...live in a world where data is a critical resource. Information is also a critical resource and consists of data that is processed into meaningful information for the purpose of organizations and users. Collected data is stored into what is known as databases where it is organized into potentially valuable information.Data also known as Raw data is a stream of facts that are not organized or arranged into a form that people can understand or use (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008) . Raw Data are facts that have not yet been processed to reveal their meaning (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008). For example when AT&T wireless ask their clients to participate in a survey about theproducts they have purchased or how was their customer service experience the data collected is useful but not until the raw data isorganized by combining it with other similar data and analyzed into meaningful information. Information is the result of processing raw data to reveal its meaning (Coronel, Morris, & Rob,2010). Data processing can be as simple as organizing data to reveal patterns or as complex as making forecasts or drawing inferences using statistical modeling (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008).Both data and information are types of knowledge which share similarities in the fact that each are used to draw a conclusion for a specific purpose. Data is gathered by observation...
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...Data and Information Summary HCI/520 11/18/2013 Data and Information Summary Today we live in a world where data is a critical resource. Information is also a critical resource and consists of data that is processed into meaningful information for the purpose of organizations and users. Collected data is stored into what is known as databases where it is organized into potentially valuable information. Data also known as Raw data is a stream of facts that are not organized or arranged into a form that people can understand or use (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008) . Raw Data are facts that have not yet been processed to reveal their meaning (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008). For example when AT&T wireless ask their clients to participate in a survey about the products they have purchased or how was their customer service experience the data collected is useful but not until the raw data is organized by combining it with other similar data and analyzed into meaningful information. Information is the result of processing raw data to reveal its meaning (Coronel, Morris, & Rob, 2010). Data processing can be as simple as organizing data to reveal patterns or as complex as making forecasts or drawing inferences using statistical modeling (Gillenson, Ponniah, Kriegel, Trukhnov, Taylor, Powell, & Miller, 2008). Both data and information are types of knowledge which share similarities...
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...relationship between data and information is an interconnected one. Data is raw facts such as phone numbers or addresses, and information is the organization of these raw facts into a meaningful manner. The information may be well organized on a report or table and yet not always be meaningful to all people. There are different ways to arrange data to make it meaningful for different people. For example, one person might be satisfied with information that shows him or her towns in which their customers live in to help him or her determine where the largest volume of customers are. A different person might want that information expanded to include those customers street addresses as well so that they may determine a better shipping route. Both of these examples contain the same customers and similar data but the first person would have no use for the street addresses in his search and the second person would not have enough information to create a shipping route from just the towns. The type of data used can also affect the information generated. You can gather a great deal of data on the needs of plants to grow but if you do not put the data in the table correctly or put inaccurate data in you will get information that is basically useless. That is the garbage in/ garbage out theory. It is also a good idea when creating a table to group the like characteristics together to make it easier to gather information from the data. Computers now make it easier to input data into tables in...
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...Meaning and definition of data and information Data is the raw material for data processing. The data relate to facts, event and transactions. Data refers to unprocessed information. Information is data that has been processed in such a way as to be meaningful to the person who receives it. It is anything that is communicated. For example, researchers who conduct market research survey might ask a member of the public to complete questionnaires about a product or a service. These completed questionnaires are data; they are processed and analyze in order to prepare a report on the survey. This resulting report is informative. Data | Information | Originated at mid 17th century | Originated in late middle time | Data are raw facts. | Information is processed data | May not always be meaningful. | Information is always meaningful. | Input to any system may be treated as Data | Output after processing the system is Information | Data must be processed to understand. | The information is already in understandable form, it may be processed further to make it more understandable. | Difficult to Understanding | Easy to Understanding | Data may not be in the order. | Information should be in the order | Example: Statistics, numbers, characters, images. | Example Reports, Knowledge | Data is used as input to the computer system. | Information is the output of data. | Data is unprocessed facts figures. | Information is processed data. | Data is not specific. |...
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...Data Analytics and Information Governance XBT Task 2- 3 Annotated Bibliographies November 2, 2015 1.) Mushtag,F. (2015). Ensuring EHR compliance for Meaningful Use. Health Management Technology, 36(7), 16-17. In this article Mushtaq is describing how Meaningful Use will improve the data quality of electronic health records. He points out what the requirements of Meaningful Use are and how it is meant to improve the EHR through The Medicare and Medicaid EHR Incentive Program. Mushtaq contends that while organizations originally put EHR systems in place to collect data, the market has shifted and health organizations as well as providers have moved more into a value based healthcare environment. He suggests that there are several steps in preparing to become complainant for the requirements for participation through the Centers for Medicare & Medicaid Services (CMS). He states that once you become complainant that EHR’s will provide relevant data, improve patient adherence, access to data that will drive improvements, and higher quality of usable data. He concludes effective compliance data will lead to higher quality of data which will allow physicians to provide better care to their patients. This article covers the importance of Meaningful Use and its relationship to obtaining quality data and is useful to bringing in or updating the EHR. The information in this article could be used to give the HIM professional ideals about how to make Meaningful Use of data...
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...collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions” (p.5). Types and levels of statistics There are two major types of statistics, descriptive and inferential. Descriptive statistics is defined by Lind (2011) as “methods of organizing, summarizing, and presenting data in an informative way” (p.6). An example of descriptive statistics would be a high school report showing that it had 300 graduates in 1990 and 450 graduates on 1991. The information that they provided described the amount of graduates that they had for each year. Inferential statistics is defined by Lind (2011) as “the methods used to estimate a property of a population on the basis of a sample” (p.7). If the same high school sent out a report showing the graduate numbers for 1999- the present to estimate the number of graduates that they would have for this school year, those statistics would be inferential because they are used to estimate future outcomes. There are four levels of statistical data: nominal, ordinal, interval and ratio. The nominal level deals with qualitative variables such as colors and blood types that can only be counted and classified. Ordinal data measurement is a variable rating system that ranks data according to predetermined categories that have only relative values such as poor, average, and good. Data can be ranked and ordered using this method. Interval data measurement adds to the ordered level by adding the characteristic...
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...presenting data. Some experts prefer to call statistics data science, a trilogy of tasks involving data modeling, analysis, and decision making. In contrast, a statistic is a single measure, reported as a number, used to summarize a sample data set. Knowing statistics will make you a better consumer of other people’s data. You should know enough to handle everyday data problems, to feel confident that others cannot deceive you with spurious arguments, and to know when you’ve reached the limits of your expertise. Statistical knowledge gives your company a competitive advantage against organizations that cannot understand their internal or external market data. And mastery of basic statistics gives you, the individual manager, a competitive advantage as you work your way through the promotion process, or when you move to a new employer. Nominal Level of Measurement The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels. Data at the nominal level is qualitative. Colors of eyes, yes or no responses to a survey, and favorite breakfast cereal all deal with the nominal level of measurement. Even some things with numbers associated with them, such as a number on the back of a football jersey, are nominal since it is used to "name" an individual player on the field. Ordinal Level of Measurement Data at this level...
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...Why are data analysis skills and thinking so important to spend analysis? Can’t software applications be used to generate the information needed automatically? Not all data presented to you may be important to make strategic decisions. One can make sense out of a large data when data analysis skills and thinking are employed. These skills help one identify the more meaningful information and understand patterns, consistencies or irregularities, to make sound decisions. Flanagan of Pepco Holdings, in the article provided, shared that one will be better able to assist internal clients in planning spend strategies and also better understand supplier’s cost proposals. Analysis is important because most automatically generated results do not provide the information needed. It is important to know what data is meaningful and understand what the output of the data means and how to employ it in a real world application How might structured process such as Six Sigma methodology be useful here? Data analysis is important for a company’s continuous improvement efforts. Six Sigma is a business process improvement tool to significantly minimize mistakes at a stringently high level. The methodology of Six Sigma itself, when followed correctly (i.e. DMAIC), will allow a person to identify process inefficiencies and address them to generate cost savings, deliver quality of service and achieve more consistent good results. Why is it important to get other functional areas,...
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...Transforming Data into Information What is Data? What is information? Data is facts; numbers; statistics; readings from a device or machine. It depends on what the context is. Data is what is used to make up information. Information could be considered to be the same characteristics I just described as data. In the context of transforming data into information, you could assume data is needed to produce information. So information there for is the meaningful translation of a set of or clusters of data that’s produces an output of meaningful information. So data is a bunch of meaningless pieces of information that needs to be composed; analyzed; formed; and so forth to form a meaningful piece of information. Transforming Data Let’s pick a context such as computer programming. You need pieces of data to be structured and formed into something that will result in an output of something; a message, a graph, or a process, in which a machine can perform some sort of action. Well now we could say that information is used to make a product, make a computer produce something, or present statistical information. That would be the output of that data. The data would be numbers, words, or symbols. The information would be a message, a graph, or a process, in which a machine can perform some sort of action. Information Information could be looked at as data as well. Let’s say we need a chart showing the cost of a business expenses in relation to employee salaries. The data for showing...
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...interpreting data to assist in making more effective decisions” (2011). There are currently two types of statistics, descriptive and inferential. Descriptive statistics is described by Lind, Marchal, and Wathen as “methods of organizing, summarizing, and presenting data in an informative way” (2011). Inferential statistics is described by Lind, Marchal, and Wathen as, “The methods used to estimate a property of a population on the basis of a sample” (2011). With the two types of statistics there are also four levels of statistics. These levels are called statistical data measurement. Each level of measurement allows data to be classified. These measurements are: nominal, ordinal, interval, and ratio. Nominal level of measurement as qualitative variables is only allowed to be classified and counted. An example of nominal level of measurement would be gender, because there is no natural order to the outcomes. The next level of measurement is ordinal. This level is used when rating or ranking date. An example of ordinal level of measurement would be how a survey rates a product or service as “Good”, “Better”, or “Best”. Next is the interval level of measurement. This level is similar to the ordinal level; however the different in the values of this level is a constant size. An example of interval level of measurement would be temperature. Finally we have the ratio level of measurement. This level is similar to the interval level, however in this case 0 is meaningful as well as...
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...and popular algorithm in clustering and was published in 1955, 50 years ago. The advancement in technology has led to many high-volume, high-dimensional data sets. These huge data sets provide opportunity for automatic data analysis, classification...
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...must be able to trust that research is being reported honestly, accurately, and free from bias. Questionable research misconduct can include poor data management, the failure to share data, questionable data selection methods, inadequate supervision of those conducting the research, and fiscal mismanagement ("Research Misconduct", 2012). Recently a large case of research fraud was brought against Dr. Kipak K. Das by the University of Connecticut. The allegations of research fraud involve 26 articles published in 11 different journals claiming the health benefits of drinking red wine. According to the article, “University Suspects Fraud by a Researcher Who Studied Red Wine”, the university received anonymous allegations in January 2009 about irregularities in Dr. Das’ research. An investigation into Das’ research articles revealed 145 instances of fabrication and falsification of data (Wade, 2012). Allegations received by the university reference lab members manipulating figures, often at the request of Dr. Das, and digitally manipulating the experiments (western-blot images) done in the studies. The fabrication and falsification of the research data will have a negative impact on Dr. Das’ reputation as a respectable doctor, could tarnish the name of the University of Connecticut, could harm those that rely upon this research data, and could also discredit the federal government in how they distribute grant money. Since Dr. Das was receiving federal grant money for his research...
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...Data is unprocessed information, the fact that is still raw. It has no meaning by itself. Data can be in any form, for examples numbers, words, and pictures. However, some data can be useable or not at all. Information is data that has been processed and given meaning. This information can be very useful in some case, but can also be useless for others. Usually information provides answers to “who, what, where, when, and how” questions. Information is also easier to remember than data which is still very wide and irregular. Most of the people are using information to make a decision and plan what to do next. So we must make sure that the information we provide meet the requirements and have the desirable qualities in it. The desirable qualities are the minimum standard to be included in the process of producing good information. The information should be relevant, accurate, complete, up to date, well presented, and adapted to user requirements. By implementing those qualities, hopefully the information provided will be easy to understand and satisfied the users. A system is a group of elements that are formed to achieve some specific objectives. All people living in a system, a system in family, home, work system, the system at school, and a system in our community. Even we ourselves are a system, a living system, for example a respiratory system. An organization is a system in which people work together to achieve some specific objectives. The point is, we need a system...
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...The computation time and the accuracy rate are the two main measurements for evaluating the performance of the IDS. So our goal is to focus on reducing the computation time and finding an efficient detection mechanism to improve the accuracy rate of the proposed algorithm for recognizing the network traffic. A. Overview The algorithm consists two phases: the classification phase and clustering phase. In the classification phase, the artificial neural network, one of the core methods of Computational Intelligence is used to create the classifier from the known network traffic data. The neural network it identifies the input pattern and tries to output the corresponding class. It can map input patterns to their associated output patterns using their mapping capabilities. They can be trained with known examples of a problem...
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...Topic 1: Overview of AIS & Business Processes Characteristics that make information useful: 1. Relevant: Reduces uncertainty, improves decision making, or confirms or corrects prior expectations 2. Reliable: Information free from bias 3. Completeness: Does not omit important aspects of events or activities 4. Timely: Information needs to be provided in time to make the decision 5. Understandable: Information must be presented in a meaningful manner 6. Verifiable: Two independent people can produce the same conclusion 7. Accessible: Available to users when when they need it Business processes (“All cycles are related”): 1. Revenue - Goods & services are sold for cash or a future promise to receive cash. Transactions in Revenue cycle: Handle customer inquiries | Update sales & A/R for sales | Take customer orders | Receive customer payments | Approve credit sales | Update A/R for collections | Check inventory availability | Handle sales returns, discounts & bad debts | Initiate back orders | Prepare management reports | Pick and pack orders | Send info to other cycles | Ship goods | | Bill customers | | 2. Expenditure - Companies purchase inventory for resale or raw materials to use in producing products in exchange for cash or a future promise to pay cash. Transactions in expenditure cycle: Requisition goods and services | Approve invoices for payment | Process purchase...
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