Data Management


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What is Data Management?

Data Management (DM) is the process of managing data resources to achieve a single version of the truth. As Gartner suggests, organizational approaches to DM vary, “but enterprises must go beyond simply ‘buying software’ if they hope to succeed with MDM.” - Gartner, October 2007

“Technology is helping managers exploit ever-greater amounts of data to make smarter decisions and develop the insights that create competitive advantages and new business models. Leaders should get out ahead of this trend to ensure that information makes organizations more rather than less effective. Information is often power; broadening access and increasing transparency will inevitably influence organizational politics and power structures.” - Eight business technology trends to watch, McKinsey Quarterly, December 2007

Our Capabilities

Our Data Management services encompass all the disciplines of Data Life-Cycle Management:

  • Data Governance - The exercise of authority, control and shared decision-making (planning, monitoring and enforcement) over the management of data assets. Data Governance is high-level planning and control over data management.
  • Data Architecture Management - The development and maintenance of enterprise data architecture, within the context of all enterprise architecture, and its connection with the application system solutions and projects that implement enterprise architecture.
  • Data Development - The data-focused activities within the system development life-cycle (SDLC), including data modeling and data requirements analysis, design, implementation and maintenance of databases data-related solution components.
  • Database Operations Management - Planning, control and support for structured data assets across the data life-cycle, from creation and acquisition through archival and purge.
  • Data Security Management - Planning, implementation and control activities to ensure privacy and confidentiality and to prevent unauthorized and inappropriate data access, creation or change.
  • Reference & Master Data Management - Planning, implementation and control activities to ensure consistency of contextual data values with a “golden version” of these data values.
  • Data Warehousing & Business Intelligence Management - Planning, implementation and control processes to provide decision support data and support knowledge workers engaged in reporting, query and analysis.
  • Document & Content Management - Planning, implementation and control activities to store, protect and access data found within electronic files and physical records (including text, graphics, image, audio, video).
  • Meta Data Management - Planning, implementation and control activities to enable easy access to high quality, integrated meta data.
  • Data Quality Management - Planning, implementation and control activities that apply quality management techniques to measure, assess, improve and ensure the fitness of data for use.

Our Goals for Data Management

  • Better business decision-making through accurate, accessible, and actionable data
  • Increased consistency and confidence in decision making
  • Decreased risk of regulatory fines
  • Improved data security
  • Maximized income generation potential of data
  • Designated accountability for information quality