Data Management Service Plan & Benefits

Data Management Service Plan & Benefits

Data management service is the practice of organizing and maintaining data processes to meet ongoing information lifecycle needs. Emphasis on data management began with the electronics era of data processing, but data management methods have roots in accounting, statistics, logistical planning and other disciplines that predate the emergence of corporate computing in the mid-20th century.

Preparing a Data management service plan before data are collected ensures that data are in the correct format, organized well, and better annotated. This saves time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It also increases research efficiency since both the data collector and other researchers will be able to understand and use well-annotated data in the future. One component of a good data management plan is data archiving and preservation.

By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. Data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have
already been conducted. Data archiving also provides insurance against loss by the data collector.

Funding agencies are beginning to require data management plans as part of the proposal and evaluation process. Data management service includes

  • Observational
  • Raw or derived
  • Physical collections
  • Models
  • Simulations
  • Curriculum materials
  • Software
  • Images

Data Management Association International, or DAMA International, chartered to improve data-related education. Data arose again as a leading descriptive term when IT professionals began to build data warehouses that employed relational techniques for offline data analytics that gave business managers a better view of their organizations’ key trends for decision-making. Modeling, schema and change management all called for different treatments with the advent of data warehousing that improved organization’s views of operations.

DAMA International and other groups have worked to advance the understanding of various approaches to data management. One such approach, master data management, for example, is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, which provides a common point of reference. Data stewardship, data quality management, data governance, MDM and data security management are among the components of many professionals’ data management practices. DAMA, among other groups which oversee certifications in data management skills proficiency, has created the DAMA Guide to the Data Management.

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