Patent Publication Number: US-2022222686-A1

Title: Analysis apparatus, analysis system, analysis method and program

Description:
TECHNICAL FIELD 
     The present invention relates to an analysis apparatus, an analysis system, an analysis method and a program. 
     BACKGROUND ART 
     As a technique for acquiring cause-and-effect relationships among events, a technique is known in which, by clustering pieces of event information that appear in news articles and then performing, for each event cluster, burst detection is performed based on the number of appearances in news, cause-and-effect relationships among events are determined (for example Non-Patent Document 1). 
     In the field of marketing, it is performed to, by analyzing purchase data configured with a plurality of items (for example, article name, article classifications, purchase price, purchase time, purchase place, purchase store name, purchaser&#39;s nationality, gender, age and the like) using, for example, the technique described in Non-Patent Document 1 or the like, utilize the analysis to solve business problems (for example, planning, promotion and the like of a new article). 
     PRIOR ART DOCUMENT 
     Non-Patent Document 
     Non-Patent Document 1: Hiroki Ono, Akira Utsumi, “Extracting Causal Knowledge by Time Series Analysis of Events”, Transactions of the Japanese Society for Artificial Intelligence, Volume 30 Issue 1 B (2015) 
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     In general, purchase data is often configured with very many records. Further, many pieces of attribute information about purchasers (for example, nationality, gender, age and the like of the purchasers), many pieces of attribute information about articles (for example, article names, article classifications and the like) and the like are often included. Therefore, for example, when it is attempted to perform various kinds of analyses, such as burst detection of time-series data and determination of cause-and-effect relationships among different pieces of data, using a combination of these many attributes, the calculation cost is very high. 
     In comparison, by selecting only certain particular attributes from among the many attributes and performing data analysis using a combination of these particular attributes, it becomes possible to reduce the calculation cost accompanying the analysis. However, it is generally not easy to select such particular attributes. Further, even if an analysis result is obtained, it may be difficult to interpret the analysis result, or it may be difficult to utilize the analysis result to solve business problems depending on the attributes. 
     An embodiment of the present invention has been made in view of the above points, and an object is to implement efficient and effective data analysis. 
     Means for Solving the Problem 
     In order to achieve the above object, an embodiment of the present invention is an analysis device for analyzing data including a plurality of attributes, the analysis device including: first analysis means for, according to a flow defined as a scenario in advance, analyzing whether, between analysis target data and comparison target data, the analysis target data takes a peculiar value in comparison with the comparison target data for a combination of one or more attributes defined for the flow in analysis order defined for the flow; and second analysis means for, if it is analyzed by the first analysis means that the peculiar value is taken, causing, according to an attribute analyzed to take the peculiar value, a name of the attribute and character strings indicating a cause of occurrence of the peculiar value in the attribute to be character strings included in a report in which results of the analyses are displayed; wherein the character strings are character strings defined in advance for each of the analyses in the analysis order defined for the flow. 
     Effects of the Invention 
     An object is to implement efficient and effective data analysis. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram showing an example of an overall configuration of a data analysis system in an embodiment of the present invention. 
         FIG. 2  is a diagram showing an example of purchase data. 
         FIG. 3  is a diagram showing an example of demographic data. 
         FIG. 4  is a diagram showing an example of a hardware configuration of a data analysis device in the embodiment of the present invention. 
         FIG. 5  is a diagram for illustrating an outline of data analysis (for each store) in the embodiment of the present invention. 
         FIG. 6  is a diagram for illustrating an outline of data analysis (grasp of overall trend) in the embodiment of the present invention. 
         FIG. 7  is a flowchart (1) showing an example of a data analysis process for each store. 
         FIG. 8  is a flowchart (2) showing an example of the data analysis process for each store. 
         FIG. 9  is a flowchart (3) showing an example of the data analysis process for each store. 
         FIG. 10  is a flowchart (4) showing an example of the data analysis process for each store. 
         FIG. 11  is a flowchart (5) showing an example of the data analysis process for each store. 
         FIG. 12  is a flowchart (1) showing an example of a data analysis process for understanding of overall trend. 
         FIG. 13  is a flowchart (2) showing an example of the data analysis process for understanding of overall trend. 
         FIG. 14  is a flowchart (3) showing an example of the data analysis process for understanding of overall trend. 
         FIG. 15  is a flowchart (4) showing an example of the data analysis process for understanding of overall trend. 
         FIG. 16  is a flowchart (5) showing an example of the data analysis process for understanding of overall trend. 
         FIG. 17A  is a diagram showing an example of an analysis result report (for each store) (1/2). 
         FIG. 17B  is a diagram showing an example of the analysis result report (for each store) (2/2). 
         FIG. 18A  is a diagram showing an example of an analysis result report (grasp of overall trend) (1/2). 
         FIG. 18B  is a diagram showing an example of the analysis result report (grasp of overall trend) (2/2). 
         FIG. 19  is a diagram showing an example of an analysis result report (correlation analysis). 
     
    
    
     DESCRIPTION OF EMBODIMENT 
     An embodiment of the present invention will be described below. In the present embodiment, a data analysis system  1  which, with purchase data of each store in a certain area as a target, implements efficient and effective data analysis using a combination of attributes of the purchase data will be described. Here, in general, it is thought that, in many cases, it is not possible for a user with little analysis experience to appropriately narrow down attributes to be analysis targets to reduce calculation costs or appropriately interpret an analysis result. Therefore, by using the data analysis system  1  in the embodiment of the present embodiment, it becomes possible for especially the user with little analysis experience to efficiently and effectively perform data analysis. 
     Note that data to be an analysis target is not limited to purchase data. The embodiment of the present invention is applicable to any data that includes a plurality of attributes (for example, medical data, people flow data, lodging data, traffic data and the like). 
     In the embodiment of the present invention, a combination of attributes used for data analysis will be also referred to as “an analysis target combination”. Further, a combination (a pair) of purchase data to be an analysis target and data to be a comparison target of the purchase data will be also referred to as “a comparison target pair”. As specific examples of the analysis target combination, for example, (“nationality”, “sales”), (“nationality”, “the number of purchasers”, “sales”) and the like are given. 
     Note that the data to be a comparison target of purchase data (hereinafter also referred to as “comparison target data”) may be purchase data in a different period (for example, purchase data in the previous month, purchase data in the same month of the previous year, or the like of the same store), may be purchase data of a store different from the store of the purchase data (for example, purchase data of another certain store in the same month, or the like), may be data obtained by aggregating purchase data of all stores in the area (for example, data obtained by aggregating purchase data of all the stores in the area in the same month, or the like), or may be data having a certain relation with the purchase data (hereinafter also referred to as “related data”. As specific examples of the related data, for example, data indicating a statistical result of the population (the number of staying people) of the area in which the store is present, data obtained by aggregating messages and the like posted to SNS&#39;s (social network services) about a certain article, and the like are provided. 
     Therefore, as the comparison target pair, for example, “the latest purchase data of a user&#39;s own store and past data of the user&#39;s own store”, “the purchase data of the user&#39;s own store and purchase data of another store”, “the purchase data of the user&#39;s own store and purchase data of all the stores” and the like are considered. As for what kind of comparison target pair is to be used to perform data analysis, a user can arbitrarily set it. 
     Further, in the embodiment of the present invention, when there exists a difference equal to or above a predetermined difference in a certain attribute included in an analysis target combination, between analysis target purchase data and comparison target data, it is said that a peculiar point exists in this attribute. For example, when, at the time of comparing the attributes “sales” between analysis target purchase data and purchase data of the same month of the previous year for the same store, the sales of the analysis target purchase data are increasing (or decreasing) in comparison with the same month of the previous year by a predetermined value or more, it is said that a peculiar point exists in the attribute “sales”. Similarly, when, at the time of comparing the attributes “the number of purchasers” between analysis target purchase data and purchase data of the same month of another certain store, the number of purchasers of the analysis target data is increasing (or decreasing) in comparison with the other store by a predetermined value or more, it is said that a peculiar point exists in the attribute “the number of purchasers”. 
     Here, existence of a peculiar point may be determined by a method other than the above. For example, in a case where, as a result of performing statistical examination about a certain attribute included in an analysis target combination between analysis target purchase data and comparison target data, there is a significant difference, it may be assumed that a peculiar point exists in this attribute. 
     [Overall Configuration] 
     First, an overall configuration of the data analysis system  1  in the embodiment of the present invention will be described with reference to  FIG. 1 .  FIG. 1  is a diagram showing an example of the overall configuration of the data analysis system  1  in the embodiment of the present invention. 
     As shown in  FIG. 1 , the data analysis system  1  in the embodiment of the present invention includes a data analysis device  10 , a plurality of store terminals  20 , one or more report display terminals  30  and one or more population statistics management servers  40 . 
     The store terminals  20  are terminals (for example, PCs (personal computers) and the like) installed in stores and the like in the area and transmit purchase data of the stores to the data analysis device  10 . In the example shown in  FIG. 1 , a case is shown where the store terminals  20  are installed in three stores of “Store 1”, “Store 2” and “Store 3”, respectively, in the area, and these store terminals  20  transmit purchase data to the data analysis device  10 . 
     Here each store terminal  20  transmits, for example, purchase data obtained by performing a predetermined aggregation process (for example, purchase data obtained by monthly aggregating the number of purchasers and a total purchase amount for each article classification, or the like) to the data analysis device  10 . However, such aggregation process may be performed by the data analysis device  10 . 
     Each report display terminal  30  is a terminal (for example, a PC, a smartphone, a tablet terminal or the like) that displays a report of a data analysis result by the data analysis device  10  (an analysis result report). It becomes possible for a user of the report display terminal  30  to utilize the analysis result report to solve business problems (for example, planning promotion and the like of a new article). 
     Each population statistics management server  40  manages population statistics data that are an example of the related data. For example, the population statistics management server  40  returns the population statistics data in response to a request from the data analysis device  10 . The population statistics data are, for example, data indicating population statistics for each time zone in a geographical area (this is also referred to as “a mesh”) determined in advance. 
     The data analysis device  10  is a computer or a computer system that performs data analysis of purchase data and provides the analysis result report to the report display terminal  30 . At this time, the data analysis device  10  in the embodiment of the present invention performs data analysis of a comparison target pair, with an analysis target combination according to a scenario defined in advance, and provides the analysis result report that includes output character strings indicating an interpretation of a result of the data analysis. Thereby, the data analysis device  10  in the embodiment of the present invention can implement effective and efficient data analysis. 
     Here, the data analysis device  10  in the embodiment of the present invention has a data analysis processing unit  110  and a report display processing unit  120  as functional units. Further, the data analysis device  10  in the embodiment of the present invention has a purchase data storage unit  130 , a related data storage unit  140  and an analysis result data storage unit  150  as storage units. 
     The purchase data storage unit  130  stores purchase data. The related data storage unit  140  stores the related data (for example, the population statistics data described above, and the like). The analysis result data storage unit  150  stores data showing a data analysis result (analysis result data) by the data analysis device  10 . Note that details of the purchase data and the population statistics data that are examples of the related data will be described later. 
     The data analysis processing unit  110  performs data analysis of a comparison target month with an analysis target combination according to a scenario defined in advance, based on the purchase data stored in the purchase data storage unit  130 , and creates analysis result data including an attribute in which a peculiar point exists and output character strings indicating a reason why the peculiar point exists in the attribute (that is, an interpretation of a data analysis result). At this time, the data analysis processing unit  110  also uses the related data stored in the related data storage unit  140  as necessary to perform the data analysis. Note that the analysis result data is stored into the analysis result data storage unit  150  by the data analysis processing unit  110 . 
     Further, the data analysis processing unit  110  performs analysis of a correlation between the attribute in which the peculiar point exists, and the related data. 
     The report display processing unit  120  creates the analysis result report including the attributes in which the peculiar point exists and the output character strings indicating the reason why the peculiar point exists in the attribute, based on the analysis result data stored in the analysis result data storage unit  150 , and causes the analysis result report to be displayed on the report display terminal  30 . At this time, the report display processing unit  120  creates the analysis result report periodically (for example, every month or the like) and causes the analysis result report to be displayed on the report display terminal  30  (therefore, data analysis by the data analysis processing unit  110  is also periodically performed). 
     Note that the configuration of the data analysis system  1  shown in  FIG. 1  is an example, and other configurations may be adopted. For example, the report display terminal  30  may not be included in the data analysis system  1 , and the analysis result report may be displayed on the store terminal  20 . Further, for example, the population statistics management server  40  may not be included in the data analysis system  1 . 
     [Purchase Data] 
     Here, the purchase data stored in the purchase data storage unit  130  will be described with reference to  FIG. 2 .  FIG. 2  is a diagram showing an example of the purchase data. In  FIG. 2 , as an example, purchase data indicating a monthly number of purchasers and total purchase amount for each article classification and for each purchaser nationality at a certain store are described. 
     As shown in  FIG. 2 , the purchase data includes one or more records. Further, each of the records includes a record ID, month/year, a store ID, article classifications, a nationality, the number of purchasers and a total purchase amount) as attributes (that is, data items). 
     The record ID is an ID (identification information) that identifies each of records constituting the purchase data. The month/year is a year and month in which the number of purchasers and a total purchase amount are aggregated for each article classification and for each purchaser nationality. The store ID is an ID (identification information) that identifies each store. 
     The article classifications are article classifications defined, for example, by JICF or the like. In the example shown in  FIG. 2 , major article classification and middle article classification are included as the article classifications. 
     The nationality indicates a nationality of purchasers of each article. The number of purchasers indicates a total number of the purchasers. The total purchase amount indicates a total of purchase prices the purchasers paid to purchase the article (that is, sales). 
     Note that, among the attributes included in the purchase data, attributes such as the article classifications, attributes such as the purchaser nationality, and attributes such as the number of purchasers and the total purchase amount are also indicated as “article information”, “target information” and “sales related information”, respectively. As the article information, for example, article name, article ID and the like are provided in addition to the article classifications. Further, as the target information, for example, gender, race, age and the like are provided in addition to nationality. As the sales information, for example, average purchase price, average purchase amount, month-to-month ratio for total purchase amount, month-to-month ratio for the number of purchasers, month-to-month ratio for average purchase price, month-to-month ratio for average purchase amount, and the like are provided in addition to the number of purchasers and the total purchase amount. 
     Therefore, the purchase data refers to data configured with records each of which includes the sales information and at least one of the article information and the target information as attributes, the records being obtained by performing a predetermined aggregation process for a predetermined period (for example, a day, a week, a month, a year or the like). Note that, according to purposes of data analysis, for example, purchase data configured with records each of which includes two pieces of information, the sales information and the target information as attributes may be used, purchase data configured with records each of which includes two pieces of information, the sales information and the article information as attributes may be used, or purchase data configured with records each of which includes three pieces of information, the sales information, the article information and the target information as attributes may be used. 
     Further, what kinds of attributes are specifically used as the article information, the target information and the sales related information, respectively (that is, for example, whether only “nationality” is to be used or two of “nationality” and “age” are to be used as the target information, and the like) also differs according to purposes of data analysis, characteristics of articles sold by each store, and the like. 
     [Population Statistics Data] 
     Next, the population statistics data, which are an example of the related data stored in the related data storage unit  140 , will be described with reference to  FIG. 3 .  FIG. 3  is a diagram showing an example of the population statistics data. 
     As shown in  FIG. 3 , the population statistics data includes one or more records. Further, each of the records includes a date, a time zone, a mesh ID, a nationality and the number of people as attributes (that is, data items). 
     The date and the time zone are a date and time zone targeted by population statistics. The mesh ID is an ID (identification information) that identifies each mesh (that is, a geographical area specified in advance). Note that, though the mesh is often specified, for example, as a 500 m to a few km square rectangular area, any area specified in advance can be caused to be the mesh. 
     The nationality and the number of people are a nationality and the number of people who stayed in the mesh on the date and time zone. 
     Thus, the population statistics data are data configured with records each of which includes the nationality and number of people who stayed in a mesh for each date and time zone. By using such population statistics data, it is possible to, for example, when a peculiar point exists in the attribute “the number of purchasers” of the purchase data, analyze that the population around the store is large (or small) and the like as described later. 
     [Hardware Configuration] 
     Next, a hardware configuration of the data analysis device  10  in the embodiment of the present invention will be described with reference to  FIG. 4 .  FIG. 4  is a diagram showing an example of the hardware configuration of the data analysis device  10  in the embodiment of the present invention. Note that each of the store terminals  20 , the report display terminals  30 , the population statistics management servers  40  and the like can be implemented by a hardware configuration similar to that of the data analysis device  10 . 
     As shown in  FIG. 4 , the data analysis device  10  in the embodiment of the present invention includes an input device  11 , a display device  12 , an external I/F  13 , a RAM (random access memory)  14 , a ROM (read-only memory)  15 , a processor  16 , a communication I/F  17  and an auxiliary storage device  18 , as a hardware. These pieces of hardware are communicably connected via a bus  19 . 
     The input device  11  is, for example, a keyboard and a mouse, a touch panel, or the like and used for a user to perform various kinds of input operations. The display device  12  is, for example, a display or the like, and displays a processing result and the like of the data analysis device  10 . Note that the data analysis device  10  may not include at least one of the input device  11  and the display device  12 . 
     The external I/F  13  is an interface with external devices. The external devices include a recording medium  13   a  and the like. The data analysis device  10  can perform reading from and writing to the recording medium  13   a  via the external I/F  13 . In the recording medium  13   a , for example, one or more programs and the like to implement the data analysis processing unit  110  and the report display processing unit  120  may be recorded. 
     As the recording medium  13   a , for example, a flexible disk, a CD (compact disc), a DVD (digital versatile disk), an SD memory card (secure digital memory card), a USB (universal serial bus), a memory card and the like are included. 
     The RAM  14  is a volatile semiconductor memory that temporarily holds programs and data. The ROM  15  is a nonvolatile semiconductor memory capable of holding programs and data even if power is turned off. In the ROM  15 , for example, setting information about an OS (operating system), setting information about a communication network and the like are stored. 
     The processor  16  is, for example, a CPU (central processing unit) or the like and is an arithmetic unit that reads out a program and data from the ROM  15 , the auxiliary storage device  18  and the like onto the RAM  14  and executes a process. The data analysis processing unit  110  and the report display processing unit  120  are implemented by reading out one or more programs stored in the ROM  15 , the auxiliary storage device  18  and the like on the RAM  14 , and causing the processor  16  to execute processes. 
     The communication I/F  17  is an interface for connecting the data analysis device  10  to the communication network. The one or more programs that implement the data analysis processing unit  110  and the report display processing unit  120  may be acquired (downloaded) from a predetermined server device or the like via the communication I/F  17 . 
     The auxiliary storage device  18  is, for example, an HDD (hard disk drive), an SSD (solid state drive) or the like and is a nonvolatile storage device that stores programs and data. The programs and data stored in the auxiliary storage device  18  include, for example, the OS, application programs that implement various kinds of functions on the OS, one or more programs that implement the data analysis processing unit  110  and the report display processing unit  120 , and the like. 
     Further, the purchase data storage unit  130 , the related data storage unit  140  and the analysis result data storage unit  150  can be implemented, for example, with the auxiliary storage device  18 . However, at least one storage unit among the purchase data storage unit  130 , the related data storage unit  140  and the analysis result data storage unit  150  may be implemented with a storage device or the like connected to the data analysis device  10  via the communication network. 
     By having the hardware configuration shown in  FIG. 4 , the data analysis device  10  in the embodiment of the present invention can implement various kinds of processes described later. Note that, though a case where the data analysis device  10  in the embodiment of the present invention is implemented by one device (computer) is shown in the example shown in  FIG. 4 , the data analysis device  10  is not limited thereto. The data analysis device  10  in the embodiment of the present invention may be implemented by a plurality of devices (computers). Further, the one device (computer) may include a plurality of processors  16  and a plurality of memories (the RAM  14 , the ROM  15 , the auxiliary storage device  18  and the like). 
     [Outline of Data Analysis] 
     Here, an outline of data analysis performed by the data analysis device  10  in the embodiment of the present invention will be described. In the embodiment of the present invention, a description will be made on a case where data analysis is performed with purchase data of a certain store as an analysis target (in other words, a report target of the analysis result report) (data analysis for each store), and a case where data analysis is performed with (purchase data obtained by aggregating) purchase data of all stores in a certain area as an analysis target (data analysis for grasp of overall trend). 
     Note that the analysis target is not limited to a store or all the stores in the area but may be, for example, particular stores or the like in a certain area or particular stores among stores operated by a certain company. Further, the area may be, for example, a range identified by a latitude, a longitude and the like, a range identified by an administrative division such as a municipality, or a range identified by a mesh. 
     (For Each Store) 
     First, an outline of the data analysis for each store will be described with reference to  FIG. 5 .  FIG. 5  is a diagram for illustrating the outline of the data analysis (for each store) in the embodiment of the present invention. In the example shown in  FIG. 5 , the comparison target pair is assumed to be monthly purchase data of a user&#39;s own store and purchase data of the same month in another store. 
     In the data analysis for each store, the data analysis is performed by flows 1a to 6a as shown in  FIG. 5 . Hereinafter, it is assumed that the monthly purchase data includes “major article classification”, “middle article classification”, “nationality”, “the number of purchasers” and “total purchase amount” as an attribute. Note that the number of purchasers and the total purchase amount refer to a total number of people of the nationality who purchased articles belonging to the major article classification and the middle article classification at the relevant store during the month and a total of purchase prices, respectively. 
     Flow 1a 
     First, in the flow 1a, data analysis is performed by (1-1) to (1-7) below. Note that a case where a peculiar point exists in a certain attribute refers to a case where there is a difference equal to or larger than a predetermined difference between the attribute of the monthly purchase data of the user&#39;s own store and the attribute of the purchase data of the same month in the other store as described above. 
     (1-1) First, it is analyzed whether or not a peculiar point exists in sales (that is, a total of all total purchase amounts of the user&#39;s own store in the current month) with “sales” as an analysis target combination. Then, if a peculiar point exists in the sales, analysis of (1-2) is performed. On the other hand, if no peculiar point exists in the sales, data analysis of the flow 2a is performed. 
     (1-2) When the peculiar point exists in the sales, it is analyzed whether or not a peculiar point exists in sales for each nationality, with “sales” and “nationality” as an analysis target combination. Then, if a peculiar point exists in the sales for each nationality, analysis of (1-3) is performed. On the other hand, if no peculiar point exists in the sales for each nationality, the data analysis of the flow 2a is performed. Hereinafter, a nationality for which it has been analyzed in (1-2) that there is a peculiar point will be referred to as “a nationality A” for simplification. 
     (1-3) When the peculiar point exists in the sales for the nationality A, it is analyzed whether or not a peculiar point exists in the number of purchasers of the nationality A, with “sales”, “nationality” and “the number of purchasers” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the nationality A, analysis of (1-4) is performed. On the other hand, if no peculiar point exists in the number of purchasers of the nationality A, analysis of (1-5) is performed. 
     (1-4) When the peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in the number of people of the nationality A around the user&#39;s own store, from the population (the number of people) of the nationality A around the user&#39;s own store and the population (the number of people) of the nationality A in the whole area, using the number of people of the nationality A in the population statistics data (that is, with “nationality” and “the number of people” in the population statistics data as an analysis target combination). Then, if no peculiar point exists, “the number of purchasers of the nationality A” in which it has been analyzed in (1-3) described above that a peculiar point exists is set as an analysis result (that is, a peculiar point to which a user should pay attention). On the other hand, if the peculiar point exists, “the number of people of the nationality A around the user&#39;s own store” in which it has been analyzed in (1-3) described above that a peculiar point exists, is set as an analysis result. Note that, at this time, analysis result data that includes the analysis result and output character strings indicating a reason why the peculiar point exists in the attribute obtained as the analysis result. 
     (1-5) When no peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in sales of each major article classification for the nationality A in which it has been analyzed in (1-2) above that a peculiar point exists, with “sales”, “nationality” and “major article classification” as an analysis target combination. Then, if a peculiar point exists in the sales of each major article classification for the nationality A, analysis of (1-6) is performed. On the other hand, if no peculiar point exists in the sales of each major article classification for the nationality A, the data analysis of the flow 2a is performed. Hereinafter, a major article classification in which it has been analyzed in (1-5) that there is a peculiar point will be referred to as “a major article classification B” for simplification. 
     (1-6) When the peculiar point exists in the major article classification B for the nationality A, it is analyzed whether or not a peculiar point exists in the number of purchasers of the major article classification B for the nationality A, with “sales”, “nationality”, “the number of purchasers” and “major article classification” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the major article classification B for the nationality A, “the number of purchasers of the major article classification B for the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists in the number of purchasers of the major article classification B for the nationality A, analysis of (1-7) is performed. 
     (1-7) When no peculiar point exists in the number of purchasers of the major article classification B for the nationality A, it is analyzed whether or not a peculiar point exists in sales of each middle article classification for the nationality A, with “sales”, “nationality”, “major article classification” and “middle article classification” as an analysis target combination. Hereinafter, a middle article classification in which it has been analyzed in (1-7) that a peculiar point exists will be referred to as “a middle article classification C” for simplification. 
     Then, if a peculiar point exists in the sales of each middle article classification for the nationality A, “the sales of the middle article classification C for the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists in the sales of the middle article classification for the nationality A, “the sales of the major article classification B for the nationality A” is set as an analysis result. This is because there is a peculiar point neither in the sales of middle article classification for the nationality A nor in the number of purchasers of the major article classification B for the nationality A. 
     Flow 2a 
     Next, in the flow 2a, data analysis is performed by (2-1) to (2-6) below. 
     (2-1) First, it is analyzed whether or not a peculiar point exists in the sales for each nationality, with “sales” and “nationality” as an analysis target combination. Then, if a peculiar point exists in the sales for each nationality, analysis of (2-2) is performed. On the other hand, if no peculiar point exists in the sales for each nationality, data analysis of the flow 3a is performed. Hereinafter, a nationality in which it has been analyzed in (2-1) that there is a peculiar point will be referred to as “a nationality A” for simplification. 
     (2-2) Then, when the peculiar point exists in the sales for the nationality A, it is analyzed whether or not a peculiar point exists in the number of purchasers of the nationality A, with “sales”, “nationality” and “the number of purchasers” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the nationality A, analysis of (2-3) is performed. On the other hand, if no peculiar point exists in the number of purchasers of the nationality A, analysis of (2-4) is performed. 
     (2-3) When the peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in the number of people of the nationality A around the user&#39;s own store, from the population (the number of people) of the nationality A around the user&#39;s own store and the population (the number of people) of the nationality A in the whole area, using the number of people of the nationality A in the population statistics data (that is, with “nationality” and “the number of people” in the population statistics data as an analysis target combination). Then, if no peculiar point exists, “the number of purchasers of the nationality A” in which it has been analyzed in (2-2) described above that a peculiar point exists is set as an analysis result (that is, a peculiar point that the user should pay attention to). On the other hand, if the peculiar point exists, “the number of people of the nationality A around the user&#39;s own store” in which it has been analyzed in (2-2) described above that a peculiar point exists is set as an analysis result. 
     (2-4) When no peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in the sales of each major article classification for the nationality A in which it has been analyzed in (2-1) above that a peculiar point exists, with “sales”, “nationality” and “major article classification” as an analysis target combination. Then, if a peculiar point exists in the sales of each major article classification for the nationality A, analysis of (2-5) is performed. On the other hand, if no peculiar point exists in the sales of each major article classification for the nationality A, the data analysis of the flow 3a is performed. Hereinafter, a major article classification in which it has been analyzed in (2-4) that there is a peculiar point will be referred to as “a major article classification B” for simplification. 
     (2-5) When the peculiar point exists in the major article classification B for the nationality A, it is analyzed whether or not there is a peculiar point in the number of purchasers of the major article classification B for the nationality A, with “sales”, “nationality”, “major article classification” and “the number of purchasers” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the major article classification B for the nationality A, “the number of purchasers of the major article classification B for the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists in the number of purchasers of the major article classification B for the nationality A, analysis of (2-6) is performed. 
     (2-6) When no peculiar point exists in the number of purchasers of the major article classification B for the nationality A, it is analyzed whether or not a peculiar point exists in the sales of each middle article classification for the nationality A, with “sales”, “nationality”, “major article classification” and “middle article classification” as an analysis target combination. Hereinafter, a middle article classification in which it has been analyzed in (2-6) that a peculiar point exists will be referred to as “a middle article classification C” for simplification. 
     Then, if a peculiar point exists in the sales of each middle article classification for the nationality A, “the sales of the middle article classification C for the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists in the sales of the middle article classification for the nationality A, “the sales of the major article classification B for the nationality A” is set as an analysis result. 
     Flow 3a 
     Next, in the flow 3a, data analysis is performed by (3-1) below. 
     (3-1) It is analyzed whether or not a peculiar point exists in the sales for each nationality, with “sales” and “nationality” as an analysis target combination. Then, if a peculiar point exists in the sales for each nationality (the nationality in which the peculiar point exists is referred to as “a nationality A”), “sales for the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists in the sales for each nationality, data analysis of the flow 4a is performed. 
     Flow 4a 
     Next, in the flow 4a, data analysis is performed by (4-1) below. 
     (4-1) It is analyzed whether or not a peculiar point exists in the sales of each major article classification, with “sales” and “major article classification” as an analysis target combination. Then, if a peculiar point exists in the sales of each major article classification (the major article classification in which the peculiar point exists is referred to as “a major article classification B”), “sales of the major article classification B” is set as an analysis result. On the other hand, if no peculiar point exists in the sales of each major article classification, data analysis of the flow 5a is performed. 
     Flow 5a 
     Next, in the flow 5a, data analysis is performed by (5-1) below. 
     (5-1) It is analyzed whether or not a peculiar point exists in the sales (that is, total sales of the current month of the user&#39;s own store), with “sales” as an analysis target combination. Then, if a peculiar point exists in the sales, “the total sales” is set as an analysis result. On the other hand, if no peculiar point exists in the sales, data analysis of the flow 6a is performed. 
     Flow 6a 
     Lastly, in the flow 6a, “no peculiar points” is set as an analysis result. This is because, in this case, no peculiar point exists in the purchase data by the analyses according to the flows 1a to 5a described above. 
     Thus, in the embodiment of the present invention, the flows 1a to 6a are defined as a scenario of the data analysis for each store, and the data analysis for each store is performed by these flows 1a to 6a. In these flows 1a to 6a, analysis granularity becomes finer from left to right in the same flow in  FIG. 5 . Meanwhile, analysis granularity becomes finer in order from the flow 1a to the flow 6a. 
     For example, in the flow 1a, while the data analysis is performed using only “sales” included in the analysis target combination in (1-1), the data analysis is performed using “sales” and “nationality” included in the analysis target combination in (1-2). Therefore, the data granularity of the data analysis in (1-2) is finer than the data analysis in (1-1). Thus, in the flow 1a, the data analysis is performed while the analysis granularity is caused to be gradually finer, in such order of “sales”→“sales”×“nationality”→“sales”×“nationality”×“the number of purchasers”→ . . . . This is because, even if a peculiar point exists when the granularity is fine, influence on the whole is small if no peculiar point exists when the granularity is large, and a possibility that it becomes a business problem is weak. Thereby, effective and efficient data analysis for each store becomes possible. 
     Note that it is an example to define the flows 1a to 6a as a scenario of the data analysis for each store, and it is possible to define various kinds of flows other than these flows 1a to 6a as a scenario. At this time, it is preferable that the definition is made so that data analysis is performed, for example, in order of (1) a flow of analyzing sales related information×target information×article information (the flows 1a and 2a), (2) a flow of analyzing sales related information×target information (the flow 3a), (3) a flow of analyzing sales related information×article information (the flow 4a) and (4) a flow of analyzing sales related information (the flow 5a). Further, at this time, a combination of a plurality of attributes may be used as each piece of information (for example, both of “sales” and “the number of purchasers” are used as the sales related information, both of “nationality” and “gender” are used as the target information, and so on). 
     Further, in the embodiment of the present invention, though a description is made on the assumption that the data analyses of (1-4) and (2-3) above are performed, these data analyses (that is, data analyses using the population statistics data) do not necessarily have to be performed. 
     (Understanding of Overall Trend) 
     Next, an outline of the data analysis for understanding of overall trend will be described with reference to  FIG. 6 .  FIG. 6  is a diagram for illustrating an outline of the data analysis (for understanding of overall trend) in the embodiment of the present invention. In the example shown in  FIG. 6 , it is assumed that the comparison target pair is data obtained by aggregating monthly purchase data of all the stores in the area (this data will be referred to as “all stores purchase data” for simplification) and all stores purchase data of the same month of the previous year). 
     In the data analysis for understanding of overall trend, the data analysis is performed by flows 1b to 6b as shown in  FIG. 6 . 
     Flow 1b 
     First, in the flow 1b, the data analysis is performed by (7-1) to (7-4) below. Note that the case where a peculiar point exists in a certain attribute refers to the case where there is a difference equal to or larger than a predetermined difference between the attribute of the monthly all stores purchase data and the attribute of the all stores purchase data of the same month of the previous year as described above. Hereinafter, it is assumed that the monthly all stores purchase data includes “nationality”, “the number of purchasers” and “total purchase amount” are included as attributes. Note that the number of purchasers and the total purchase amount refer to a total of the numbers of people of the nationality who purchased the article in all the stores in the area during the relevant month, and a total of total purchase amounts, respectively. 
     (7-1) First, it is analyzed whether or not a peculiar point exists in overall total sales (that is, a total of all the total purchase amounts in the current month) with “overall total sales” as an analysis target combination. Then, if a peculiar point exists in the overall total sales, analysis of (7-2) is performed. On the other hand, if no peculiar point exists in the overall total sales, data analysis of the flow 2b is performed. 
     (7-2) When the peculiar point exists in the overall total sales, it is analyzed whether or not a peculiar point exists in overall total sales for each nationality, with “overall total sales” and “nationality” as an analysis target combination. Then, if a peculiar point exists in the overall total sales for each nationality, analysis of (7-3) is performed. On the other hand, if no peculiar point exists in the overall total sales, the data analysis of the flow 2b is performed. Hereinafter, a nationality in which it has been analyzed in (7-2) that there is a peculiar point will be referred to as “a nationality A” for simplification. 
     (7-3) When the peculiar point exists in the overall total sales for the nationality A, it is analyzed whether or not a peculiar point exists in the number of purchasers of the nationality A, with “overall total sales”, “nationality” and “the number of purchasers” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the nationality A, analysis of (7-4) is performed. On the other hand, if no peculiar point exists in the number of purchasers of the nationality A, “overall total sales for the nationality A” is set as an analysis result. 
     (7-4) When the peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in the number of people of the nationality A in the whole area in the current month, from the population (the number of people) of the nationality A in the whole area in the current month and the population (the number of people) of the nationality A in the whole area in the same month of the previous year, with the number of people of the nationality A in the population statistics data (that is, with “nationality” and “the number of people” in the population statistics data as an analysis target combination). Then, if no peculiar point exists, “the number of purchasers of the nationality A” in which it has been analyzed in (7-3) described above that a peculiar point exists is set as an analysis result. On the other hand, if the peculiar point exists, “a total number of staying people of the nationality A in the whole area” is set as an analysis result. This is because, in this case, the number of people of the nationality A in the whole area is increasing (or decreasing) in comparison with the same month of the previous year by a predetermined threshold or above. 
     Flow 2b 
     Next, in the flow 2b, data analysis is performed by (8-1) to (8-3) below. 
     (8-1) It is analyzed whether or not a peculiar point exists in the overall total sales for each nationality, with “overall total sales” and “nationality” as an analysis target combination. Then, if a peculiar point exists in the overall total sales for each nationality, analysis of (8-2) is performed. On the other hand, if no peculiar point exists in the overall total sales for each nationality, data analysis of the flow 3b is performed. Hereinafter, a nationality in which it has been analyzed in (8-1) that there is a peculiar point will be referred to as “a nationality A” for simplification. 
     (8-2) When the peculiar point exists in overall total sales for the nationality A, it is analyzed whether or not a peculiar point exists in the number of purchasers of the nationality A, with “overall total sales”, “nationality” and “the number of purchasers” as an analysis target combination. Then, if a peculiar point exists in the number of purchasers of the nationality A, analysis of (8-3) is performed. On the other hand, if no peculiar point exists in the number of purchasers of the nationality A, “overall total sales for the nationality A” is set as an analysis result. 
     (8-3) When the peculiar point exists in the number of purchasers of the nationality A, it is analyzed whether or not a peculiar point exists in the number of people of the nationality A in the whole area in the current month, from the population (the number of people) of the nationality A in the whole area in the current month and the population (the number of people) of the nationality A in the whole area in the same month of the previous year, using the number of people of the nationality A in the population statistics data (that is, with “nationality” and “the number of people” in the population statistics data as an analysis target combination). Then, if no peculiar point exists, “the number of purchasers of the nationality A” is set as an analysis result. On the other hand, if the peculiar point exists, “the total number of staying people of the nationality A in the whole area” is set as an analysis result. 
     Flow 3b 
     Next, in the flow 3b, data analysis is performed by (9-1) to (9-3) below. 
     (9-1) It is analyzed whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, using the number of people in the population statistics data (that is, with “the number of people” in the population statistics data as an analysis target combination). Then, if a peculiar point exists in the population of the whole area in the current month, analysis of (9-2) is performed. On the other hand, if no peculiar point exists in the population of the whole area in the current month, data analysis of the flow 4b is performed. 
     (9-2) When the peculiar point exists in the population of the whole area in the current month, it is analyzed whether or not a peculiar point exists in the population (the number of people) for each mesh between the current month and the same month of the previous year, using the number of people for each mesh in the population statistics data (that is, with “the number of people” and “mesh ID” in the population statistics data as an analysis target combination). Then, if the peculiar point exists, analysis of (9-3) is performed. On the other hand, if no peculiar point exists, the data analysis of the flow 4b is performed. Hereinafter, a mesh in which it has been analyzed in (9-2) that there is a peculiar point will be referred to as “a mesh A” for simplification. 
     (9-3) When the peculiar point exists in the population of the mesh A in the current month, it is analyzed whether or not a peculiar point exists in the population of each nationality in the mesh A between the current month and the same month of the previous year, using the number of people of each nationality in the mesh A in the population statistics data (that is, with “the number of people”, “nationality” and “mesh ID” in the population statistics data as an analysis target combination). Then, if the peculiar point exists (the nationality in which the peculiar point exists is referred to as “a nationality A”), “a total number of staying people of the nationality A in the mesh A” is set as an analysis result. On the other hand, if no peculiar point exists, “a total number of staying people in the mesh A” is set as an analysis result. 
     Flow 4b 
     Next, in the flow 4b, data analysis is performed by (10-1) to (10-2) below. 
     (10-1) It is analyzed whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, using the number of people in the population statistics data. Then, if a peculiar point exists in the population of the whole area in the current month, analysis of (10-2) is performed. On the other hand, if no peculiar point exists in the population of the whole area in the current month, data analysis of the flow 5b is performed. 
     (10-2) When the peculiar point exists in the population of the whole area in the current month, it is analyzed whether or not a peculiar point exists in the population (the number of people) of each nationality between the current month and the same month of the previous year, using the number of people of each nationality in the population statistics data. Then, if the peculiar point exists (the nationality in which the peculiar point exists is referred to as “a nationality A”), “a total number of staying people of the nationality A” is set as an analysis result. On the other hand, if no peculiar point exists, the data analysis of the flow 5b is performed. 
     Flow 5b 
     Next, in the flow 5b, data analysis is performed by (11-1) below. 
     (11-1) It is analyzed whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, using the number of people in the population statistics data. Then, if a peculiar point exists in the population of the whole area in the current month, “the number of staying people of the whole area” is set as an analysis result. On the other hand, if no peculiar point exists in the population of the whole area in the current month, data analysis of the flow 6b is performed. 
     Flow 6b 
     Lastly, in the flow 6b, “no peculiar points” is set as an analysis result. This is because, in this case, no peculiar point exists in the all stores purchase data by the analyses according to the flows 1b to 5b described above. 
     Thus, in the embodiment of the present invention, the flows 1b to 6b are defined as a scenario of the data analysis for understanding of overall trend, and the data analysis for understanding of overall trend is performed by these flows 1b to 6b. In these flows 1b to 6b, analysis granularity becomes finer from left to right in the same flow in  FIG. 6 . 
     Note that it is an example to define the flows 1b to 6b as a scenario of the data analysis for understanding of overall trend, and it is possible to define various kinds of flows other than these flows 1b to 6b as a scenario. At this time, it is preferable that the definition is made so that data analysis is performed, for example, in order of (1) a flow of analyzing sales related information×target information (the flows 1b and 2b) and (2) a flow of analyzing target information (the flows 3b to 5a). Further, at this time, a combination of a plurality of attributes may be used as each piece of information. 
     Further, in the embodiment of the present invention, though a description is made on the assumption that the data analyses of (7-4), (8-3), (9-1) to (9-4), (10-1) and (10-2), and (11-1) above are performed, these data analyses (that is, data analyses using the population statistics data) do not necessarily have to be performed. 
     [Data Analysis Process (for Each Store)] 
     Hereinafter, a flow of a data analysis process for each store will be described with reference to  FIGS. 7 to 11 .  FIGS. 7 to 11  are flowcharts (1) to (5) showing an example of the data analysis process for each store. Note that, as for these flowcharts (1) to (5), the flowcharts (1) to (4) correspond to the flows 1a to 4a shown in  FIG. 5 , respectively, and the flowchart (5) corresponds to the flowcharts 5a and 6a. 
     Further, hereinafter, it is assumed that the comparison target pair is “monthly purchase data of the user&#39;s own store and purchase data of another store in the same month”, and the monthly purchase data includes “major article classification”, “middle article classification”, “nationality”, “the number of purchasers” and “total purchase amount” as attributes, similarly to  FIG. 5 . These pieces of purchase data are stored in the purchase data storage unit  130 . Note that the comparison target pair is set, for example, by the user. 
     (Flowchart (1)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales (that is, the total of all the total purchase amounts of the user&#39;s own store in the current month) with “sales” as an analysis target combination (step S 101 ). For example, if there is a difference in sales equal to or above a predetermined threshold between the user&#39;s own store and the other store, the data analysis processing unit  110  determines that a peculiar point exists in the sales in the purchase data of the current month of the user&#39;s own store. Note that a ratio may be used instead of the difference (the same goes for the time of analyzing existence of a peculiar point hereinafter). 
     Then, if no peculiar point exists in the sales (step S 102 : NO), the data analysis processing unit  110  executes the flowchart (2). On the other hand, if a peculiar point exists in the sales (step S 102 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales (for example, “Increasing against the previous month in comparison with sales of other store”, “Decreasing against the previous month in comparison with sales of other store” or the like) to output character strings (step S 103 ). Note that, for example, which of “Increasing against the previous month in comparison with sales of the other store” and “Decreasing against the previous month in comparison with sales of the other store” is to be added to the output character strings is determined based on whether the sales of the user&#39;s own store has been analyzed to be high or low in comparison with the sales of the other store as a result of the analysis of step S 101  described above. The same goes for the time of adding predetermined character strings to the output character strings hereinafter. 
     Subsequent to step S 103 , the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales for each nationality, with “sales” and “nationality” as an analysis target combination (step S 104 ). For example, if such a nationality that there is a difference equal to or above a predetermined threshold in the sales for each nationality between the user&#39;s own store and the other store, the data analysis processing unit  110  determines that a peculiar point exists in the sales for each nationality in the purchase data of the current month of the user&#39;s own store. 
     Then, if no peculiar point exists in the sales for each nationality (step S 105 : NO), the data analysis processing unit  110  executes the flowchart (2). On the other hand, if a peculiar point exists in the sales for each nationality (step S 105 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales for each nationality (hereinafter, a nationality in which the peculiar point exists will be referred to as “a nationality A”) (for example, “Sales for the nationality A is increasing in comparison with other store”, “Sales for the nationality A is decreasing in comparison with sales of other store”, or the like) to the output character strings (step S 106 ). 
     Next, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of purchasers of the nationality A, with “sales”, “nationality” and “the number of purchasers” as an analysis target combination (step S 107 ). For example, if there is a difference in the number of purchasers of the nationality A equal to or above a predetermined threshold between the user&#39;s own store and the other store, the data analysis processing unit  110  determines that a peculiar point exists in the number of purchasers of the nationality A in the purchase data of the current month of the user&#39;s own store. 
     Then, if a peculiar point exists in the number of purchasers of the nationality A (step S 108 : YES), the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of the nationality A around the user&#39;s own store in the current month, from the number of people of the nationality A around the user&#39;s own store in the current month and the number of people of the nationality A in the whole area in the same month, with “nationality” and “the number of people” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 109 ). For example, if there is a difference in the number of people of the nationality A in the current month equal to or above a predetermined threshold between an area around the user&#39;s own store and the whole area, the data analysis processing unit  110  determines that a peculiar point exists in the number of people of the nationality A around the user&#39;s own store. Note that, at this time, the data analysis processing unit  110  may determine that a peculiar point exists in a case where the number of people of the nationality A in the current month is increasing by a predetermined threshold or above (that is, a case where more people of the nationality A can be attracted to the user&#39;s own store than the other store, and the like). 
     Then, if a peculiar point exists in the number of people of the nationality A around the user&#39;s own store (step S 110 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of the nationality A around the user&#39;s own store (for example, “People of the nationality A could be attracted to the area around the user&#39;s own store unlike the other store”, “People of the nationality A are not around the user&#39;s own store unlike the other store”, or the like) to the output character strings (step S 111 ). 
     On the other hand, if no peculiar point exists in the number of people of the nationality A around the user&#39;s own store (step S 110 : NO), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of purchasers of the nationality A in which it has been analyzed at step S 107  above that a peculiar point exists (for example, “Purchasers of the nationality A are increasing unlike the other store”, “Purchasers of the nationality A are decreasing unlike the other store”, or the like) to the output character strings (step S 112 ). 
     In the case of NO at step S 108  above (that is, in a case where no peculiar point exists in the number of purchasers of the nationality A), the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales of each major article classification for the nationality A (step S 113 ) with “sales”, “nationality” and “major article classification” as an analysis target combination (step S 113 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a major article classification in which the peculiar point exists will be referred to as “a major article classification B”. 
     Then, if a peculiar point exists in the sales of each major article classification for the nationality A (step S 114 : YES), the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of purchasers of the major article classification B for the nationality A, with “sales”, “nationality”, “major article classification” and “the number of purchasers” as an analysis target combination (step S 115 ). 
     On the other hand, if no peculiar point exists in the sales of each major article classification for the nationality A (step S 114 : NO), the data analysis processing unit  110  executes the flowchart (2). 
     If a peculiar point exists in the number of purchasers of the major article classification B for the nationality A (step S 116 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of purchasers of the major article classification B for the nationality A (for example, “The number of purchasers of the major article classification B is increasing especially for the nationality A”, “The number of purchasers of the major article classification B is decreasing especially for the nationality A”, or the like) to the output character strings (step S 117 ). 
     On the other hand, if no peculiar point exists in the number of purchasers of the major article classification B for the nationality A (step S 116 : NO), the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales of each middle article classification of the major article classification B for the nationality A, with “sales”, “nationality”, “major article classification” and “middle article classification” as an analysis target combination (step S 118 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a middle article classification in which the peculiar point exists will be referred to as “a middle article classification C”. 
     If a peculiar point exists in the sales of each middle article classification of the major article classification B for the nationality A (step S 119 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the middle article classification C of the major article classification B for the nationality A (for example, “Sales of the middle article classification C of the major article classification B are increasing especially for the nationality A”, “Sales of the middle article classification C of the major article classification B are decreasing especially for the nationality A”, or the like) to the output character strings (step S 120 ). 
     On the other hand, if no peculiar point exists in the sales of each middle article classification of the major article classification B for the nationality A (step S 119 : NO), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales of the major article classification B for the nationality A (for example, “The sales of the major article classification B are increasing especially for the nationality A”, “The sales of the major article classification B are decreasing especially for the nationality A”, or the like) to the output character strings (step S 121 ). 
     Subsequent to step S 111 , S 112 , S 117 , S 120  or S 121 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (records in the purchase data) and the related data stored in the related data storage unit  140  (step S 122 ). Here, as the related data, for example, various kinds of data such as data obtained by aggregating messages and the like posted to SNS&#39;s, and data about visitors to tourist facilities. By analyzing the correlations between the records in which peculiar points exist and their related data, it becomes possible to extract elements having some relation with the peculiar points (for example, an event, an incident, a matter and the like). Note that a result of the correlation analysis is, for example, coefficients of correlations with the related data. 
     Lastly, the data analysis processing unit  110  creates analysis result data that includes the peculiar points of the purchase data, the output character strings to which the character strings corresponding to the peculiar points are added, and the result of the correlation analysis (step S 123 ). Note that this analysis result data is stored into the analysis result data storage unit  150 . 
     (Flowchart (2)) 
     Steps S 201  to S 220  of  FIG. 8  are similar to steps S 104  to S 123  of  FIG. 7 , respectively, except that the attribute “sales” is not a peculiar point. In the flowchart (2), however, the data analysis processing unit  110  executes the flowchart (3) in the case of NO at step S 202  and in the case of NO at step S 211 . Note that the case of the flowchart (2) being executed is, for example, a case where there are no peculiar points in sales in comparison with the other store (that is, the sales are almost similar to sales of the other store), but there is a difference in the sales for each nationality. 
     (Flowchart (3)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales for each nationality, with “sales” and “nationality” as an analysis target combination (step S 301 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a nationality in which the peculiar point exists will be referred to as “a nationality A”. 
     Then, when the peculiar point exists in the sales for the nationality A (step S 302 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales for each nationality (that is, the sales for the nationality A) (for example, “Sales for the nationality A is increasing against the previous month in comparison with the other store”, “Sales for the nationality A is decreasing against the previous month in comparison with the other store”, or the like) to output character strings (step S 303 ). Further, at this time, the data analysis processing unit  110  may further add character strings, “It was not found that, in the sales for the nationality A, a particular article classification influences the sales” to the output character strings. 
     On the other hand, if no peculiar point exists in the sales for the nationality A (step S 302 : NO), the data analysis processing unit  110  executes the flowchart (4). 
     Subsequent to step S 303 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (records in the purchase data) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 304 ). 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 305 ). 
     (Flowchart (4)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales of each major article classification, with “sales” and “major article classification” as an analysis target combination (step S 401 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a major article classification in which the peculiar point exists will be referred to as “a major article classification B”. 
     Then, if a peculiar point exists in the sales of each major article classification (step S 403 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales of each major article classification (that is, the sales of the major article classification B) (for example, “Sales of the major article classification B is increasing against the previous month in comparison with the other store”, “Sales of the major article classification B is decreasing against the previous month in comparison with the other store”, or the like) to output character strings (step S 403 ). Further, at this time, the data analysis processing unit  110  may further add character strings, “It was not found that, in the sales of the major article classification B, a particular nationality influences the sales” to the output character strings. 
     On the other hand, if no peculiar point exists in the sales of each major article classification (step S 403 : NO), the data analysis processing unit  110  executes the flowchart (5). 
     Subsequent to step S 403 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (records in the purchase data) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 404 ). 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 405 ). 
     (Flowchart (5)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the sales (that is, total sales of the user&#39;s own store in the current month), with “sales” as an analysis target combination (step S 501 ). 
     Then, if a peculiar point exists in the sales (step S 502 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the sales (for example, “Sales are increasing against the previous month in comparison with the other store”, “Sales are decreasing against the previous month in comparison with the other store” or the like) to output character strings (step S 503 ). Further, at this time, the data analysis processing unit  110  may further add character strings, “A nationality or a major article classification with a peculiar point influencing the sales was not found” to the output character strings. 
     Next, the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (in this case, all records in the purchase data of the current month or records obtained by aggregating these records for sales) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 504 ). 
     On the other hand, if no peculiar point exists in the sales (step S 502 : NO), the data analysis processing unit  110  adds character strings indicating that there are no peculiar points, to the output character strings (step S 505 ). This is because, in this case, no peculiar points were found by the scenario of the data analysis for each store. 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 506 ). 
     [Data Analysis Process (Understanding of Overall Trend)] 
     Hereinafter, a flow of a data analysis process for understanding of overall trend will be described with reference to  FIGS. 12 to 16 .  FIGS. 12 to 16  are flowcharts (1) to (5) showing an example of the data analysis process for understanding of overall trend. Note that, as for these flowcharts (1) to (5), the flowcharts (1) to (4) correspond to the flows 1b to 4b shown in  FIG. 6 , respectively, and the flowchart (5) corresponds to the flowcharts 5b and 6b. 
     Further, hereinafter, it is assumed that the comparison target pair is “monthly all stores purchase data and all stores purchase data of the same month of the previous year”, and the monthly all stores purchase data includes “nationality”, “the number of purchasers” and “total purchase amount” are included as attributes similarly to  FIG. 6 . These pieces of all stores purchase data are stored in the purchase data storage unit  130 . Note that the comparison target pair is set, for example, by the user. 
     (Flowchart (1)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the overall total sales (that is, a total of all total purchase amounts of all the stores in the area in the current month) with “overall total sales” as an analysis target combination (step S 601 ). For example, if there is a difference in the overall total sales equal to or above a predetermined threshold between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the overall total sales of all stores purchase data of the current month. Note that a ratio may be used instead of the difference (the same goes for the time of analyzing existence of a peculiar point hereinafter). 
     Then, if no peculiar point exists in the sales (step S 602 : NO), the data analysis processing unit  110  executes the flowchart (2). On the other hand, if a peculiar point exists in the sales (step S 602 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the overall total sales (for example, “Overall total sales are increasing against the same month of the previous year”, “Overall total sales are decreasing against the same month of the previous year” or the like) to output character strings (step S 603 ). Note that, for example, which of “Overall total sales are increasing against the same month of the previous year” and “Overall total sales are decreasing against the same month of the previous year” is to be added to the output character strings is determined based on whether the overall total sales of the current month has been analyzed to be higher or lower in comparison with the overall total sales of the same month of the previous year as a result of the analysis of step S 601  described above. The same goes for the time of adding predetermined character strings to the output character strings hereinafter. 
     Subsequent to step S 603 , the data analysis processing unit  110  analyzes whether a peculiar point exists in the overall total sales for each nationality, with “overall total sales” and “nationality” as an analysis target combination (step S 604 ). For example, if such a nationality that there is a difference equal to or above a predetermined threshold in the overall total sales for each nationality between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the overall total sales for each nationality in the purchase data of the current month. Note that the overall total sales for each nationality refers to sales for each nationality in the total of all the total purchase amounts of all the stores in the whole area. 
     Then, if no peculiar point exists in the overall total sales for each nationality (step S 605 : NO), the data analysis processing unit  110  executes the flowchart (2). On the other hand, if a peculiar point exists in the overall total sales for each nationality (step S 605 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the overall total sales for each nationality (hereinafter, a nationality in which the peculiar point exists will be referred to as “a nationality A”) (for example, “And sales for the nationality A is increasing as a whole”, “And sales for the nationality A is decreasing as a whole”, or the like) to the output character strings (step S 606 ). 
     Next, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of purchasers of the nationality A, with “overall total sales”, “nationality” and “the number of purchasers” as an analysis target combination (step S 607 ). For example, if there is a difference in the number of purchasers of the nationality A equal to or above a predetermined threshold between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the number of purchasers of the nationality A in the all stores purchase data of the current month. 
     Then, if a peculiar point exists in the number of purchasers of the nationality A (step S 608 : YES), the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of the nationality A in the whole area in the current month, from the number of people of the nationality A in the whole area in the current month and the number of people of the nationality A in the whole area in the same month of the previous year, with “nationality” and “the number of people” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 609 ). For example, if there is a difference in the number of people of the nationality A in the whole area equal to or above a predetermined threshold between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the number of people of the nationality A in the whole area in the current month. 
     Then, if a peculiar point exists in the number of people of the nationality A in the whole area in the current month (step S 610 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of the nationality A in the whole area in the current month (for example, “The number of people of the nationality A is increasing in the whole area” or “The number of people of the nationality A is decreasing in the whole area”) to the output character strings (step S 611 ). 
     On the other hand, if no peculiar point exists in the number of people of the nationality A in the whole area in the current month (step S 610 : NO), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of purchasers of the nationality A (for example, “The number of purchasers of the nationality A is increasing”, “The number of purchasers of the nationality A is decreasing”, or the like) to the output character strings (step S 612 ). 
     Further, if a peculiar point exists in the number of purchasers of the nationality A (step S 608 : NO), the data analysis processing unit  110  adds character strings corresponding to the fact that there is a peculiar point in overall total sales for the nationality A, and there are no peculiar points in the number of purchasers of the nationality A (for example, “It is conceivable that the unit price has increased because the tendency of the number of purchasers of the nationality A has not changed”, “It is conceivable that the unit price has decreased because the tendency of the number of purchasers of the nationality A has not changed”, or the like) to the output character strings (step S 613 ). 
     Subsequent to step S 611 , S 612  or S 613 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (records in the all stores purchase data) and the related data stored in the related data storage unit  140  similarly to step S 122  in  FIG. 7  (step S 614 ). 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 615 ). 
     (Flowchart (2)) 
     Steps S 701  to S 712  of  FIG. 13  are similar to steps S 604  to S 615  of  FIG. 12 , respectively, except that the attribute “overall total sales” is not a peculiar point. In the flowchart (2), however, the data analysis processing unit  110  executes the flowchart (3) in the case of NO at step S 702 . Note that the case of the flowchart (2) being executed is, for example, a case where there are no peculiar points in the overall total sales in comparison with the same month of the previous year, but there is a difference in the overall total sales for each nationality. 
     (Flowchart (3)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, with “the number of people” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 801 ). For example, if there is a difference in the number of people of the whole area equal to or above a predetermined threshold between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the number of people of the whole area in the current month. 
     Then, if a peculiar point exists in the number of people of the whole area in the current month (step S 802 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of the whole area in the current month (for example, “The number of people of the whole area is increasing against the same month of the previous year”, “The number of people of the whole area is decreasing against the same month of the previous year”, or the like) to output character strings (step S 803 ). 
     On the other hand, if no peculiar point exists in the number of people of the whole area in the current month (step S 802 : NO), the data analysis processing unit  110  executes the flowchart (4). 
     Subsequent to step S 803 , the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people for each mesh in the area in the current month, from the population (the number of people) for each mesh in the area in the current month and the population (mesh) for each mesh in the area in the same month of the previous year, with “the number of people” and “mesh ID” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 804 ). For example, if such a mesh that there is a difference equal to or above a predetermined threshold in the number of people for each mesh in the area between the current month and the same month of the previous year, the data analysis processing unit  110  determines that a peculiar point exists in the number of people for each mesh in the area in the current month. Hereinafter, when it is analyzed at this step that a peculiar point exists, a mesh in which the peculiar point exists will be referred to as “a mesh A”. 
     Then, if a peculiar point exists in the number of people for each mesh in the area in the current month (step S 805 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people for each mesh (for example, “And the number of people of the mesh A is increasing”, “And the number of people of the mesh A is decreasing”, or the like) to the output character strings (step S 806 ). 
     On the other hand, if no peculiar point exists in the number of people for each mesh in the area in the current month (step S 805 : NO), the data analysis processing unit  110  executes the flowchart (4). 
     Subsequent to step S 806 , the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of each nationality in the mesh A in the area in the current month, from the population (the number of people) of each nationality in the mesh A in the area in the current month and the population (mesh) of each nationality in the mesh A in the area in the same month of the previous year, with “the number of people”, “mesh ID” and “nationality” as an analysis target combination (step S 807 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a nationality in which the peculiar point exists will be referred to as “a nationality A”. 
     Then, if a peculiar point exists in the number of people of each nationality in the mesh A in the area in the current month (step S 808 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of each nationality in the mesh A (for example, “The number of people of the nationality A in the mesh A is increasing”, “The number of people of the nationality A in the mesh A is decreasing”, or the like) to the output character strings (step S 809 ). 
     On the other hand, if no peculiar point exists in the number of people of each nationality in the mesh A in the area in the current month (step S 808 : NO), the data analysis processing unit  110  adds character strings corresponding to the fact that there is a peculiar point in the number of people of the mesh A, and there are no peculiar points in the number of people of each nationality (for example, “There are no remarkable changes in nationality”, or the like) to the output character strings (step S 810 ). 
     Subsequent to step S 809  or S 810 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (in this case records of the population statistics data) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 811 ). 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 812 ). 
     (Flowchart (4)) 
     First, the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, with “the number of people” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 901 ). 
     Then, if a peculiar point exists in the number of people of the whole area in the current month (step S 902 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of the whole area in the current month (for example, “The number of people of the whole area is increasing against the same month of the previous year”, “The number of people of the whole area is decreasing against the same month of the previous year”, or the like) to output character strings (step S 903 ). 
     On the other hand, if no peculiar point exists in the number of people of the whole area in the current month (step S 902 : NO), the data analysis processing unit  110  executes the flowchart (5). 
     Subsequent to step S 903 , the data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of each nationality in the area in the current month, from the population (the number of people) of each nationality in the area in the current month and the population (mesh) of each nationality in the area in the same month of the previous year, with “the number of people” and “nationality” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 904 ). Hereinafter, when it is analyzed at this step that a peculiar point exists, a nationality in which the peculiar point exists will be referred to as “a nationality A”. 
     Then, if a peculiar point exists in the number of people of each nationality in the area in the current month (step S 905 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of each nationality (for example, “And the number of people of the nationality A is increasing”, “And the number of people of the nationality A is decreasing”, or the like) to the output character strings (step S 906 ). 
     On the other hand, if no peculiar point exists in the number of people of each mesh in the area in the current month (step S 905 : NO), the data analysis processing unit  110  executes the flowchart (5). 
     Subsequent to step S 806 , the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (in this case, the records of the population statistics data) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 907 ). 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 908 ). 
     (Flowchart (5)) 
     The data analysis processing unit  110  analyzes whether or not a peculiar point exists in the number of people of the whole area in the current month, from the population (the number of people) of the whole area in the current month and the population (the number of people) of the whole area in the same month of the previous year, with “the number of people” in the population statistics data stored in the related data storage unit  140  as an analysis target combination (step S 1001 ). 
     Then, if a peculiar point exists in the number of people of the whole area in the current month (step S 1002 : YES), the data analysis processing unit  110  adds character strings corresponding to the peculiar point of the number of people of the whole area in the current month (for example, “The number of people of the whole area is increasing against the same month of the previous year”, “The number of people of the whole area is decreasing against the same month of the previous year”, or the like) to output character strings (step S 1003 ). 
     Next, the data analysis processing unit  110  analyzes correlations between records in which peculiar points exist (in this case, the records of the population statistics data) and the related data stored in the related data storage unit  140  similarly to step S 122  of  FIG. 7  (step S 1004 ). 
     On the other hand, if no peculiar point exists in the number of people of the whole area in the current month (step S 1002 : NO), the data analysis processing unit  110  adds character strings indicating that there are no peculiar points, to the output character strings (step S 1005 ). This is because, in this case, no peculiar points were found by the scenario of the data analysis for understanding of overall trend. 
     Lastly, the data analysis processing unit  110  creates analysis result data similarly to step S 123  of  FIG. 7  (step S 1006 ). 
     [Analysis Result Report] 
     Next, the analysis result report displayed on the report display terminal  30  by the report display processing unit  120  will be described. The analysis result report is created by the report display processing unit  120  based on analysis result reports stored in the analysis result data storage unit  150  and transmitted to the report display terminal  30 . Hereinafter, an analysis result report for each store, an analysis result report for understanding of overall trend and an analysis result report for correlation analysis will be described as examples. 
     (Analysis Result Report (for Each Store)) 
     An analysis result report  1000  for each store is shown in  FIGS. 17A and 17B . The analysis result reports  1000  shown in  FIGS. 17A and 17B  are, for example, a screen of the first page and a screen of the second page of the analysis result report  1000 , respectively. 
     The analysis result report  1000  includes, for example, “1. Report target” field  1100 , “2. Conclusion” field  1200 , “3. Details” field  1300  and “4. Flow schematic diagram” field  1400 . In “1. Report target” field  1100 , information for identifying a comparison target pair that includes analysis target data (for example, purchase data) is displayed. In “2. Conclusion” field  1200 , a result of data analysis (for example, an analysis result of sales) is displayed. Here, in “2. Conclusion” field  1200 , output character strings  1201  to  1203 , and the like are displayed. These output character strings  1201  to  1203 , and the like are character strings corresponding to peculiar points and indicate causes (factors) of relevant attributes having become the peculiar points. By defining data analysis flows in advance as a scenario, these output character strings can also be defined in advance according to peculiar points in analyses in the flows. Since it is possible for the user to know a reason for a data analysis result by referring to these output character strings  1201  to  1203 , and the like, it becomes possible for him to easily perform interpretation of the data analysis result and perform effective solution of business problems. 
     In “3. Details” field  1300 , detailed information about the data analysis result displayed in “2. Conclusion” field  1200  is displayed. In “3. Details” field  1300 , for example, a data analysis result of a comparison target pair in which it has been analyzed that a peculiar point exists, and output character strings  1301  and  1302 , and the like of the data analysis result are displayed. By referring to these output character strings  1301  and  1302 , and the like, it becomes possible for the user to perform effective solution of business problems as described above. 
     Furthermore, in “4. Flow schematic diagram” field  1400 , a flow in which data analysis was performed is visualized and displayed. In the example shown in  FIG. 17B , it is displayed that data analysis was performed according to the flow 1a. By referring to this flow schematic diagram, the user can know what kind of data analysis was performed. 
     Note that the analysis result report  1000  also includes a display switching tab  1001 , and it is possible to mutually switch display among the analysis result report for each store, the analysis result report for understanding of overall trend and the analysis result report for correlation analysis. 
     (Analysis Result Report (Understanding of Overall Trend)) 
     An analysis result report for understanding of overall trend  2000  is shown in  FIGS. 18A and 18B . The analysis result reports  2000  shown in  FIGS. 18A and 18B  are, for example, a screen of the first page and a screen of the second page of the analysis result report  2000 , respectively. 
     The analysis result report  2000  includes, for example, “1. Report target” field  2100 , “2. Conclusion” field  2200 , “3. Details” field  2300  and “4. Flow schematic diagram” field  2400 . In “1. Report target” field  2100 , information for identifying a comparison target pair that includes analysis target data (for example, all stores purchase data) is displayed. In “2. Conclusion” field  2200 , a result of data analysis (for example, an analysis result of sales) is displayed. Here, in “2. Conclusion” field  2200 , output character strings  2201  and  2202 , and the like are displayed. These output character strings  2201  and  2202 , and the like are character strings corresponding to peculiar points. Since it is possible for the user to know a reason for a data analysis result by referring to these output character strings  2201  and  2202 , and the like, it becomes possible for him to easily perform interpretation of the data analysis result and perform effective solution of business problems. 
     In “3. Details” field  2300 , detailed information about the data analysis result displayed in “2. Conclusion” field  2200  is displayed. In “3. Details” field  2300 , for example, a data analysis result of a comparison target pair in which it has been analyzed that a peculiar point exists, and output character strings  2301  and  2302 , and the like of the data analysis result are displayed. By referring to these output character strings  2301  and  2302 , and the like, it becomes possible for the user to perform effective solution of business problems as described above. 
     Furthermore, in “4. Flow schematic diagram” field  2400 , a flow in which data analysis was performed is visualized and displayed. In the example shown in  FIG. 17B , it is displayed that data analysis was performed according to the flow 1b. By referring to this flow schematic diagram, the user can know what kind of data analysis was performed. 
     Note that the analysis result report  2000  also includes a display switching tab  2001 , and it is possible to mutually switch display among the analysis result report for each store, the analysis result report for understanding of overall trend and the analysis result report for correlation analysis. 
     (Analysis Result Report (Correlation Analysis)) 
     An analysis result report  3000  for correlation analysis is shown in  FIG. 19 . The analysis result report  300  includes, for example, “1. Report target” field  3100 , “2. Correlation model” field  3200  and “3. Reference information” field  4300 . In “1. Report target” field  3100 , analysis target data, and information for identifying attributes targeted by analysis (that is, attributes in which it has been analyzed that peculiar points exist) are displayed. In “2. Correlation model” field  3200 , a correlation direction between the analysis target data and related data (that is, which of a positive correlation and a negative correlation exists) and importance thereof are displayed. Note that the importance is determined, for example, by the magnitude of the absolute value of a correlation coefficient. In “3. Reference information” field  4300 , values of correlation coefficients between the analysis target data and the related data are displayed in a matrix format. By referring to a correlation analysis result, the user can know elements (for example, events, incidents, matters and the like) related to the peculiar points and it becomes possible for him to perform effective solution of business problems. 
     Note that the analysis result report  3000  also includes a display switching tab  3001 , and it is possible to mutually switch display among the analysis result report for each store, the analysis result report for understanding of overall trend and the analysis result report for correlation analysis. 
     The present invention is not limited to the above embodiment specifically disclosed, but various kinds of modifications and changes are possible without departing from Claims. 
     REFERENCE SIGNS LIST 
     
         
         
           
               1  Data analysis system 
               10  Data analysis device 
               20  Store terminal 
               30  Report display terminal 
               40  Population statistics management server 
               110  Data analysis processing unit 
               120  Report display processing unit 
               130  Purchase data storage unit 
               140  Related data storage unit 
               150  Analysis result data storage unit