billion bytes of data in digital form be it on social media‚ blogs‚ purchase transaction record‚ purchasing pattern of middle class families‚ amount of waste generated in a city‚ no. of road accidents on a particular highways‚ data generated by meteorological department etc. This huge size of data generated is known as big data. Generally managers use data to arrive at decision. Marketers use data analytics to determine customer preferences and their purchasing pattern. Big data has tremendous potential
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Data warehousing is the process of collecting data in raw form for analyzing trends. The benefits to data warehousing are improved end-user access‚ increased data consistency‚ various kinds of reports can be made from the data collected‚ gather the data in a common place from separate sources and additional documentation of data. Potential lower computing costs‚ increased productivity‚ end-users can query the database without using overhead of the operational systems and creates an infrastructure
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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Introduction to Data Mining Assignment 1 Ex1.1 what is data mining? (a) Is it another hype? Data mining is Knowledge extraction from data this need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. So‚ data mining definitely is not another hype it can be viewed as the result of the natural evolution of information technology. (b) Is it a simple transformation of technology developed
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Activity 1 Reasons why organisations need to collect HR Data. It is important for organisations to collect and retain HR data as this will be key for strategic and HR planning. It will also help to have all the information necessary to make informed decisions‚ for the formulation and implementation of employment policies and procedures‚ to monitor fair and consistent treatment of staff‚ to contribute to National Statistics and to comply with statutory requirements. The key organisational
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Secondary data refers to the data which an investigator does not collect himself for his purpose rather he obtains them from some other source‚ agency or office. In other words‚ this data has already been collected by some other source and an investigator makes use of it for his purpose. Secondary data is different from primary data on the basis of the sources of their collection. The difference between the two is relative - data which is primary at one place become secondary at another place.
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Q) What are Secondary Data? Secondary Data Secondary data is information gathered for purposes other than the completion of a research project. Data previously collected by someone else‚ possibly for some other purpose that can be used later for making decisions if found suitable for the purpose‚ other than the original one. Secondary data can be acquired from the internal records of the organization‚ their departments‚ subsidiaries or sister organizations and also from external sources‚ such
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Trang Vuong Big Data and Its Potentials Data exists everywhere nowadays. It flows to every area of the economy and plays an important role in the decision-making process. Indeed‚ “businesses‚ industries‚ governments‚ universities‚ scientists‚ consumers‚ and nonprofits are generating data at unprecedented levels and at an incredible pace” to ensure the accuracy and reliability of their data-driven decisions (Gordon-Murnane 30). Especially when technology and economy are growing at an unbelievable
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Turning data into information © Copyright IBM Corporation 2007 Course materials may not be reproduced in whole or in part without the prior written permission of IBM. 4.0.3 Unit objectives After completing this unit‚ you should be able to: Explain how Business and Data is correlated Discuss the concept of turning data into information Describe the relationships between DW‚ BI‚ and Data Insight Identify the components of a DW architecture Summarize the Insight requirements and goals of
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The Difference Between Data Centers and Computer Rooms By Peter Sacco Experts for Your Always Available Data Center White Paper #1 EXECUTIVE SUMMARY The differences between a data center and a computer room are often misunderstood. Furthermore‚ the terms used to describe the location where companies provide a secure‚ power protected‚ and environmentally controlled space are often used inappropriately. This paper provides a basis for understanding the differences between these locations
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