Dika model
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The document discusses the D-I-K-A model which represents the process of transforming data into action. It describes each stage of the model - data, information, knowledge, and action - and lists key questions to consider at each stage, such as whether the data is useful, who has the relevant knowledge, and whether plans can be effectively communicated and implemented. The overall purpose is to provide a framework for ensuring data is properly analyzed and leveraged to create meaningful actions within an organization.
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This eBook chapter focuses on the best practices of HR Marketing Communications and provides case studies and references to help Human Resource professionals understand the importance and differences with various HR marketing communication tactics.
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The Role of Community-Driven Data Curation for Enterprises
With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.
E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.
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Are You Using Data To Drive Your HR Strategy
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Student 11) Data is a set of undefined objects which are form.docx
Student 1:
1) Data is a set of undefined objects which are formed due to a course of action irrespective of the consequences and contain both useful and useless information which address aligned in an unorganized manner. Data does not comply with time or situation in which they do not even make sense of using it directly. Information is formed from data that is aligned in an organized manner or an organized set of data is known as information. Information is valuable data that provides meaningful data and can be used to accomplish various purposes. Information is critical to extract, use, and protect because information involves sensitive content, and hence, information is an adjunct to organizations to enhance performance. Knowledge is the ability of a separate or a group to make use of the acquired information in a sensible and elegant manner. The best use of information represents two knowledge of organizations (Araújo, Guimarães & Ferneda, 2016).
2) It becomes unpredictable for organizations to know what information should be acquired from what sources, but organizations have ideas about what information has to be acquired. The gap between these two aspects will make organizations to stifle between the need for information and the ability to acquire information. Even if they are able to acquire information, it is hard to manage the information because of a lack of technical sources and security aspects (Chen, You, & Ruan, 2020).
Hence, organizations are forced to leave or discard their information according to situations and their abilities. In order to acquire adequate information, organizations have to improve their security standards and incorporate effective technical resources. Moreover, firms should have to maintain a vision, and therefore, their goals and objectives lead organizations to gain the required information. This is the most important thing because inappropriate vision and clumsy ideologies of firms will not help companies to get adequate information as tend resources (Gillingham, 2014).
Student 2:
1. Information, Data, and Knowledge:
From the article by (Rowley, 2005), a great model to show the contrast between how data, information, and knowledge co-identify with one another. In a specialized assessment of the distinction between the data, information, and knowledge the worth chain begins from Data, Data is a crude source where the nature of data will be theoretical until the data is kneaded and handled. In a straightforward case situation data breaks in organizations will lead the programmer or penetrating gathering to a trove of data. be that as it may, this data will be in the crude organization which must be kneaded and handled to change over into information. The data doesn't manage quality or convenience. In short data all alone doesn't have significance yet the data fills in as the structure hinders in figuring the information.
As indicated by the article (Chisholm, and Warman, 2007), a form of Informatio.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Mule event processing models | MuleSoft Mysore Meetup #47
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Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
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For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
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His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Dika model
- 1. The D-I-K-A Model Data Information Knowledge Action
- 2. Data Raw Data can come from various places. Is the data flawed? Can it be useful? What areas of the business is the data relevant? Is the data time sensitive?
- 3. Information Is the data timely, accurate, and relevant enough to be useful to the business? Beware of information overload Provide the relevant information to the right groups of the organization and at the right time to use it
- 4. Knowledge Who has the knowledge to pull the data from the system or accurately collect it in the process and procedures? Who has the knowledge to know what data is credible and relevant? Who has the knowledge to know which groups the data is valuable to? Who has the knowledge to create actionable and valuable reports, processes and procedure?
- 5. Action Are the employee’s empowered to take action on the information that is provided from the analysis? Has the information been used to create and actionable plan? Are the plans and the actionable data likely to be resisted by employees or management? Can the plan be effectively communicated to make it more likely to be successful?
- 6. References Clampitt, P. (2017). Communication for managerial effectiveness: Challenges, strategies, solutions. Thousand Oaks, California: Sage Publications Inc. Khedhaouria, A., & Jamal, A. (2015). Sourcing knowledge for innovation: Knowledge reuse and creation in project teams. Journal of Knowledge Management, 19(5), 932-948. doi:10.1108/JKM-01-2015- 0039 Lavtar, R. (2013). Ways and side ways of using the information and communication technology (ICT) in knowledge sharing in organizations. Lex Localis, 11(4), 871.