TECHNOLOGY EFFECTS ON DATA ANALYSIS AND COMPUTING


Technology has affected society and its surroundings in a number of ways. In many societies, technology has helped develop more advanced economies (including today’s global economy) and has allowed the rise of a leisure class. Many technological processes produce unwanted by-products, known as pollution, and deplete natural resources, to the detriment of the Earth and its environment. Various implementations of technology influence the values of a society and new technology often raises new ethical questions. Examples include the rise of the notion of efficiency in terms of human productivity, a term originally applied only to machines, and the challenge of traditional norms.
Technology is the making, usage, and knowledge of tools, machines, techniques, crafts, systems or methods of organization in order to solve a problem or perform a specific function. It can also refer to the collection of such tools, machinery, and procedures. Technologies significantly affect human as well as other animal species’ ability to control and adapt to their natural environments. The word technology comes from Greek (technología); and from (téchnē), meaning “art, skill, craft”, The term can either be applied generally or to specific areas: examples include construction technology, medical technology, and information technology.
Many organizations have large amounts of data which has been collected and stored in massive datasets which needs be processed and analyzed to provide business intelligence, improve products and services for customers, or to meet other internal data processing requirements.[1] For example, Internet companies need to process data collected by Web crawlers as well as logs, click data, and other information generated by Web services. Parallel relational database technology has not proven to be cost-effective or provide the high-performance needed to analyze massive amounts of data in a timely manner. As a result several organizations developed technology to utilize large clusters of commodity servers to provide high-performance computing capabilities for processing and analysis of massive datasets. Clusters can consist of hundreds or even thousands of commodity machines connected using high-bandwidth networks. Examples of this type of cluster technology include Google’s MapReduce, Apache Hadoop, Aster Data Systems, Sector/Sphere, and LexisNexis HPCC platform.
More companies are using technology to handle customer service in an efficient and cost-effective way. Here’s how you can use data management and analytics and insight-driven marketing to improve your customer care systems. There’s no denying the fact that customer service is important to a small or mid-sized business. The quality of that service will either enhance or degrade customer loyalty to your brand and your business. With the economy in recession, customers have more alternatives than ever. The business that proves to be responsive to customer questions, complaints, or other needs can gain a clear competitive advantage. That’s why it’s so important to understand how new technologies can help you anticipate customer needs, tailor business processes to best serve customers, and ultimately improve the efficiency of your business – the latter of which can keep costs down. Technology offers a potential medium through which DATA computing and analysies implementation could be made easier and more likely to occur (Ysseldyke & McLeod, 2007). The use of technology makes ongoing data collection, data consumption, and data-based decision making a more plausible proposition, and it can keep these important aspects of RtI from monopolizing teacher time. Previous research found that the use of technology substantially facilitated collecting, managing, and analyzing educational data (McIntire, 2002; McLeod, 2005; Pierce, 2005; Wayman, 2005). Thus, technology-enhanced assessment (TEA) would likely support RtI implementation, but applying technology to other aspects of RtI would likely enhance the implementation of those components as well. The RTI Action Network of the National Center for Learning Disabilities has identified high-quality classroom instruction, tiered instruction/intervention, ongoing student assessment, and family involvement as the essential components of RtI. Below is information about those essential components and suggestions for ways in which technology could facilitate the successful implementation of each. It should be noted that the information presented below does not suggest endorsement of a product.S
Using technology to compute and analyses data is any process by which through technological means, computer program are use to enter data and summaries, analyses or otherwise convert data into usable information. The process may be automated and run on a computer. It involves recording, analyzing, sorting, summarizing, calculating, disseminating and storing data. Because data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Nevertheless, the terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output. Data processing may or may not be distinguished from data conversion, when the process is merely to convert data to another format, and does not involve any data manipulation.
Type of data
Data can be of several types
Quantitative data data is a number
o Often this is a continuous decimal number to a specified number of significant digits
o Sometimes it is a whole counting number
Categorical data data one of several categories
Qualitative data data is a pass/fail or the presence or lack of a characteristic
The process of data analysis
Data analysis and computation is a process that uses technological softwares, within which several phases can be distinguished:
Data cleaning
Data cleaning is an important procedure during which the data are inspected, and erroneous data are—if necessary, preferable, and possible—corrected. Data cleaning can be done during the stage of data entry. If this is done, it is important that no subjective decisions are made. The guiding principle provided by Adèr (ref) is: during subsequent manipulations of the data, information should always be cumulatively retrievable. In other words, it should always be possible to undo any data set alterations. Therefore, it is important not to throw information away at any stage in the data cleaning phase. All information should be saved (i.e., when altering variables, both the original values and the new values should be kept, either in a duplicate data set or under a different variable name), and all alterations to the data set should carefully and clearly documented, for instance in a syntax or a log.
Initial data analysis
The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:[3]
Quality of data
The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, normal probability plots), associations (correlations, scatter plots).
Other initial data quality checks are: The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase and the type of technological package adopted

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