FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
L Title of the invention: ASSESSMENT OF DATA MANAGEMENT SYSTEM
Applicants)
NAME I NATIONALITY I ADDRESS
TATA CONSULTANCY Indian Nirmal Building, 9th Floor, Nariman
SERVICES LIMITED Point, Mumbai - 400021
__ India
3. Preamble to the description
COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it is to
be performed.

TECHNICAL FIELD
[0001] The present subject matter relates, in general, to a data management system and,
in particular, to assessment of a data management system.
BACKGROUND
[0002] Data is an important asset to any organization. The data may relate to information
pertaining to processes, which can be both internal and external to the organization. Examples of data include information related to infrastructure, human resources, and business processes. Such data is processed, stored, retrieved, managed, and utilized in the organization, generally in an automated manner. The automated processing and management of data reduces the cost of certain tasks, for example, billing. Usually, the data is acquired, processed, consolidated, and managed using dedicated data management systems, in accordance with the requirements of the organization and its operational model.
[0003] However, the automated processing and management of the data through the data
management systems may provide unreliable or inaccurate results, especially when the processed data is itself incorrect. This may happen if the processes, tools and technology used are not suitable or the data management systems are not well designed or people designing, developing and maintaining are not competent enough. The time, effort, and expense incurred due to the use of such data can be costly and can negatively impact the organization's growth. Such scenarios can be avoided if the data management systems can be assessed and corrective actions can be implemented.
[0004] A number of assessment systems and schemes have been devised to monitor and
assess the data management systems. Such systems focus on various areas of data management, for example, data architecture and modeling management, data quality management, etc. However, these systems fail to provide a structured and integrated understanding of the organization.
SUMMARY
[0005] This summary is provided to introduce concepts related to assessment of a data
management system, which are further described below in the detailed description. This

summary is not intended to identify essential features of the claimed subject matter nor is it
intended for use in determining or limiting the scope of the claimed subject matter.
[0006] In one implementation, the method includes identifying at least one dimension
pertinent to a data management system and obtaining responses to a plurality of queries. The queries are generated based on each of the identified dimensions. Further, the responses are analyzed to provide an actual state assessment corresponding to each of the identified dimensions. The actual state assessment is compared with a corresponding planned state and an assessment report is provided based at least on the comparison between the actual state assessment and the planned state.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The detailed description is described with reference to the accompanying figures.
In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and components.
[0008] Fig. 1 illustrates an exemplary system implementing a data management
assessment server to assess a data management system of an organization, in accordance with an embodiment of the present subject matter.
[0009] Fig. 2 illustrates exemplary components of a data management assessment server,
in accordance with an embodiment of the present subject matter.
[00010] Fig. 3(a) illustrates an exemplary data structure of dimensions and queries associated with the dimensions embodied on the data management assessment server, in accordance with an embodiment of the present subject matter.
[00011] Fig. 3(b) illustrates one or more visual charts generated by an analysis module of the data management assessment server, in accordance with an embodiment of the present subject matter.
[00012] Fig. 4 illustrates an exemplary method to assess the data management system of an organization, in accordance with an implementation of the present subject matter. [00013] Fig. 5 illustrates an exemplary method to identify the dimensions and sub-dimensions, in accordance with an implementation of the present subject matter.

[00014] Fig 6 illustrates an exemplary method to analyze one or more responses received from user groups, in accordance with an implementation of the present subject matter. [00015] Fig .7 depicts, in the form of flowchart, art exemplary method to generate an assessment report and a project plan, in accordance with an implementation of the present subject matter.
DETAILED DESCRIPTION
[00016] Typically, an organization implements a number of systems for data
management. Processes implemented in a data management system can be dictated based on the manner in which data flows through the organization and the manner in which the data is utilized. The processes can also be modified based on whether the data is suitable for its intended use in decision making or planning.
[00017] Generally, the processes implemented in the data management system focus on a
plurality of aspects related to data management or a specific area of data management, for example, data quality management. All such aspects are considered while implementing the data management system in an organization.
[00018] It will be appreciated by a person skilled in the art that such aspects are
considered, for example, by an administrator implementing the data management system, during the design phase. To avoid complexity during the design phase, all aspects may not be considered for data management. Due to this, some aspects that may be either pertinent to the organization or instrumental in catering to certain objectives of the organization such as enterprise view of data, regulatory compliance may be neglected. In such cases, the data management system may not be best suited to the needs of the organization or its customers. As a result, the growth of the organization may be adversely affected. Additionally, due to insufficient selection of aspects in the design phase, the data management system may provide unreliable and incorrect results and the root cause of which may remain unidentifiable in absence of methods and systems to assess the data management system. Further, due to die use of disparate set of technologies for implementing the same processes in different lines of business within an organization, growth through mergers and acquisitions, new business requirements and regulations may result in data being highly fragmented, dispersed, redundant, and constantly changing.

[00019] For this reason, methods and systems for assessing processes and mechanisms
implemented for data management in an organization are disclosed herein. The assessment of the data management system of an organization is based on one or more aspects, such as dimensions, which are to be considered while implementing data management. The dimensions can further include one or more sub-dimensions. Such dimensions and sub-dimensions relate to all possible aspects of data management, for example, data management methodology, tools, competency, etc.
[00020] In order to assess the data management system, one or more dimensions and
sub-dimensions are identified. Once the relevant dimensions and sub-dimensions are identified, queries are generated. The generated queries are intended for one or more user groups of the organization. In said implementation, the queries are generated based on their association with the dimensions and the sub-dimensions. The generated queries are presented to members of the user groups and subsequently, their responses are gathered.
[00021] Once gathered, the responses are processed and analyzed. In one
implementation, the processing involves computing a score for each response and further, taking a weighted mean of all the scores associated with the responses pertaining to a dimension. Each of the dimensions may be associated with a criticality factor based on the score of the responses for that dimension.. At least one visual chart, representing the weighted mean of all the dimensions along with the criticality factor associated with each dimension, may be generated.
[00022] The visual chart can be used for depicting differences based on the comparison
between a planned state of the data management system that the organization intends to achieve and an actual state assessment of the data management system. The actual state assessment of the data management system is made based on the responses received from the user groups. Based on the visual chart, an assessment report and a project plan can be provided. The assessment report and the project plan may be used to improve the existing data management systems and processes of the organization.
[00023] While aspects of described systems and methods for the assessment of the data
management system can be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).

EXEMPLARY SYSTEMS
[00024] Fig. 1 illustrates an exemplary system 100 implementing a data management
assessment server 102 to assess processes and systems implemented for data management in an organization or an enterprise, according to an embodiment of the present subject matter. The system 100 includes the data management assessment server 102 configured to assess the policies, architectures, procedures and practices, which are implemented to manage data of the organization. The data management assessment server 102 may include mainframes, personal computers, laptops, personal digital assistants (PDAs), etc.
[00025] The data, policies, architectures, procedures, and practices are processed and
consolidated in a data management system (not shown in this figure) specific to an organization. The data management system may be a collection of a number of systems such as a data quality management system, a data architecture and modeling management system, a data security management system, a master data management system, a metadata management system, a data governance system, and so on. Each organization also includes a user group, for example, user group 104-1,104-2,...104-N, collectively referred to as user groups 104. Each of the user groups 104 include one or more members, for example, administrators, stakeholders, technical staff of the organization, owners of the data, consumers of the data, clients of the organization, etc. In one implementation, the assessment of the data management system is achieved with the assistance of the user groups 104. In another implementation, various data profiling tools, such as the IBM Information Analyzer, TCS Data Profiler can be used to gather characteristics of the data management system.
[00026] The data management assessment server 102 is provided with one or more user
interfaces to interact with the members of the user groups 104. It will be appreciated that the members of the user groups 104 interact with the data management assessment server 102 through one or more computing devices. In another implementation, the members of the user groups 104 may directly interact with one or more analysts. Even though the description, hereinafter, is in terms of the user group 104-1 associated with one organization, it will be understood that the description may be extended to other user groups 104 associated with other organizations.
[00027] The data management assessment server 102 communicates with the user group
104-1 through a network 106. The network 106 may be a wireless network, wired network or a

combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network 106 may either be a dedicated network or a shared network, which 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), etc., to communicate with each other.
[00028] Assessment of the data management system of the organization is based on one
or more dimensions. The dimensions may include one or more sub-dimensions. These dimensions and sub-dimensions relate to all possible aspects of data management, for example, data management methodology, tools, competency, etc., and are therefore contribute towards achieving a comprehensive and holistic assessment of the data management system.
[00029} In one embodiment, an identification module 108 identifies various dimensions
and sub-dimensions. In said embodiment, the dimensions and sub-dimensions vary based on a system for which the data management assessment server 102 is implemented. For example, if a data quality management system, included within the data management system, is being assessed, the dimensions include aspects related to strategy, governance, competency, lifecycle, metrics, and process excellence. In another example, if a data architecture and modeling management system is being assessed, the dimensions include aspects related to types of data, data governance, data lifecycle, data infrastructure, and so on. In yet another example, if a master data management system is being assessed, the dimensions include data security, governance and compliance, strategy and methods, architecture, data quality, executive sponsorship and business involvement, and competency tools and technology.
[00030] In another embodiment, the dimensions and the sub-dimensions may be
predetermined and stored in a table. The manner in which the dimensions and the sub-dimensions are identified is further described in detail in this detailed description.
[00031] Once the identification module 108 identifies the relevant dimensions and
related sub-dimensions, a query module 110 generates one or more queries for presenting to the members of the user group 104-1. The members to be presented with the queries are selected based on their roles in the organization, and their usage of the data management system. It will

be appreciated that the generated queries are transmitted to the members of the user group 104-1 through the network 106.
[00032] As mentioned previously, the responses provided by the user group 104-1 are
indicative of an actual state of the data management system of the organization with which the user group 104-1 is associated. Hence, the responses are used to make an actual state assessment of each dimension. The responses also indicate the degree to which the data management system is operating and its level of efficiency and performance. Further, the responses also help in evaluating whether the performance conforms to the organization's goals, requirements and needs. In one implementation, the actual state assessment is associated with a criticality factor indicating a level of risk associated with the dimension. Along with making the actual state assessment, a planned state of the data management system is determined based on the goals of the organization. In one implementation, the goals of the organization are provided by the user group 104-1 based on their expectations from the data management system, such as a level of accuracy of data.
[00033] On the basis of the responses received from the user group 104-1, the data
management assessment server 102 compares the actual state assessment with the planned state of the organization's data management system. The data management assessment server 102 also identifies one or more potential risks within the organization's data management system and subsequently, generates an assessment report and a project plan. In one implementation, the assessment report and the project plan are generated by a rule-based engine (not shown in the figure).
[00034] The assessment report and the project plan, together aim to bridge a gap between
the actual state assessment and the planned state of the data management system of the
organization with which the user group 104-1 is associated. A configuration of the data
management assessment server 102 is discussed in detail in subsequent figures.
[00035] Fig. 2 illustrates exemplary components of the data management assessment
server 102, in accordance with an embodiment of the present subject matter. In said embodiment, the data management assessment server 102 includes a processors) 202, input-output (I/O) interface(s) 204, and a memory 206. The processors) 202 are coupled to the memory 206. The processors) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state

machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processors) 202 are configured to fetch and execute computer-readable instructions stored in the memory 206.
[00036] The I/O interface(s) 204 may include a variety of software and hardware
interfaces, for example, a web interface allowing the data management assessment server 102 to interact with the user group 104-1. Further, the I/O interface^) 204 may enable the data management assessment server 102 to communicate with other computing devices, such as web servers and external repositories. The I/O interface(s) 204 can facilitate multiple communications within a wide variety of networks add protocol types, including wired networks, for example LAN, cable, etc., and wireless networks such as WLAN, cellular, or satellite. The I/O interface(s) 204 may include one or more ports for connecting a number of computing devices to each other or to another server.
[00037] The memory 206 can include any computer-readable medium known in the art
including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, etc.). In one embodiment, the memory 206 includes program module(s) 208 and program data 210. The program module(s) 208 further include the identification module 108, the query module 110, an analysis module 212, and other module(s) 214. Additionally, the memory 206 further includes program data 210 that serves, amongst other things, as a repository for storing data processed, received and generated by one or more of the program module(s) 208. The program data 210 includes, for example, identification data 216, query data 218, response data 220, and other data 222. In one embodiment, the identification data 216, the query data 218, and the response data 220, may be stored in the memory 206 in the form of data structures.
[00038] As mentioned previously, the data management assessment server 102 assesses
the processes and mechanisms for data management implemented in an organization or an enterprise. To this end, the identification module 108 identifies various dimensions and sub-dimensions pertinent for assessing an organization's data management system. The dimensions and the sub-dimensions may vary based on the aspect of the data management system that is being assessed. In one implementation, the dimensions are identified based on a preliminary inquiry that is presented to the members of the user group 104-1. As mentioned previously, the user group 104-1 is a group of members who are associated with the organization, which is to

be assessed by the data management assessment server 102. For example, members of the user group 104-1 who are responsible for an aspect, such as data quality, will be presented with the preliminary inquiry that is related to data quality. In such a case, the dimensions and the related sub-dimensions are identified based on the responses received for the preliminary inquiry. In one implementation, the preliminary inquiry is performed by the query module 110. In said implementation, information in relation to the identified dimensions and sub-dimensions is stored in the identification data 216.
[00039] Once the dimensions and related sub-dimensions are identified the query module
110 generates a plurality of queries based on the identified dimensions and sub-dimensions. In
one implementation, the query module 110 obtains the queries from the query data 218. The
queries that are generated by the query module 110 are then presented to the members of the
user group 104-1. In one implementation, the queries can be presented to the members of the
user group 104-1 through a user-interface generated at one or more computing devices
accessible to them. The members of the user group 104-1, on receiving the queries, can provide
their responses, for example, through a computer generated user-interface.
[00040] In one implementation, the responses to the queries received from the members
of the user group 104-1, are stored in the response data 220. The responses received from the
members of the user group 104-1 are compared with one or more predetermined responses. The
predetermined responses are stored in the other data 222. The predetermined responses are
prepared in accordance with a planned state of the data management system. The planned state
of the data management system can include a desired state at which the members of the user
groups 104 perceive an optimum functioning of the data management system.
[00041] Based on the comparison, the analysis module 212 associates a score with each
of the responses received from the members of the user group 104-1. Once obtained, the analysis module 212 processes the scored responses to provide an actual state assessment. The actual state assessment indicates, in part, the assessment of a current state of the data management system of the organization, in respect of a particular dimension. In one implementation, the analysis module 212 makes the actual state assessment based on the weighted mean of the scored responses. In another implementation, the actual state assessment of the data management system in respect of all the relevant dimensions, is represented in various visual forms, such as in the form of dashboards, graphical charts, tabular charts, etc.

[00042] In one implementation, the analysis module 212 generates visual charts, such as
spider charts, to compare the actual state assessment with the planned state of the organization's data management system. Such a visual chart depicts gaps between the actual state assessment and the planned state for each dimension. The visual charts also distinguish the more pertinent dimensions from the less pertinent ones. This will be discussed in the description of subsequent figures.
[00043] As indicated previously, the data management assessment server 102 is
associated with a knowledge base 224. The data management assessment server 102 and the knowledge base 224 interact through the network 106. The knowledge base 224 either can be externally associated with the data management assessment server 102 or can be internal to the data management assessment server 102.
[00044] The knowledge base 224 includes rules, procedural representations, use case
scenarios, etc. These rules are implemented as conditional statements providing a result in response to various scenarios. In one implementation, the knowledge base 224 is periodically updated and modified to reflect new rules and best practices followed in the industry to which the organization pertains.
[00045] The analysis module 212 obtains information stored in the knowledge base 224
to evaluate risks associated with the actual state assessment of the data management system. The risks are evaluated based on the rules and other information obtained from the knowledge base 224, and based on the responses received from the members of the user group 104-1. For example, the responses provided by the user group 104-1 and stored in the response data 220, are analyzed on the basis of one or more conditions specified in the rules. Accordingly, the analysis module 212 generates an assessment report, which indicates risks associated with the responses.
[00046] Once the risks are obtained, a project plan is generated. The project plan is based
at least on the planned state. The project plan indicates processes or mechanisms that can be implemented by the organization in order to achieve optimum functionality of the data management system as depicted by the planned state. The project plan can also be based on one or more business related goals, which the organization seeks to achieve over a specific period of time. In one implementation, the project plan is generated by the analysis module 212. The

analysis module 212 generates the project plan based on the actual state assessment and based on information provided in the knowledge base 224.
[00047] Even though the identification module 108, the query module 110 and the
analysis module 212 have been shown to be included in a single device such as the data management assessment server 102, it will be appreciated that the identification module 108, the query module 110 and the analysis module 212 may be included in separate devices too.
[00048] Fig. 3(a) illustrates an exemplary data structure 300 indicating dimensions and
queries that are associated with the dimensions. The data structure 300 is included in the memory 206 of the data management assessment server 102. As described previously, when an assessment of a particular system within a data management system, for example a data quality management system, is requested the data management assessment server 102 identifies various dimensions 302-1, 302-2,..., 302-N, collectively referred to as dimensions 302, pertinent to the data management system, using the identification module 108.
[00049] Each of the dimensions 302 includes one or more sub-dimensions. For example,
the dimension 302-1 includes sub-dimensions 304-1, 304-2,..,304-N, collectively referred to as sub-dimensions 304 and the dimension 302-2 includes sub-dimensions 306-1, 306-2,...306-N, collectively referred to as sub-dimensions 306. The identification module 108 stores the dimensions 302 and their respective sub-dimensions, such as sub-dimensions 304 and 306, in the identification data 216.
[00050] In one implementation, the data structure 300 is implemented as a table, for
example, a table stored in identification data 216. In another implementation, the data structure 300 can be implemented as a table stored in an external storage device, accessible over the network 106. Also, for assessing a data quality management system of the organization, the dimensions 302 include, but are not limited to, aspects related to strategy, governance, lifecycle, and metrics, as shown in table 1. Examples of the dimensions and related sub-dimensions for evaluating a data architecture and modeling management system are listed in table 2.

Table 1
Data Quality Management
Dimensions Sub-Dimensions
Strategy Objectives and Goals, Management and Business Users Buy in, Initiatives Planning
Governance Organization Structure, Roles and Responsibilities, Policies and Procedures
Metrics Sufficiency, Consistency, Uniqueness, Accuracy, Latency
Lifecycle Profiling , Cleansing and Standardization, Matching and Consolidation, Data Enrichment, Monitoring, Implementation
Process And Quality Assurance Compliance Process Improvement, Quality Assurance Compliance
Competency and Operational Infrastructure None
Table 2
Data Archite cture and Modeling Management System
Dimensions Sub-Dimensions
Data lifecycle Data Create, Data Read, Data Update, Data Delete
Modeling Lifecycle Data Flow Diagram, Conceptual Data Modeling, Logical Data Modeling, Physical Data Modeling
Data Governance People, Process, Technology
Data Infrastructure Database Maintenance and Monitoring, Capacity Planning, Business Continuity, Backup Archiving and Recovery, Database Tuning, Clustering and High Availability
Types of Data Transactional Data, Operational Data, Analytic Data, Reference Data, Master Data, Metadata , Market Data
Data Architecture Principles Data Integrity, Data Quality and Data Security


[00051] Similarly, the dimensions pertinent for evaluating a master data management
system include data security, governance and compliance, strategy and methods, architecture,
data quality, executive sponsorship and business involvement, and competency tools and
technology. As explained previously, once the dimensions, such as the dimensions 302 are
identified, the query module 110 generates queries based on their association with the
dimensions 302. Each of the queries may be assigned a weight based on the importance of the
query to the overall assessment. In one implementation, the queries related to the sub-
dimensions 304 and 306 can be implemented as a separate table within the query data 218. The
query table can indicate the association between, the dimensions 302 and the related sub-
dimensions 304 and 306, and the queries. Once the relevant dimensions 302, sub-dimensions
304 and 306 are identified, the query module 110 fetches one or more queries from the query
data 218 that are associated with the identified dimensions 302, and the sub-dimensions 304
and 306. The queries are then presented to the members of the user group 104-1. In one
implementation, the responses received from the members of the user group 104-1 are
associated with the queries stored in the queries 218 in the data structure 300. As explained
previously, the responses to the queries are stored in the response data 220.
[00052] In another implementation, the analysis module 212 evaluates the responses and
generates a table as shown in Figure 3(b). Such a table 350 is stored in the memory 206 as a data structure having one or more fields . Referring to Fig. 3(b), the analysis module 212 compares the responses with pre-determined responses and provides a weighted score to each response based on the comparison. The analysis module 212 computes a weighted mean of all the responses corresponding to a particular dimension, for example, dimension 302-1. The weighted mean represents an actual state assessment of the dimension 302-1, represented by the field actual state assessment 352, as part of the data structure 350. Similarly, weighted mean for all the other relevant dimensions, for example, dimensions 302-1,...,302-7, is computed which collectively are present in the field actual state assessment 352. Further, a planned state is prescribed, which is stored in the field planned state 354. In one implementation, the planned state 354 is computed based on the business goals of the organization
[00053] Furthermore, the analysis module 212 generates at least one visual chart, such as
a spider chart 356. The spider chart 356 depicts a comparison and differences between the actual state assessment 352 and the planned state 354 of the data management system, with

respect to all the pertinent dimensions 302 and related sub-dimensions, of the organization. A
gap area 358 between the actual state assessment 352 and the planned state 354 of the
organization's data management system is an indicator of the extent of changes that have to be
implemented so as to achieve an optimum functioning of the data management system. For
example, the actual state assessment 352 of the dimension 302-1 and related sub-dimensions
304, computed from the responses to the queries, is a weighted mean of 7. The planned state
354 computed on the basis of goals of the organization is equal to 9. The gap area 358
equivalent to 2 exists between the actual state assessment 352 and the planned state 354.
[00054] In one embodiment, the analysis module 212 associates criticality factors with
the actual state assessment. The criticality factors can be appended to the data structure 350 as the field criticality factor 360 for the respective actual state assessment (i.e., as depicted by the field actual state assessment 352). The field criticality factor 360 is based on the degree of differences between the actual state assessment 352 and the planned state 354. In case the difference is more than a certain value, a high criticality factor can be associated with the respective actual state assessment 352 and the related dimension, say dimension 302-1.
[00055] For example, the criticality factors 360 may be computed based on the planned
state 354. Thus, if the actual state assessment 352, calculated using the weighted mean, is less than half of the planned state 354, then a maximum criticality factor 360 equivalent to 5 may be associated with the actual state assessment 352. However, if the actual state assessment 352 is almost equal to the planned state 354, then a minimum criticality factor 360 equivalent to 1 may be associated with the actual state assessment 352. In another example, as shown in Fig. 3(b), the criticality factors 360 may be computed on a scale of 1 to 10, where a weighted mean between 1 to 3 represents high risk, between 3 to 7 represents medium risk and between 7 to 10 represents low risk. In this way, the analysis module 212 helps in distinguishing the more critical dimensions from the less critical dimensions.
[00056] Furthermore, based on the spider chart 356 and the table 350, the analysis
module 212 interacts with the knowledge base 224, to provide an assessment report and a project plan for the organization, which may be used by the organization to make its existing data management systems more effective. The project plan includes recommendations to overcome the risks associated with the actual state assessment 352 and a roadmap to implement the recommendations.

[00057J Exemplary methods for assessing data management systems are described with
reference to Figs. 4 to 7. These exemplary methods may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like that perform particular functions or implement particular abstract data types. The methods may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[00058] The order in which the methods are described is not intended to be construed as
a limitation, and any number of the described method blocks can be combined in any order to implement the methods, or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the methods can be implemented in any suitable hardware, software, firmware, or combination thereof.
[00059] Fig. 4 illustrates an exemplary method 400 for evaluating a data management
system of an organization.
[00060] At block 402, at least one dimension and sub-dimension is identified. For
example, the identification module 108 identifies various dimensions 302 and sub-dimensions, such as sub-dimensions 304 and 306, pertinent to the assessment of an organization's data management system. In one implementation, the dimensions 302 can also be specified by the members of the user group 104-1 of the organization.
[00061] At block 404, a plurality of queries are generated for user groups. For example,
the query module 110 receives the dimensions 302 from the identification module 108 and generates queries based on the dimensions 302 and the related sub-dimensions 304 and 306. Each of such queries is associated with a weight. The query module 110 stores the queries in the query data 218. Further, the queries are provided to members of the user group 104-1 of the organization. Examples of such members of the user group 104-1 include, but are not limited to, administrators of an organization, owners of data and consumers of the data, clients of the organization, etc. The user group 104-1 provide responses to the queries, which are gathered in the response data 220.

[00062] At block 406, the responses received from the user groups are analyzed. For
example, the responses gathered in the response data 220 are processed and analyzed by the analysis module 212 to provide an actual state assessment 352 of the organization's data management system. Further, a planned state 354 of the organization may be computed based on the business goals of the organization. The analysis module 212 compares the actual state assessment 352 of the data management system with the planned state 354 of the data management system. The comparison may be depicted in the form of the spider chart 356 and may depict one or more gap areas 358 between the actual state assessment 352 and the planned state 354 with respect to the pertinent dimensions.
[00063] At block 408, an assessment report and a project plan are generated. For
example, the analysis module 212 interacts with the knowledge base 224 and analyzes the responses stored in the response data 220 with respect to pre-defined rules stored in the knowledge base 224 . Further, the analysis module 212 provides an assessment report, which identifies risks associated with the actual state assessment 352 of the dimensions 302, and in turn, the data management system of the organization. Accordingly, a project plan is also generated by the analysis module 212. The project plan includes recommendations to overcome the risks associated with the actual state assessment 352. In addition, the project plan provides a roadmap to implement one or more recommendations.
[00064] Fig. 5 illustrates an exemplary method to identify the dimensions and sub-
dimensions, in accordance with an implementation of the present subject matter.
[00065] At block 502, a request for assessment of a data management system is received.
The members of user group 104-1, for example, administrators of an organization, request assessment of the data management system. In one implementation, the user group 104-1 requests assessment of only a particular system included within the data management system, such as the data quality management system, the data architecture and modeling management system, or the master data management system.
[00066] At block 504, the dimensions associated with the data management system are
determined. In one implementation, the identification module 108 can determine the dimensions that are associated based on a preliminary inquiry presented to the user group 104-1. In another implementation, an analyst may also specify the dimensions. In yet another implementation, the identification module 108 selects the dimensions 302 stored in the

identification data 216. The identified or selected dimensions 302 are related to the data management system which is being assessed. When the dimensions 302 associated with the data management system are identified, the method moves to block 506.
[00067] At block 506, it is determined whether any sub-dimensions are associated with
the dimensions. For example, the identification module 108 identifies sub-dimensions, such as sub-dimensions 304 and 306, corresponding to the dimensions 302 identified in block 504 ("Yes" branch from block 506). Once identified, the information in relation to the identified dimensions, such as dimensions 302, and information in relation to the sub-dimensions, such as sub-dimensions 304 and 306, is stored in the identification data 216 (block 510). On the other hand, if the determination yields that the dimensions 302 are not associated with sub-dimensions 304 and 306 ("No" branch from block 506), then one or more members of the user group 104-1 can specify the sub-dimensions (block 508).
[00068] Fig 6 illustrates an exemplary method to analyze one or more responses received
from members of the user groups.
[00069] At block 602, one or more responses are obtained from user groups. In one
implementation, the analysis module 212 obtains responses to the queries from the user group 104-1, and stores them in the response data 220. The response data 220 may be associated to the query data 218.
[00070] At block 604, a planned state is computed. For example, based on the business
goals of the organization, the planned state 354 of the organization may be computed and stored in the other data 222.
[00071] At block 606, an actual state assessment is made based on the responses. For
example, the analysis module 212 associates a score to each response received from the user group 104-1. In one implementation, the analysis module 212 computes a weighted mean for each dimension. The weighted mean represents an actual state assessment 352 of data management system of the organization being assessed. The actual state assessment 352 is expressed in terms of the actual state assessment of the dimensions 302 pertinent to the data management system.
[00072] At block 608, the actual state assessment is compared with the planned state.
The analysis module 212 compares the actual state assessment 352 of the data management system with the planned state 354 computed in block 602.

[00D73] At block 610, a visual chart is generated. For example, the analysis module 212
generates at least one visual chart, such as the spider chart 356, depicting the gap area 358 between the actual state assessment 352 and the planned State 354 of the organization. On the basis of the criticality factor 360 associated with each dimension, such a spider chart 356 may be configured to distinguish the most critical dimensions from the less critical dimensions. [00074] Fig .7 depicts, in the form of flowchart, an exemplary method to generate an assessment report and a project plan, in accordance with an implementation of the present subject matter.
[00075] At block 702, rules are obtained. In one example, the rules are stored in the
knowledge base 224. The rules may be conditional statements based on best practices and goals
of the organization. For example, the rules may state that if the actual state assessment 352 is
less than half of the planned state 354 for a certain dimension, perform a certain task.
[00076] At block 704, risks are identified with respect to rules. For example, the analysis
module 212 interacts with the knowledge base 224 and applies pre-defined rules to the responses stored in the response data 220. Additionally, in one implementation, analysts may intervene through the I/O interfaces) 204 to create, modify or remove rules to reflect the dynamic changes in the organization.
[00077] At block 706, an assessment report is generated. The analysis module 212
provides the assessment report, which lists risks associated with the actual state assessment 352 of the dimensions 302, and in turn, the risks associated with the data management system of the organization.
[00078] At block 708, a project plan is generated. The project plan is formulated by the
analysis module 212. The project plan may take input from the goals or expectations defined by the user group 104-1. In another implementation, the criticality factor 360 associated with each of the dimensions 302 may also serve as one of the parameters for generating the project plan.
CONCLUSION
[00079] Although embodiments for assessment of a data management system have been
described in language specific to structural features and/or methods, it is to be understood that the invention is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as exemplary embodiments for assessment of the data management system.

We claim:
1. A method comprising:
identifying one or more dimensions (302) pertinent to a data management system;
obtaining responses (220) to a plurality of queries (218), wherein the queries (218) are generated based on each of the identified dimensions (302);
analyzing the responses (220) to make an actual state assessment (352) corresponding to each of the identified dimensions (302);
comparing the actual state assessment (352) of each of the identified dimensions (302) with a corresponding planned state (354) for each of the identified dimensions (302); and
providing an assessment report based, at least in part, on the comparing.
2. The method as claimed in claim 1, wherein the identified dimensions (302) comprise at least one sub-dimension (304,306).
3. The method as claimed in claim 2 further comprising storing, in a computer-readable medium (206), the identified dimensions (302) and the corresponding sub-dimension (304, 306).
4. The method as claimed in claim 1, wherein the obtaining comprises associating a weight with each of the generated queries (218).
5. The method as claimed in claim 1, wherein the analyzing comprises:
associating a score with each of the responses (220);
computing a weighted mean of the score associated with each of the responses (220); and
providing the actual state assessment (352), corresponding to each of the identified dimensions (302) based on the weighted mean.
6. The method as claimed in claim 1 further comprising generating a visual chart (356) based at least on the comparing.
7. The method as claimed in claim 1 further comprising associating a criticality factor (360) with the actual state assessment (352) based on a difference between the actual state assessment (352) and the planned state (354).
8. The method as claimed in claim 1, wherein the providing further comprises: obtaining a plurality of predetermined rules from a knowledge base (224);
evaluating the responses (220) based on the predetermined rules; and
identifying risks based on the evaluating.
20

9. The method as claimed in claim 8, wherein the providing further comprises generating a project plan based at least on the actual state assessment (352), the planned state (354), and the identified risks.
10. A system (102) comprising:
a processor (202);
a memory (206) coupled to the processor (202), wherein the memory (206) comprises,
an identification module (108) configured to identify one or more dimensions (302) for assessing a data management system; and
a query module (110) configured to generate a plurality of queries (218) based on the identified dimensions (302).
11. The system (102) as claimed in claim 10, wherein the query module (110) is configured to provide the generated queries (218) to at least one member of a user group (104).
12. The system (102) as claimed in claim 10 further comprising an analysis module (212) configured to determine, based on one or more responses (220) to the generated queries (218), a planned state (354) and an actual state assessment (352) corresponding to each of the identified dimensions (302).
13. The system (102) as claimed in claim 12, wherein the analysis module (212) is further configured to generate at least one of an assessment report, a visual chart (356), and a project plan.
14. The system (102) as claimed in claim 12, wherein the analysis module (212)is associated with a knowledge base (224).
15. The system (102) as claimed in claim 12, wherein the analysis module (212) is further configured to associate a criticality factor (360) to the actual state assessment (352), wherein the criticality factor (360) indicates a degree of difference between the actual state assessment (352) and the planned state (354).
16. The system (102) as claimed in claim 13, wherein the project plan comprises a plurality of recommendations for reducing a gap area (358) between the actual state assessment (352) and the planned state (354).
17. The system (102) as claimed in claim 10, wherein the data management system comprises at least one of a data architecture and modeling management system, a data security management system, a master data management system, a metadata management system, a data quality management system, and a data governance system.

18. The system (102) as claimed in claim 17, wherein the dimensions (302) for assessing the data quality management system are selected from a group of strategy, governance, metrics, lifecycle, process and quality assurance compliance, and competency and operational infrastructure.
19. The system (102) as claimed in claim 17, wherein the dimensions (302) for assessing the data architecture and modeling management system are selected from a group of data governance, data architecture principles, modeling lifecycle, types of data, data lifecycle, and data infrastructure.
20. The system (102) as claimed in claim 17, wherein the dimensions (302) for assessing the master data management system are selected from a group of data security, governance and compliance, strategy and methods, architecture, data quality, executive sponsorship and business involvement, and competency tools and technology.
21. A computer readable medium (206) having stored thereon a data structure (300, 350) comprising:
a first field for representing at least one dimension (302) associated with a data management system; and
a second field for representing entities selected from a group of queries (218), responses (220), an actual state assessment (352) of each dimension (302), and a planned state (354) of each dimension (302).
22. The computer readable medium (206) of claim 21, wherein the data structure (300, 350) further comprises a third field representing at least one sub-dimension (304, 306), wherein the at least one sub-dimension (304,306) is associated with the at least one dimension (302) in the first field.
23. The computer readable medium (206) of claim 21, wherein each of the queries (218) in the second field is associated with the at least one dimension (302) in the first field.
24. The computer readable medium (206) of claim 21, wherein the data structure (300, 350) further comprises a fourth field containing a criticality factor (360) associated with the at least one dimension (302) in the first field.
25. The computer readable medium (206) of claim 21, wherein the data structure (300, 350) further comprises:
at least one field for representing the responses (220);

at least one field for representing the actual state assessment (352) of each dimension
(302); and
at least one field for representing the planned state (354) of each dimension (302).