Notes
Slide Show
Outline
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Results of IAC Study of Metrics
in
Electronic Records Management (ERM)
Systems
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Introduction and Principal Conclusions
  • How does one measure the impact of an ERM system to the bottom line business or mission of an organization?
  • What is the business case for an enterprise ERM system?
  • Principal conclusions:
    • No silver bullet
    • No universal COTS tool or product
    • No one metric captures the success of an ERM system and relates unambiguously to the bottom line
      • Notwithstanding: Some common categories of metrics in use today
      • Some metrics less burdensome to capture than others
      • Some metrics just reflect a measure of IT system performance
      • Some metrics reflect mission success more directly than others
    • Measurement of ERM performance is currently immature
    • Most measurements tend to be IT-related rather than related to records management itself
    • Valid comparisons of ERM practices across organizations are difficult to make, and probably should not be made
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Bottom Line
  • The inescapable conclusion:
    • There is no simple, single answer!
    • There is no Swiss Army Knife-like tool
    • Tradeoffs must be made to arrive at metrics that are:
      • Meaningful to measure ERM success (e.g., “good” vs. “bad” metrics), and
      • Not too burdensome to capture on an enterprise-wide basis
    • “What gets measured is what gets done”
    • Aggregation of metrics into a single coherent picture of bottom line performance is
      problematic
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Concerns to Consider
  • Metrics for Public Services Relating to ERM
    • Spirit of the eGovernment initiative is to provide a Government that “works better and costs less.”
      • Quantifiable and well-defined ERM metrics relating to capacity, throughput, security (especially data and records integrity), assured service availability, ubiquitous access, lower cost, improved turnaround times, etc. are of interest.
      • Also concerned about particular metrics that are unreliable, non-specific, intractable to interpret, or too burdensome or onerous to collect.
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Major Factors to Consider
  • Who is the Consumer?
    • Nature of the “consumer” is an important factor
    • “Who” and/or “what” the metrics are sampling
      • “Public at large”
      • Specific customers
      • Agency/company employees
      • Federal agencies,
      • Other government agencies
      • corporations, or
      • Foreign users, etc.

  • What is the ERM Business Practice?
    • What specific “bottom-line” agency and/or industry business practices the metrics supported.  For example:
      • Servicing FOIA requests
      • Support for legal discovery
      • Historical research
      • Genealogy
      • Auditing and controls
      • Regulatory compliance
      • Public information dissemination
      • Statistical analysis
      • Archival records management
      • Grants management
      • ERM systems operations and management
      • Specific mission support (e.g., medical, environmental, emergency
        and disaster, defense)
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Principals in Defining ERM Metrics
  • Not everything that can be measured needs to be measured nor should it be
  • Metrics should have a purpose for continuing improvement
  • Best to design the capture and management of metrics into a system upfront or provide for an SLM approach
  • Important “paper vs. electronic” paradigm issues to be understood
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Broad Categories of ERM Metrics
  • Access to ERM Services
  • Accuracy
  • Capacity
  • Efficiency
  • Participation
  • Productivity
  • Search and Retrieval
  • System
  • User Satisfaction
  • Utilization
  • Legal *
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“Good” vs. “Bad” Metrics
  • Many metrics are potentially ambiguous, intractable, unreliable, or burdensome to capture
  • Among the more problematic metrics:
    • Record search time
    • Record retrieval time
    • Number of seats (or licenses)
    • Session time, and the
    • Raw number of records in the system
  • All of the above can be captured
  • However, interpretation of each can be quite controversial
    • A long session time, for example, could be indicative of great success or utter failure
    • Search times can be curiosity-driven as in surfing the Web
    • Level of commitment and persistence of user can not be easily measured
    • Some people are just better than others at
      “finding things”
    • Training, domain knowledge, and time-of-day
      can be important mitigating factors
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Sample Candidate Metrics for ERM Systems
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Sample Candidate Metrics for ERM Systems (cont.)
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Sample Candidate Metrics for ERM Systems (cont.)
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Summary