved January 7 2010, from http://www.ibmpressbooks.com/articles/article.asp?p=344241
Saugatuck Technology Inc. (2004) Utility Computing: The Current and Future Business Reality. Retrieved January 10, 2009, from http://www.saugatech.com/reports/UC-ACS%2001Oct04.pdf
SchoolDude.com (2010) Why SaaS for Education? Retrieved January 11, 2010, from http://www.schooldude.com/solutions/saas-for-education/
SearchDataCenter.com (2007) Utility Computing. Retrieved January 6, 2010, from http://searchdatacenter.techtarget.com/sDefinition/0,,sid80_gci904539,00.html
Strickland, J. (2008) How Utility Computing Works. Retrieved January 6, 2010, from http://communication.howstuffworks.com/utility-computing.htm
Subramanian, K. (2009) How Cloud Computing Can Help School Education? Retrieved January 7, 2010, from http://www.cloudave.com/link/how-cloud-computing-can-help-school-education
Turban, E., Leidner, D., Mclean, E., & Wetherbe, J. (2008) Information technology for management: Transforming organizations in the digital economy (6th ed.). New York, NY: John Wiley & Sons.
Wagner, D. (2006) Delivering the Advantages of Utility Computing Today Through Proactive Resource Management. Retrieved January 7, 2010, from http://www.enterprisenetworksandservers.com/monthly/art.php?2082
Willard, C.G., Joseph, E. & Lamy, L. (2007) An Overview of Compute-Intensive Utility Computing. Retrieved January 10, 2010, from http://www-03.ibm.com/systems/resources/systems_deepcomputing_cod_pdf_idcutilitycomputingwhitepaper.pdf
3PAR Inc. (2010) Utility Computing. Retrieved January 5, 2010, from http://www.3par.com/solutions/utility_computing.html
Tuesday, March 16, 2010
Data Management
Computers and the Internet have made possible Web-based or network-based software systems for managing not only business processes but also many school processes and in fact, today, everything relating to the school environment such as attendance rosters can be handled by computer systems while also, more and more student assessment can be managed with computers, generating data results that can be used for additional software manipulation (Doe, 2009). Education data, that are simply numerical information and are gathered about the operations of the education system, are essential tools for educational decision-making (Durosaro, n.d). The current essay provides a report on the data problems that have been encountered in an educational organization in Cyprus and the measures that have been taken in order to solve these problems. In addition, this report unveils the data problems as they are related to some data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness.
Data management systems are developing large amounts of information that can be stored, combined, and analyzed for data-driven instructional leadership, and the need for this type of information and analysis is further fueled by funding accountability as well as the demands of state and national standards, including the No Child Left Behind (NCLB) legislation (Doe, 2009). As Turban, Leidner, Mclean and Wetherbe (2008) outlined, the goal of data management is to provide the infrastructure that is necessary in order to transform raw data into corporate information that has the highest quality while also, the foundation of data management has some building blocks such as data profiling, data quality management, data integration, and data augmentation.
An important issue related to the data gathered in any organization whether that is business, industry, medicine, or education refers to data quality dimensions. Data quality is an important and crucial issue because quality determines not only the usefulness of the data but also the quality of the decisions based on the data (Turban et al., 2008). As stated by the Melissa Data Corporation (2010), “In order for the analyst to determine the scope of the underlying root causes and to plan the ways that tools can be used to address data quality issues, it is valuable to understand…data quality dimensions” (¶2). The most common and basic data quality dimensions include: accuracy, accessibility, relevance, timeliness, and completeness. “Accuracy of data is the degree to which data correctly reflects the real world object or an event being described” (Building Intelligent and Performing Enterprises Institute, n.d, ¶2) For instance, as reported by the Melissa Data Corporation, in correct spellings of the names of persons or products, their addresses or even untimely or not current data can impact operational and analytical applications.
Accessibility refers to the ease with which customers can identify, obtain, and use the information in the data products and relevance refers to the degree to which the data products provide information that meets the needs of the customers (Tupek, 2006). As supported by the Building Intelligent and Performing Enterprises Institute (n.d), the timeliness of the data is an also important data quality dimension and this is reflected in issues such as the following: the organizations and companies are required to publish their quarterly results within a given frame of time; and customers’ services need to provide customers with up-to date information. As Wand and Wang (1996) noted, “Timeliness has been defined in terms of whether the data is out of date and availability of output on time…Timeliness is affected by three factors: How fast the information system stated is updated after the real-world system changes (system currency); the rate of change of the real-world system (volatility); and the time the data is actually used” (p.8).
As Wand and Wang (1996) reported, completeness is achieved when all the necessary values for a certain variable are included, and hence a set of data is complete. For instance, in some cases, missing data is irrelevant but when the information that is missing is critical to a specific process, then completeness becomes an issue (Melissa Data Corporation, 2010). As noted by the Building Intelligent and Performing Enterprises Institute (n.d), data completeness refers to the extent to which the expected attributes of data are provided and in fact, data completeness refers actually to the expected completeness and therefore, it is possible for data not to be available but it is still considered completed as it meets the expectations of the user.
Going further, the author has interviewed a knowledge worker in an educational organization in Cyprus and has identified the data problems encountered in the organization as well as the measures taken to solve these problems. Next, follows a report on those data problems that are related to the data quality dimensions that were mentioned and discussed above as well as a report on the measures that were taken in order to solve those data problems. The educational organization faced data problems due to the fact that the gathered and collected data had not met the data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In general, data problems that were encountered included and involved incorrect data, redundant data, irrelevant data, missing data, etc.
The data available at the educational organization lacked of an important data quality dimension, and that is accuracy. As a result, the organization encountered problems that involved data validity, data inconsistency, data integrity, data inaccuracy as well as concurrency problems. In specific, the educational organization’s data were created and used offline and due to the fact that these data do not go through quality control checks, the validity and hence the accuracy of the data is not assured but rather is questionable (Turban et al., 2008). In addition to this, problems that were also encountered involved the inconsistency and integrity of the data as well as concurrency problems. As it was also reported by Turban et al., the actual values across various copies of the data were not synchronized and for instance, changes in students’ information were not made in all applications in the educational organization that require this information.
Moreover, the educational organization encountered data integrity and concurrency problems, which, according to Turban et al., had as a result, the data values not to meet the integrity constraints and in fact, while an application was updating a record, another application could not access that specific record and hence it could not get the desired information. Furthermore, problems of inaccuracy occurred due to the fact that the data had not accurately represented the real-world values they were expected to model and for instance, the operational applications were affected by incorrect spellings of students’ and teachers’ names and addresses (Melissa Data Corporation, 2010). Apart from these, the problem of data inaccuracy occurred due to the fact that the head teachers had not kept and allocated the records and data for their schools accurately deliberately, in order to influence financial allocation to their schools while they had also done this because of ignorance about record keeping (Durosaro, n.d).
The educational organization encountered problems not only because of the low quality of the data regarding accuracy but also because of the lack of accessibility to the data. In specific, the problem of accessibility arose due to the data increase, poor storage and retrieval of the data, and also due to the fact that the data were redundant and scattered as well. In particular, the amount of data gathered at the educational organization was increasing rapidly due to the fact that time to time and year by year, the number of students and teachers was increasing while also, data about old students and teachers was also increasing. As Turban et al. (2008) noted, much past data must be kept for a long time, and new data are added quickly but while only a small portion of the educational organization’s data are relevant to be used for any specific application, that relevant data must also be identified and found in order to be useful.
Problems with accessibility to the data occurred also because the educational organization’s data were scattered. Due to the fact that the data were stored in several servers and in different computing systems, databases, and formats, consequently, the data were scattered throughout the organization and were collected by many individuals using various methods and devices (Turban et al., 2008). As a result, this sometimes presented difficulties in and problems with accessing some of the data. The problem of accessibility was also caused because of the poor storage and retrieval of the data. As it was also reported by Durosaro (n.d), much of the educational organization’s data were kept in files and folders and stored in drawers while also, there were no statistics units in the educational organization to help gather and store the necessary data and there was an insufficient data-based management information system, and this method of collection hindered and prevented retrieval while it also resulted in the loss of data. In addition to these, as Durosaro outlined, “The problems facing educators in the area of data storage are such that people are careless with data. People don’t preserve documents even personal documents such as pay slips, declaration of age, marriage certificates, receipts of payment made on schools fees and even certificates are being poorly kept and lost” (p.4).
Apart from these, the fact that the data were redundant also caused problems with accessibility. As it was also reported by Turban et al. (2008), the data throughout the educational organization were often out-of-date and redundant and hence, data managers faced problems in their maintenance while also, due to the fact that applications and their data files were created by different programmers over a period of time, another problem occurred that involved the duplication of the same data in various files. In addition to these, as Turban et al. noted, as a consequence of accessing data from different applications, the problem of data isolation occurred since the data were organized differently, were stored in different formats and were often inaccessible to other applications.
The educational organization’s data lacked of another data quality dimension, timeliness and this was due to some other data problems such as the non availability of the data. In specific, some important records and data that were necessary were not kept while others were poorly kept and therefore, the needed information could not be found because it had not been obtained by the educational organization and also because data have been lost due to poor storage (Durosaro, n.d). In addition to these, as Durosaro also reported, some data have been mixed up to the extent that retrieval was very difficult when required for use.
The educational organization encountered also problems regarding data security. Due to the fact that new applications were continually added to the system on an add-hoc basis, and hence, with more applications more people had access to data, security was very difficult to enforce in the file environment (Turban et al., 2008). In addition to these, as Turban et al. also reported, another problem that was encountered involved the selection of the data management tool and this was due to the large number of the products that were available while also, another problem that was encountered was that the applications were developed with regard to how the data were stored and in fact, the applications and data in computer systems were not independent but rather they were dependent on each other. Apart from these, another problem that was also reported by Turban et al. and the educational organization also encountered, involved the delegation of data-quality responsibilities to the technical teams that impacted and affected negatively the high-quality of the data.
The particular educational organization in Cyprus encountered a variety of data problems and this has had as a result the data to lack of important data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In order to solve this kind of problems, the educational organization took some measures that are next mentioned and discussed. The educational organization used data and analysis in order to drive decision-making practices and in specific, it made use of an Education Data Warehouse (EDW) in order to store, manage, and analyze the data. “A data warehouse is a repository of data that are organized to be readily acceptable for analytical processing activities (such as data mining, decision support, querying, and other applications)” (Turban et al., 2008, p.100). The data warehouse gathers data into reports that help guide decision making at schools, districts, and individual student levels (Durosaro, n.d). As Mills (2008) noted, data warehouses are structured in order to facilitate data collection, management, querying and reporting for decision making. Data warehouse can be used to address issues in academic institutions regarding the effectiveness of new instructional techniques, student satisfaction, etc. (Chaplot, 2007). In addition to these, as supported by the Michigan Association of Intermediate School Administrators (2005), data warehousing is a tool that can help districts become data driven in order to meet the requirements of the No Child Left Behind legislation and allows districts to find answers and ask complex questions that uncover underlying problems, thus leading to the design of data driven student achievement and school improvement strategies. As Chaplot supported, the fundamental goal of the data warehouse is to support strategic planning, modeling and forecasting at the organizational level while also, it must fulfill the need for knowledge for an area of uncertainty or growth in the organization and therefore, in order to achieve this goal, the data warehouse must provide a comprehensive and consistent view of the organization.
As Turban et al. (2008) noted, data warehousing makes it easier and faster for organizations to process, analyze, and query data while it also provides for improved analytical processing which involves analysis of accumulated data and it includes decision support systems, data mining, Web applications, enterprise information systems, querying, etc. As Mills (2008) stated, “A successful and sustainable data warehouse can be an important contributor to a district’s ongoing success” (¶9) Additionally, effective data warehousing can help create a meaningful relationship between information technology and organizations, thus facilitating enterprise-level strategic planning and growth as well (Chaplot, 2007). A data warehouse may include data such as: personnel data, student demographics and achievement data, financial data as well as assessment data (Michigan Association of Intermediate School Administrators, 2005).
Moreover, as Chaplot noted, a data warehouse can address various phenomena and issues such as the following three: (a) how can instruction be modified in order to help students learn to write more effective essays; (b) are students who attend classes full-time more likely to succeed academically than those who take classes on a part-time basis; and (c) what kind of training is necessary for new employees?
As Chaplot (2007) reported, a data warehouse has four main components: operational systems of record, the data staging area, the data presentations area, and data access tools, and each of these components serves a unique function in preparing data for manipulation and examination. According to Turban et al. (2008), a data warehouse has the following eight characteristics: (a) organization, where the data are organized by subject and include information relevant for decision support; (b) time variant, where the data are kept for many years in order to be used for trends, forecasting, and comparisons over time; (c) consistency, where the data in various databases may be encoded differently; (d) integration, where data from various sources are integrated while also integration is supported by the use of Web services; (e) real time, where it is possible to arrange for real-time capabilities despite the fact that most applications of data warehousing are not in real time; (f) nonvolatile, where the data are not updated once entered into the data warehouse; (g) web-based, where data warehouses are designed to provide an efficient computing environment for web-based applications; and (h) relational, where the data warehouse uses the client/server architecture to provide the user an easy access to its data.
The educational organization as noted previously, faced various data problems such as inaccurate and inaccessible data, irrelevant data as well as problems regarding the security of the data, the completeness of the data, etc. In order to solve this kind of problems and hence provide for a data management, the educational organization decided to use an Education Data Warehouse (EDW). In addition to solving these particular data problems, as Turban et al. (2008) noted, this kind of data management will help the educational organization ease the burden of maintaining data and will enhance the power from their use while also, it will be able to support easy data access and quick, accurate and effective decision making. Apart from these, the aim of using an Education Data Warehouse is for better management and resource allocation decisions to flow if information can be made available (Durosaro, n.d).
In order to solve the data problems related to education, the educational organization has built and used an education data warehouse. The education data warehouse is a place that helped the organization to easily view and analyze the data collected from multiple data sources and a key support to data-driven decision making (Sanders, Romond & Ferrara, n.d). In specific, the education data warehouse was created in order to unify data collection efforts and to allow the organization to conduct trend analyses and track students and teachers to evaluate programs (The Center for Teaching Quality, n.d). As it was also supported by the Florida Department of Education (2005), the education data warehouse integrates existing, transformed data extracted from various sources that are available at the state level and it provides a single repository of data concerning students served in the public education system as well as educational facilities, curriculum and staff involved in instructional activities.
The education data warehouse has the following characteristics, as reported by the Florida Department of Education (2005): it allows longitudinal analyses, ensures confidentiality, includes historical and current data, is student-centric, and has state-of-the-art analytical capabilities. The data sources include student demographics and background, progression through grades, staff data, basic teacher data such as demographics, addresses, certification data, instructional activities, teacher salary and compensations, licensure test scores, degrees earned, course assignments, licensure status as well as endorsement areas and these data about the teachers are extracted from the databases of the Department of Education (The Center for Teaching Quality, n.d). In addition to these, as it was also reported by the Florida Department of Education, the education data warehouse includes student courses taken, enrollment, test scores, financial aid, awards, educational curriculum as well as educational institutions.
At the education data warehouse, each teacher record is assigned a different unique ID and each year new assignments of IDs are checked using ethnicity, name, and birthday while also, the warehouse includes data on teacher demographics, current work, and certification data with information on preparation (The Center for Teaching Quality, n.d). Additionally, as noted by the The Center for Teaching Quality, the warehouse includes individual records from public higher education institutions that allow tracking of teacher preparation graduates to work in the schools, including information on their coursework.
The education data warehouse was chosen as a solution to the data problems that the educational organization was facing due to the following three benefits it provides, as they were also reported by the Florida Department of Education (2005): (a) it provides capabilities to perform trend analyses and to track students and teachers over time and across delivery systems; (b) it allows the users of the educational organization to run their own queries against summarized data in a timely and efficient manner; and (c) it provides decision-makers with tools and information necessary to make informed, fact-based decisions about education.
Apart from these, the education data warehouse stores information on student achievement and outcomes from various sources that help teachers and administrators to better serve every student in the school and in fact, these data allow for the continual and longitudinal tracking of individual student achievement (Edudata Canada Team, 2005). In addition to these, as noted by the Edudata Canada Team, the education data warehouse provides students’ data that help identify specific learning strengths and weaknesses and help develop strategies to address them and this is achieved by identifying students who need additional help and by informing teachers about student specific weaknesses across the elementary instruction while also, information about individual students from elementary schools can be easily shared with secondary schools through the data warehouse. As Doe (2009) stated, the warehouse forms a longitudinal history from the data that can provide insights into student achievement and educational effectiveness. Furthermore, as Doe supported, users can query the data warehouse in order to compare different types of data such as assessment scores according to demographics while also, the warehouse enables the evaluation of the same kinds of data for various reasons.
The use of the education data warehouse for consolidation of information helped the educational organization in Cyprus, as it was also reported by the Microsoft Corporation (2010), to: manage information in its many forms such as documents, sensor data, imagery, etc.; access information where it is useful such as from desktop to mobile devices; share information with the people who need it within and across the organization; and to secure information for different users in various operations. The education data warehouse, as outlined by the Microsoft Corporation, “provide[s] the technologies that academic institutions need to contain proliferation, manage data in all its divergent forms, and make information easy to access and use” (¶12).
The current essay provided a report on the data problems encountered in an educational organization in Cyprus and the measures taken in order to solve them. The data problems that were mentioned and discussed previously, occurred due to the fact that the organization’s data lacked of important data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In specific, the data problems that were encountered in the educational organization included the following: data increase, data validity, data integrity, data inconsistency, data security, data redundancy, non availability of data, inaccuracy of data, poor storage and retrieval of the data, problems regarding data accessibility, concurrency problems as well as other problems that were presented due to the fact that the data were scattered. In order to solve this kind of data problems the educational organization made use of an education data warehouse that integrates and stores education data from multiple sources in various methods in order to support organizational decision-making (Chaplot, 2007). The use of an education data warehouse was found to be effective and helped the educational organization to manage the data and solve the data problems that were encountered. Closing up, as Chaplot outlined, a data warehouse “provide[s] users access and control to a wide variety of centralized and formatted data to choose the best course to action and support…decisions. Users can manipulate and customize the data to support specific queries that will enable positive changes at various…levels. Since the various stages increase data accuracy and integrity, complex queries can be conducted with a strong sense of confidence” (p.5)
References
Building Intelligent and Performing Enterprises Institute (n.d) Data Quality
Definition-What is Data Quality? Retrieved January 19, 2010, from http://www.bipminstitute.com/data-quality/accuracy-consistency-audit.php
Chaplot, P. (2007) An Introduction to Data Warehousing. Retrieved January 17,
2010, from http://www.mtsac.edu/administration/research/pdf/tips/DataWarehouses.pdf
Doe, C. G. (2009) A Look At…Data Management and Analysis Systems. Retrieved
January 18, 2010, from http://www.mmischools.com/Articles/Editorial/Features/A-LOOK-AT...-Data-Management-and-Analysis-Systems-59877.aspx
Durosaro, D.O. (n.d) Problems Confronting School Personnel in Educational Data
Collection, Analysis and Storage. Retrieved January 16, 2010, from http://www.kwsubeb.com/data-collection-collation-analysis/PROBLEMS_CONFRONTING_SCHOOL_PERSONNEL_IN_EDUCATIONAL_DATA_COLLECTION_ANALYSIS_AND_STORAGE.pdf
Edudata Canada Team (2005) Data Warehouse. Retrieved January 18, 2010, from
http://edudata.educ.ubc.ca/exampleproject/NorthVan/datawarehouse.htm
Florida Department of Education (2005) Education Data Warehouse Fact Sheet.
Retrieved January 20, 2010, from http://edwapp.doe.state.fl.us/EDW_Facts.htm
Melissa Data Corporation (2010) 6 Key Quality Dimensions. Retrieved January 17,
2010, from http://www.melissadata.com/enews/articles/1007/2.htm
Michigan Association of Intermediate School Administrators (2005) Data
Warehousing in Michigan Schools: Executive Summary. Retrieved January 20, 2010, from http://michiganedusource.org/Technology/DataWarehousingSummary.pdf
Microsoft Corporation (2010) Server Consolidation and Data Warehousing.
Retrieved January 19, 2010, from http://www.microsoft.com/education/solutions/datamanagement.aspx
Mills, L. (2008) Getting Started with Data Warehousing. Retrieved January 19,
2010, from http://www.schoolcio.com/showarticle/1048
Sanders, D., Romond, B. & Ferrara, J. (n.d) Vermont’s Education Data Warehouse &
Analyzer. Retrieved January 23, 2010, from
http://www.setda.org/c/document_library/get_file?folderId=23&name=Vermont+Education+Datahouse.pdf
The Center for Teaching Quality (n.d) Florida TQ Data Landscape (K-20 Education
Data Warehouse). Retrieved January 18, 2010, from http://www.teachingdata.org/pdfs/cpre_data_fl.pdf
Tupek, A.R. (2006) Definition of Data Quality. Retrieved January 21, 2010, from
http://www.census.gov/quality/P01-0_v1.3_Definition_of_Quality.pdf
Turban, E., Leidner, D., Mclean, E. & Wetherbe, J. (2008) Information technology
for management: Transforming organizations in the digital economy (6th ed.). New York, NY: John Wiley & Sons.
Wand, Y. & Wang.R.Y. (1996) Anchoring Data Quality Dimensions in Ontological
Foundations. Communications of the ACM, 39(11). Retrieved January 17, 2010, from http://web.mit.edu/tdqm/www/tdqmpub/WandWangCACMNov96.pdf
Data management systems are developing large amounts of information that can be stored, combined, and analyzed for data-driven instructional leadership, and the need for this type of information and analysis is further fueled by funding accountability as well as the demands of state and national standards, including the No Child Left Behind (NCLB) legislation (Doe, 2009). As Turban, Leidner, Mclean and Wetherbe (2008) outlined, the goal of data management is to provide the infrastructure that is necessary in order to transform raw data into corporate information that has the highest quality while also, the foundation of data management has some building blocks such as data profiling, data quality management, data integration, and data augmentation.
An important issue related to the data gathered in any organization whether that is business, industry, medicine, or education refers to data quality dimensions. Data quality is an important and crucial issue because quality determines not only the usefulness of the data but also the quality of the decisions based on the data (Turban et al., 2008). As stated by the Melissa Data Corporation (2010), “In order for the analyst to determine the scope of the underlying root causes and to plan the ways that tools can be used to address data quality issues, it is valuable to understand…data quality dimensions” (¶2). The most common and basic data quality dimensions include: accuracy, accessibility, relevance, timeliness, and completeness. “Accuracy of data is the degree to which data correctly reflects the real world object or an event being described” (Building Intelligent and Performing Enterprises Institute, n.d, ¶2) For instance, as reported by the Melissa Data Corporation, in correct spellings of the names of persons or products, their addresses or even untimely or not current data can impact operational and analytical applications.
Accessibility refers to the ease with which customers can identify, obtain, and use the information in the data products and relevance refers to the degree to which the data products provide information that meets the needs of the customers (Tupek, 2006). As supported by the Building Intelligent and Performing Enterprises Institute (n.d), the timeliness of the data is an also important data quality dimension and this is reflected in issues such as the following: the organizations and companies are required to publish their quarterly results within a given frame of time; and customers’ services need to provide customers with up-to date information. As Wand and Wang (1996) noted, “Timeliness has been defined in terms of whether the data is out of date and availability of output on time…Timeliness is affected by three factors: How fast the information system stated is updated after the real-world system changes (system currency); the rate of change of the real-world system (volatility); and the time the data is actually used” (p.8).
As Wand and Wang (1996) reported, completeness is achieved when all the necessary values for a certain variable are included, and hence a set of data is complete. For instance, in some cases, missing data is irrelevant but when the information that is missing is critical to a specific process, then completeness becomes an issue (Melissa Data Corporation, 2010). As noted by the Building Intelligent and Performing Enterprises Institute (n.d), data completeness refers to the extent to which the expected attributes of data are provided and in fact, data completeness refers actually to the expected completeness and therefore, it is possible for data not to be available but it is still considered completed as it meets the expectations of the user.
Going further, the author has interviewed a knowledge worker in an educational organization in Cyprus and has identified the data problems encountered in the organization as well as the measures taken to solve these problems. Next, follows a report on those data problems that are related to the data quality dimensions that were mentioned and discussed above as well as a report on the measures that were taken in order to solve those data problems. The educational organization faced data problems due to the fact that the gathered and collected data had not met the data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In general, data problems that were encountered included and involved incorrect data, redundant data, irrelevant data, missing data, etc.
The data available at the educational organization lacked of an important data quality dimension, and that is accuracy. As a result, the organization encountered problems that involved data validity, data inconsistency, data integrity, data inaccuracy as well as concurrency problems. In specific, the educational organization’s data were created and used offline and due to the fact that these data do not go through quality control checks, the validity and hence the accuracy of the data is not assured but rather is questionable (Turban et al., 2008). In addition to this, problems that were also encountered involved the inconsistency and integrity of the data as well as concurrency problems. As it was also reported by Turban et al., the actual values across various copies of the data were not synchronized and for instance, changes in students’ information were not made in all applications in the educational organization that require this information.
Moreover, the educational organization encountered data integrity and concurrency problems, which, according to Turban et al., had as a result, the data values not to meet the integrity constraints and in fact, while an application was updating a record, another application could not access that specific record and hence it could not get the desired information. Furthermore, problems of inaccuracy occurred due to the fact that the data had not accurately represented the real-world values they were expected to model and for instance, the operational applications were affected by incorrect spellings of students’ and teachers’ names and addresses (Melissa Data Corporation, 2010). Apart from these, the problem of data inaccuracy occurred due to the fact that the head teachers had not kept and allocated the records and data for their schools accurately deliberately, in order to influence financial allocation to their schools while they had also done this because of ignorance about record keeping (Durosaro, n.d).
The educational organization encountered problems not only because of the low quality of the data regarding accuracy but also because of the lack of accessibility to the data. In specific, the problem of accessibility arose due to the data increase, poor storage and retrieval of the data, and also due to the fact that the data were redundant and scattered as well. In particular, the amount of data gathered at the educational organization was increasing rapidly due to the fact that time to time and year by year, the number of students and teachers was increasing while also, data about old students and teachers was also increasing. As Turban et al. (2008) noted, much past data must be kept for a long time, and new data are added quickly but while only a small portion of the educational organization’s data are relevant to be used for any specific application, that relevant data must also be identified and found in order to be useful.
Problems with accessibility to the data occurred also because the educational organization’s data were scattered. Due to the fact that the data were stored in several servers and in different computing systems, databases, and formats, consequently, the data were scattered throughout the organization and were collected by many individuals using various methods and devices (Turban et al., 2008). As a result, this sometimes presented difficulties in and problems with accessing some of the data. The problem of accessibility was also caused because of the poor storage and retrieval of the data. As it was also reported by Durosaro (n.d), much of the educational organization’s data were kept in files and folders and stored in drawers while also, there were no statistics units in the educational organization to help gather and store the necessary data and there was an insufficient data-based management information system, and this method of collection hindered and prevented retrieval while it also resulted in the loss of data. In addition to these, as Durosaro outlined, “The problems facing educators in the area of data storage are such that people are careless with data. People don’t preserve documents even personal documents such as pay slips, declaration of age, marriage certificates, receipts of payment made on schools fees and even certificates are being poorly kept and lost” (p.4).
Apart from these, the fact that the data were redundant also caused problems with accessibility. As it was also reported by Turban et al. (2008), the data throughout the educational organization were often out-of-date and redundant and hence, data managers faced problems in their maintenance while also, due to the fact that applications and their data files were created by different programmers over a period of time, another problem occurred that involved the duplication of the same data in various files. In addition to these, as Turban et al. noted, as a consequence of accessing data from different applications, the problem of data isolation occurred since the data were organized differently, were stored in different formats and were often inaccessible to other applications.
The educational organization’s data lacked of another data quality dimension, timeliness and this was due to some other data problems such as the non availability of the data. In specific, some important records and data that were necessary were not kept while others were poorly kept and therefore, the needed information could not be found because it had not been obtained by the educational organization and also because data have been lost due to poor storage (Durosaro, n.d). In addition to these, as Durosaro also reported, some data have been mixed up to the extent that retrieval was very difficult when required for use.
The educational organization encountered also problems regarding data security. Due to the fact that new applications were continually added to the system on an add-hoc basis, and hence, with more applications more people had access to data, security was very difficult to enforce in the file environment (Turban et al., 2008). In addition to these, as Turban et al. also reported, another problem that was encountered involved the selection of the data management tool and this was due to the large number of the products that were available while also, another problem that was encountered was that the applications were developed with regard to how the data were stored and in fact, the applications and data in computer systems were not independent but rather they were dependent on each other. Apart from these, another problem that was also reported by Turban et al. and the educational organization also encountered, involved the delegation of data-quality responsibilities to the technical teams that impacted and affected negatively the high-quality of the data.
The particular educational organization in Cyprus encountered a variety of data problems and this has had as a result the data to lack of important data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In order to solve this kind of problems, the educational organization took some measures that are next mentioned and discussed. The educational organization used data and analysis in order to drive decision-making practices and in specific, it made use of an Education Data Warehouse (EDW) in order to store, manage, and analyze the data. “A data warehouse is a repository of data that are organized to be readily acceptable for analytical processing activities (such as data mining, decision support, querying, and other applications)” (Turban et al., 2008, p.100). The data warehouse gathers data into reports that help guide decision making at schools, districts, and individual student levels (Durosaro, n.d). As Mills (2008) noted, data warehouses are structured in order to facilitate data collection, management, querying and reporting for decision making. Data warehouse can be used to address issues in academic institutions regarding the effectiveness of new instructional techniques, student satisfaction, etc. (Chaplot, 2007). In addition to these, as supported by the Michigan Association of Intermediate School Administrators (2005), data warehousing is a tool that can help districts become data driven in order to meet the requirements of the No Child Left Behind legislation and allows districts to find answers and ask complex questions that uncover underlying problems, thus leading to the design of data driven student achievement and school improvement strategies. As Chaplot supported, the fundamental goal of the data warehouse is to support strategic planning, modeling and forecasting at the organizational level while also, it must fulfill the need for knowledge for an area of uncertainty or growth in the organization and therefore, in order to achieve this goal, the data warehouse must provide a comprehensive and consistent view of the organization.
As Turban et al. (2008) noted, data warehousing makes it easier and faster for organizations to process, analyze, and query data while it also provides for improved analytical processing which involves analysis of accumulated data and it includes decision support systems, data mining, Web applications, enterprise information systems, querying, etc. As Mills (2008) stated, “A successful and sustainable data warehouse can be an important contributor to a district’s ongoing success” (¶9) Additionally, effective data warehousing can help create a meaningful relationship between information technology and organizations, thus facilitating enterprise-level strategic planning and growth as well (Chaplot, 2007). A data warehouse may include data such as: personnel data, student demographics and achievement data, financial data as well as assessment data (Michigan Association of Intermediate School Administrators, 2005).
Moreover, as Chaplot noted, a data warehouse can address various phenomena and issues such as the following three: (a) how can instruction be modified in order to help students learn to write more effective essays; (b) are students who attend classes full-time more likely to succeed academically than those who take classes on a part-time basis; and (c) what kind of training is necessary for new employees?
As Chaplot (2007) reported, a data warehouse has four main components: operational systems of record, the data staging area, the data presentations area, and data access tools, and each of these components serves a unique function in preparing data for manipulation and examination. According to Turban et al. (2008), a data warehouse has the following eight characteristics: (a) organization, where the data are organized by subject and include information relevant for decision support; (b) time variant, where the data are kept for many years in order to be used for trends, forecasting, and comparisons over time; (c) consistency, where the data in various databases may be encoded differently; (d) integration, where data from various sources are integrated while also integration is supported by the use of Web services; (e) real time, where it is possible to arrange for real-time capabilities despite the fact that most applications of data warehousing are not in real time; (f) nonvolatile, where the data are not updated once entered into the data warehouse; (g) web-based, where data warehouses are designed to provide an efficient computing environment for web-based applications; and (h) relational, where the data warehouse uses the client/server architecture to provide the user an easy access to its data.
The educational organization as noted previously, faced various data problems such as inaccurate and inaccessible data, irrelevant data as well as problems regarding the security of the data, the completeness of the data, etc. In order to solve this kind of problems and hence provide for a data management, the educational organization decided to use an Education Data Warehouse (EDW). In addition to solving these particular data problems, as Turban et al. (2008) noted, this kind of data management will help the educational organization ease the burden of maintaining data and will enhance the power from their use while also, it will be able to support easy data access and quick, accurate and effective decision making. Apart from these, the aim of using an Education Data Warehouse is for better management and resource allocation decisions to flow if information can be made available (Durosaro, n.d).
In order to solve the data problems related to education, the educational organization has built and used an education data warehouse. The education data warehouse is a place that helped the organization to easily view and analyze the data collected from multiple data sources and a key support to data-driven decision making (Sanders, Romond & Ferrara, n.d). In specific, the education data warehouse was created in order to unify data collection efforts and to allow the organization to conduct trend analyses and track students and teachers to evaluate programs (The Center for Teaching Quality, n.d). As it was also supported by the Florida Department of Education (2005), the education data warehouse integrates existing, transformed data extracted from various sources that are available at the state level and it provides a single repository of data concerning students served in the public education system as well as educational facilities, curriculum and staff involved in instructional activities.
The education data warehouse has the following characteristics, as reported by the Florida Department of Education (2005): it allows longitudinal analyses, ensures confidentiality, includes historical and current data, is student-centric, and has state-of-the-art analytical capabilities. The data sources include student demographics and background, progression through grades, staff data, basic teacher data such as demographics, addresses, certification data, instructional activities, teacher salary and compensations, licensure test scores, degrees earned, course assignments, licensure status as well as endorsement areas and these data about the teachers are extracted from the databases of the Department of Education (The Center for Teaching Quality, n.d). In addition to these, as it was also reported by the Florida Department of Education, the education data warehouse includes student courses taken, enrollment, test scores, financial aid, awards, educational curriculum as well as educational institutions.
At the education data warehouse, each teacher record is assigned a different unique ID and each year new assignments of IDs are checked using ethnicity, name, and birthday while also, the warehouse includes data on teacher demographics, current work, and certification data with information on preparation (The Center for Teaching Quality, n.d). Additionally, as noted by the The Center for Teaching Quality, the warehouse includes individual records from public higher education institutions that allow tracking of teacher preparation graduates to work in the schools, including information on their coursework.
The education data warehouse was chosen as a solution to the data problems that the educational organization was facing due to the following three benefits it provides, as they were also reported by the Florida Department of Education (2005): (a) it provides capabilities to perform trend analyses and to track students and teachers over time and across delivery systems; (b) it allows the users of the educational organization to run their own queries against summarized data in a timely and efficient manner; and (c) it provides decision-makers with tools and information necessary to make informed, fact-based decisions about education.
Apart from these, the education data warehouse stores information on student achievement and outcomes from various sources that help teachers and administrators to better serve every student in the school and in fact, these data allow for the continual and longitudinal tracking of individual student achievement (Edudata Canada Team, 2005). In addition to these, as noted by the Edudata Canada Team, the education data warehouse provides students’ data that help identify specific learning strengths and weaknesses and help develop strategies to address them and this is achieved by identifying students who need additional help and by informing teachers about student specific weaknesses across the elementary instruction while also, information about individual students from elementary schools can be easily shared with secondary schools through the data warehouse. As Doe (2009) stated, the warehouse forms a longitudinal history from the data that can provide insights into student achievement and educational effectiveness. Furthermore, as Doe supported, users can query the data warehouse in order to compare different types of data such as assessment scores according to demographics while also, the warehouse enables the evaluation of the same kinds of data for various reasons.
The use of the education data warehouse for consolidation of information helped the educational organization in Cyprus, as it was also reported by the Microsoft Corporation (2010), to: manage information in its many forms such as documents, sensor data, imagery, etc.; access information where it is useful such as from desktop to mobile devices; share information with the people who need it within and across the organization; and to secure information for different users in various operations. The education data warehouse, as outlined by the Microsoft Corporation, “provide[s] the technologies that academic institutions need to contain proliferation, manage data in all its divergent forms, and make information easy to access and use” (¶12).
The current essay provided a report on the data problems encountered in an educational organization in Cyprus and the measures taken in order to solve them. The data problems that were mentioned and discussed previously, occurred due to the fact that the organization’s data lacked of important data quality dimensions such as accuracy, accessibility, relevance, timeliness, and completeness. In specific, the data problems that were encountered in the educational organization included the following: data increase, data validity, data integrity, data inconsistency, data security, data redundancy, non availability of data, inaccuracy of data, poor storage and retrieval of the data, problems regarding data accessibility, concurrency problems as well as other problems that were presented due to the fact that the data were scattered. In order to solve this kind of data problems the educational organization made use of an education data warehouse that integrates and stores education data from multiple sources in various methods in order to support organizational decision-making (Chaplot, 2007). The use of an education data warehouse was found to be effective and helped the educational organization to manage the data and solve the data problems that were encountered. Closing up, as Chaplot outlined, a data warehouse “provide[s] users access and control to a wide variety of centralized and formatted data to choose the best course to action and support…decisions. Users can manipulate and customize the data to support specific queries that will enable positive changes at various…levels. Since the various stages increase data accuracy and integrity, complex queries can be conducted with a strong sense of confidence” (p.5)
References
Building Intelligent and Performing Enterprises Institute (n.d) Data Quality
Definition-What is Data Quality? Retrieved January 19, 2010, from http://www.bipminstitute.com/data-quality/accuracy-consistency-audit.php
Chaplot, P. (2007) An Introduction to Data Warehousing. Retrieved January 17,
2010, from http://www.mtsac.edu/administration/research/pdf/tips/DataWarehouses.pdf
Doe, C. G. (2009) A Look At…Data Management and Analysis Systems. Retrieved
January 18, 2010, from http://www.mmischools.com/Articles/Editorial/Features/A-LOOK-AT...-Data-Management-and-Analysis-Systems-59877.aspx
Durosaro, D.O. (n.d) Problems Confronting School Personnel in Educational Data
Collection, Analysis and Storage. Retrieved January 16, 2010, from http://www.kwsubeb.com/data-collection-collation-analysis/PROBLEMS_CONFRONTING_SCHOOL_PERSONNEL_IN_EDUCATIONAL_DATA_COLLECTION_ANALYSIS_AND_STORAGE.pdf
Edudata Canada Team (2005) Data Warehouse. Retrieved January 18, 2010, from
http://edudata.educ.ubc.ca/exampleproject/NorthVan/datawarehouse.htm
Florida Department of Education (2005) Education Data Warehouse Fact Sheet.
Retrieved January 20, 2010, from http://edwapp.doe.state.fl.us/EDW_Facts.htm
Melissa Data Corporation (2010) 6 Key Quality Dimensions. Retrieved January 17,
2010, from http://www.melissadata.com/enews/articles/1007/2.htm
Michigan Association of Intermediate School Administrators (2005) Data
Warehousing in Michigan Schools: Executive Summary. Retrieved January 20, 2010, from http://michiganedusource.org/Technology/DataWarehousingSummary.pdf
Microsoft Corporation (2010) Server Consolidation and Data Warehousing.
Retrieved January 19, 2010, from http://www.microsoft.com/education/solutions/datamanagement.aspx
Mills, L. (2008) Getting Started with Data Warehousing. Retrieved January 19,
2010, from http://www.schoolcio.com/showarticle/1048
Sanders, D., Romond, B. & Ferrara, J. (n.d) Vermont’s Education Data Warehouse &
Analyzer. Retrieved January 23, 2010, from
http://www.setda.org/c/document_library/get_file?folderId=23&name=Vermont+Education+Datahouse.pdf
The Center for Teaching Quality (n.d) Florida TQ Data Landscape (K-20 Education
Data Warehouse). Retrieved January 18, 2010, from http://www.teachingdata.org/pdfs/cpre_data_fl.pdf
Tupek, A.R. (2006) Definition of Data Quality. Retrieved January 21, 2010, from
http://www.census.gov/quality/P01-0_v1.3_Definition_of_Quality.pdf
Turban, E., Leidner, D., Mclean, E. & Wetherbe, J. (2008) Information technology
for management: Transforming organizations in the digital economy (6th ed.). New York, NY: John Wiley & Sons.
Wand, Y. & Wang.R.Y. (1996) Anchoring Data Quality Dimensions in Ontological
Foundations. Communications of the ACM, 39(11). Retrieved January 17, 2010, from http://web.mit.edu/tdqm/www/tdqmpub/WandWangCACMNov96.pdf
Subscribe to:
Posts (Atom)