OntoSpace™ Data Analysis Report
INPUT DATA SUMMARY
| Input File |
Example_Input.csv |
| Number of Inputs |
20 |
| Number of Samples |
27 |
1. RATING AND COMPLEXITY MEASURES
Every system can only sustain its specific maximum value of complexity. This is known as critical complexity. Close to criticality, systems become vulnerable, fragile and difficult to manage. The difference between the current and critical complexity is a measure of the overall "health" of the system. Closer to criticality the system is less healthy and therefore generally more exposed.
2. COMPLEXITY & RISK MAP
The Complexity & Risk Map illustrates the most significant relationships. In a properly functioning system, it is important to maintain the structure of the map intact. Understanding the functioning of a system is easy if one analyzes its Complexity & Risk Map. The map is made of nodes (arranged in diagonal) and connectors (red and blue dots linking the nodes). Blue connectors correspond to strong links, while the ones in red indicate weaker links. The weak relationships concentrate most of the system's complexity and it is here that one must intervene if one wishes to reduce the overall complexity of the system in question. The coloring of the connectors corresponds to that in the table in paragraph 4 of this report. The total number of connectors is indicated in the figure below as the number of rules. This is because each relationship between two variables constitutes a rule. The complete set of rules establishes how a system functions.
Figure 1. Complexity & Risk Map
A variable is defined as not active if it does not relate to any other variable and may be regarded as irrelevant. In such cases, it will not be indicated in the System Map and a blank space will appear in the corresponding location. Highly connected variables - known as hubs - are indicated in red. It is these variables that require most attention.
The density of the map is also important. In highly dense maps problems spread very quickly. Moreover, in such cases it is very difficult to reach goals. Changing the value of any variable immediately changes the values of many others. A very low map density, on the other hand, reflects systems with a very low degree of redundancy.
The structure of a given Complexity & Risk Map may be appreciated based on the number of relationships that variables have with eachother. This number is known as degree. Variables with the highest degree - the hubs - are critical to your business. The table below lists all variables ranked from highest to the lowest degree. Irrelevant variables have a degree of 0.
| Nº |
Variable |
Degree |
| 1 |
GDP Growth (qtr) |
15 |
| 2 |
Employees |
15 |
| 3 |
NewHouseConstr (monthly) |
13 |
| 4 |
Net Earnings (million $) |
13 |
| 5 |
Number of customer transactions (thousands) |
13 |
| 6 |
Total Assets (million $) |
12 |
| 7 |
DA |
12 |
| 8 |
Long term debt (million $) |
11 |
| 9 |
Ave Sq Ft per store (thousands) |
11 |
| 10 |
YoY Revenue growth (%) |
11 |
| 11 |
Oil Prices (annual) |
10 |
| 12 |
EPS ($) |
10 |
| 13 |
Ave Sale per trans ($) |
10 |
| 14 |
Number of stores |
9 |
| 15 |
YoY Income growth (% |
7 |
| 16 |
GDP (Qtr) |
6 |
| 17 |
Net Sales (Million $) |
2 |
| 18 |
Current Ratio |
2 |
| 19 |
MortgageInterest (monthly) |
0 |
| 20 |
YoY Income growth (%) |
0 |
An additional ranking logic for variables in a given System Map is based on the strength of all the correlations with the neighbors of each variable. A generalized correlation coefficient is used for the purpose. Variables having a higher "Correlation Strenght" are most significant and, possibly, critical. This alternative ranking mechanism is very important in that sometimes a variable is classified as hub but the relationships with its neighbors, although potentially numerous, are weak. From a pure map topology standpoint, the variable is a hub but may be quite insignificant from a correlation point of view.
3. RANKING OF VARIABLES
In order to establish the relative importance of the variables in a system they are ranked based on the cumulative strength of all the relationships with their neighbors, not only on the number of relationships. This ranking is reported in the table below. This table provides vital information for decision-making.
| Nº |
Variable |
Correlation Strength |
| 1 |
GDP Growth (qtr) |
3.34 |
| 2 |
NewHouseConstr (monthly) |
2.61 |
| 3 |
Net Earnings (million $) |
2.59 |
| 4 |
EPS ($) |
2.06 |
| 5 |
Oil Prices (annual) |
2.01 |
| 6 |
Total Assets (million $) |
1.91 |
| 7 |
DA |
1.88 |
| 8 |
Ave Sale per trans ($) |
1.68 |
| 9 |
Long term debt (million $) |
1.68 |
| 10 |
Number of customer transactions (thousands) |
1.55 |
| 11 |
Employees |
1.52 |
| 12 |
GDP (Qtr) |
1.25 |
| 13 |
Number of stores |
1.11 |
| 14 |
Net Sales (Million $) |
0.78 |
| 15 |
Ave Sq Ft per store (thousands) |
0.59 |
| 16 |
Current Ratio |
0.20 |
| 17 |
MortgageInterest (monthly) |
0.00 |
| 18 |
YoY Revenue growth (%) |
0.00 |
| 19 |
YoY Income growth (%) |
0.00 |
| 20 |
YoY Income growth (% |
0.00 |
4. SYSTEM ANALYSIS
How will the system start to fail? Where is the system most vulnerable? Failure can occur in different ways. In general, failure is equivalent to the loss of structure in the Complexity & Risk Map - the map starts to break down causing loss of functionality.
The system will generally start to fail at the weakest relationships. The table below reports all the relationships (rules), together with an index called robustness. The closer the index is to 0, the more vulnerable the relationship in question. Values close to 1 indicate healthy and strong relationships. The relationships at the bottom of the list (those having lowest robustness) are those which concentrate most of your system's complexity. If you plan to reduce the complexity of your system, it is these relationships that must be addressed first. On the contrary, relationships at the top of the list are those that contribute less to making your system highly complex.
| Nº |
Variable 1 |
Variable 2 |
Robustness |
| 1 |
GDP Growth (qtr) |
Employees |
0.77 |
| 2 |
Number of customer transactions (thousands) |
Ave Sq Ft per store (thousands) |
0.77 |
| 3 |
Net Earnings (million $) |
Long term debt (million $) |
0.76 |
| 4 |
GDP Growth (qtr) |
YoY Revenue growth (%) |
0.76 |
| 5 |
Net Earnings (million $) |
Number of customer transactions (thousands) |
0.75 |
| 6 |
EPS ($) |
Long term debt (million $) |
0.75 |
| 7 |
Ave Sale per trans ($) |
Ave Sq Ft per store (thousands) |
0.74 |
| 8 |
Employees |
YoY Revenue growth (%) |
0.74 |
| 9 |
GDP Growth (qtr) |
Ave Sale per trans ($) |
0.74 |
| 10 |
GDP Growth (qtr) |
Total Assets (million $) |
0.73 |
| 11 |
DA |
Employees |
0.72 |
| 12 |
Net Earnings (million $) |
EPS ($) |
0.70 |
| 13 |
Ave Sale per trans ($) |
Employees |
0.70 |
| 14 |
Long term debt (million $) |
Number of customer transactions (thousands) |
0.69 |
| 15 |
Net Earnings (million $) |
Ave Sq Ft per store (thousands) |
0.69 |
| 16 |
NewHouseConstr (monthly) |
YoY Revenue growth (%) |
0.69 |
| 17 |
GDP Growth (qtr) |
Ave Sq Ft per store (thousands) |
0.67 |
| 18 |
Number of customer transactions (thousands) |
Employees |
0.67 |
| 19 |
GDP Growth (qtr) |
EPS ($) |
0.67 |
| 20 |
EPS ($) |
Number of customer transactions (thousands) |
0.64 |
| 21 |
NewHouseConstr (monthly) |
Employees |
0.64 |
| 22 |
Employees |
Ave Sq Ft per store (thousands) |
0.64 |
| 23 |
GDP Growth (qtr) |
Long term debt (million $) |
0.63 |
| 24 |
EPS ($) |
Employees |
0.63 |
| 25 |
GDP Growth (qtr) |
Oil Prices (annual) |
0.63 |
| 26 |
NewHouseConstr (monthly) |
GDP Growth (qtr) |
0.62 |
| 27 |
EPS ($) |
Total Assets (million $) |
0.62 |
| 28 |
NewHouseConstr (monthly) |
Ave Sale per trans ($) |
0.62 |
| 29 |
GDP Growth (qtr) |
Number of customer transactions (thousands) |
0.61 |
| 30 |
Long term debt (million $) |
Employees |
0.61 |
| 31 |
GDP Growth (qtr) |
Net Earnings (million $) |
0.61 |
| 32 |
Net Earnings (million $) |
Employees |
0.60 |
| 33 |
Oil Prices (annual) |
YoY Revenue growth (%) |
0.60 |
| 34 |
NewHouseConstr (monthly) |
Number of customer transactions (thousands) |
0.60 |
| 35 |
Net Earnings (million $) |
DA |
0.59 |
| 36 |
Total Assets (million $) |
Employees |
0.59 |
| 37 |
Long term debt (million $) |
DA |
0.58 |
| 38 |
Oil Prices (annual) |
Number of customer transactions (thousands) |
0.58 |
| 39 |
DA |
Number of customer transactions (thousands) |
0.57 |
| 40 |
NewHouseConstr (monthly) |
Ave Sq Ft per store (thousands) |
0.57 |
| 41 |
Total Assets (million $) |
Long term debt (million $) |
0.57 |
| 42 |
Net Sales (Million $) |
Employees |
0.55 |
| 43 |
Oil Prices (annual) |
Net Earnings (million $) |
0.55 |
| 44 |
Ave Sale per trans ($) |
YoY Revenue growth (%) |
0.55 |
| 45 |
NewHouseConstr (monthly) |
Oil Prices (annual) |
0.53 |
| 46 |
Total Assets (million $) |
Ave Sale per trans ($) |
0.53 |
| 47 |
GDP Growth (qtr) |
DA |
0.53 |
| 48 |
Oil Prices (annual) |
Employees |
0.53 |
| 49 |
DA |
YoY Revenue growth (%) |
0.52 |
| 50 |
DA |
Ave Sale per trans ($) |
0.52 |
| 51 |
Net Earnings (million $) |
Total Assets (million $) |
0.52 |
| 52 |
DA |
Ave Sq Ft per store (thousands) |
0.51 |
| 53 |
GDP (Qtr) |
Ave Sale per trans ($) |
0.51 |
| 54 |
Oil Prices (annual) |
DA |
0.50 |
| 55 |
Total Assets (million $) |
Number of customer transactions (thousands) |
0.50 |
| 56 |
Total Assets (million $) |
Ave Sq Ft per store (thousands) |
0.50 |
| 57 |
GDP Growth (qtr) |
Net Sales (Million $) |
0.49 |
| 58 |
GDP Growth (qtr) |
Number of stores |
0.49 |
| 59 |
NewHouseConstr (monthly) |
Long term debt (million $) |
0.47 |
| 60 |
EPS ($) |
DA |
0.47 |
| 61 |
Employees |
YoY Income growth (% |
0.46 |
| 62 |
NewHouseConstr (monthly) |
Net Earnings (million $) |
0.45 |
| 63 |
Number of stores |
Ave Sale per trans ($) |
0.44 |
| 64 |
Oil Prices (annual) |
Long term debt (million $) |
0.43 |
| 65 |
Oil Prices (annual) |
YoY Income growth (% |
0.43 |
| 66 |
Number of customer transactions (thousands) |
YoY Revenue growth (%) |
0.41 |
| 67 |
Ave Sq Ft per store (thousands) |
YoY Revenue growth (%) |
0.41 |
| 68 |
GDP (Qtr) |
Total Assets (million $) |
0.41 |
| 69 |
Number of stores |
Number of customer transactions (thousands) |
0.40 |
| 70 |
Net Earnings (million $) |
Number of stores |
0.39 |
| 71 |
GDP Growth (qtr) |
YoY Income growth (% |
0.38 |
| 72 |
Total Assets (million $) |
Number of stores |
0.38 |
| 73 |
NewHouseConstr (monthly) |
EPS ($) |
0.38 |
| 74 |
EPS ($) |
Number of stores |
0.37 |
| 75 |
Total Assets (million $) |
YoY Revenue growth (%) |
0.37 |
| 76 |
Number of stores |
Employees |
0.36 |
| 77 |
Number of stores |
Ave Sq Ft per store (thousands) |
0.36 |
| 78 |
NewHouseConstr (monthly) |
GDP (Qtr) |
0.35 |
| 79 |
GDP (Qtr) |
Number of customer transactions (thousands) |
0.34 |
| 80 |
GDP (Qtr) |
Ave Sq Ft per store (thousands) |
0.34 |
| 81 |
Oil Prices (annual) |
EPS ($) |
0.34 |
| 82 |
GDP (Qtr) |
Net Earnings (million $) |
0.34 |
| 83 |
Total Assets (million $) |
DA |
0.34 |
| 84 |
Long term debt (million $) |
Number of stores |
0.33 |
| 85 |
Net Earnings (million $) |
YoY Revenue growth (%) |
0.32 |
| 86 |
Long term debt (million $) |
YoY Revenue growth (%) |
0.32 |
| 87 |
DA |
YoY Income growth (% |
0.32 |
| 88 |
NewHouseConstr (monthly) |
YoY Income growth (% |
0.29 |
| 89 |
Ave Sale per trans ($) |
YoY Income growth (% |
0.28 |
| 90 |
NewHouseConstr (monthly) |
Current Ratio |
0.15 |
| 91 |
Current Ratio |
YoY Income growth (% |
0.14 |
End of Report
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Date: Thu Mar 13 08:09:39 2008
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