Academic journal publishing reform

From Wikipedia, the free encyclopedia

 

Academic journal publishing reform is the advocacy for changes in the way academic journals are created and distributed in the age of the Internet and the advent of electronic publishing. Since the rise of the Internet, people have organized campaigns to change the relationships among and between academic authors, their traditional distributors and their readership. Most of the discussion has centered on taking advantage of benefits offered by the Internet's capacity for widespread distribution of reading material.

History

Before the advent of the Internet it was difficult for scholars to distribute articles giving their research results. Historically publishers performed services including proofreading, typesetting, copy editing, printing, and worldwide distribution. In modern times all researchers became expected to give the publishers digital copies of their work which needed no further processing. For digital distribution printing was unnecessary, copying was free, and worldwide distribution happens online instantly. In science journal publishing, Internet technology enabled the four major scientific publishers—Elsevier, Springer, Wiley, and Informa—to cut their expenditures such that they could consistently generate profits which exceed a third of their revenue.

The Internet made it easier for researchers to do work which had previously been done by publishers, and some people began to feel that they did not need to pay for the services of publishers. This perception was a problem for publishers, who stated that their services were still necessary at the rates they asked. Critics began to describe publishers' practices with terms such as "corporate scam" and "racket". Scholars sometimes obtain articles from fellow scholars through unofficial channels, such as posting requests on Twitter using the hashtag "#icanhazpdf" (a play on the I Can Has Cheezburger? meme), to avoid paying publishers' access charges.

Motivations for reform

Although it has some historical precedent, open access became desired in response to the advent of electronic publishing as part of a broader desire for academic journal publishing reform. Electronic publishing created new benefits as compared to paper publishing but beyond that, it contributed to causing problems in traditional publishing models. 

The premises behind open access are that there are viable funding models to maintain traditional academic publishing standards of quality while also making the following changes to the field:

1. Rather than making journals be available through a subscription business model, all academic publications should be free to read and published with some other funding model. Publications should be gratis or "free to read".
2. Rather than applying traditional notions of copyright to academic publications, readers should be free to build upon the research of others. Publications should be libre or "free to build upon".
3. Everyone should have greater awareness of the serious social problems caused by restricting access to academic research.
4. Everyone should recognize that there are serious economic challenges for the future of academic publishing. Even though open access models are problematic, traditional publishing models definitely are not sustainable and something radical needs to change immediately.

Open access also has ambitions beyond merely granting access to academic publications, as access to research is only a tool for helping people achieve other goals. Open access advances scholarly pursuits in the fields of open data, open government, open educational resources, free and open-source software, and open science, among others.

Problems addressed by academic publishing reform

The motivations for academic journal publishing reform include the ability of computers to store large amounts of information, the advantages of giving more researchers access to preprints, and the potential for interactivity between researchers.

Various studies showed that the demand for open access research was such that freely available articles consistently had impact factors which were higher than articles published under restricted access.

Some universities reported that modern "package deal" subscriptions were too costly for them to maintain, and that they would prefer to subscribe to journals individually to save money.

The problems which led to discussion about academic publishing reform have been considered in the context of what provision of open access might provide. Here are some of the problems in academic publishing which open access advocates purport that open access would address:

1. A pricing crisis called the serials crisis has been growing in the decades before open access and remains today. The academic publishing industry has increased prices of academic journals faster than inflation and beyond the library budgets.
2. The pricing crisis does not only mean strain to budgets, but also that people actually are losing access to journals.
3. Not even the wealthiest libraries in the world are able to afford all the journals that their users are demanding, and less rich libraries are severely harmed by lack of access to journals.
4. Publishers are using "bundling" strategies to sell journals, and this marketing strategy is criticized by many libraries as forcing them to pay for unpopular journals which their users are not demanding.
5. Libraries are cutting their book budgets to pay for academic journals.
6. Libraries do not own electronic journals in permanent archival form as they do paper copies, so if they have to cancel a subscription then they lose all subscribed journals. This did not happen with paper journals, and yet costs historically have been higher for electronic versions.
7. Academic publishers get essential assets from their subscribers in a way that other publishers do not. Authors donate the texts of academic journals to the publishers and grant rights to publish them, and editors and referees donate peer-review to validate the articles. The people writing the journals are questioning the increased pressure put upon them to pay higher prices for the journal produced by their community.
8. Conventional publishers are using a business model which requires access barriers and creates artificial scarcity. All publishers need revenue, but open access promises models in which scarcity is fundamental to raising revenue.
9. Scholarly publishing depends heavily on government policy, public subsidies, gift economy, and anti-competitive practices, yet all of these things are in conflict with the conventional academic publishing model of restricting access to works.
10. Toll access journals compete more for authors to donate content to them than they compete for subscribers to pay for the work. This is because every scholarly journal has a natural monopoly over the information of its field. Because of this, the market for pricing journals does not have feedback because it is outside of traditional market forces, and the prices have no control to drive it to serve the needs of the market.
11. Besides the natural monopoly, there is supporting evidence that prices are artificially inflated to benefit publishers while harming the market. Evidence includes the trend of large publishers to have accelerating prices increases greater than small publishers, when in traditional markets high volume and high sales enables cost savings and lower prices.
12. Conventional publishers fund "content protection" actions which restrict and police content sharing.
13. For-profit publishers have economic incentives to decrease rates of rejected articles so that they publish more content to sell. No such market force exists if selling content for money is not a motivating factor.
14. Many researchers are unaware that it might be possible for them to have all the research articles they need, and just accept it as fate that they will always be without some of the articles they would like to read.
15. Access to toll-access journals is not scaling with increases in research and publishing, and the academic publishers are under market forces to restrict increases in publishing and indirectly because of that they are restricting the growth of research.

Motivations against reform

Publishers state that if profit was not a consideration in the pricing of journals then the cost of accessing those journals would not substantially change. Publishers also state that they add value to publications in many ways, and without academic publishing as an institution these services the readership would miss these services and fewer people would have access to articles.

Critics of open access have suggested that by itself, this is not a solution to scientific publishing’s most serious problem – it simply changes the paths through which ever-increasing sums of money flow. Evidence for this does exist and for example, Yale University ended its financial support of BioMed Central’s Open Access Membership program effective July 27, 2007. In their announcement, they stated,

The libraries’ BioMedCentral membership represented an opportunity to test the technical feasibility and the business model of this open access publisher. While the technology proved acceptable, the business model failed to provide a viable long-term revenue base built upon logical and scalable options. Instead, BioMedCentral has asked libraries for larger and larger contributions to subsidize their activities. Starting with 2005, BioMed Central article charges cost the libraries $4,658, comparable to single biomedicine journal subscription. The cost of article charges for 2006 then jumped to $31,625. The article charges have continued to soar in 2007 with the libraries charged $29,635 through June 2007, with $34,965 in potential additional article charges in submission.

A similar situation is reported from the University of Maryland, and Phil Davis commented that,

The assumptions that open access publishing is both cheaper and more sustainable than the traditional subscription model are featured in many of these mandates. But they remain just that — assumptions. In reality, the data from Cornell show just the opposite. Institutions like the University of Maryland would pay much more under an author-pays model, as would most research-intensive universities, and the rise in author processing charges (APCs) rivals the inflation felt at any time under the subscription model.

Opponents of the open access model see publishers as a part of the scholarly information chain and view a pay-for-access model as being necessary in ensuring that publishers are adequately compensated for their work. "In fact, most STM [Scientific, Technical and Medical] publishers are not profit-seeking corporations from outside the scholarly community, but rather learned societies and other non-profit entities, many of which rely on income from journal subscriptions to support their conferences, member services, and scholarly endeavors". Scholarly journal publishers that support pay-for-access claim that the "gatekeeper" role they play, maintaining a scholarly reputation, arranging for peer review, and editing and indexing articles, require economic resources that are not supplied under an open access model. Conventional journal publishers may also lose customers to open access publishers who compete with them. The Partnership for Research Integrity in Science and Medicine (PRISM), a lobbying organization formed by the Association of American Publishers (AAP), is opposed to the open access movement. PRISM and AAP have lobbied against the increasing trend amongst funding organizations to require open publication, describing it as "government interference" and a threat to peer review.

For researchers, publishing an article in a reputable scientific journal is perceived as being beneficial to one's reputation among scientific peers and in advancing one's academic career. There is a concern that the perception of open access journals do not have the same reputation, which will lead to less publishing. Park and Qin discuss the perceptions that academics have with regard to open access journals. One concern that academics have "are growing concerns about how to promote [Open Access] publishing." Park and Qin also state, "The general perception is that [Open Access] journals are new, and therefore many uncertainties, such as quality and sustainability, exist."

Journal article authors are generally not directly financially compensated for their work beyond their institutional salaries and the indirect benefits that an enhanced reputation provides in terms of institutional funding, job offers, and peer collaboration.

There are those, for example PRISM, who think that open access is unnecessary or even harmful. David Goodman argued that there is no need for those outside major academic institutions to have access to primary publications, at least in some fields.

The argument that publicly funded research should be made openly available has been countered with the assertion that "taxes are generally not paid so that taxpayers can access research results, but rather so that society can benefit from the results of that research; in the form of new medical treatments, for example. Publishers claim that 90% of potential readers can access 90% of all available content through national or research libraries, and while this may not be as easy as accessing an article online directly it is certainly possible." The argument for tax-payer funded research is only applicable in certain countries as well. For instance in Australia, 80% of research funding comes through taxes, whereas in Japan and Switzerland, only approximately 10% is from the public coffers.

For various reasons open access journals have been established by predatory publishers who seek to use the model to make money without regard to producing a quality journal. The causes of predatory open access publishing include the low barrier to creating the appearance of a legitimate digital journal and funding models which may include author publishing costs rather than subscription sales. Research reviewer Jeffrey Beall publishes a "List of Predatory Publishers" and an accompanying methodology for identifying publishers who have editorial and financial practices which are contrary to the ideal of good research publishing practices.

Reform initiatives

Public Library of Science

The Public Library of Science is a nonprofit open-access scientific publishing project aimed at creating a library of open access journals and other scientific literature under an open content license. The founding of the organization had its origins in a 2001 online petition calling for all scientists to pledge that from September 2001 they would discontinue submission of papers to journals which did not make the full-text of their papers available to all, free and unfettered, either immediately or after a delay of several months. The petition collected 34,000 signatures but the publishers took no strong response to the demands. Shortly thereafter, the Public Library of Science was founded as an alternative to traditional publishing.

HINARI

HINARI is a 2002 project of the World Health Organization and major publishers to enable developing countries to access collections of biomedical and health literature online at reduced subscription costs.

Research Works Act

The Research Works Act was a bill of the United States Congress which would have prohibited all laws which would require an open access mandate when US-government-funded researchers published their work. The proposers of the law stated that it would "ensure the continued publication and integrity of peer-reviewed research works by the private sector". Critics of the law stated that it was the moment that "academic publishers gave up all pretence of being on the side of scientists." In February 2012, Elsevier withdrew its support for the bill. Following this statement, the sponsors of the bill announced they will also withdraw their support.

The Cost of Knowledge

The Cost of Knowledge is a campaign begun in 2012 specifically targeting the scientific publishing company Elsevier. It was begun by a group of prominent mathematicians who each made a commitment to not participate in publishing in Elsevier's journals.

Access2Research

Access2Research is a 2012 United States-based campaign in which open access advocates appealed to the United States government to require that taxpayer-funded research be made available to the public under open licensing.

PeerJ

PeerJ is an open-access journal launched in 2012 that charges publication fees per researcher, not per article, resulting in what has been called "a flat fee for 'all you can publish'".

Public Knowledge Project

Since 1998, PKP has been developing free open source software platforms for managing and publishing peer-reviewed open access journals and monographs, with Open Journal Systems used by more than 7,000 active journals in 2013.

Schekman boycott

2013 Nobel Prize winner Randy Schekman called for a boycott of traditional academic journals including Nature, Cell, and Science. Instead he promoted the open access journal eLife.

Initiative for Open Citations

Initiative for Open Citations is a CrossRef initiative for improved citation analysis. It was supported by majority of the publishers effective from April 2017.

Information management

From Wikipedia, the free encyclopedia

 

Information management (IM) concerns a cycle of organizational activity: the acquisition of information from one or more sources, the custodianship and the distribution of that information to those who need it, and its ultimate disposition through archiving or deletion.

This cycle of organisational involvement with information involves a variety of stakeholders, including those who are responsible for assuring the quality, accessibility and utility of acquired information; those who are responsible for its safe storage and disposal; and those who need it for decision making. Stakeholders might have rights to originate, change, distribute or delete information according to organisational information management policies.

Information management embraces all the generic concepts of management, including the planning, organizing, structuring, processing, controlling, evaluation and reporting of information activities, all of which is needed in order to meet the needs of those with organisational roles or functions that depend on information. These generic concepts allow the information to be presented to the audience or the correct group of people. After individuals are able to put that information to use, it then gains more value.
Information management is closely related to, and overlaps with, the management of data, systems, technology, processes and – where the availability of information is critical to organisational success – strategy. This broad view of the realm of information management contrasts with the earlier, more traditional view, that the life cycle of managing information is an operational matter that requires specific procedures, organisational capabilities and standards that deal with information as a product or a service.

History

Emergent ideas out of data management

In the 1970s, the management of information largely concerned matters closer to what would now be called data management: punched cards, magnetic tapes and other record-keeping media, involving a life cycle of such formats requiring origination, distribution, backup, maintenance and disposal. At this time the huge potential of information technology began to be recognised: for example a single chip storing a whole book, or electronic mail moving messages instantly around the world, remarkable ideas at the time. With the proliferation of information technology and the extending reach of information systems in the 1980s and 1990s, information management took on a new form. Progressive businesses such as British Petroleum transformed the vocabulary of what was then "IT management", so that “systems analysts” became “business analysts”, “monopoly supply” became a mixture of “insourcing” and “outsourcing”, and the large IT function was transformed into “lean teams” that began to allow some agility in the processes that harness information for business benefit. The scope of senior management interest in information at British Petroleum extended from the creation of value through improved business processes, based upon the effective management of information, permitting the implementation of appropriate information systems (or “applications”) that were operated on IT infrastructure that was outsourced. In this way, information management was no longer a simple job that could be performed by anyone who had nothing else to do, it became highly strategic and a matter for senior management attention. An understanding of the technologies involved, an ability to manage information systems projects and business change well, and a willingness to align technology and business strategies all became necessary.

Positioning information management in the bigger picture

In the transitional period leading up to the strategic view of information management, Venkatraman (a strong advocate of this transition and transformation, proffered a simple arrangement of ideas that succinctly brought together the managements of data, information, and knowledge (see the figure)) argued that:

• Data that is maintained in IT infrastructure has to be interpreted in order to render information.
• The information in our information systems has to be understood in order to emerge as knowledge.
• Knowledge allows managers to take effective decisions.
• Effective decisions have to lead to appropriate actions.
• Appropriate actions are expected to deliver meaningful results.

This simple model summarises a presentation by Venkatraman in 1996, as reported by Ward and Peppard (2002, page 207).

 

This is often referred to as the DIKAR model: Data, Information, Knowledge, Action and Result, it gives a strong clue as to the layers involved in aligning technology and organisational strategies, and it can be seen as a pivotal moment in changing attitudes to information management. The recognition that information management is an investment that must deliver meaningful results is important to all modern organisations that depend on information and good decision-making for their success.

Theoretical background

Behavioural and organisational theories

It is commonly believed that good information management is crucial to the smooth working of organisations, and although there is no commonly accepted theory of information management per se, behavioural and organisational theories help. Following the behavioural science theory of management, mainly developed at Carnegie Mellon University and prominently supported by March and Simon, most of what goes on in modern organizations is actually information handling and decision making. One crucial factor in information handling and decision making is an individual's ability to process information and to make decisions under limitations that might derive from the context: a person's age, the situational complexity, or a lack of requisite quality in the information that is at hand – all of which is exacerbated by the rapid advance of technology and the new kinds of system that it enables, especially as the social web emerges as a phenomenon that business cannot ignore. And yet, well before there was any general recognition of the importance of information management in organisations, March and Simon argued that organizations have to be considered as cooperative systems, with a high level of information processing and a vast need for decision making at various levels. Instead of using the model of the "economic man", as advocated in classical theory  they proposed "administrative man" as an alternative, based on their argumentation about the cognitive limits of rationality. Additionally they proposed the notion of satisficing, which entails searching through the available alternatives until an acceptability threshold is met - another idea that still has currency.

Economic theory

In addition to the organisational factors mentioned by March and Simon, there are other issues that stem from economic and environmental dynamics. There is the cost of collecting and evaluating the information needed to take a decision, including the time and effort required. The transaction cost associated with information processes can be high. In particular, established organizational rules and procedures can prevent the taking of the most appropriate decision, leading to sub-optimum outcomes. This is an issue that has been presented as a major problem with bureaucratic organizations that lose the economies of strategic change because of entrenched attitudes.

Strategic information management

Background

According to the Carnegie Mellon School an organization's ability to process information is at the core of organizational and managerial competency, and an organization's strategies must be designed to improve information processing capability and as information systems that provide that capability became formalised and automated, competencies were severely tested at many levels. It was recognised that organisations needed to be able to learn and adapt in ways that were never so evident before  and academics began to organise and publish definitive works concerning the strategic management of information, and information systems. Concurrently, the ideas of business process management and knowledge management although much of the optimistic early thinking about business process redesign has since been discredited in the information management literature. In the strategic studies field, it is considered of the highest priority the understanding of the information environment, conceived as the aggregate of individuals, organizations, and systems that collect, process, disseminate, or act on information. This environment consists of three interrelated dimensions which continuously interact with individuals, organizations, and systems. These dimensions are the physical, informational, and cognitive.

Aligning technology and business strategy with information management

Venkatraman has provided a simple view of the requisite capabilities of an organisation that wants to manage information well – the DIKAR model (see above). He also worked with others to understand how technology and business strategies could be appropriately aligned in order to identify specific capabilities that are needed. This work was paralleled by other writers in the world of consulting, practice and academia.

A contemporary portfolio model for information

Bytheway has collected and organised basic tools and techniques for information management in a single volume. At the heart of his view of information management is a portfolio model that takes account of the surging interest in external sources of information and the need to organise un-structured information external so as to make it useful (see the figure). 

This portfolio model organizes issues of internal and external sourcing and management of information, that may be either structured or unstructured.

Such an information portfolio as this shows how information can be gathered and usefully organised, in four stages:

Stage 1: Taking advantage of public information: recognise and adopt well-structured external schemes of reference data, such as post codes, weather data, GPS positioning data and travel timetables, exemplified in the personal computing press.

Stage 2: Tagging the noise on the world wide web: use existing schemes such as post codes and GPS data or more typically by adding “tags”, or construct a formal ontology that provides structure. Shirky provides an overview of these two approaches.

Stage 3: Sifting and analysing: in the wider world the generalised ontologies that are under development extend to hundreds of entities and hundreds of relations between them and provide the means to elicit meaning from large volumes of data. Structured data in databases works best when that structure reflects a higher-level information model – an ontology, or an entity-relationship model.

Stage 4: Structuring and archiving: with the large volume of data available from sources such as the social web and from the miniature telemetry systems used in personal health management, new ways to archive and then trawl data for meaningful information. Map-reduce methods, originating from functional programming, are a more recent way of eliciting information from large archival datasets that is becoming interesting to regular businesses that have very large data resources to work with, but it requires advanced multi-processor resources.

Competencies to manage information well

The Information Management Body of Knowledge was made available on the world wide web in 2004  and sets out to show that the required management competencies to derive real benefits from an investment in information are complex and multi-layered. The framework model that is the basis for understanding competencies comprises six “knowledge” areas and four “process” areas:

This framework is the basis of organising the "Information Management Body of Knowledge" first made available in 2004. This version is adapted by the addition of "Business information" in 2014.

The information management knowledge areas

The IMBOK is based on the argument that there are six areas of required management competency, two of which (“business process management” and “business information management”) are very closely related.

• Information technology: The pace of change of technology and the pressure to constantly acquire the newest technological products can undermine the stability of the infrastructure that supports systems, and thereby optimises business processes and delivers benefits. It is necessary to manage the “supply side” and recognise that technology is, increasingly, becoming a commodity.
• Information system: While historically information systems were developed in-house, over the years it has become possible to acquire most of the software systems that an organisation needs from the software package industry. However, there is still the potential for competitive advantage from the implementation of new systems ideas that deliver to the strategic intentions of organisations.
• Business processes and Business information: Information systems are applied to business processes in order to improve them, and they bring data to the business that becomes useful as business information. Business process management is still seen as a relatively new idea because it is not universally adopted, and it has been difficult in many cases; business information management is even more of a challenge.
• Business benefit: What are the benefits that we are seeking? It is necessary not only to be brutally honest about what can be achieved, but also to ensure the active management and assessment of benefit delivery. Since the emergence and popularisation of the Balanced scorecard  there has been huge interest in business performance management but not much serious effort has been made to relate business performance management to the benefits of information technology investments and the introduction of new information systems until the turn of the millennium.
• Business strategy: Although a long way from the workaday issues of managing information in organisations, strategy in most organisations simply has to be informed by information technology and information systems opportunities, whether to address poor performance or to improve differentiation and competitiveness. Strategic analysis tools such as the value chain and critical success factor analysis are directly dependent on proper attention to the information that is (or could be) managed 

The information management processes

Even with full capability and competency within the six knowledge areas, it is argued that things can still go wrong. The problem lies in the migration of ideas and information management value from one area of competency to another. Summarising what Bytheway explains in some detail (and supported by selected secondary references):

• Projects: Information technology is without value until it is engineered into information systems that meet the needs of the business by means of good project management.
• Business change: The best information systems succeed in delivering benefits through the achievement of change within the business systems, but people do not appreciate change that makes new demands upon their skills in the ways that new information systems often do. Contrary to common expectations, there is some evidence that the public sector has succeeded with information technology induced business change.
• Business operations: With new systems in place, with business processes and business information improved, and with staff finally ready and able to work with new processes, then the business can get to work, even when new systems extend far beyond the boundaries of a single business.
• Performance management: Investments are no longer solely about financial results, financial success must be balanced with internal efficiency, customer satisfaction, and with organisational learning and development.

Summary

There are always many ways to see a business, and the information management viewpoint is only one way. It is important to remember that other areas of business activity will also contribute to strategy – it is not only good information management that moves a business forwards. Corporate governance, human resource management, product development and marketing will all have an important role to play in strategic ways, and we must not see one domain of activity alone as the sole source of strategic success. On the other hand, corporate governance, human resource management, product development and marketing are all dependent on effective information management, and so in the final analysis our competency to manage information well, on the broad basis that is offered here, can be said to be predominant.

Operationalising information management

Managing requisite change

Organizations are often confronted with many information management challenges and issues at the operational level, especially when organisational change is engendered. The novelty of new systems architectures and a lack of experience with new styles of information management requires a level of organisational change management that is notoriously difficult to deliver. As a result of a general organisational reluctance to change, to enable new forms of information management, there might be (for example): a shortfall in the requisite resources, a failure to acknowledge new classes of information and the new procedures that use them, a lack of support from senior management leading to a loss of strategic vision, and even political manoeuvring that undermines the operation of the whole organisation. However, the implementation of new forms of information management should normally lead to operational benefits.

The early work of Galbraith

In early work, taking an information processing view of organisation design, Jay Galbraith has identified five tactical areas to increase information processing capacity and reduce the need for information processing.

• Developing, implementing, and monitoring all aspects of the “environment” of an organization.
• Creation of slack resources so as to decrease the load on the overall hierarchy of resources and to reduce information processing relating to overload.
• Creation of self-contained tasks with defined boundaries and that can achieve proper closure, and with all the resources at hand required to perform the task.
• Recognition of lateral relations that cut across functional units, so as to move decision power to the process instead of fragmenting it within the hierarchy.
• Investment in vertical information systems that route information flows for a specific task (or set of tasks) in accordance to the applied business logic.

The matrix organisation

The lateral relations concept leads to an organizational form that is different from the simple hierarchy, the “matrix organization”. This brings together the vertical (hierarchical) view of an organisation and the horizontal (product or project) view of the work that it does visible to the outside world. The creation of a matrix organization is one management response to a persistent fluidity of external demand, avoiding multifarious and spurious responses to episodic demands that tend to be dealt with individually.

Data modeling

From Wikipedia, the free encyclopedia

 

The data modeling process. The figure illustrates the way data models are developed and used today . A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model. The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used as the start point for interface or database design.

 

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques.

Overview

Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system. 

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. The data requirements are initially recorded as a conceptual data model which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The conceptual model is then translated into a logical data model, which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:

• to assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand and use an agreed semi-formal model the concepts of the organization and how they relate to one another
• to manage data as a resource
• for the integration of information systems
• for designing databases/data warehouses (aka data repositories)

Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten et al. (2004) determined two types of data modeling:

• Strategic data modeling: This is part of the creation of an information systems strategy, which defines an overall vision and architecture for information systems. Information technology engineering is a methodology that embraces this approach.
• Data modeling during systems analysis: In systems analysis logical data models are created as part of the development of new databases.

Data modeling is also used as a technique for detailing business requirements for specific databases. It is sometimes called database modeling because a data model is eventually implemented in a database.

Data modeling topics

Data models

How data models deliver benefit.

 

Data models provide a framework for data to be used within information systems by providing specific definition and format. If a data model is used consistently across systems then compatibility of data can be achieved. If the same data structures are used to store and access data then different applications can share data seamlessly. The results of this are indicated in the diagram. However, systems and interfaces are often expensive to build, operate, and maintain. They may also constrain the business rather than support it. This may occur when the quality of the data models implemented in systems and interfaces is poor.

Some common problems found in data models are:

• Business rules, specific to how things are done in a particular place, are often fixed in the structure of a data model. This means that small changes in the way business is conducted lead to large changes in computer systems and interfaces. So, business rules need to be implemented in a flexible way that does not result in complicated dependencies, rather the data model should be flexible enough so that changes in the business can be implemented within the data model in a relatively quick and efficient way.
• Entity types are often not identified, or are identified incorrectly. This can lead to replication of data, data structure and functionality, together with the attendant costs of that duplication in development and maintenance.Therefore, data definitions should be made as explicit and easy to understand as possible to minimize misinterpretation and duplication.
• Data models for different systems are arbitrarily different. The result of this is that complex interfaces are required between systems that share data. These interfaces can account for between 25-70% of the cost of current systems. Required interfaces should be considered inherently while designing a data model, as a data model on its own would not be usable without interfaces within different systems.
• Data cannot be shared electronically with customers and suppliers, because the structure and meaning of data has not been standardised. To obtain optimal value from an implemented data model, it is very important to define standards that will ensure that data models will both meet business needs and be consistent.

Conceptual, logical and physical schemas

The ANSI/SPARC three level architecture. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models.

 

In 1975 ANSI described three kinds of data-model instance:

• Conceptual schema: describes the semantics of a domain (the scope of the model). For example, it may be a model of the interest area of an organization or of an industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationships assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model. In that sense, it defines the allowed expressions in an artificial "language" with a scope that is limited by the scope of the model. Simply described, a conceptual schema is the first step in organizing the data requirements.
• Logical schema: describes the structure of some domain of information. This consists of descriptions of (for example) tables, columns, object-oriented classes, and XML tags. The logical schema and conceptual schema are sometimes implemented as one and the same.
• Physical schema: describes the physical means used to store data. This is concerned with partitions, CPUs, tablespaces, and the like.

According to ANSI, this approach allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual schema. The table/column structure can change without (necessarily) affecting the conceptual schema. In each case, of course, the structures must remain consistent across all schemas of the same data model.

Data modeling process

Data modeling in the context of Business Process Integration.

 

In the context of business process integration (see figure), data modeling complements business process modeling, and ultimately results in database generation.

The process of designing a database involves producing the previously described three types of schemas - conceptual, logical, and physical. The database design documented in these schemas are converted through a Data Definition Language, which can then be used to generate a database. A fully attributed data model contains detailed attributes (descriptions) for every entity within it. The term "database design" can describe many different parts of the design of an overall database system. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data. In the relational model these are the tables and views. In an object database the entities and relationships map directly to object classes and named relationships. However, the term "database design" could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the Database Management System or DBMS.

In the process, system interfaces account for 25% to 70% of the development and support costs of current systems. The primary reason for this cost is that these systems do not share a common data model. If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them. Most systems within an organization contain the same basic data, redeveloped for a specific purpose. Therefore, an efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization

Modeling methodologies

Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston (1997) only two modeling methodologies stand out, top-down and bottom-up:

• Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an enterprise perspective. They may not promote data sharing, especially if they are built without reference to other parts of the organization.
• Top-down logical data models, on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template.

Sometimes models are created in a mixture of the two methods: by considering the data needs and structure of an application and by consistently referencing a subject-area model. Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred. In addition, some CASE tools don't make a distinction between logical and physical data models.

Entity relationship diagrams

Example of an IDEF1X Entity relationship diagrams used to model IDEF1X itself. The name of the view is mm. The domain hierarchy and constraints are also given. The constraints are expressed as sentences in the formal theory of the meta model.

 

There are several notations for data modeling. The actual model is frequently called "Entity relationship model", because it depicts data in terms of the entities and relationships described in the data. An entity-relationship model (ERM) is an abstract conceptual representation of structured data. Entity-relationship modeling is a relational schema database modeling method, used in software engineering to produce a type of conceptual data model (or semantic data model) of a system, often a relational database, and its requirements in a top-down fashion.

These models are being used in the first stage of information system design during the requirements analysis to describe information needs or the type of information that is to be stored in a database. The data modeling technique can be used to describe any ontology (i.e. an overview and classifications of used terms and their relationships) for a certain universe of discourse i.e. area of interest.

Several techniques have been developed for the design of data models. While these methodologies guide data modelers in their work, two different people using the same methodology will often come up with very different results. Most notable are:

• Bachman diagrams
• Barker's notation
• Chen's Notation
• Data Vault Modeling
• Extended Backus–Naur form
• IDEF1X
• Object-relational mapping
• Object-Role Modeling
• Relational Model
• Relational Model/Tasmania

Generic data modeling

Example of a Generic data model.

 

Generic data models are generalizations of conventional data models. They define standardized general relation types, together with the kinds of things that may be related by such a relation type. The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of whole, regardless the kind of things that are related.

Given an extensible list of classes, this allows the classification of any individual thing and to specify part-whole relations for any individual object. By standardization of an extensible list of relation types, a generic data model enables the expression of an unlimited number of kinds of facts and will approach the capabilities of natural languages. Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation (usage) of such a model only allows expressions of kinds of facts that are predefined in the model.

Semantic data modeling

The logical data structure of a DBMS, whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS. That is unless the semantic data model is implemented in the database on purpose, a choice which may slightly impact performance but generally vastly improves productivity. 

Semantic data models.

 

Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure the real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.

A semantic data model can be used to serve many purposes, such as:

• planning of data resources
• building of shareable databases
• evaluation of vendor software
• integration of existing databases

The overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful abstraction concepts known from the Artificial Intelligence field. The idea is to provide high level modeling primitives as integral part of a data model in order to facilitate the representation of real world situations.