Technical Field
This disclosure relates generally to data analytics and machine learning, and more particularly to a system and method for determining integration maturity of an entity using data analytics and machine learning.

BACKGROUND
Presently, in the digital era, all the organizations ranging from small to large scale are digitally equipped with the latest technologies, leveraging many domain-specific and software-as-a-service (SaaS) applications. Since these applications have easy adaptation, each department of the organization incorporates their own set of applications in order to simplify their operations as well as meet the ever-growing expectations of the organizations with respect to different criteria. Moreover, the organizations need to integrate their applications, systems, data, and processes obtained from across the departments in order to meet the expectations with respect to automation, ease of working, and so on. In an organization, these applications require an integration strategy for continuous integration and improvement without any possibility of bottlenecks arising out of integration.
However, for respective organizations, the goals, objectives and digital integration requirements vary depending on their type and size of businesses and hence a bottleneck may arise in implementing the integration strategy. Therefore, in order to overcome the bottlenecks and maintain a smooth integration, various trained systems and methods could be used which continuously guide users to determine the factors causing the bottlenecks. However, due to the complex nature of the present systems and methods, the organizations struggle with such systems which are continuously trained on the integration maturity models related to defined data. Further, the organizations struggle in getting a clear visibility through the system to understand where and how they are lacking in integrations based on their data and responses. Hence, there exists a muddle between existing integration practices and standards to implement the integration strategy.
Therefore, there is a requirement to provide a system and method to assess the integration process that meets the organizations goals and objectives in an efficient way.
SUMMARY OF THE INVENTION
In an embodiment, a system for determining integration maturity of an entity is disclosed. The system may include one or more processors in an integration and maturity determination device and may further comprise of a display and a memory. The memory stores a plurality of processor-executable instructions, which upon execution by the processor, may cause the processor to receive, by the integration maturity determination device a user input comprising an entity type. The system may determine a set of criteria based on the user input wherein each of the set of criteria may comprise of a plurality of pre-defined attributes and a pre-defined weight. Further, each of the plurality of pre-defined attributes may be associated with a pre-defined capability score. The system may render on a user device each of the set of criteria along with the plurality of pre-defined attributes through a graphical user interface (GUI). The system may receive from the user device a response for each of the set of criteria from the user through the graphical user interface. It may be noted that the response may comprise of a user selected pre-defined attribute based on a selection from the plurality of pre-defined attributes and a user defined weight. The system may determine a system maturity score for each of the set of criteria based on the pre-defined capability score for a user selected pre-defined attribute and the pre-defined weight. The system may further determine a user maturity score for each of the set of criteria based on the pre-defined capability score for the user selected pre-defined attribute and the user-defined weight. Further, the system may determine a system maturity level and a user maturity level for each of the set of criteria based on a mapping of the system maturity score and the user maturity score respectively with a pre-defined range corresponding to a level value in a mapping table. The system may determine a cumulative system maturity level and a cumulative user maturity level based on the system maturity level and the user maturity level of each of the set of criteria. The system may determine the integration maturity level of the entity upon determining the cumulative system maturity level and the cumulative user maturity level to be within a pre-defined threshold.
In another embodiment, a method of determining integration maturity of an entity is disclosed. The method may include receiving, by an integration maturity determination device, a user input comprising an entity type. The method may include determining a set of criteria based on the user input wherein each of the set of criteria comprises a plurality of pre-defined attributes and a pre-defined weight and wherein each of the plurality of pre-defined attributes may be associated with a pre-defined capability score. The method may include to render, by the integration maturity determination device and on a user device, each of the set of criteria along with the plurality of pre-defined attributes through a graphical user interface (GUI). The method may further include receiving, by the integration maturity determination device and from the user device, a response for each of the set of criteria from the user through the GUI. It may be noted that the response from the user may comprise a user selected pre-defined attribute based on a selection from the plurality of pre-defined attributes, and a user defined weight. The method may include determining a system maturity score for each of the set of criteria based on the pre-defined capability score for a user selected pre-defined attribute and the pre-defined weight. Further, the method may include determining a user maturity score for each of the set of criteria based on the pre-defined capability score for the user selected pre-defined attribute and the user-defined weight. The method may include determining a system maturity level and a user maturity level for each of the set of criteria based on a mapping of the system maturity score and the user maturity score, respectively, with a pre-defined range corresponding to a level value in a mapping table. The method may further include determining a cumulative system maturity level and a cumulative user maturity level based on the system maturity level and the user maturity level of each of the set of criteria. The method may include determining the integration maturity level of the entity upon determining the cumulative system maturity level and the cumulative user maturity level to be within a pre-defined threshold.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
FIG. 1 is a block diagram indicating a network implementation of a maturity integration system for determining integration maturity of an entity, in accordance with an embodiment of the present disclosure
FIG. 2 is a functional block diagram of the integration maturity determination device, in accordance with an embodiment of the present disclosure.
FIG. 3 is a table depicting a dataset comprising a set of criteria, in accordance with an embodiment of the present disclosure.
FIG. 4 is a table depicting a dataset comprising a plurality of pre-defined attributes of a criteria, in accordance with an embodiment of the present disclosure.
FIG. 5 is a table depicting a dataset comprising user input corresponding to the set of criteria, in accordance with an embodiment of the present disclosure.
FIG. 6 depicts a table of a dataset comprising system maturity score (SMS) and user maturity score (UMS), in accordance with an embodiment of the present disclosure.
FIG. 7A depicts a dataset comprising a level mapping table, in accordance with an embodiment of the present disclosure.
FIG. 7B depicts a dataset comprising determined system maturity level and user maturity level, in accordance with an embodiment of the present disclosure.
FIG. 8 is a flowchart of a method of determining integration maturity of an entity, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS
Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims. Additional illustrative embodiments are listed.
In order to provide visibility about integration maturity of an entity, an intuitive and effective methodology is disclosed for determining integration maturity of an entity. The present disclosure provides methods and systems for determining an integration maturity of an entity. The system comprises an integration maturity determination device which may further include a processor and a memory coupled to it. The device may perform the system integration using machine learning modules. Those skilled in the art may recognize that the machine learning modules may be implemented using the processor to determine the integration maturity of an entity. An administrator, who may be a chief operator, chief executive, or a top management, may login in the system via the integration maturity determination device. The administrator, who may have the default access to the system, may login with their valid credentials and upload different datasets. The datasets uploaded by the administrator may include a set of criteria corresponding to a specific industry and integration areas or aspects. Thereafter, the admin may provide rights and access to a user, using which the user may login in the system and select and download the preconfigured industry related dataset uploaded by the administrator. The datasets uploaded by the administrator may be used to train the system on different aspects of integration maturity. Further, each value of the set of criteria may be assigned a specific pre-defined weightage indicating the importance of that criteria value in the process of evaluating the integration maturity. Additionally, each set of criteria value may further comprise of a plurality of predefined attributes along with a pre-defined score. The users may download the dataset and may further present them to the business users who may then provide with their responses corresponding to the set of criteria included in the dataset. The response provided by the business users may be in the form of numerical values. Thereafter, upon receiving the responses the system may first determine scores corresponding to the set of criteria included in the datasets. Thereafter, using the scores, the system may determine a user maturity level based on the determined scores corresponding to the set of criteria included in the datasets. The user level may be compared with a system maturity levelcorresponding to the set of criteria to determine integration maturity within the enterprise.
Referring now to FIG. 1, a block diagram indicating a network implementation of a maturity integration system 100 for determining integration maturity of an entity is illustrated, in accordance with an embodiment of the present disclosure.
The system 100 may include an integration maturity determination device 102. By way of an example, the integration maturity determination device 102 (hereafter referred to as the device 102) may be implemented in any computing device which may be configured in or operatively connected to a server (not shown). An administrator 112 may communicate with the device 102 via an administrator device 110 through a wireless or wired communication network 108. In an embodiment, the administrator 112 may be a senior executive, chief operator, or a top-level management associate.
In an embodiment, multiple users 116-1, 116-2…116-N (which are collectively referred to as users 116 and individually referred to as the user 116, hereinafter) can communicate with the device 102 through one or more user devices 114-1, 114-2…114-N (which are collectively referred to as user devices 114, hereinafter) that can be communicatively coupled to the device 102 through a network 108.
In an embodiment, examples of the administrator device 110 and/or user device 114 may include, but are not limited to a desktop, a laptop, a notebook, a netbook, a tablet, a smartphone, a mobile phone, an application server, or a web server.
In an embodiment, the device 102 may include one or more processor(s) 104, a memory 106, and an input/output device 118. The one or more processor(s) 104 may be implemented as one or more microprocessors, microcomputers, single board computer, microcontrollers, digital signal processors, central processing units, graphics processing units, logic circuitries, and/or any devices that manipulate data received from a memory 106. Among other capabilities, the one or more processor(s) 104 are configured to fetch and execute computer-readable instructions stored in a memory 106 of the device 102.
The memory 106 may store instructions that, when executed by the processor 104, cause the processor 104 to run and execute the required calculations and algorithms to determine the integration maturity of an entity, as discussed in greater detail below. The memory 106 may be a non-volatile memory or a volatile memory. Examples of non-volatile memory may include, but are not limited to a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Examples of volatile memory may include but are not limited to Dynamic Random Access Memory (DRAM), and Static Random-Access memory (SRAM). The memory may also store programmable instructions and machine learning algorithms to iteratively perform the determination of the integration maturity of an entity. Further, the machine learning algorithm may be trained based on a historical data with respect to the steps and efforts to improve the integration maturity for a plurality of entity types.
In an embodiment, a network 108 may be a wireless or a wired network. The network 108 may be implemented as one of the different types of networks, such as Common Industrial Protocol (CIP) network, DeviceNet network, intranet, local area network (LAN), wide area network (WAN), the internet, Wi-Fi, LTE network, CDMA network, and the like. Further, the network 108 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 108 may be configured using a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
The device 102 may also comprise input/output devices 118. The input/output devices 118 may comprise of variety of interface(s), for example, interfaces for data input and output devices, and the like. The input/output devices 118 may facilitate inputting of instructions by a user 116 communicating with the device 102 through a user device 114. In an embodiment, the input/output device 118 may be wirelessly connected to the device 102 through wireless network interfaces such as Bluetooth®, infrared, or any other wireless radio communication known in the art. In an embodiment, the input/output devices 118 may be connected to a communication pathway for one or more components of the smart assistance device 102 to facilitate the transmission of inputted instructions and output the results of data generated by various components such as, but not limited to, processor(s) 104 and memory 106.
In an embodiment, the administrator 112 may have a default access to the device 102 and may login into the device 102 using pre-defined credentials inputted through the input/output devices 118. The administrator 112 upon authentication may gain access to the device 102. through an application enabled on a cloud or installable on the administrator device 110. The administrator 112 may, via a graphic user interface of the application of the device 102, define different types of entities and upload datasets corresponding to different entity types. In an embodiment, entity types may include, but not limited to, different types of businesses or enterprises related to manufacturing industries, supply chain and logistics, software technology, ecommerce, small businesses, and so on. It may be noted that the administrator 112 may modify the datasets as and when required. Additionally, the administrator 112 may also supervise the authentication and authorization of the various users 116a-n (which are collectively referred to as user 116, hereinafter) and may grant them with access and rights to login to the device 102. Further, upon verification of the user 116 by the administrator 112, the user 116 may login with their pre-defined credentials to gain access to the device 102 using corresponding user device 114.
Further, upon gaining access to the device 102, the user 116 may using the GUI of the application of the device 102 may select one of the pre-defined entity types and may download the corresponding datasets. In an embodiment, the integration maturity determination device 102 may allow the user 116 to input the corresponding response dataset obtained in order for the device to determine integration maturity of the entities as described in detail below.
Referring now to FIG. 2, a functional block diagram of the integration maturity determination device 102 is illustrated, in accordance with an embodiment of the present disclosure. The integration maturity determination device 102 may be configured to process data obtained from the users 116 to determine integration maturity of an entity. The integration maturity determination device 102 may include an administrative module 202, a user module 204, a maturity calculation module 206, maturity learning module 208, and a data module 210.
It may be noted that all such aforementioned modules 202-210 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202-210 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202-210 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202-212 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202-210 may be implemented in software for execution by various types of processors. An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the modules. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
In an embodiment, the administrative module 202 may be configured by the processor 104 to enable administrator 112 to login to the device 102 by inputting pre-defined login credentials using a corresponding administrator device 110. In an embodiment, the administrator 112 may access an administrator GUI (not shown) in an application of the device 102 on the administrator device 110 to manage user access. In an embodiment, various user access levels may be defined based on a hierarchical structure of an entity. In an exemplary embodiment, a user 116 with manager level access may be able to manage access of a user 116 with employee level access and may be able to view and edit the responses provided by a user 116 with employee level access. In an embodiment, an administrator 112 may manage user access by adding new users or blocking or deleting user access of the device 102.
In an embodiment, the administrator 112 may define and input entity types and its corresponding dataset in order to determine an integration maturity of an entity. In an embodiment, the administrator 112 may also define dataset which may be include a set of criteria for each entity type defined.
In an embodiment, a debt dataset defining a number of steps in form of recommendations which may be implemented by a user 116 in case determined user integration maturity level is not equal to the system integration maturity level for an entity.
FIG. 3 is a table 300 depicting a dataset comprising a set of criteria, in accordance with an embodiment of the present disclosure. In an embodiment, a dataset of an entity type may include a set of criteria 302. The set of criteria may include a list of criterias C1-C5 each representing a pre-defined criteria for measuring of integration maturity of an entity with respect to various aspects such as operational, administrative, managerial, human, technical, etc. In an embodiment, the number of criteria defined for each entity may vary based on the entity type or system requirements. In an exemplary embodiment, number of criteria for determining integration maturity of an entity may be five in number. In an embodiment, number criteria defined for determining integration maturity of an entity may be more or less than five. In an embodiment, each criteria in the set of criteria 302 may be assigned a pre-defined weight 304 by the administrator 112 depicting importance of a criteria in determining and improving the integration maturity of an entity. In an embodiment, a pre-defined weight 304 may be assigned to each criteria C1-C5 using machine learning algorithms based on which a system integration maturity level may be determined for an entity. In an embodiment, the system integration maturity level may be determined based on inputs provided by an administrator 112 for an entity as per evaluation of entity’s target integration maturity level. In an embodiment, the pre-defined weight 304 may be in range of 0 to 1. In an embodiment, the pre-defined weight 304 may be even or odd values.
FIG. 4 illustrates a table 400 depicting a dataset comprising a plurality of pre-defined attributes of a criteria, in accordance with an embodiment of the present disclosure. In an embodiment, the administrator 112 may define a set of attributes 402 for each criteria C1-C5 in the set of criteria 302. In an embodiment, each criteria C1-C5 in the set of criteria 302 may include a set of attributes 402 depicting capabilities. In an embodiment, each of the attributes A1-A5 may be assigned a capability score 404 ranging in value from 1 to 5, with minimum value being 1 and maximum value being 5. The capability score assigned to each attribute A1-A5 may be modified by the administrator 112 after uploading new dataset. In an embodiment, the administrator 112 may also modify the set of attributes 402 and the corresponding capability score 404 based on the entity type and the pre-defined target integration maturity level. In an embodiment, the capability score 404 may depict a capability level of the attribute of a criteria and are on a graded scale. In an embodiment, a higher capability score 404 may depict capability of each of the criteria C1-C5 inclusive of the attributes having lesser capability scores 404. For example, attribute A3 having a capability score of 3 may have its individual capability in addition to the existing capabilities of earlier attributes A1 and A2 having capability score of 1 and 2 respectively. However, the attribute A3 may be determined to be less capable than attributes A4 and A5 having capability score of 4 and 5 respectively which is higher than 3. For example, the criteria C5 from the set of criteria 302, is related to ‘managing an enterprise-wide integration from one platform’. The criteria C5 is associated with multiple predefined attributes A1-A5, out of which A5 is selected which related to ‘comprising business users enabled to developed changes by themselves with minimum support’ may be assigned as highest capability score of 5 as it may be judged by the administrator 112 depicting highest capability. Thus, the capability score of 5 for a criteria may be inclusive of the attributes A1-A5. In an embodiment, the capability score 404 of the attributes 402 of a criteria C1-C5 from the set of criterias 302 may be pre-defined by the administrator 112 based on an entity type and the pre-defined system integration maturity level.
In an embodiment, the user module 204 may be configured by the processor 104 to enable a user 116 to login to the device 102 by inputting pre-defined login credentials using a corresponding user device 114. In an embodiment, the one or more users 116 may be authenticated by the device 102 based on the input of various authentication information including the corresponding username and password. In an embodiment, the user 116 may be provided access to the system 100 based on a user access level information such as managers level, employee level, etc. In an embodiment, each user 116 may access to the device 102 using a GUI of implemented by the user module 204 based on access granted by the administrator 112.
In an embodiment, the user 116 may access the device 102 through the corresponding user device 114 using the GUI of the application. Thereafter, the user 116 may select the entity type and may download the corresponding datasets associated with the entity type.
FIG. 5 is a table 500 depicting a dataset comprising user input corresponding to the set of criteria, in accordance with an embodiment of the disclosure. In an embodiment, table 500 depicts the dataset comprising a list of criteria 302 as shown in FIG. 3, which may be presented to the users 116 on their corresponding user device 114 over a GUI comprising forms or drop downs. Further, the user 116 may provide their response in form of user defined weights (UWE) 506 to each of the criteria C1-C5. In an embodiment, the UWE 506 may be in range of 0 to 1 and may be selected based on pre-defined options presented in form of a drop-down list (not shown) on the GUI. In an embodiment, the UWE 506 may be even or odd values in the range of 0 to 1. Thereafter, the users 116 may select an attribute A1-A5 as shown in table 400 of FIG. 4. Based on the user selected attribute for each criteria C1-C5, a user capability score (UCS) 502 may be determined which may be equal to the corresponding system assigned capability score 404 to an attribute A1-A5 as shown in Table 400.
The maturity calculation module 206, may utilize the uploaded responses by the user 116 as shown in table 500, comprising user defined weights (UWE) 506 assigned by user 116 to each of the plurality of criteria 302 and the UCS 502 based on the attribute selected by the user 116 for each criteria C1-C5 from the set of criteria 302. The maturity calculation module 206 may include score calculation module 212 and level calculation module 214 to perform the necessary calculations to determine integration maturity of the entity selected by the user 116.
Further, the integration maturity determination device 102 may assign or preconfigure system generated weight (SWE) 304 for each criteria C1-C5, which may be populated corresponding to each criteria (C1-C5) from the set of criteria 302 in the table 500. The SWE 304 generated by the device 102 for each of the set of criteria 302 may be represented by SWE1, SWE2, … to SWEn, where ‘n’ may represent ‘n’ number of preconfigured criteria defined by the device 102 in order to determine the integration maturity score. In an exemplary embodiment, as shown in table 500, for the criteria C3, the system may provide a system generated weight SWE3, which in the embodiment may be represented with a value 1. Moreover, the response table 500 may further include a user generated weight (UWE) 506 which is equal to value 0.6. It may be noted that, the UWE 506 may indicate weights assigned by the user 116 as a response corresponding to each criteria (C1-C5) from the set of criteria 302 indicative of the current situation with respect to determining the integration maturity of an entity. In an embodiment, the range may include both the even and odd numerical values.
FIG. 6 depicts a table 600 of a dataset comprising system maturity score (SMS) 602 and user maturity score (UMS) 604, in accordance with an embodiment of the present disclosure. In an embodiment, the score calculation module 212 may determine SMS 602 for each criteria C1-C5 from the set of criteria 302. The SMS 602 for each criteria 302 may be determined based on a product of system defined weight (SWE) 304 and system assigned capability score 404 for the user selected pre-defined attribute 502 as shown in FIG. 5, as selected by user 116 for each criteria 302.
In an embodiment, the score calculation module 212 may determine user maturity score (UMS) 604 for each criteria C1-C5 from the set of criteria 302. In an embodiment, the SMS 602 may be calculated using formula: SMSn = UCSn * SWEn, wherein ‘n’ is a sequence number of the criteria. In an embodiment, the UMS 604 may be calculated using formula: UMSn = UCSn * UWEn, wherein ‘n’ is a sequence number of the criteria.
In an exemplary embodiment, the score calculation module 212 may determine that for criteria C3 of the set of criteria 302, the user 116 has selected a UCS3 value of 2. It may be noted that the values of the UCS3 502 may be determined as 2 based on the capability score assigned by the device 102 to the attribute A2 from list of attributes 402 as shown in FIG. 4. Further, user defined weight (UWE) 506 as shown in FIG. 5, may be inputted by user 116 using the corresponding user device 114 based on his/her preference or understanding of the importance of a criteria in its contribution towards integration maturity target of an entity. In an embodiment, the UWE 506 may be in a range of 0 to 1. In an embodiment, the UWE 506 may be, but not limited to, an even value in the range of 0 to 1. In an embodiment, each criteria may be assigned to a different user 116 who may be responsible for overlooking an operation to which a criteria is related. For example, a criteria related to automation may be assigned to a user 116 who is responsible for enabling automation in an entity in order to provide an appropriate UWE to the criteria and select an appropriate attribute for the criteria which may be utilized to determine the UMS 604 for the criteria assigned to him/her. In an embodiment, the user 116 may be able to view the criteria and the associated attributes of the assigned criteria only.
FIG. 7A depicts a level mapping table, in accordance with an embodiment of the present disclosure. The level mapping table 700A includes various pre-defined level threshold ranges 702 corresponding to a level value 704 and a corresponding level type 706. FIG. 7B depicts a dataset comprising determined system maturity level and user maturity level, in accordance with an embodiment of the present disclosure. In an embodiment, the level calculation module 214 of the maturity calculation module 206 may be configured to determine a system maturity level (SL) 708 and a user maturity level (UL) 710. In an embodiment, the SL 708 for each criteria C1-C5 may be determined based on a mapping of the SMS 602 with a respective level threshold ranges 702 as shown in Table 700A. In an exemplary embodiment, in case the value of SMS1 lies in the range 702a of the Table 700A, accordingly the SL 708 for C1 may be determined as ‘1’ and the level type 706 may be determined as ‘ad-hoc’ as provided in the level mapping table 700A.
Accordingly, user maturity levels 710 for each UMS 604 value for each criteria C1-C5 may be determined based on the level mapping table 700A as shown in FIG. 7A. In an exemplary embodiment, the UL2 corresponding to UMS2 is determined as 3 as the UMS2 lies in the level threshold range of 702c of the level mapping table 700A.
The level calculation module 214 may then calculate a cumulative system maturity level using following formula:
((?_(i=1)^n¦?SLi= SL1+SL2+SL3+SL4+?+SLn?)/n), wherein ‘n’ is the number of criteria determined by the device 102 for determining the cumulative system maturity level of the entity.
The level calculation module 214 may then calculate a cumulative user maturity level using following formula:
((?_(i=1)^n¦?ULi= UL1+UL2+UL3+UL4+?+ULn?)/n) , wherein ‘n’ is the number of criteria determined by the device 102 for determining the cumulative user maturity level of the entity.
In an embodiment, based on the determination of the cumulative user maturity level, a final user maturity level may be determined based on the level mapping table 700A shown in FIG. 7A. In an exemplary embodiment, if the cumulative user maturity level is determined to be ‘1.9’ then the level calculation module 214 may determine a level threshold range 702 in which the value of the cumulative user maturity level falls. Accordingly, the level calculation module 214 may determine that the value ‘1.9’ is within the level threshold range 702b and hence, the final user maturity level may be determined as ‘2’ and the level type 706 may be determined as ‘transactional’ from the level mapping table 700A.
In an embodiment, based on the determination of the cumulative system maturity level, a final system maturity level may be determined based on the level mapping table 700A as shown in FIG. 7A. In an exemplary embodiment, if the cumulative system maturity level is determined to be ‘2’ then the level calculation module 214 may determine a level threshold range 702 in which the value of the cumulative system maturity level lies. Accordingly, the level calculation module 214 may determine that the value ‘2’ is in the level threshold range 702b according to which the final system maturity level may be determined as ‘2’ and the level type 706 may be determined as ‘transactional’ from the level mapping table 700A.
In the exemplary embodiment, the final system maturity level and the final user maturity level are both determined to be ‘2’, and hence, no mismatch is determined. Accordingly, the entity’s operations and performance are determined to be at par with the system expected level.
In case, there is a mismatch in the level type 706 determined for the system and the user based on the respective final system maturity level and the final user maturity level, the user 116 may be again presented with the list of criteria 302 or an assigned criteria as shown in table 300 of FIG. 3. The user 116 may provide his input using the user device 114 corresponding to the user capability score 502 and the user generated weight 506. A user maturity score 604 may be determined by the score calculation module 212 based on which a final user maturity level and a level type 706 may be determined until the determined level type 706 is as same as the level type 706 determined for the corresponding final system maturity level. Accordingly, the cumulative user maturity level may be iteratively determined until the cumulative system maturity level and the cumulative user maturity level is within a pre-defined threshold level range 702.
In case there is no mismatch determined between the level type 706 of the user and the system based on the respective final user maturity level and the final system maturity level determined, the user 116 may be provided some recommendations by the maturity learning module 208 in order to improve the level type 706 of the user. In an embodiment, the user 116 may be asked to provide his inputs with respect to the set of criteria 302 again in order to determine if the level type 706 corresponding to the final user maturity level has improved than earlier determination.
The maturity learning module 208 of the device 102 may include a training module 216 and a debt module 218. The maturity learning module 208 may present recommendations in form of efforts and steps on the user device 114 associated to an entity in order to improve the integration maturity level. In order for the maturity learning module 208 to provide the recommendation to the user 116, the training module 216 may be configured to learn from a training dataset provided by an administrator 112. Thus, the training datasets may be used for training the maturity learning module 208 in order for it to provide recommendations in form of efforts and key recommended steps which may be taken by different teams of the entity in improving the integration maturity of the entity. In an embodiment, the administrator 112 may input the training dataset in a pre-defined format. In an embodiment, the training dataset may be fed to the training module 216 continuously, thus, the system 100 enables continuous enhancement in the integration maturity across business units of an entity.
In an embodiment, the training module 216 may use various machine learning algorithms, such as but not limited to, logistic regression, k-nearest neighbour (KNN), Support vector machine (SMM), etc. In an embodiment, the datasets which are inputted by the administrator and historical datasets may be utilized by the training module 216 in order to build, re-build, train and re-train the maturity learning module 208. In an embodiment, the machine learning algorithms of the training module 216 may increase accuracy of output of the maturity learning module 208. In an embodiment, the training module 216 may utilize two or more different machine learning algorithms and may select output of the algorithm with the highest accuracy level.
In an embodiment, the training module 216 may utilize training datasets which may be split into subsets as a training subset and a test data subset. Moreover, the training data subset and the test data subset may be further arranged into features and target utilized for determining the criteria 302 for an entity or providing recommendation to an entity for improving the maturity level of an entity.
In an embodiment, the training dataset may be sampled randomly to split the training dataset into training subset and test subset. Accordingly, once the training module 216 has been trained using the trained subset, the training module 216 may be tested using the test subset in order to determine the accuracy of the algorithms being trained in the training module 216. In an embodiment, size of test subset from the training dataset may be pre-defined or may be randomly determined.
The debt module 218 of the maturity learning module 208 may determine recommendations to be presented to a user 116 in order to improve the integration maturity of an entity. The debt module 218 may also utilize the training data utilized by the training module 216 to search and optimize, best matching results to be shared with the user using the user device 114. In an embodiment, the debt module 218 may provide recommendations to the user 116 to achieve next higher integration maturity level. In an exemplary embodiment, if the level type of an entity has been determined as “transactional” as per the level mapping table 700A of FIG. 7A, then the recommendation would enable the entity to achieve next level of integration maturity which is “automated”. In an embodiment, the users 116 of an entity may follow the steps and recommendations to further improve the integration maturity level of an entity. In an embodiment, the debt module 218 may always search for the results that users may take to achieve the next best integration maturity level of an entity.
The data module 210 may serve as a repository for storing the data uploaded and generated by the users 116 and the administrators 112 using the various modules. In an embodiment, the data module 210 may include system data comprising the datasets uploaded by the administrator 112. The datasets uploaded by the administrator 112 may include the set of criteria 302 along with their predefined attributes 402 and their corresponding predefined system generated weights 304 and system capability score 404 as defined in conjunction with FIG. 3 and FIG. 4. Further, data module 210 may store all numerical values determined and calculated by the modules of the device 102 as result of inputted by the user 116 and/or the administrator 112 during the determination of the integration maturity score of an entity.
FIG. 8 is a flowchart of a method of determining integration maturity of an entity, in accordance with an embodiment of the present disclosure. The flowchart 800 depicts a methodology of determining integration maturity of an entity and is explained in conjunction with FIG. 1 to FIG. 7B.
At step 802, a user input may be received from a user 116. The user input may include selection of an entity type by the user 116. In an embodiment, the user input may also include entity size. At step 804, a set of criteria 302 based on the user input may be determined by the device 102. Further, each of the set of criteria comprises a plurality of pre-defined attributes 402 and a pre-defined weight 304. In an embodiment, each of the plurality of pre-defined attributes 402 may be associated with a pre-defined capability score 404.
At step 806, each of the set of criteria along with the plurality of predefined attributes may be rendered on a user devices 114 through a graphical user interface (GUI) by the device 102.
At step 808, a response for each of the set of criteria 302 may be received from the user 116, through the GUI of the user device 114. It may be noted that the user response received may include a user selected pre-defined attribute based on a selection from the plurality of pre-defined attributes 402 and a user defined weight 506 for each of the set of criteria 302.
At step 810, a system maturity score 602 for each of the criteria may be determined by the integration maturity determination device 102. It may be noted that the system maturity score 602 for each of the set of criteria may be based on the predefined capability score 404 for a user selected predefined attribute 502 and the predefined weight 304.
At step 812, a user maturity score 604 for each of the set of criteria 302 may be determined by the integration maturity determination device 102. It may be noted that the user maturity score 604 for each of the set of criteria 302 may be based on the predefined capability score for the user selected predefined attribute 502 and user defined weight 506 which may be defined by the users 116.
At step 814, a system maturity level 708 and a user maturity level 710 may be determined for each of the set of criteria 302, wherein the system maturity level 708 and the user maturity level 710 may be based on a mapping of the system maturity score 602 and the user maturity score 604 respectively with a pre-defined range 702 corresponding to a level value 704 in a mapping table 700A.
At step 816, a cumulative system maturity level and a cumulative user maturity level which may further be determined based on the system maturity level 708 and the user maturity level 710 of each of the set of criteria 302 may be determined.
At step 818, the integration maturity level of the entity may be determined upon determining the cumulative system maturity level and the cumulative user maturity level to be within a pre-defined threshold. In an embodiment, the pre-defined threshold may be determined based on the mapping of the cumulative system maturity level and the cumulative user maturity level with respect to the level mapping table 700A.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

I/We claim:

1. A method of determining integration maturity of an entity, comprising:
receiving, by an integration maturity determination device, a user input comprising an entity type;
determining, by the integration maturity determination device, a set of criteria based on the user input, wherein each of the set of criteria comprises a plurality of pre-defined attributes and a pre-defined weight, and wherein each of the plurality of pre-defined attributes is associated with a pre-defined capability score;
rendering, by the integration maturity determination device and on a user device, each of the set of criteria along with the plurality of pre-defined attributes through a graphical user interface (GUI);
receiving, by the integration maturity determination device and from the user device, a response for each of the set of criteria from the user through the GUI, wherein the response comprises:
a user selected pre-defined attribute based on a selection from the plurality of pre-defined attributes; and
a user-defined weight;
determining, by the integration maturity determination device, a system maturity score for each of the set of criteria based on the pre-defined capability score for a user selected pre-defined attribute and the pre-defined weight;
determining, by the integration maturity determination device, a user maturity score for each of the set of criteria based on the pre-defined capability score for the user selected pre-defined attribute and the user-defined weight;
determining, by the integration maturity determination device, a system maturity level and a user maturity level for each of the set of criteria based on a mapping of the system maturity score and the user maturity score, respectively, with a pre-defined range corresponding to a level value in a mapping table;
determining, by the integration maturity determination device, a cumulative system maturity level and a cumulative user maturity level based on the system maturity level and the user maturity level of each of the set of criteria; and
determining, by the integration maturity determination device, the integration maturity level of the entity upon determining the cumulative system maturity level and the cumulative user maturity level to be within a pre-defined threshold.

2. The method as claimed in claim 1, wherein the user input further comprises entity size.

3. The method as claimed in claim 1, wherein the pre-defined capability score for each of the plurality of pre-defined attributes are on a graded scale.

4. The method as claimed in claim 1, wherein the pre-defined weight and the user-defined weight for each of the set of criteria is in a range of 0 to 1.

5. The method as claimed in claim 1, further comprising:
generating, by the integration maturity determination device, one or more recommendations to increase the integration maturity level of the entity using a machine learning algorithm, wherein the machine learning algorithm is trained based on historical data with respect to steps and efforts to improve the integration maturity for a plurality of entity types, and wherein the steps and efforts are provide in a pre-defined standard format; and
rendering, by the integration maturity determination device and on the user device, the one or more recommendations through the GUI.

6. The method as claimed in claim 1, further comprising iterating receiving the response, determining the user maturity score, determining the user maturity level, and determining the cumulative user maturity level when the cumulative system maturity level and the cumulative user maturity level is not within the pre-defined threshold.

7. The method as claimed in claim 1, wherein:
rendering each of the set of criteria further comprises rendering each of the set of criteria along with a plurality of pre-defined weights through the GUI; and
receiving the user-defined weight based on a selection from the plurality of pre-defined weights.

8. A system for determining integration maturity of an entity comprising:
an integration maturity determination device comprising:
one or more processors; and
a memory communicatively connected to the one or more processors, wherein the memory stores a plurality of processor-executable instructions, which, upon execution, cause the processor to:
receive, a user input comprising an entity type;
determine a set of criteria based on the user input,
wherein each of the set of criteria comprises a plurality of pre-defined attributes and a pre-defined weight, and
wherein each of the plurality of pre-defined attributes is associated with a pre-defined capability score;
render on a user device, each of the set of criteria along with the plurality of pre-defined attributes through a graphical user interface (GUI);
receive from the user device a response for each of the set of criteria, from the user through the GUI, wherein the response comprises:
a user selected predefined attribute based on a selection from the plurality of pre-defined attributes; and
a user-defined weight;
determine a system maturity score for each of the set of criteria based on the pre-defined capability score for a user selected pre-defined attribute and the pre-defined weight;
determine a user maturity score for each of the set of criteria based on the pre-defined capability score for the user selected pre-defined attribute and the user-defined weight;
determine a system maturity level and a user maturity level for each of the set of criteria based on a mapping of the system maturity score and the user maturity score, respectively, with a pre-defined range corresponding to a level value in a mapping table;
determine a cumulative system maturity level and a cumulative user maturity level based on the system maturity level and the user maturity level of each of the set of criteria; and
determine the integration maturity level of the entity upon determining the cumulative system maturity level and the cumulative user maturity level to be within a pre-defined threshold.

9. The system as claimed in claim 8, wherein the processors are further configured to:
generate one or more recommendations to increase the integration maturity level of the entity using a machine learning module, wherein the machine learning module is trained based on historical data with respect to steps and efforts to improve the integration maturity for a plurality of entity types, and wherein the steps and efforts are provided in a pre-defined standard format; and
render on the user device, the one or more recommendations through the GUI.

10. The system as claimed in claim 8, wherein the processors are further configured to iterate receiving the response, determining the user maturity score, determining the user maturity level, and determining the cumulative user maturity level when the cumulative system maturity level and the cumulative user maturity level is not within the pre-defined threshold.