Discussion of Topic
Take a leading retailer such as Target; in 2014, its internal data resources were breached, leading to the theft of information and other data for customers for almost 110 million of its customers. Again, imagine the retailer has 1807 stores in the USA alone, each handling hundreds or even thousands of clients every day, who have e-mail addresses, are registered to one of its loyalty programs, have an address, and shop using debit or credit cards, with others shopping online. The sheer amount of client information the retailer has to capture, update, and manipulate is tremendous; add to this its internal stock, financial, CRM, and employee management systems and the volumes of data they have to handle (Isidore, 2014). Today, organizations are confronted with large volumes of data and information that they must manage, with threats placing greater stress on ensuring the proper and management of this data. Banks face more service requests by clients who can access banking services through the web and using mobile devices. Supermarkets and other retail chains have many loyalty programs and customer details, as well as web portals where shoppers use credit/ debit cards for online shopping. Law enforcement agencies capture increasingly large amounts of data, in light of new types of security threats such as terrorism; for instance video files from security cameras, and profiles of potential suspects.
All these mean that enterprises are faced with huge volumes of data to manage, and manage efficiently and securely, consistent with existing laws and regulations on data management. As a consequence, organizations are required to define data precisely, integrate and retrieve this data and information effectively and efficiently, both for internal applications and purposes and external communications (McAfee & Brynjolfsson, 2012). Businesses enterprises also must create accurate, transparent, and consistent content and effectively integrate this information into its business applications. Enterprise systems data management is based on the concept of enterprise data management, which is a concept denoting an organizations ability to define data precisely, integrate this data into its operations, and be able to retrieve such data as an when needed; both for external use in communication and for internal use. Enterprise data management is also a business objective where organizations integrate data that is accurate, transparent, and consistent into its business applications. Because of the increased use of data by organizations and businesses, it is imperative that a coordinated and seamless approach is used in managing this data is used by all segments and components within an organization. This is to ensure quality standards are maintained and data conflicts are avoided so that the data and information remains trustworthy for use in business operations such as in reporting and decision making (Aiken & Billings, 2013). This report is a team members’ effort in which each member has selected and summarized a peer reviewed article on the subject of Enterprise Systems and Data Management. Each of the team members’ articles on the topic is then combined to form a single report. The report will then discuss Enterprise Systems and Data Management in the context of a case study and will then draw conclusions at the end.
Using Enterprise Architecture Standards in Managing Information Technology
In Using Enterprise Architecture Standards in Managing Information Technology by Wai Fong Boh and Daniel Yellin (2014); the authors recognize the increasing need for businesses to build enterprise-wide capabilities for leveraging technology distributed in different business units. Organizations achieve this by setting EA (enterprise architecture) standards to allow for greater compatibility of IT components as well as enable integration of data and applications across the enterprise. In investigating how different mechanisms for governance impact EA standards use and the extent that EA standards help organizations better integrate IT resources in the entire enterprise, the authors undertook a firm level survey and established that using EA standards is an effective approach in assisting organizations better manage, integrate, and use its IT resources enterprise-wide (Boh & Yellin, 2006).
The use of EA standards for data governance is among the most important aspects of managing data and information in the enterprise as it is interrelated with all other data management function disciplines. Master data management cannot be successful if data governance is ineffective, although the level of dependency for different data management functions varies from function to function (Boh & Yellin, 2006). Governance is premised on specific levels of control and so governance entails establishing the right level and amount of control. Using iterative approaches minimizes risks and helps focus on high control levels to ensure that data management within the enterprise is successfully integrated into business operations (Federoff, 2014).
In Traceability data management for food chains by Dmitris Folinas, Ioannis manikas, and Basil Manos (2006), the authors investigated the data needs considered essential for the efficient traceability of food and introduced a generic framework of managing traceability data that food business operators and other entities can use. Using the extensible markup language methodology (XML) and physical markup language (PML), the authors developed a simple framework for traceability that can communicate information for use in business decisions and operations using means that are easily accessible, such as the internet and mobile phones. The authors conclude that an integrated system for data and information traceability must be capable of filing and communicating information on various aspects such as consumer safety and product origin. Such a system must be capable of adequately filtering information, extracting information from existing databases, and harmonize this information with international standards of codification and current technologies.
Financial institutions and food industries, for example, have placed greater emphasis on the accuracy of their market and reference data, it is even more important to maintain a clear vision and insights into enterprise data management processes and systems, and as such data traceability has gained even greater importance (Folinas, Manikas, & Manos, 2006). Each time data is retrieved and run through validation checks, a workflow is triggered; it is essential that details of such workflows are recorded. The traceability of the workflow details are essential in aiding an organization accomplish two important goals of informing management and informing users. By implementing traceability through workflow details recording, decision makers have a high level visibility and use this information for the constant improvement of processes enterprise –wide. Traceability can help management in the food and other industries determine and prioritize what needs to be fixed/ sorted first, using other resources and data repositories. Traceability in enterprise data management allows managers to measure processes and functionalities, allowing for improvement of these processes. Traceability also informs users, such as data consumers, with better insights into the sourced content. Thus, managing data in the enterprises enables users throughout the enterprise to know data sources, its validation procedures, its derivation, and the process executors; this enables seamless operations within a business (Sentance, 2016).
Traceability Data Management for Food Chains
In Towards Security of Integrated Enterprise Systems Management by Alexander Korzyk, the author examines how business process re-engineering can be achieved through sharing of data and information. The use of packaged enterprises systems to transform enterprises has continued unabated globally. The opportunity for structuring enterprises requires that data be shared within an enterprise and between enterprises within a supply chain. However, the author notes that information security poses one of the challenges for sharing data and information within and outside the enterprise. As such, an integrated enterprise framework for system security management is necessary to control access to, and use of information. The author proposes sharing of data that is accurate, secure, and valid within the value chain as well as management of knowledge as a way for effective enterprise re-engineering (Korzyk, 1999).
Inconsistent and inaccurate systems for managing data can have disastrous consequences; research shows that data of poor quality on average, costs organizations $ 8.2 million annually. Thus, data that is inconsistent or inaccurate can result in poor compliance with regulations attracting fines, delays in customer service, and operational inefficiencies; all that can cause significant financial losses (Cyber Data, 2017). Recent ransomware attacks demonstrate the value of integrating security when managing data within the enterprise
In Putting the Enterprise into the Enterprise by Thomas Davenport (1998), the author describes how commercial software applications such as ERP systems offer seamless integration of information flows within companies, including accounting and financial information, human resource information, customer information, and supply chain information (Davenport, 1998).
A successful enterprise system is required to be created by utilizing the local and the global knowledge regarding the product sold by the company and the targeted consumers. By worldwide client learning we mean information that is free of the specific item area, for example, knowing how individuals settle on buy choices or how best to “chat” with them on the web. Worldwide item learning incorporates the item’s traits, their functionalities, and whatever else that is autonomous of the specific dealers offerings (Braglia & Frosolini, 2014). A space master in PCs, for example, knows video cards, their execution capacities (e.g., which sort of card is required for showing photos, playing computer games, or demonstrating films), their estimated costs, and even how quickly their innovation is evolving (Lam, 2014). Neighborhood client learning alludes to the capacity to connect a client’s close to home needs, uses, and inclinations to the central item. In this way, a purchaser may have a high requirement for status among associates that may impact many buys. Notwithstanding, nearby customer learning empowers an endeavor framework to coordinate the shopper toward a specific item, for example, a PC with the picture of the most recent and most prominent innovation or a car that signs status (in any event to the intended interest group of colleagues). Neighborhood item learning centers on the merchant’s offerings. Such information not just incorporates models, styles, parts, additional items, and so forth (Galliers & Leidner, 2014). Additionally the segments can be arranged with others to-minute accessibility of any prescribed item or exceptional offers. In this manner, an endeavor framework ought to be associated with the seller’s item database and to the apropos showcasing detailing frameworks. The unstable way of both genuine items like PCs and sellers’ data frameworks requires satisfactory database upkeep in the venture framework. At last, a fruitful venture framework must incorporate every one of the four sorts of learning, as well as it must make the greater part of this information unmistakable to the client (Bi, Da Xu & Wang, 2014). That is, this information ought not exclusively be “in the engine” of a decent recommender framework, however clients must have the capacity to perceive how the full exhibit of learning is utilized and why the subsequent proposal has been keenly tweaked to their own needs.
Towards Security of Integrated Enterprise Systems Management
A case study is selected based on the Enterprise Systems and Data Management that clearly meets the topic discusses above in the report. The case study is “Putting the Enterprise into the Enterprise System” by Thomas H. Davenport. The case study discusses about the different enterprise systems and integration of the commercial software in the information system of an organization to increase the efficiency and flow of the information of the information system of the organization. The case study also discusses about some of the popular software vendors and their sales in different years. It helps us to identify the increases in the use of the enterprise software in the industry with the increase in the growth of the internet (Rosemann & vom Brocke, 2015). The failed outcomes of the use of the enterprise software in an organization is also discussed in the case study and it helps to identify the changes that are required to be made in an organizational infrastructure before the application of the enterprise system and gain maximum amount of benefit from it.
Keeping up a wide range of PC frameworks prompts tremendous expenses for putting away and defending excess information, for rekeying and reformatting information from one framework for use in another, for refreshing and investigating old programming code, for programming correspondence connects between frameworks to mechanize the exchange of information (Lam, 2014). Be that as it may, much more critical than the immediate expenses are the circuitous ones. In the event that an organization’s deals and requesting frameworks can’t chat with its creation planning frameworks, then its assembling profitability and client responsiveness endure. In the event that its deals and showcasing frameworks are inconsistent with its money related announcing frameworks, then administration is left to settle on essential choices by sense instead of as per a point by point comprehension of item and client benefit (Stark, 2015). To put it obtusely if an organization’s frameworks are divided, its business is divided. Enter the endeavor framework. A decent ES is an innovative visit de compel. At its center is a solitary far reaching database. The database gathers information from and nourishes information into particular applications supporting for all intents and purposes the majority of an organization’s business exercises crosswise over capacities, crosswise over specialty units, over the world (Fang et al., 2014). At the point when new data is entered in one place, related data is consequently refreshed. For understanding the working of the enterprise system and identification of the risk the information required to be included in the information system is required to be analyzed and a large quantity of data is required to be gathered from the computer system of the organization. Resolving the risk associated with the enterprise system can lead an organization to improve the business performance and productivity and take the leading position in the market.
Clearly, enterprise systems offer the potential of big benefits. But the very quality of the systems that makes those benefits possible their almost universal applicability also presents a danger. When developing information systems in the past, companies would first decide how they wanted to do business and then choose a software package that would support their proprietary processes. They often rewrote large portions of the software code to ensure a tight fit. With enterprise systems, however, the sequence is reversed (Stadtler, 2015). The business often must be modified to fit the system. An enterprise system is, after all, a generic solution. Its design reflects a series of assumptions about the way companies operate in general. Vendors try to structure the systems to reflect best practices, but it is the vendor, not the customer, that is defining what “best” means (Wixom et al., 2014). In many cases, the system will enable a company to operate more efficiently than it did before. In some cases, though, the system’s assumptions will run counter to a company’s best interests. The enterprise system deployed in an organization can be customized upto some level for aligning the business requirement with the information system and develop it accordingly to give the maximum productivity. Since the frameworks are measured, for example, organizations can introduce just those modules that are most fitting to their business. Nonetheless, the framework’s unpredictability makes significant alterations impracticable (Katerattanakul, Lee & Hong, 2014). Subsequently, most organizations introducing endeavor frameworks should adjust or even totally improve their procedures to fit the prerequisites of the framework. Arranging an undertaking framework is to a great extent a matter of making bargains, of adjusting the way you need to work with the way the framework gives you a chance to work. You start by choosing which modules to introduce. At that point, for every module, you alter the framework utilizing setup tables to accomplish the most ideal fit with your organization’s procedures.
Conclusion
From the above report it can be concluded that with the implementation of the enterprise the organization would be benefited but there are certain considerations that should be noted before the deployment of the enterprise system. Notwithstanding having vital ramifications, the enterprise systems likewise have a direct, and frequently confusing effect on an organization’s association and culture. From one viewpoint, by giving general, ongoing access to working and money related information, the frameworks enable organizations to streamline their administration structures, making compliment, more adaptable, and more majority rule associations. Then again, they additionally include the centralization of control over data and the institutionalization of procedures, which are qualities more steady with various leveled, summon and-control associations with uniform societies.
References
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Boh, W. F., & Yellin, D. (2006). Using Enterprise Architecture Standards in Managing Information Technology. Journal of Management Information Systems.
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Folinas, D., Manikas, I., & Manos, B. (2006). Traceability data management for food chains. British Food Journal, 622-633.
Galliers, R. D., & Leidner, D. E. (2014). Strategic information management: challenges and strategies in managing information systems. Routledge.
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