The emergence of various technological advances has caused the competition in the ever-changing global market to become tighter and harsher. Consequently, companies continuously seek the ways on how to use these technologies to their advantage in order to gain competitive edge. Such ways include the necessary systems for data management which are highly essential to a firm’s operations. Most companies of today employ a data management program, the aim of which is to parallelize data for use within the whole organization.
It seeks to make processes simpler, distribute or reuse data, and simplify the operational design (Siliato, 2004). This is achieved through synchronization of processes and systems integration, as well as “stewardship, quality processes, development of new systems and support operations” (Dziedzic, 2005, p. 12). Data management programs include various processes such as constructing, acquiring, recording, organizing, updating, and storing data.
They also involve processing and utilizing data for evaluation and inspection (Back & Moreau, 2001). Data management is highly important in operational functions such as planning and strategizing (Back & Moreau, 2001). It aims to further improve the vital resources and capabilities that an organization possesses (Siliato, 2004). Hence, companies that have good data management systems consider information as their lifeblood, as these systems turn information into competitive advantages for the organization (Dziedzic, 2005).
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For this reason, the importance of data management and information systems to a company cannot be disregarded, because when these systems fail, it can lead to huge financial losses or even the collapse of the firm itself, if not addressed properly (Dziedzic, 2005). Problems in Data Management Unfortunately, a recent comprehensive study in April 2007 conducted by Virginia Commonwealth University (VCU), which is cited in Nikki Swartz’s (2007) article entitled “Data Management Problems Widespread,” reveals that many companies are not managing data effectively (p. 28).
The ineffectiveness of data management of organizations is caused by several problems faced by organizations in managing their data. One of these is the inaccuracy of data. According to the study led by Aiken (2007 as cited in Swartz, 2007), erroneous data within organizations have caused business ventures, functional areas of management, and even the whole company to crumble. These findings are consistent with an earlier research accomplished by Gartner, which report that over one-fourth of crucial data of large organizations seems faulty or incomplete (as cited in Swartz, 2007).
In fact, the 2004 PricewaterhouseCoopers study shows that approximately 33 percent of the participating organizations in the study were certain of the accuracy of its data, while less than 20 percent were confident about the information provided by other organizations. As a result, due to low-quality data, many companies faced various kinds of problems, which resulted in losses or rise in overhead expenses (Data Warehousing Institute, 2005 as cited in Swartz, 2007). The inaccuracy or poor quality of data is caused by another problem in data management of organizations—inadequate investment.
While others regard it as an asset, it was discovered that numerous organizations perceive data only as a “maintenance cost” (Aiken, Allen, Parker & Mattia, 2007; VCU, 2007; all cited in Swartz, 2007, p. 29). This suggests only too well that many organizations neglect the significance of effective data management strategies to operational processes of an organization. Another related problem to the low quality of data could be the lack of formal training of data managers.
According to the study accomplished by Aiken and his associates (2007), less than 70 percent of data managers completed formal training on managing information (as cited in Swartz, 2007). The deficiency in formally trained data managers may be the cause of the lack of formal data management processes, which is another problem in managing information in organizations. In the research of Aiken and company (2007), among the 175 organizations that participated, they reported that not more than 10 percent of the firms are utilizing documented processes for managing information (as cited in Swartz, 2007).
Moreover, only 40 percent make use of a documented strategy endorsed by the board (PricewaterhouseCoopers, 2004 as cited in Swartz, 2007). Because of this, fewer than 70 percent currently employ or intend to employ “formal metadata management techniques,” while not more than 50 percent use “computer-aided software engineering tools” and data warehousing technologies (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007, p. 29). Solutions to Data Management Problems
VCU (2007) identified the reasons for the poor data management practices of organizations (as cited in Swartz, 2007). From the results of its study, VCU (2007) recommended that organizations should establish a formal feedback system to enhance their techniques in data management (as cited in Swartz, 2007). It also devised some guidelines which organizations can use to improve their practices in data management (as cited in Swartz, 2007).
Some of these guidelines created by VCU (2007) include changing the way organizations perceive data management which can help in improving data management practices (as cited in Swartz, 2007). This can be achieved by acknowledging data management as a fundamental process that aids in realizing certain company goals and objectives, and as the foundation of resources shared within the organization (VCU, 2007 as cited in Swartz, 2007).
The VCU guidelines also suggest that data management should be incorporated to company processes and policies, such as strategic planning, devising standards on the components of data, and creating, implementing, and maintaining databases (as cited in Swartz, 2007). In addition to this, Swartz (2007) stated that the organization leaders, for the most part, and all its members should realize and learn the significance of efficient data management practices in order to effectively implement them.
Moreover, in their article, Aiken and his co-authors (2007) suggested several ways on how organizations can improve their data management practices (as cited in Swartz, 2007). One of these is to distinguish “involuntary” data, such as the multiple copies of data for separate use which results in inconsistencies, from “controlled data,” such as creating exact copies of data intended to be shared with other for consistency purposes (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007, p.
29; “Data Duplication,” 2005; “McKinstry, 2005). Another way is to acknowledge the fact that problems associated to data are a manifestation of bigger, unseen problems caused by malfunctioning systems (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007). Effective management of metadata (i. e. , “semantics” and “syntax”) can also help enhance data management practices, specifically in implementing organizational-wide strategies in data management, which necessitate “enterprise-wide data stewardship” (i.
e. , team that handles selection of the “data management process” involved in “enterprise-wide metadata”) (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007, p. 29). Acquiring and preserving an “ISO 11179 standard-based data registry” can definitely help enhance the management of data. In relation to this, an “enterprise-wide data management program” would also be advantageous in attaining effective database management practices (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007, p. 29).
Finally, it is highly important to secure the support of top management who will be staying in the company for a long time (i. e. , minimum of five years), especially the Chief Information Officer (CIO) and Chief Executive Officer (CEO), in order to establish an effective and enduring “enterprise-wide data management. ” However, without solid and long-lasting commitment of the whole organization, all these methods to attain successful data management practices would not be realized (Aiken, Allen, Parker & Mattia, 2007 as cited in Swartz, 2007).
Summing up, the independent studies cited in Swartz’s (2007) article suggest that many organizations do not see how pertinent effective data management practices are to an organization’s success. Nevertheless, by realizing the role of data management strategies in the functional operations, integrating these strategies in the company processes and policies, implementing effective management of metadata, and securing the support of the top leaders, these problems on data management can be addressed and avoided in the future.
Back, W. E.& Moreau, K. A. (2001 March). Information management strategies for project management. Project Management Institute 32 (1), 10-19. Retrieved June 22, 2008 from AllBusiness Database: http://www. allbusiness. com/management/984616-1. html Data duplication. (2005). Ecommerce Technology. Retrieved June 22, 2008 from http://ecommercetechnology. org/english/data/06. htm Dziedzic, L. (2005). Foreword. In Data Management, Storage and Security Review 2005 (page 12). London: Business Briefings Ltd. Retrieved June 22, 2008 from Touch Briefings Database: http://www.
touchbriefings. com/pdf/1426/Foreword. pdf McKinstry, J. (2005, April 28). Data replication. Computer Technology Review. Retrieved June 22, 2008 from http://www. wwpi. com/index. php? option=com_content&task=view&id=446&Itemid=44 Siliato, A. (2004, December 1). The cost of bad data & the value of good data management. The Interpreter. Retrieved June 22, 2008 from http://www. allbusiness. com/government/3581425-1. html Swartz, N. (2007, September/October). Data management problems widespread. Information Management Journal, 41 (5), 28-30.
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