Data Analysis The first question of the set of 15 questions was about the age limit of the respondents. We collected all data from the age group starting from 15years. Most of the respondents fall into the age limit of 16-25 years which is 54% of the total respondents. 18of the 50 respondents were 26-35 years of age which is 36%. [pic] [pic] Q1: your most preferable Schemes when you are Thinking about a savings account? This was the question that gives the critical information
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team‚ collect different types of data. One of them is meeting legal requirements. In order to satisfy legal obligations we collect such information as contractual arrangements‚ employees’ duties‚ privileges‚ salaries‚ working hours‚ vacation accruals‚ bonuses‚ as well as documents relating to health and safety. The Russian Labor Inspection can check any data regarding individual employees and it is important for the organization to timely provide accurate and valid data in order to avoid fees or other
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Enhancing Customer Data Enhanced Customer Data Repository is a secure and fully supported data repository with problem determination tools and functions. It updates problem management records (PMR) and maintains full data life cycle management. · combination of all the internal structured business data (CRM‚ ERP‚ POS and all the internal system data) and external unstructured data ( Social media data‚ feedback surveys‚ Audios‚ Videos‚ streaming data‚ Call center data‚ images) · unmanageable volumes
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|Case Study: Data for Sale | |Management Information System | | | |
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Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology‚ if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining
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Big Data Management: Possibilities and Challenges The term big data describes the volumes of data generated by an enterprise‚ including Web-browsing trails‚ point-of-sale data‚ ATM records‚ and other customer information generated within an organization (Levine‚ 2013). These data sets can be so large and complex that they become difficult to process using traditional database management tools and data processing applications. Big data creates numerous exciting possibilities for organizations‚
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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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UNCLASSIFIED UNCLASSIFIED 1 Open Data Strategy June 2012 UNCLASSIFIED UNCLASSIFIED 2 Contents Summary ................................................................................................... 3 Introduction ................................................................................................ 5 Information Principles for the UK Public Sector ......................................... 6 Big Data .......................................................................
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HARD NEWS 1.Mumbai: 5 dead in wall collapse after explosion in chemical factory Mumbai: Five people were killed and seven others injured after the wall of a house collapsed following an explosion in a makeshift aluminium mould producing unit at Sakinaka area in Andheri. The police said that the explosion was caused by a vacuum in the machines that were being used to produce aluminum moulds. What aggravated the situation was the nitrogen liquid that was being used at the factory‚ they said. "We
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An Oracle White Paper July 2010 Data Masking Best Practices Oracle White Paper—Data Masking Best Practices Executive Overview ........................................................................... 1 Introduction ....................................................................................... 1 The Challenges of Masking Data ....................................................... 2 Implementing Data Masking .............................................................. 2
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