Technology & Engineering
Data Management
Data management involves the process of collecting, storing, organizing, and maintaining data to ensure its accuracy, accessibility, and security. It encompasses various activities such as data integration, cleansing, and governance to support the efficient use of data for decision-making and business operations. Effective data management is crucial for leveraging technology and engineering solutions to derive insights and drive innovation.
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9 Key excerpts on "Data Management"
- Richa Tiwari(Author)
- 2023(Publication Date)
- Society Publishing(Publisher)
1.6.2. Types of Data Management The Data Management tends to play a number of different roles in the data environment of an organization. This in turn makes the important functions easier and less time-intensive. Such Data Management techniques include some pointers mentioned below: Introduction to Data Management 15 • Data preparation is used in order to clean as well as transform raw data into the right shape and format for the purpose of analysis and this includes making corrections and combining data sets. • Data pipelines tend to enable the automated transfer of data from one system to another. • Extract, Transform, Load that is ETLs are built so as to take the data from one system, transform it and also, load it into the data warehouse (DWH) of the organization. • Data catalogs further help in managing metadata for creating a complete picture of the data and this provides a summary of its changes, locations, and quality and also it makes the data easy to find. • Data warehouses refer to the places in order to consolidate different data sources, contend with a number of data types businesses store, and also, provide a clear route for the purpose of data analysis. • Data governance refers to the standards, processes, and policies in order to maintain data security as well as integrity. • Data architecture tends to provide a formal approach in order to create and manage the data flow. • Data security further protects data from unauthorized access as well as corruption. • Data modeling refers to the flow of data that is done through an application or organization. 1.7. WHY Data Management IS IMPORTANT? Data Management is considered to be an important first step in order to employ effective data analysis at scale. This in turn results in important insights which further add value to the customers and tend to improve the bottom line.
- Maggi Savin-Baden, Gemma Tombs(Authors)
- 2017(Publication Date)
- Bloomsbury Academic(Publisher)
CHAPTER EIGHT Data Management Introduction This chapter examines the ways in which digital data are managed, analysed and interpreted, addressing the advantages and disadvantages associated with the use of data software. We propose ways of integrating increasingly disparate forms of digital data, such as the integration of student YouTube videos with online forum analyses, with learning analytics. The chapter then offers suggestions for analytical approaches, concluding by identifying innovative and creative theories for interpreting data in the digital age. Whilst this chapter has been treated as distinct from Chapter 7 for ease of understanding and reference, it will also address the interrelated natures of data creation and collection, and data analysis and interpretation. Defining Data Management Data Management is generally taken to refer to the security, storage, access and archiving of data. Whilst this has been an issue of importance for as long as research has been undertaken, the rapid growth in digital data availability, and the rise of the open data movement, has pushed Data Management to the forefront of research scholarship. Organizations such as the international Data Documentation Initiative and the UK-based Digital RESEARCH METHODS FOR EDUCATION IN THE DIGITAL AGE 168 Curation Centre offer guidelines for Data Management, whilst librarians, archivists, managers, researchers and administrators are engaged in discussions about how to manage the challenges associated with digital data. These include (but are not restricted to) the following: ● Data access: Who has access to data, and how they should be accessed. This includes issues not only about open access, but also about technological challenges such as changing digital formats. For example, digital data saved in 2015 software may not be accessible in 2030 software, let alone 2065 software. Data access also includes issues relating to corruption of data files and technological failures.
eBook - ePubQualitative Secondary Research
A Step-By-Step Guide
- Claire Largan, Theresa Morris(Authors)
- 2019(Publication Date)
- SAGE Publications Ltd(Publisher)
10 Managing your DataThis chapter supports your ability to- understand the need for effective Data Management
- construct a Data Management plan
- identify and respond to the demands of data ownership
- store and dispose of your data securely
Chapter overview
An important undertaking in any research project is the organisation, storage and tracking of your collected data and this is called Data Management. This way of working with your data requires the adoption of a set of skills and behaviours that should enable you to work with your data in an appropriate, systematic and consistent manner. To aid you in the development of systematic processes, we provide an overview of the role and function of Data Management plans.This chapter therefore, examines a range of strategies for Data Management that should enable you to work proficiently with the type and quantity of data you may have amassed. These strategies are relevant whether you are working with hard (paper) copies or storing your work using various technological devices and formats. As part of our focus on digital forms of storage, we explore the fundamental importance of backing up all of your work.Underpinning this chapter is the role of Data Management in enhancing your researcher integrity because being able to show where your data has come from and recording it accurately enable your reader to trace the data you have utilised. This can be a way of enhancing research credibility and increasing confidence in your findings. By the end of this chapter, you should feel you can handle your data in an effective, systematic and secure way.What is Data Management?
Data Management is a general term which covers how researchers manage and organise the information used or generated during the research process (see Figure 10.1
eBook - PDFKnowledge Management, Business Intelligence, and Content Management
The IT Practitioner's Guide
- Jessica Keyes(Author)
- 2006(Publication Date)
- Auerbach Publications(Publisher)
93 5 Data Management The Data Management Association (www.dama.org) defines Data Management as the development and execution of archi-tectures, policies, practices, and procedures that properly man-age the full data life-cycle needs of an enterprise. Disciplines in Data Management include the following: 1. Data modeling 2. Database administration 3. Data warehousing 4. Data movement 5. Data mining 6. Data quality assurance 7. Data security 8. MetaData Management (data repositories and their management) 9. Strategic data architecture There is a difference between data and information. Data is stored in multiple applications systems on multiple platforms using multiple methods and is used to perform day-to-day operations. If this distributed data is grouped together in a meaningful format, it can provide valuable information to business organizations and their decision makers. Data is captured using online transaction processing (OLTP) systems to perform mission-critical daily operations. Typically, many users simultaneously add, modify, delete, and view data using OLTP applications. OLTP systems are characteristically designed to process one transaction record at a time. 94 Knowledge Management Information is derived from online analytical processing (OLAP) systems used for analysis, planning, and management reporting through access to a variety of sources. An OLAP system usually references information that is stored in a data warehouse. Use of this technology provides the facility to present a comprehensive view of the enterprise. Data and information are extremely valuable assets. Data architecture defines an infrastructure for providing high-qual-ity, consistent data to be used as the basis for decision support and executive information services as well as traditional trans-action applications statewide. In this chapter, we will focus on how data should best be organized so that it can provide the basis for knowledge man-agement.
- Mark L. Gillenson, Paulraj Ponniah, Alex Kriegel, Boris M. Trukhnov, Allen G. Taylor, Gavin Powell, Frank Miller(Authors)
- 2012(Publication Date)
- Wiley(Publisher)
1.1.2 Understanding Data Management Data is a difficult corporate resource to manage. In data, you have a resource of tremendous volume, with billions, trillions, and more individual pieces, each piece of which is different from the next. And much of it is in a state of change at any one time. As far back as the early to mid-1960s, companies began to realize that stor- ing each application’s data separately, in simple files, was problematic for the fol- lowing reasons: ▲ The increasing volume of data. ▲ The increasing demand for data access. ▲ The need for data security, privacy, backup, and recovery. ▲ The desire to share data and cut down on data redundancy (unwanted duplicate data in a database). It soon became clear that a new kind of software was needed to help man- age the data, as well as faster hardware to keep up with the increasing volume of data and data access demands. In terms of personnel, Data Management spe- cialists would have to be developed, educated, and given the responsibility for managing the data as a corporate resource. Out of this need was born a new kind of software, the database manage- ment system (DBMS), and a new category of personnel, with titles like database administrator and Data Management specialist. And, yes, hardware has progres- sively gotten faster and cheaper for the degree of performance that it provides. The integration of these advances adds up to much more than the simple sum of their parts; they add up to the database environment. 1.1.3 The Need for Data Management It is practically impossible to buy anything, sell anything, or travel anywhere by air, rail, or sea without the fact being recorded in a database somewhere. With the recent rash of mergers of all kinds of organizations into larger entities, this data is becom- ing centralized.
- Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang(Authors)
- 2009(Publication Date)
- Auerbach Publications(Publisher)
We believe that Data Management is essential to many information technolo-gies, including data mining, multimedia information processing, interoperability, and collaboration and knowledge management. This appendix stresses data man-agement. Security is critical for all Data Management technologies. References 1. Thuraisingham, B., Data Management Systems Evolution and Interoperation , CRC Press, Boca Raton, FL, 1997. 2. Codd, E.F., A relational model of data for large shared data banks, Communications of the ACM , Vol. 13, No. 6, June 1970. 3. Date, C.J., An Introduction to Database Management Systems , Addison-Wesley, Reading MA, 1990 (6th edition published in 1995 by Addison-Wesley). 4. Cattell, R., Object Data Management Systems , Addison-Wesley, Reading MA, 1991. Web Data Mining Technologies and Their Applications in Counter-Terrorism Data Mining Tools Database and Applications Security: Integrating Database Management and Information Security Managing and Mining Multimedia Databases Figure A.12 Relationships between texts—Series II. 242 ◾ Data Management Systems: Developments and Trends 5. Proceedings of the Database Systems Workshop, Report published by the National Science Foundation, 1990 (also in ACM SIGMOD Record, December 1990). 6. Next Generation Database Systems, ACM SIGMOD Record, December 1990. 7. Special Issue on Heterogeneous Database Systems, ACM Computing Surveys , September 1990. 8. Proceedings of the Database Systems Workshop, Report published by the National Science Foundation, 1995 (also in ACM SIGMOD Record, March 1996). 9. Thuraisingham, B., Data Mining: Technologies, Techniques, Tools and Trends , CRC Press, Boca Raton, FL, 1998. 10. Thuraisingham, B., Web Data Management and Electronic Commerce , CRC Press, Boca Raton, FL, 2000. 11. Thuraisingham, B., Managing and Mining Multimedia Databases for the Electronic Enterprise , CRC Press, Boca Raton, FL, 2001.
No longer available |Learn more- (Author)
- 2014(Publication Date)
- University Publications(Publisher)
____________________ WORLD TECHNOLOGIES ____________________ Chapter- 6 Product Data Management Product Data Management ( PDM ) is the business function often within product lifecycle management that is responsible for the creation, management and publication of product data. Introduction Product Data Management (PDM) is the use of software or other tools to track and control data related to a particular product. The data tracked usually involves the technical specifications of the product, specifications for manufacture and development, and the types of materials that will be required to produce goods. The use of product Data Management allows a company to track the various costs associated with the creation and launch of a product. Product Data Management is part of product life cycle management, and is primarily used by engineers. Within PDM the focus is on managing and tracking the creation, change and archive of all information related to a product. The information being stored and managed (on one or more file servers) will include engineering data such as Computer-aided design (CAD) models, drawings and their associated documents. Product Data Management (PDM) serves as a central knowledge repository for process and product history, and promotes integration and data exchange among all business users who interact with products — including project managers, engineers, sales people, buyers, and quality assurance teams. The central database will also manage metadata such as owner of a file and release status of the components. The package will: control check-in and check-out of the product data to multi-user; carry out engineering change management and release control on all versions/issues of components in a product; build and manipulate the product structure ____________________ WORLD TECHNOLOGIES ____________________ bill of materials (BOM) for assemblies; and assist in configurations management of product variants.
- David Rich(Author)
- 2002(Publication Date)
- CRC Press(Publisher)
This means that unnecessary costs, whether in time or money, must be minimized. Electronic Data Management can help contain costs by saving time and minimizing lost data. People tend to start working on a database without giving a lot of thought to what a database really is. It is more than an accumulation of numbers and letters. It is a special way to help us understand information. Here are some general thoughts about databases: A database is a model of reality – In many cases, the data that we have for a facility is the only representation that we have for conditions at that facility. This is especially true in the subsurface, and for chemical constituents that are not visible, either because of their physical condition or their location. The model helps us understand the reality – In general, conditions at sites are nearly infinitely complex. The total combination of geological, hydrological and engineering factors usually exceeds our ability to understand it without some simplification. Our model of the site, based on the data that we have, helps us to perform this simplification in a meaningful way. This understanding helps us make decisions – Our simplified understanding of the site allows us to make decisions about actions to be taken to improve the situation at the site. Our model lets us propose and test solutions based on the data that we have, identify additional data that we need, and then choose from the alternative solutions. The clearer the model, the better the decisions – Since our decisions are based on our data-based model, it follows that we will make better decisions if we have a clear, accurate, up-to-date model. The purpose of a database management system for environmental data is to provide us the information to build accurate models and keep them current. Clearly information technology, including Data Management, is important to organizations.
eBook - PDFStraight Through Processing for Financial Services
The Complete Guide
- Ayesha Khanna(Author)
- 2010(Publication Date)
- Academic Press(Publisher)
The rest of this chapter examines strate-gies that both address the old reference data problems faced by firms and create a Data Management system powerful enough to meet the new demands of the market. 6.3 Data Management Basics Data Management is a set of procedures and infrastructure that allow a firm to store, transport, and manipulate data. Databases form the core of any Data Management Fixed income trading application Fixed income Equity Risk management Credit derivatives Equity trading application Risk management value-at-risk system Data vendor Exchange Data aggregator Credit derivatives trading and analytics system Data vendors often feed different databases in the same firm at various times. These databases exist in silos , and thus carry redundant data in different formats. Figure 6-1 Financial firms find that their data is stuck in silos. Data Management 117 system. They are software programs that store information in an organized and structured manner. Lightweight databases, such as Microsoft Access, can be installed locally on a PC desktop, but firms usually deploy databases, such as Oracle or Sybase that can handle large volumes of data and queries, and are installed on servers. The most common type of a database is a relational database, in which data is organized in tables and relationships exist between data in different tables. 6.3.1 RELATIONAL DATABASES Relational databases organize information by categorizing it into entities that have attributes, and then linking these entities through relationships. An entity is a real-world item or concept that exists on its own. For example, BOND, EQUITY, and PARTY are all entities. Each entity has attributes , or particular properties that describe the entity. For example, a BOND has such attributes as Issuer, Principal, Maturity, and Coupon Rate. The value of the attribute Principal may be $100. See Table 6-1. A relationship type is a set of associations among entity types.
Index pages curate the most relevant extracts from our library of academic textbooks. They’ve been created using an in-house natural language model (NLM), each adding context and meaning to key research topics.
