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To improve the performance of your processes, the categorization of the variation in the data reported by the indicators is the first step to take. You can discern between different variation patterns, each with its implication including:
Points "dominant": a possible signal process (or fault data) that cause an overflow outside the natural statistical limits, given by + - 3 sigma variation from the mean
Points "moderate": signal a possible change process that moves the average natural boundary near the bottom or top
Points "weak": signal a possible process that causes a shift down, or up, the average
Points "near average": a possible signal that keeps the process close to the average performance
Points "ascendant": signal a possible change process that moves something the average downward or upward
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Measurements analyst has all the information necessary to determine past performance, current and future estimated that each indicator provides so we can support better decision-making and continuous improvement of processes available through graphics, rules of pattern analysis variation, filters and other facilities like comparing baseline and extrapolation or comparing individual series. You can access this type of analysis directly from the tools
Fig. 22. Statistical signals and user defined baseline
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Fig. 22. Normal Distribution
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Fuzzy signals
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Data Quality
The quality of the information is based on the quality of the basic data with which measurements and indicators are built. As data quality we means complete, with valid values (not null), correct type (eg Date, Number), and is in the proper range (zero to N , a valid date, a valid name).
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By entering certain mandatory fields
Monitoring a "data quality index" using the defined indicator. This indicator is calculated for each data import and individual field level obtained as a ratio between the number of field values considered correct vs. the number of values considered incomplete
We can create an indicator to monitor this quality index over time, along with detailed data from the associated context for easy review.
Fig. 23. Example of variation of quality indicator data entry
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There are several types of context. A type of context, called "Global" is a set of events (dates) on a timeline. A special event is "SourceOrigin" and can be used to filter data from that date. This context is generated and maintained by the user through "Global Notes".
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The data sources, and certain indicators, contain metadata to represent the components of the related activity system, in particular, the instruments, operations and intervening actors.
Fig. 25. An activity system under analysis
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Activity patterns
Self-constructed graphical formalism which is a socio-technical systemic view that relates the following components in a solution pattern from the signals and other clues in the data:
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It is used in Story to explain / argue the set and is based on the theory of Activity Systems and Patterns
Fig. 25. An activity system under analysis
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Activity Pattern as part of a Story
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Categories
You can explore the indicator repository in a structured way by using categories. These categories represent dimensions or aspects used to represent reality from a business point of view.
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