Data Essay

Data: Data is studied as the lowest part of abstraction level from which knowledge and information can be derived. Data is always a raw form of information. It can be a collection of images, numbers, inputs, characters or any other outputs that can be converted into symbolic representation. Information: Information refers to data that provides a meaningful connection between them. Here, data refers to the collection that can be processed to provide useful answers which leads to an increase in knowledge

diverse customer data. The telecom industry in which our organization operates in, owns a great deal of customer data that portray customer buying behavior daily. This customer data provides a source of insight and a new opportunity for generating revenue, which any organization would want to pursue. To understand how data can transform into revenues, we must first understand why an organization should monetize their data. The reasons why an organization should monetize their data The uncertain business

pooled data. Splunk central receiver receives variety of data from many different ports and displays them as events on a single server. From one place, one can be able to access the log files in any of the web servers of the business, all the databases, routers, load balancers, and firewalls from all the companies’ operating systems. All the logs and configuration files can be accessed and analyzed, as well all from one device. 3.7 Conversion of data into answers Splunk is used to analyze the data

Data and Information Many people actually think that data and information can be used interchangeably but this is not true. Data is raw material that has not been processed and has been extracted from the source the way it is. Data is also unorganized therefore cannot be used for a meaningful purpose. On the other hand, information is processed data therefore the latter has to be organized into a meaningful manner for the former to exist. A good example of how data and information interact is through

Discuss the importance of data accuracy. Inaccurate data leads to inaccurate information. What can be some of the consequences of data inaccuracy? What can be done to ensure data accuracy? Data accuracy is important because inaccurate data leads may lead to such things as the closing down of business, it may also lead to the loosing of jobs, and it may also lead to the failure of a new product. To ensure that one’s data is accurate one may double check the data given to them, as well as has more

4. Data source analysis Data is one of the important factors in data forecasting studies because data represents the whole source of the business purpose of the study. There are several reasons that the difference of data source makes it hard to compare prediction accuracy from each other. First, the result of a prediction model may differ with different data sources. Theoretically, the more data we test, the more accurate result we can get, however, in real-world, it is often hard to collect as

Introduction The term big data came into the picture to refer the big volumes of information’s both the companies and governments are storing. The data may be where we live, where we go, what we buy and what we say etc. all will be recorded and stored forever. More than 90% of data is generated in the past 2 years only and this volume is increasing day by day and doubling for every two years. In this world, the organizations are using the data generated by us and no one knows what they are doing

1. INTRODUCTION Data in its original shape contains sensitive information about individuals, and publishing such data will breach individual privacy. The recent practice in data publishing relies mainly on rules and guidelines as to what kinds of data can be published and on agreements on the use of published data. This approach may lead to extreme data distortion or inadequate protection. Privacy-preserving data publishing provides methods and tools for publishing useful information

Data: Data is a set of values of measuring or quantitative variables. It contain raw facts, no context and just numbers and text. Data is also called as collection of small matters/ pieces information. Example: 12122014 in this number we don’t have any exact information, so it is a good example for data. Information: Information is data that has been developed and arranged in a regular way. Examples: 12/12/2014 – Final day of classes at Murray State University. $1,000 – My Father’s salary

Executive Summary Big Data is garnering great recognition for its data-driven decision making methodology. Right from data acquisition where there is a flood of data available, we need to make effective decisions about usage of data. Privacy, scalability, complexity and timeliness are the problems that hinder the progress of Big Data. Today, most of the data available is not obtained in a structured format; therefore data transformation for analysis is a major objection. Data integration is also

hospital data has been stored in hard copy format, however, with EHRs the availability data from various sources becomes widely available. And in this digital age, data is integral to our healthcare as it likely holds the promise of supporting a wide range of medical and healthcare functions.   This of course identifies the need to effectively understanding and build knowledge around data analytic techniques to transform healthcare data into meaningful outcomes. There is an abundance of data, yet,

amount of data being generated nowadays has increased tremendously. With these large volumes of data, there is a need for efficient storage and processing of data. The data generated from a variety of sources like Social networking sites, Emails, audio and video files, text files and other various files is unstructured. The traditional database cannot handle this complex and high volumes of data efficiently. The Hadoop framework provides an effective solution for storing large volumes of data and processing

ABSTRACT Data mining is a popular technology for extracting interesting information for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of several types of data set. Multimedia data are unstructured data or semi-structured data. These data are stored in multimedia database, multimedia mining which is used to find information from large multimedia database system, using multimedia techniques and powerful tools. This paper analyzes about the use of essential

type of data, and it has a massive amount of processing power, and can handle a boundless number of jobs or tasks. Data Management, Data ingestion, Warehouse, and ETL provides features for effective management and data warehousing for data managing as a valuable resource. The Stream computing features pulls streams of data and then streams it back out as a single flow and then processes that data. Analytics/ Machine Learning features advanced analytics and machine learning. Content Management which

Urban Data Collection and Data Privacy (Case Study on LinkNYC Kiosk) Part I As the data are getting more and more easily accessed and analyzed by software and algorithms, the development of Urban Science has entered a new era. The urban technologies have being created to tackle the universe of the big data, and the popularized use of Mobile devices has made data collection never so easy. Data is everywhere, anytime and in any format. The science of data can be highly applicable to almost any industries

1.1 Procedures To analysis the collected qualitative data, the five steps for qualitative data analysis was applied: data immersion, data coding, data reduction, data display, and interpretation (Lui 2014). In the data immersion step, besides reading and rereading the transcription of recording, the observation note and report also been reviewed to familiar with the research topic and context; meanwhile, other general information before the research also been reviewed, such as memos and relative

Liverpool defines data mining “as a set of mechanisms and techniques, realized in software, to extract hidden information from data" [1]. Data mining ventures to make it easier for humans to make decisions by making data easier to manage and patterns easier to find. With more information, humans can make better business and financial decisions. The term was first written down in the 1980’s and has sense then become one of the fastest growing components of the computer science world. Data Architects make

amounts of data being generated each year make getting useful information from that data more and more critical. The information frequently is stored in a data warehouse, a repository of data gathered from various sources, including corporate databases, summarized information from internal systems, and data from external sources. Analysis of the data includes simple query and reporting, statistical analysis, more complex multidimensional analysis, and data mining. Data analysis and data mining are

25) Ashok Yaganti (Class ID: 46) Article: Data Mining with Big Data (Paper-1) This paper addresses the complications being faced by Big Data because of increase in the volume, complexity of data and due to multiple sources, which produces large number of data sets. With the increase of big data in different fields like medicine, media, social networking etc., there is a need for better processing model which can access the data at the rate at which the data increases. This paper proposed a processing

section presents data process involved in data management and analysis, it discusses data entry, data cleaning and data analysis. The section starts with data entry where the data cleaning and analysis were presented thereafter. 3.14.1 Data entry Data entry refers to the process of recording data, regularly into a computer programmes (Rahm & Hai Do, 2010). During the evaluation, data were entered into computerised software packages to assist in analysis process. Quantitative data from questionnaires