What is Story

A story is an artifact of arguing the meaning of the data, and consists of the following components (pages):

• Data: considered not only the numbers, but together with the statistical graphs (particularly with statistical signals),        

• Texts that are as annotations and / or presentation of points of view, explanations, etc.

• Various morphs arranged on a board as attachments (for example with bar graphs, correlations, graphs, texts, etc.)

• Patterns of activity, a self-constructed graphic formalism that is a socio-technical systemic view that relates the context, the problem (such as contradictions / tensions), the causes (from the signals), the questions, the arguments, and possible solutions, which is used to explain / argue the set and is based on the theory of Activity Systems and Patterns

    
    The presentation is made by the user creating the different pages according to the specific case that the user wants to present. A summary panel is automatically updated showing the indicators grouped by a user-selectable dimension, and a natural language description of the nature of the process (stable, unstable), patterns that can be detected in the data, and other contextual information. The presentation is made by the user creating the different pages according to the specific case that the user wants to present. A tree-shaped structure allows to visualize and select the signals detected in the data corresponding to any indicator present in the Story

User Interface - Story

You can monitor and analyze your organizations processes using the indicators present in the tool Dashboard. This tool, with multiple pages, contains indicators of measurement repository, text pages with your notes and comments, background information accompanying the data, and automatic content analysis. Although a board may contain any number of pages, it is recommended to use a limited set of well-selected, according to business goals. Can be generated many different boards, recorded to disk or shared in a central repository, for example, use a board per process, by area, or by some other criteria, temporary or more permanent.

You can navigate your board from the first page (table of contents), sequentially, or directly from the menu option "Go". You can sort the information according to their own preference panel for consultation on the screen.

The home page has a flap to open a pre-analysis of indicator data.

The board has a bar of icons for the most important operations:

    Go to homepage
    Go to previously visited
    Go to previous page
    Go to the next page
    Create a new dashboard
    Saving a stored locally board
    Save a board in a local file
    Update all indicators
    Submenu insert
    Submenu actions

You have facilities to build your boards quickly. You can add indicators to the board (one per page) from the same tool or from the tool repository. The ability to add more than one indicator at a time is essential, so that you can add indicators that shows signs of variation in the data, or for a certain process, your favorite indicators, etc.. with no need to search the repository one by one.

You can update all indicators with current data repository with a click of the mouse. From the dashboard you establish baselines to compare the ability of processes, documents the analysis of measurements, and assigns responsibility for any tasks or actions to improve the processes.

This tool can be displayed full-screen or normal window. You can enter full screen by clicking on the corresponding icon in the system tray (top right). Another mode of visualization is a double-page, useful for making comparisons

Fig. 6. Icon bar

Open

Fig. 7. Story

Open

Fig. 8. Story full screen

Sharing

You can share the board with other people. You can use the graphics and data into another application saving and exporting individual pages from the "File" menu

You can also share as object files directly usable by another user from another application.

Use the Export and Import from the menu to save and read from the local disk. If you want to share the file, you can publish in the Repository defined a shared folder. From there, another user (or yourself) can take your file with the "Read the Repository" from the "Share" menu

Logical-statistical analysis

You proceed to the analysis of indicators interpreting the presence of signals of variation in the data. You can do this by using the previous analysis performed by the application, which comes at two levels:

1. Statistical: analyzes statistical variation in a normal distribution (+-3 sigma variation). It looks for recognized statistical variation patterns (we called dominant, moderate, weak, ascendant, and near average). The indicators are grouped by type of project and in order to analyze period of homogeneous data. You can see the result of this analysis displaying the tab "Summary" or see it in a new window (navigable) from the option menu. The report shows the nature of the process in relation to the detected signal level: it says stable if no signals are detected, in these cases you can query the process parameters, such as maximum, minimum, average. For indicators with statistical variation signals, you can access a possible diagnostic process that is showing on each shift pattern. Can use in your analysis the qualification level of information (or lack of entropy) present in the data.

2. Fuzzy: grouping qualitative data sets ("low", "medium", "high", and its qualifiers "very", "slightly", "quite") with respect to another data set taken as a comparison (currently, consolidated data by project type). You can see the information from this "point of view" by selecting the "labels" on the graph of the indicator to get additional insight, "nuanced" change

Since the change in your organization is a dynamic phenomenon, in order to study it you should monitor the capacity of each process using "baseline" for comparison. By setting a new baseline, you persist current data for future comparison (values, statistical limits, statistical and logical signs, context). You may in the future open this "photo" so imprinted on the indicator and thus visualize the evolution of the parameters (called the "voice of the process") and compare it with the desired values (we call the "voice of the customer")Fig. 9. Membership functions used to calculate fuzzy membership grades

Information Analysis

You base the analysis of the information in the study of the temporal variation of data from a selected set of indicators that make up your board. The indicators are designed to show particular phenomena of sub-critical processes of the organization or sector. The basic tools includes statistical analysis, fuzzy logic and fractal analysis.

The first tool you are looking for statistics on varying signals in the data to determine the degree of stability of the monitored process. Able to distinguish the "noise" of possible events that are statistically significant (for example, a major task was modified, it began to use other technology, there was a change in personnel, etc..) Helps you make decisions and to investigate possible causes of change. Has available five different types of statistics signals (see section in this help). The second tool uses fuzzy logic to show data from a qualitative perspective, complementary to the statistics. The use of quantifiers like "no", "very", "slightly", "little" will communicate information using language closer to common sense. The fractal analysis is used to visualize the data variation to determine cycles (periodic and non-periodic) and process stability.

You can check on summary tab of the board an analysis of the indicators developed using these techniques. This information may use it as a navigational guide for the information, but also raised questions about the causes of the observed signals

Comparing processes

You have available a simple mechanism to compare the change in one variable: the establishment of a baseline. From the menu, you can set the "snaphots" of all indicators of the board (to date) and then in the future to review the changes. To see the evolution, select from the menu "View" desired baseline of any indicator of the board and can compare the two moments in graphical form, with its parameters (mean, minimum, maximum, statistical boundaries, etc..).

To visualize relationships and correlations (linear and not linear) you can use the option to add an extra serie from the "Edit" menu. It allows compose a page in the board with several sets of indicators (curve type), whether the same or different. The page displays all the curves and data of the selected indicators. 

If you are in double-page mode, you can open a scatter chart between the two visible pages to detect correlations, visually compare two indicators, their context and associated trends (menu)

Fig. 10. Comparing Contexts to Double Page

Fig. 11. Example: correlation chart between two variables (scatter)

Extrapolating the future performance of a process

You can get a trend curve, linear or exponential, to project possible future values of an indicator. You can control the parameters from the repository tool to visualize the tendency of a project or a group of similar projects (project category). The menu allows you to check the correlation coefficient, which indicates how good the approximation of the curve (values greater than 0.75 are considered good enough).

Fig. 12. Viewing a trend example

Analyzing statistical variations

You can detect the variation data indicative of a possible type curve underlying process that causes a shift of the mean value of the both is down or up. This behavior is one of the statistical signals interpreted by your application (called "weak") and allows you to analyze and understand the data variation of an indicator in full, not only through specific measures (minimum, maximum, and average) than are not usually sufficient for understanding a process and indistinguishable with the latter noise information. At this level of ability of your organization, you use the information about the behavior (or performance) processes as the basis of analysis of the indicators and decision-making.

Another sign that indicates a shift in the process mean is the named "moderate". In this case it is traveling close to the statistical limits (upper or lower), and indicate a possible change in the process: whether a process will become unstable (eg having increased informality of tasks, rotations, lack of institutionalization / standards, etc..), or in another process, stable, but with other average (for example for having been a major change in the technology used). You can detect small shifts in the mean by the signs called "ascendant". The signal called "near average" tells you otherwise, values closer to the mean, with very little variation.

The signals are automatically displayed in the curve graphs. From the "View" menu of the Dashboard or the Repository Browser you can exclude isolated or dominant signals show only those weak signals that may represent a limited phenomenon.

You can see the "dispersion" of the values with respect to the bands of variation from the mean (+-3sigma) opening a histogram that shows the frequency of occurrence for each band

Fig. 13. Viewing data variation frecuencies example

Detect important changes

You can detect a potential process which causes an shift out of natural statistical limits. These limits correspond to +-3sigma of variation from the average of the measure values. You can determine if the process exhibits behavior "statistically stable" or to determine the need to explain the reason for the event or change that causes significant variation. In this way, you can get a glimpse of the actual performance, called the "voice of the process", from which action points can be assessed improvement objectives setting new limits for your goals. 

In a complementary way, you can analyze the variability using fractal analysis. Using the technique of "Scaled Range" is calculated the fractal dimension, a coefficient that characterizes the variation, displayed graphically, allowing also detect periodic and non-periodic cycles (see section in this Help).

In addition to this quantitative view, you can see the reality "qualitatively", by using "fuzzy signal" in the data. Such signals are calculated using the formal method of fuzzy logic (see section in this Help)

Another level of change analysis, monitoring of a longer range than you can perform using the mechanism called "setting baselines" can observe the evolution of the performance of the process with respect to these limits in pursuit of improvement.

Fig. 14. Process performance evolution comparing example

Fig. 15. Adjusting the threshold of a process

Show two variables in the same indicator 

Normally indicators show a single variable, whether by graphic curves, pie or bar type. You have two ways to include another variable in the same graph (curve):

1. Add an extra series

2. Adding a polymetric

With the first technique you can project more than one data series on the same graph X. Usually aggregate series will be the same indicator for another group of projects, or in some cases another indicator to observe a possible correlation.

The second technique, the polymetric, is to compose a new measure whose values are calculated from the points of two (or more) indicators. You use it to watch a graphical relationship between the two variables (eg between effort and duration). In IMPROVEKIT, the polymetric is the size of each point which is calculated using an index (1 to 10) from the values of the added serie.

The indicators related can be any indicator currently displayed in the Repository Browser.

You can add and remove series from the Repository Browser and Dashboard "Edit" menu

Fig. 16. Visualizing the relationship duration/effort example

Determining the entropy of an indicator

You use the statistical variation of the indicator data to detect various types of statistical signals (it has five different types available). Each signal level gives information about the "noise" distribution considering a normal distribution. You may consider those indicators that give greater information taking into account the degree of entropy estimated by a fuzzy logic from the data signals.

Another important factor to consider is the quality of the data. Each category has an associated indicator named "quality index" that is calculated daily dynamic from a heuristic on the input data used to calculate these indicators. You can check this value (which varies between 0 and 1, from lowest to highest quality data) from the lapels "context" of the board to determine, on the one hand, actions to improve data quality (eg greater controls on the data capture, required fields, etc..), and secondly, to have an estimate of the uncertainty or error of the data used in the analysis.

You can see that the process "stable" will indicate that the process is "in regime" and will warn if you are consistent with the goals of your organization. Using the grade "fuzzy" information allows estimating the degree of "changes" in the data, which could have a correlation with change processes in the activities, equipment, and / or the technology used in the company, and thus in the direction or future projection where it is headed the same. Depending on the nature of the change may find that you need to stabilize the process, ie, it is very unstable, hazardous, and does not meet the specifications or objectives. Or rather, motivate and support will indicate the direction of change emerging.

Fig. 17. Example: considering the level of information presented by the analysis

Group project by relative performance/cost

You may need to use a dynamic project grouping, which depends on the performance of the projects. To do so, has the project category "performance", as well as being dynamic (calculated with data from performance of projects), is comparative and qualitative. You can see the projects through pooling from the tool repository ("View" menu) to analyze as a whole according to its performance from the point of view of the business (eg "very" expensive), beyond the definition of predefined projects statically (for example size of the project)

Using "cualitative" indicators

The indicators show the numerical variation in time of a process variable. These values can be analyzed quantitatively using statistical variation signals considering a normal distribution. You can complement this analysis with a "qualitative" oriented approach such fuzzy logic. This logic differs from traditional logic in which an item can belong "while" more than a set, ie its degree of membership is a value ranging from 0 (does not belong) to 1 (belongs "entirely" ), unlike traditional logic, where the membership value is 0 or 1. For more information of change to a different dimension, you can display "qualitative changes" or "diffuse signals" via "View Labels". You can view natural language qualifiers each signal variation in series points, of a different color in the pattern of variation (analogous to statistical variation patterns)

Fig. 18. Viewing data from a cualitative point of view example

Analyzing data variability dimension (fractal)

Persistence (positive correlation) allows modeling phenomena that tend to cluster first one way and then the average across while antipersistence (negative correlation) allows modeling phenomena strongly fluctuating around the average. Persistence is associated with stable structures with high probability of fulfilling specific functions, while antipersistence relates unstable structures seeking functionality.

The persistence of a time series is measured by the Hurst exponent, also known as the "dependency ratio" or "dependency ratio over time." Quantifies the relative tendency of a time series regression either strongly or clustered together mean one direction. H value in the range of 0.5 <H <1 indicates a time series with positive long-term autocorrelation therefore a high value in the range likely to be followed by another high value and future values also will tend to be high. A value in the range 0 <H <0.5 indicates a time series with long-term change between high and low values in adjacent pairs, which means that a single high likely be followed by a low value and the next value will tend to be high, with the tendency to switch between high and low values persistently. A value of M = 0.5 can indicate a number of uncorrelated data.

This exponent measures the fractal dimension of a curve, which provides information about the degree of "chaos", about the possible presence of cycles, and that provides qualitative clues about the "health" of the process (according to recent research, a process of nature healthy organic exhibits a characteristic fractal behavior, which does not occur in processes that have become rigid on one side, or too chaotic on the other)

Fig. 19. Viewing the data variability (hurst) and cycles example

Working with Story

Building the Story

The Dashboard is the tool that you use to analyze a set of indicators to monitor their processes. The tool consists of a series of pages, one for each indicator. The first page (front page) is the table of contents of the board. you can insert and sort any number of pages, including text and graphics pages free (for example to use them as section divider, comments, or embed images with drag & drop). You may have many boards stored, but can only be opened one at a time.

You can from an existing panel to quickly create a new board, and then add one or more pages of indicators and text notes. Using the "Insert" menu can add all or a favorite indicators in particular. Once you have at least one indicator on the dash, the menu "Insert" are extended to provide the ability to add other indicators similar to that shown in the current page: in the same category or process with statistical variation signals in the data, or a particular type of signal.

Navigating the information of Story

You can check the information board in an easy and intuitive, scrolling through pages of it using the menu "Go", or using the arrows (previous, next, previous page) and the "home" icon of the board.

To go to a particular indicator or note, select the link for the page listed in the index.

You can choose to move directly to the front page. This content page contains a list of the contents of the Board (as iconic or list as configure and select), and the user name and the date of the last update of the indicators. Open the tab "Summary" for information that will assist you to make the analysis of the information board. Opening this tab in a new window you can also "signals browsing".

An alternative way to navigate indicators signals is to use the "Go" menu, with which you can navigate sequentially jumping on pages with indicators that show a statistical signal in the variation of the data.

Fig. 20. Example: locating the important signals of the presentation document

Updating data of Story

It is not necessary to build a new board each time you perform an analysis, you simply need to update the data of the indicators of a board. To do this use the "upgrade" of the icon bar. You can also update a single page from the menu without upgrading all board indicators.

It may be that over time add or remove indicators of their boards. Both operations, modifications and deletion of pages that do can undo them by always timely and flexible "Undo" option

Saving and retrieving a Story

To monitor several processes you can use more than one control board. You can save a board to local or shared disk remotely. If the user preference so indicates, each time you record a board creates a direct link to it on the desktop of the application, so you can quickly access.

You can also share a board using through a list of favorite indicators. From the "Go" menu create a favorite indicator with the indicator of a page, or all pages of the board, and proceed to export them from the tool "Repository". Another user can then import your favorite indicators and create with them a new board

Sharing information

With the Share menu you can share your board or the selected page. You can publish to a shared folder on a server or in your DropBox. You can import all the pages of a published board or partition page, or adopt a whole new board, both from the shared folder and from your DropBox.

Another way of sharing is through the ' Morph Chat ' of the connection panel. Simply drag any morph from the desktop to the avatar of any currently logged in user to send you a copy.

You can also export your board itself to file and then send it by email or print. You can send any email page indicators, with graphic and data. If you prefer, you can export the graphics and data image files and text which can be imported into MS-Excel or other tools

Fig. 21. Share Menu