Predefined and Reinforced Data Standards

You’ve heard it a million times, “garbage in, garbage out.” But this axiom couldn’t be more true than in the case of organizational performance measures where in so many instances even a minute change in the data entered results in a profoundly different indicated performance. So how can an organization’s leaders be confident in the accuracy of their performance measurement data and the resulting measures? By defining and reinforcing a comprehensive set of organizational performance measure data standards.[wcm_restrict plans=”41561, 25542, 25653″]

Data Standards

Comprehensive data standards govern performance measurement data throughout its lifecycle. These standards cover: data definition, data gathering, data storage, data manipulation, and data presentation. While not intended to be all inclusive, the examples below highlight some of the standards common to each topical area:

Data Definition

  • units of measure
  • number of characters
  • number of significant digits
  • alpha-numeric nature of the data (alpha, numeric, or alpha-numeric)
  • standardized abbreviations

Data Gathering

  • time, day, and/or date data is gathered
  • frequency of data gathering
  • method of data gathering, including the use of cross-checks
  • instrumentation accuracy

Data Storage

  • medium of data storage (electronic, hardcopy, microfilm, etcetera)
  • access controls for stored data
  • change controls for stored data
  • time references associated with stored data (snapshots in time)
  • data retention policies

Data Manipulation

  • mathematical definitions for metrics calculations
  • definitions for conversion of characteristic/quality data into numeric data (significant = 1, important = 2, average = 3, unimportant = 4, insignificant = 5)
  • equality conversion factors for dissimilar data (1 FTE = 45 weeks of labor per year)
  • standard values (24 hours = 1 day, 7 days = 1 week, 4.3 weeks = 1 month, 12 months = 1 year, 52 weeks = 1 year, 365.25 days = 1 year, etcetera)
  • weighting factors (significant = 10000, important = 1000, average = 100, unimportant = 10, insignificant = 1)

Data Presentation

  • zero referenced graphics
  • common X and Y axis scales for comparable metrics
  • frequency of metric publication
  • timeframe of metric representation
  • aligned color coding of common metrics

Data Standards Reinforcement

Like all performance expectations, data standards must be reinforced to ensure application effectiveness. Reinforcement can be automated through software applications or administrative through establishment of policies, procedures, and practices. In either case, the reinforcement mechanisms should be periodically tested either through system testing or through management observation. Such follow-up helps ensure the quality of data and accuracy of performance measurement output.[/wcm_restrict][wcm_nonmember plans=”41561, 25542, 25653″]

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