Patent Publication Number: US-11379533-B2

Title: Assessing quality of an active directory

Description:
TECHNICAL FIELD 
     The subject matter disclosed herein generally relates to a special-purpose machine that validates user profile data of a directory application, including computerized variants of such special-purpose machines and improvements to such variants. Specifically, the present disclosure addresses systems and methods for assessing a quality of an enterprise active directory. 
     BACKGROUND 
     Enterprise applications (e.g., document editing application) provide a platform for users to collaborate and interact with one another. However, user interactions within an enterprise setting may be difficult to be evaluated given the noise of the interaction data between users of the enterprise. Furthermore, user profile data of a directory application may outdated because the users often time do not update their profile data in the active directory. An administrator of the enterprise application typically updates the profile data of a user when the user joins or leaves the enterprise. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. 
         FIG. 1  is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, in accordance with some example embodiments. 
         FIG. 2  is a diagrammatic representation of interactions between users of an application in an enterprise, in accordance with some example embodiments. 
         FIG. 3  is a block diagram illustrating a heuristics validation engine in accordance with one example embodiment. 
         FIG. 4  is a block diagram illustrating a heuristics module in accordance with one example embodiment. 
         FIG. 5  is a flow diagram illustrating a method for determining a quality of an active directory in accordance with one example embodiment. 
         FIG. 6  is a flow diagram illustrating a method for quantifying a quality of an active directory in accordance with one example embodiment. 
         FIG. 7  is a flow diagram illustrating a method for providing validated users in accordance with one example embodiment. 
         FIG. 8  is a flow diagram illustrating a method for determining a quality of an active directory in accordance with one example embodiment. 
         FIG. 9  is a flow diagram illustrating a method for generating a recommendation in accordance with one example embodiment. 
         FIG. 10  illustrates a routine  1000  in accordance with one embodiment. 
         FIG. 11  illustrates a graphical user interface  1100  in accordance with one example embodiment. 
         FIG. 12  is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows describes systems, methods, techniques, instruction sequences, and computing machine program products that illustrate example embodiments of the present subject matter. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the present subject matter. It will be evident, however, to those skilled in the art, that embodiments of the present subject matter may be practiced without some or other of these specific details. Examples merely typify possible variations. Unless explicitly stated otherwise, structures (e.g., structural components, such as modules) are optional and may be combined or subdivided, and operations (e.g., in a procedure, algorithm, or other function) may vary in sequence or be combined or subdivided. 
     Directories of enterprise users&#39; profile are often prone to be outdated because they rely on proactive input and regular updates from users and administrators. There is little incentive for users to update profile attributes such as skills attribute or manager attribute in an enterprise directory of an enterprise. An enterprise represents organizations or groups of users associated with an organization. An example of a directory application includes an active directory application that manages the user profile data. The present application describes a system that applies heuristics (e.g., a set of rules or computations) on the user profile data. The system evaluates the results of the heuristics with collaboration data to assess a quality of hierarchy data of an active directory of the enterprise. The collaboration data includes, for example, people organizational network relations, collaborations topics, collaboration patterns, network metrics, skills, and projects. In one example, if the quality of the active directory below a threshold, the system recommends a collaboration analysis module to use an alternative source of active directory or to perform an analysis on the collaboration data using parameters that do not rely on the profile attributes of the active directory. In another example, the system uses the collaboration data to correct and/or validate manager attributes of the user profile data of the users in the active directory. 
     In one example embodiment, a system and method for validating user profile data from a directory application is described. The system accesses user collaboration data from a plurality of users of an application. The system also accesses a directory application that manages user profile data for each user of the plurality of users. A set of heuristics is applied on the user profile data. The system validates the results of the applied set of heuristics on the user profile data with the user collaboration data. The quality of the user profile data is assessed based on the validation. 
     As a result, one or more of the methodologies described herein facilitate solving the technical problem of manually correcting and updating profile attributes of an active directory. Data in a directory is often manually inputted by an administrator or the user. However, manual update of data is often prone to error and inaccuracies. Furthermore, any analysis performed based on data from the active directory may be inaccurate. As such, one or more of the methodologies described herein may obviate a need for certain efforts or computing resources (e.g., manually identifying incorrect attributes, correcting the incorrect attributes, validating profile attributes, updating the profile attributes of users of an enterprise). Examples of such computing resources include processor cycles, network traffic, memory usage, data storage capacity, power consumption, network bandwidth, and cooling capacity. 
       FIG. 1  is a diagrammatic representation of a network environment  100  in which some example embodiments of the present disclosure may be implemented or deployed. One or more application servers  104  provide server-side functionality via a network  102  to a networked user device, in the form of a client device  106 . A user  132  from an enterprise operates the client device  106 . The client device  106  includes a web client  110  (e.g., a browser), a programmatic client  108  (e.g., an email/calendar application such as Microsoft Outlook™, an instant message application, a document writing application, a shared document storage application) that is hosted and executed on the client device  106 . In one example embodiment, the programmatic client  108  accesses interaction data from a combination of the enterprise application  122 , the programmatic client  108 , or the web client  110 . The interaction data identifies items that are communicated from other users of the enterprise to the user  132  and from the user  132  to other users of the enterprise. Examples of interaction data includes and are not limited to email communications, meeting communications, instant messages, shared document comments, and any communication with a recipient (e.g., a user from the enterprise). 
     An Application Program Interface (API) server  118  and a web server  120  provide respective programmatic and web interfaces to application servers  104 . A specific application server  116  hosts an enterprise application  122  and a heuristics validation engine  126 . Both enterprise application  122  and heuristics validation engine  126  include components, modules and/or applications. 
     The enterprise application  122  may include collaborative applications (e.g., a server side email/calendar enterprise application, a server side instant message enterprise application, a document writing enterprise application, a shared document storage enterprise application) that enable users of an enterprise to collaborate and share document, messages, and other data (e.g., meeting information, common projects) with each other. Collaboration data identifies items communicated with the collaboration applications. Examples of collaboration data includes and are not limited to meeting information (e.g., frequency and duration of one-on-one meeting between user x and user y) and document sharing information (e.g., frequency and duration of user x and user y sharing and editing a common document). The user  132  at the client device  106  may access the enterprise application  122  to edit documents that are shared with other users of the same enterprise. In another example, the client device  106  accesses the enterprise application  122  to retrieve or send messages or emails to and from other peer users of the enterprise. Other examples of enterprise application  122  includes enterprise systems, content management systems, and knowledge management systems. 
     The directory application  124  includes, for example, an active directory. An active directory is used to manage computers and other devices on a network. The active directory may operate on an operating system that runs both local and Internet-based servers. The active directory allows network administrators to create and manage domains, users, and objects within a network. For example, an administrator can create a group of users and give them specific access privileges to certain directories on the server. As a network grows, the active directory provides a way to organize a large number of users into logical groups and subgroups, while providing access control at each level. 
     In one example embodiment, the heuristics validation engine  126  communicates with both the enterprise application  122  and the directory application  124 . The heuristics validation engine  126  accesses interaction data from users in the enterprise from the enterprise application  122 . The heuristics validation engine  126  accesses user profile data from the directory application  124 . In another example embodiment, the heuristics validation engine  126  communicates with the programmatic client  108  to receive instructions to perform an analysis based on the user profile data from the directory application  124  and collaboration data from the enterprise application  122 . In one example, the programmatic client  108  communicates with the application server  116  via the web server  120 . In another example, the web client  110  communicate with the heuristics validation engine  126  and enterprise application  122  via the programmatic interface provided by the Application Program Interface (API) server  118 . 
     The heuristics validation engine  126  applies a set of rules by performing a computation on the user profile data. In one example, the heuristics validation engine  126  assesses the quality of the user profile data based on the results of the computation. In another example, the heuristics validation engine  126  assesses a quality of a managerial hierarchy of users of the enterprise from the active directory based on results of heuristics applied to the user profile data. 
     In another example embodiment, the heuristics validation engine  126  determines that the quality of the user profile data in the active directory is below a threshold. In response, the heuristics validation engine  126  performs a clean-up operation of the user profile data (e.g., manager attribute) of the active directory based on the collaboration data. For example, the heuristics validation engine  126  determines a missing manager attribute for a user based on the collaboration data and updates the user profile data for the user. In another example, the heuristics validation engine  126  validates an existing manager attribute for a user based on the collaboration data. In another example, the heuristics validation engine  126  updates the existing manager attribute for a user based on the collaboration data. 
     In another example embodiment, the heuristics validation engine  126  determines that the quality of the user profile data in the active directory is below a threshold (after applying the set of rules from the set of heuristics). In response, the heuristics validation engine  126  generates a recommendation to the enterprise application  122  to avoid performing a collaboration analysis (performed by an analysis module—not shown—at the enterprise application  122  or by the heuristics validation engine  126 ) of the users of the enterprise application  122  using metrics that depend on an unreliable attribute (e.g., manager attribute) in the user profile data. In another example, the heuristics validation engine  126  generates a recommendation to the collaboration analysis to use another source of directory given the low reliability of the active directory. In another example, the heuristics validation engine  126  generates a recommendation to generate an alternative user experience that does not display any metrics that depend on the unreliable attribute (e.g., manager attribute) of the user profile data from the active directory. 
     The application server  116  is shown to be communicatively coupled to database servers  128  that facilitates access to an information storage repository or databases  130 . In an example embodiment, the databases  130  includes storage devices that store information to be processed by the enterprise application  122  and the heuristics validation engine  126 . 
     Additionally, a third-party application  114  may, for example, store another part of the enterprise application  122 , or include a cloud storage system. The third-party application  114  executing on a third-party server  112 , is shown as having programmatic access to the application server  116  via the programmatic interface provided by the Application Program Interface (API) server  118 . For example, the third-party application  114 , using information retrieved from the application server  116 , may supports one or more features or functions on a website hosted by the third party. 
       FIG. 2  is a diagram representation of interactions  200  between users in an enterprise  224 , in accordance with some example embodiments. The diagram illustrates peer users (e.g., user  202 , user  206 , user  208 , user  222 ) that are part of the enterprise  224  and are users of the enterprise application  122 . The peer users use a respective client device (not shown) using one or more client applications (not shown) to communicate and collaborate with one another (e.g., via the enterprise application  122 ). 
     For example, the user  222  sends an email  210  to user  208 , sends a shared document  220  to user  206 , and sends a meeting request  214  to user  202 . The user  208  receives the email  210  from user  222 , sends an email  212  to user  202 . The user  202  receives the meeting request  214  from user  222 , receives the email  212  from user  208 , and shares a comment on shared document  218  with user  204 . The user  204  receives the comment on shared document  218  from  202  and sends an instant message  216  to user  206 . 
     In one example embodiment, the heuristics validation engine  126  and/or enterprise application  122  collect user collaboration data representing the interactions between peer users of the enterprise  224  described above. In another example embodiment, the heuristics validation engine  126 /enterprise application  122  collect interaction data from a corresponding user of a client device. The heuristics validation engine  126 /enterprise application  122  may collect the interaction data for a preset period of time at a preset frequency (e.g., a one-week interval, year to date, or other time frame). 
     In another example embodiment, the heuristics validation engine  126 /enterprise application  122  collects user interaction data for a user up to a preset degree of contacts. For example, if the preset degree is one, the interaction data for the user  222  includes interaction data for user  208 , user  202 , and user  206 . A core working group of the user  222  would thus include user  208 , user  202 , and user  206 . 
       FIG. 3  is a block diagram illustrating a heuristics validation engine  126  in accordance with one example embodiment. The heuristics validation engine  126  comprises an enterprise application interface  302 , a heuristics module  304 , a quality assessment module  306 , an analytics module  308 , and a directory application interface  310 . 
     The enterprise application interface  302  communicates with the client devices of the users in the enterprise  224  and/or with the enterprise application  122 . In one example embodiment, the enterprise application interface  302  retrieves collaboration data (e.g., frequency and duration of one-on-one meetings between user x and user y, frequency and time of communication between user x and user y on topic z) and topic data (e.g., topic z, subject header in a message, keyword in a meeting request) from the enterprise application  122 /heuristics validation engine  126  or the client device  106 . In another example embodiment, the enterprise application interface  302  accesses collaboration data from the web client  110  or the programmatic client  108 . In another example embodiment, the enterprise application interface  302  retrieves user interaction data from the enterprise application  122 , heuristics validation engine  126 , client device  106 , web client  110 , or programmatic client  108 . 
     The directory application interface  310  retrieves enterprise organizational data from the directory application  124 . An example of enterprise organizational data includes user profile data. The user profile data includes the profile data for one or more users of the enterprise  224 . In one example, the user profile data include a manager attribute that indicate a manager hierarchy. Other examples of attributes include, but are not limited to, a leader attribute, a team attribute, a group attribute, a project attribute, a supervisor attribute. In another example embodiment, the heuristics module  304  generates a tree based on the enterprise organizational data where the parent nodes represent the managers and the leaf nodes represent users that report to the managers. 
     The heuristics module  304  computes a set of heuristics to measure a quality of the enterprise organizational data based on the collaboration data. For example, the heuristics module  304  identifies a list of users and their corresponding managers from the user profile data. The heuristics module  304  generates a team hierarchy based on the list of users and managers. The heuristics module  304  applies a set of rules (e.g., clean-up operations) to the team hierarchy. For example, the heuristics module  304  performs a function that identifies and removes isolated users (e.g., a user with no child node and no parent node). In another example, the heuristics module  304  performs a function that identifies and removes users caught in loops (e.g., a user with reporting to a manager who is one or more level below the user). The heuristics module  304  modifies the manager attribute of a user profile data based on the results of the heuristics. 
     In another example embodiment, the heuristics module  304  uses the collaboration data from the directory application interface  310  to validate the manager attribute of the user profile data. In one example, the heuristics module  304  validates the modified manager attribute of the user profile data after applying the set of heuristics to the user profile data. For example, the heuristics module  304  identifies a manager of a user based on a collaboration data between the manager and the user (e.g., how often and when the user and the manager communicate with each other). An example embodiment of the heuristics module  304  is described below further with respect to  FIG. 4 . 
     The quality assessment module  306  determines a quality of the user profile data based on the results of the heuristics and the collaboration data from the heuristics module  304 . For example, the quality assessment module  306  determines that 80% of the values in the manager attribute of the user profile data are valid (based on the results of the heuristics and comparing with the collaboration data). In another example, the quality assessment module  306  classifies the user data profile into one of five categories: Excellent, Very Good, Good, Acceptable, and Poor. These categories may be based on preset percentage validity ranges (e.g., 90% to 100% is excellent). In another example, the quality assessment module  306  generates a quality score based on the percentage validity (e.g., score of 10 when at least 95% of the values of the manager attributes of the profile data are valid). 
     In another example embodiment, the quality assessment module  306  compares the quality score of the user profile data with a preset threshold (for the user, the group, or the enterprise) and generates a recommendation based on the comparison. For example, if the quality score of the user profile data is below a preset threshold (e.g., score of 6), the quality assessment module  306  provides one or more recommendations to the analytics module  308 . 
     The analytics module  308  performs a statistical computation on the collaboration data and/or interaction data. The analytics module  308  may also generate a graphical user interface that relies on the values in the manager attributes of the user profile data in the directory application  124 . For example, the analytics module  308  may render a graphical tree that illustrates the enterprise organization hierarchy. The analytics module  308  receives a recommendation from the quality assessment module  306 . The recommendation may be for the analytics module  308  to use an alternative directory source instead of the directory application  124 . The recommendation may be for the analytics module  308  to provide an alternative user interface that is not dependent on the active directory data (e.g., manager attribute of the user profile data). The recommendation may be for the analytics module  308  to modify a user experience workflow that does not depend on the manager attribute of the user profile data). In another example, the recommendation may prevent the analytics module  308  from computing a metric that is dependent on the manager attribute. 
       FIG. 4  is a block diagram illustrating a heuristics module  304  in accordance with one example embodiment. The heuristics module  304  includes a rules module  402 , and a dataset module  404 . 
     The dataset module  404  accesses the enterprise application interface  302  to retrieve the collaboration data and topic data, and the directory application interface  310  to retrieve the enterprise organization data (e.g., user profile data). 
     The rules module  402  applies a set of rules on the user profile data. The rules module  402  accesses the user profile data and identifies the users and managers based on the user profile data. The rules module  402  generates a tree hierarchy based on the identified users and managers. The rules module  402  includes for example, a first rule that identifies and removes isolated users and a second rule that identifies and removes users caught in a loop. 
     In the first rule, the rules module  402  detects that a user does not have a valid value in the manager attribute of its user profile. The following table illustrates an example where the managerID  425  of person ID  123  and  325  is not valid because managerID  425  does not exist in the user profile data: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 PersonID 
                 Name 
                 ManagerID 
               
               
                   
               
             
            
               
                 123 
                 Faisal 
                 425 
               
               
                 325 
                 Binyan 
                 425 
               
               
                 633 
                 Scott 
                 526 
               
               
                   
               
            
           
         
       
     
     In the above example, personID  123 ,  325 ,  633  do not have any child nodes. 
     In another example, the rules module  402  detects that a user does not have a value in the manager attribute of its user profile. The following table illustrates an example where the managerID of personID  123  is blank or null: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 PersonID 
                 Name 
                 ManagerID 
               
               
                   
               
             
            
               
                 123 
                 Faisal 
                 null 
               
               
                 325 
                 Binyan 
                 633 
               
               
                 633 
                 Scott 
                 526 
               
               
                   
               
            
           
         
       
     
     In the above example, personID  123 ,  325 ,  633  do not have any child nodes. 
     In the second rule, the rules module  402  detects that a manager at a higher level in a hierarchy reports to another user in one or more levels below the manager, thereby creating a loop. The following table illustrates an example where the managerID  234  reports to a user (personID  425 ) that also reports to the managerID  234 : 
     
       
         
           
               
               
               
             
               
                   
               
               
                 PersonID 
                 Name 
                 ManagerID 
               
               
                   
               
             
            
               
                 123 
                 Faisal 
                 425 
               
               
                 234 
                 Binyan 
                 425 
               
               
                 345 
                 Anji 
                 425 
               
               
                 425 
                 Scott 
                 234 
               
               
                   
               
            
           
         
       
     
     The following table illustrates an example where the personID  123  reports to the same personID  123 : 
     
       
         
           
               
               
               
             
               
                   
               
               
                 PersonID 
                 Name 
                 ManagerID 
               
               
                   
               
             
            
               
                 123 
                 Faisal 
                 123 
               
               
                 325 
                 Binyan 
                 425 
               
               
                 633 
                 Scott 
                 526 
               
               
                   
               
            
           
         
       
     
     In another example, the rules module  402  forms two groups: a first group for users who have a manager after running the operations (from the first and second rules) above, and a second group for users who do not have a manager after running the operations above. 
     For the second group, the rules module  402  identifies and assigns a manager for a user in the second group. For example, user John Doe of enterprise  224  does not have a manager assigned in the user profile data. The rules module  402  performs the following algorithm based on collaboration data to identify and assign a manager to John Doe. 
     The rules module  402  calculates a table of all users with whom John has recurring  1 : 1  meetings (hours and/or count). One example a definition of a recurring  1 : 1  meeting definition is the total meeting hours where the meeting is recurring=and the total number of attendees is 2 and the participant included John Doe. For a predefined time period (X months), the rules module  402  determines the following from the collaboration data: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 S. No 
                 Employee 
                 Total recurring 1:1 hours 
               
               
                   
               
             
            
               
                 1 
                 Mark 
                 40 
               
               
                 2 
                 Jennifer 
                 20 
               
               
                 3 
                 James 
                 10 
               
               
                   
               
            
           
         
       
     
     If the rules module  402  finds no candidate managers, the rules module  402  performs another calculation based on the following 1:1 meeting definition=total meeting hours where total attendees=2 and participant=John Doe. For a predefined time period (X months). In those X months, the rules module  402  determines the following from the collaboration data: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 S. No 
                 Employee 
                 Total 1:1 hours 
               
               
                   
               
             
            
               
                 1 
                 Mark 
                 40 
               
               
                 2 
                 Jennifer 
                 20 
               
               
                 3 
                 James 
                 10 
               
               
                   
               
            
           
         
       
     
     The rules module  402  selects the first  3  users and check if they are managers in the user profile data from the directory application  124 . If they are not, the rules module  402  eliminates them. If they are, the rules module  402  continues to the next step. 
     For example, the user profile data indicates that Mark is not a manager, but Jennifer and James are. In this example, the rules module  402  eliminates Mark, finds Jennifer and Mark&#39;s team (individuals). The rules module  402  then calculates John&#39;s total collaboration time with Jennifer and James&#39; team (for X months): 
     
       
         
           
               
               
               
             
               
                   
               
               
                 S. No 
                 Team 
                 Total collaboration hours 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 1 
                 Jennifer&#39;s team 
                 80 
               
               
                 2 
                 James&#39;s team 
                 190 
               
               
                   
               
            
           
         
       
     
     The rules module  402  determines that John belongs to James&#39; team because total collaboration hours with James&#39;s team exceed the total collaboration hours with Jennifer&#39;s team by a margin (e.g., 138%) that exceeds a preset threshold margin (e.g., 50%). In one example embodiment, the rules module  402  modifies the manager attribute and adds the value of the manager attribute with the newly identified manager (e.g., James). 
     Once the rules module  402  has completed this computation for all of the users from the second group, the rules module  402  may perform the same computation for all of the users from the first group to validate the corresponding value in the manager attribute of the users in the first group. In one example embodiment, the rules module  402  modifies the manager attribute and replaces the value of the manager attribute with the newly identified manager (e.g., James). 
     The following tables illustrate other examples of rules for manager heuristics from the rules module  402 : 
     
       
         
           
               
               
               
             
               
                   
               
             
            
               
                 3. Measure 
                 Calculate the proportion of 
                 Consider a 10,000-person company. If 
               
               
                 ‘remaining’ 
                 employees remaining 
                 100 employees are removed in Rule 1 
               
               
                 employees 
                 relative to the total company 
                 and 2,400 employees are removed in 
               
               
                   
                 size (referred to as value A %). 
                 Rule 2 then: A = 25% 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 PersonID 
                 Name 
                 ManagerID 
               
               
                   
               
               
                 4. Identify 
                 Case where a root node has 
                 123 
                 Faisal 
                 425 
               
               
                 fragmented trees 
                 at least 2 children and 1 or 
                 234 
                 Binyan 
                 425 
               
               
                   
                 more layers of hierarchy, 
                 345 
                 Anji 
                 425 
               
               
                   
                 but the root node has: 
                 425 
                 Scott 
                 null 
               
               
                   
                 No manager ID 
               
               
                   
                 A manager ID that does 
               
               
                   
                 not appear 
               
               
                   
               
            
           
           
               
               
               
            
               
                 4.1 Calculate the 
                 Count employees in the 
                 R = 6:4 = 1.5 
               
               
                 ratio of employees 
                 largest tree. Count 
               
               
                 in largest tree to 
                 employees in all ‘small’ 
               
               
                 employees in all 
                 trees (trees that are not the 
               
               
                 smaller trees 
                 largest ones). 
               
               
                   
                 Divide the first number by 
               
               
                   
                 the second. 
               
               
                   
                 Call this Output: R 
               
               
                 4.2 Count the 
                 Count total number of 
                 F = 2/10 = 0.2 
               
               
                 average number of 
                 employees in ‘small’ trees 
               
               
                 employees per 
                 (trees that are not the 
               
               
                 fragmented tree 
                 largest ones). Count total 
               
               
                 (divided by total 
                 number of employees. 
               
               
                 number of 
                 Divide the first number by 
               
               
                 employees) 
                 the second. 
               
               
                   
                 Call this Output: F 
               
               
                 4.3. Count layers in 
                 Count number of layers in 
                 L = 3 
               
               
                 the tallest tree 
                 the tallest tree (of all 
               
               
                   
                 fragmented trees). 
               
               
                   
                 Call this Output: L 
               
               
                 5. Measure number of 
                 For each layer in the hierarchy 
                 [Assume 3-layered 
               
               
                 managers per X 
                 calculate the number of managers 
                 hierarchy and N = 100] 
               
               
                 employees at each layer 
                 (L1, L2, . . . , Ln) per N employees. 
                 L1: For a group of 100 
               
               
                 (on tallest &amp; largest trees) 
                 Output: a set ‘S M ’ of proportions. 
                 employees, measure 25 L1 managers. 
               
               
                   
                   
                 L2: For a group of 100 
               
               
                   
                   
                 employees, measure 5 L2 managers. 
               
               
                   
                   
                 L3: For a group of 100 
               
               
                   
                   
                 employees, measure 3 L3 managers. 
               
               
                   
                   
                 ‘SM’ = [25, 5, 3] 
               
               
                 6. Measure number of 
                 For each layer in the hierarchy 
                 [Assume 3-layered 
               
               
                 direct reports per X 
                 calculate the number of employees 
                 hierarchy and N = 10] 
               
               
                 managers at each layer 
                 (L1, L2, . . . , Ln) per N managers. 
                 L1: For a group of 10 L1 
               
               
                 (on tallest &amp; largest trees) 
                 Output: a set ‘S R ’ of proportions. 
                 managers, measure 250 employees. 
               
               
                   
                   
                 L2: For a group of 10 L2 
               
               
                   
                   
                 managers, measure 50 employees. 
               
               
                   
                   
                 L3: For a group of 10 L3 
               
               
                   
                   
                 managers, measure 30 employees. 
               
               
                   
                   
                 ‘SR’ = [250, 50, 30] 
               
               
                 7. Compute decision 
                 Use A, R, F, L, and SM as inputs 
               
               
                   
                 to the decision function ƒM 
               
               
                   
                 If ƒM (A, R, F, L, SM, SR) = 
               
               
                   
                 True: 
               
               
                   
                 Display results metrics of a 
               
               
                   
                 collaboration analysis (e.g., 
               
               
                   
                 Manager insights) 
               
               
                   
                 If ƒM (A, R, F, L, SM, SR) = 
               
               
                   
                 False: 
               
               
                   
                 Hide results metrics of a 
               
               
                   
                 collaboration analysis (e.g., 
               
               
                   
                 Manager insights) 
               
               
                   
               
            
           
         
       
     
     The following table illustrates a set of rules are for organization heuristics: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 Rule # 
                 Description 
                 Example 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 PersonID 
                 Name 
                 Org 
               
               
                   
               
               
                 1. Remove 
                 Case where a PersonID 
                 123 
                 Faisal 
                 null 
               
               
                 Nulls 
                 has no Organization 
                 325 
                 Binyan 
                 B 
               
               
                   
                 attribute 
               
               
                   
               
               
                   
                   
                   
                 Org Size 
               
               
                   
                   
                   
                 (% of total 
                 Outside 
               
               
                   
                   
                 Org 
                 emp&#39;s) 
                 {tilde over (x)} − 2σ? 
               
               
                   
               
               
                 2. Remove 
                 Case where an Org&#39;s 
                 L 
                 B 
                 Yes 
               
               
                 ‘small’ 
                 size/total company size 
                 M 
                 C 
                 No 
               
               
                 Orgs 
                 falls 2σ outside of {tilde over (x)} (on 
                 N 
                 D 
                 No 
               
               
                   
                 the left-hand side of the 
               
               
                   
                 distribution). 
               
               
                   
               
               
                   
                   
                   
                 Org Size 
               
               
                   
                   
                   
                 (% of total 
                 Outside 
               
               
                   
                   
                 Org 
                 emp&#39;s) 
                 {tilde over (x)} + 2σ? 
               
               
                   
               
               
                 3. Remove 
                 Case where an Org&#39;s 
                 L 
                 B 
                 Yes 
               
               
                 ‘large’ 
                 size/total company size 
                 M 
                 C 
                 No 
               
               
                 Orgs 
                 falls 2σ outside of {tilde over (x)} (on 
                 N 
                 D 
                 No 
               
               
                   
                 the right-hand side of the 
               
               
                   
                 distribution) 
               
               
                   
               
            
           
           
               
               
               
            
               
                 4. Measure 
                 After applying heuristic 
                 Consider a 1000-person company. If 90 Orgs 
               
               
                 total number 
                 rules 1-3: Calculate 
                 are removed as a result of Steps 1-3 then: D = 9% 
               
               
                 of Orgs 
                 total number of 
               
               
                   
                 Orgs/Total Company Size 
               
               
                   
                 (call this value D) 
               
               
                 5. Measure 
                 Calculate the proportion 
                 Consider a 10,000-person company. If 1,000 
               
               
                 remaining 
                 of employees remaining 
                 employees are removed as a result of Steps 1-3 
               
               
                 Employees 
                 relative to the total 
                 then: E = 90% 
               
               
                   
                 company size. (call this 
               
               
                   
                 value E %). 
               
               
                 6. Compute 
                 Use D &amp; E as inputs to 
               
               
                 Decision 
                 the decision function ƒO 
               
               
                   
                 If ƒO (D, E) = true: 
               
               
                   
                 Display results on 
               
               
                   
                 organization analytics 
               
               
                   
                 (e.g., Organization 
               
               
                   
                 insights) 
               
               
                   
                 If ƒO (D, E) = false: 
               
               
                   
                 Do not display results on 
               
               
                   
                 organization analytics 
               
               
                   
                 (e.g., Organization 
               
               
                   
                 insights) 
               
               
                   
               
            
           
         
       
     
     The following example illustrates hypothetical baselines for each metric/rule for an evaluation of manager and organization data: 
     
       
         
           
               
               
               
             
               
                   
               
               
                 Metric # 
                 Metric Description 
                 Threshold 
               
               
                   
               
             
            
               
                 1 
                 % employees remaining after Rules 
                 [65%, 90%] 
               
               
                   
                 1 and 2 
               
               
                 2 
                 % employees in tallest tree 
                 [1.5, 12]  
               
               
                 3 
                 # of fragmented trees/total 
                 [0.01%, 0.15%] 
               
               
                   
                 employees 
               
               
                 4 
                 Layers in tallest tree 
                 [3, 6] 
               
               
                 5 
                 Number of (Ln) managers per 1000 
                 L1: [20, 30] 
               
               
                   
                 employees 
                 L2: [2.5, 7.3] 
               
               
                   
                   
                 L3: [1, 4] 
               
               
                 6 
                 Number of direct employees per 50 
                 L1: [230, 270] 
               
               
                   
                 (Ln) managers (tallest tree) 
                 L2: [30, 72.5] 
               
               
                   
                   
                 L3: [25, 31] 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                   
               
             
            
               
                 7 &amp; 8 
                 Organization size bounds/ 
                 {tilde over (x)} = 0.07 s = 0.03 
               
               
                   
                 total employees 
                 [0.01, 0.13] 
               
               
                 9 
                 % Orgs remaining after Rules 1-3 
                 [64%, 90%] 
               
               
                 10 
                 % Emp&#39;s remaining Rules 1-3 
                 [72%, 95%] 
               
               
                   
               
            
           
         
       
     
     The following table illustrates an example case where all results of an analysis based on the user profile data are displayed: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 Case 1: 
                 The system determines that the quality of the user profile data is good. 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Function ƒM 
                   
                   
                   
                   
                   
                   
               
               
                 (parameters): 
                 A 
                 R 
                 F 
                 L 
                 SM 
                 SR 
               
               
                   
               
               
                 ƒM inputs for 
                 87% 
                 10 
                 0.05% 
                 4 
                 L1: 18 
                 L1: 231 
               
               
                 this company: 
                   
                   
                   
                   
                 L2: 3 
                 L2: 36 
               
               
                   
                   
                   
                   
                   
                 L3: 2.8 
                 L3: 29 
               
               
                   
               
            
           
           
               
               
            
               
                 ƒM (output): 
                 TRUE 
               
            
           
           
               
               
               
            
               
                 Function ƒO 
                 D 
                 E 
               
               
                 (parameters): 
               
               
                 ƒO inputs for 
                 70% 
                 80% 
               
               
                 this company: 
               
            
           
           
               
               
            
               
                 ƒO (output): 
                 TRUE 
               
               
                 Conclusion: 
                 The outputs for the two decision functions are true. 
               
               
                   
                 Both manager and organization experiences are enabled. 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Behavior: 
                 Enabled/Disabled 
               
               
                   
                   
               
               
                   
                 Focus time analytics (e.g., 
                 Enabled 
               
               
                   
                 scheduled time spent without 
               
               
                   
                 distractions) 
               
               
                   
                 Decision Making Meetings 
                 Enabled 
               
               
                   
                 analytics (e.g., time in meeting 
               
               
                   
                 resulting in a decision) 
               
               
                   
                 Cross group collaboration 
                 Enabled 
               
               
                   
                 analytics (e.g., time spent 
               
               
                   
                 collaborating with 
               
               
                   
                 users/colleagues from other 
               
               
                   
                 groups) 
               
               
                   
                 Manager connectedness 
                 Enabled 
               
               
                   
                 analytics (e.g., time spent 
               
               
                   
                 collaborating with a 
               
               
                   
                 manager/supervisor) 
               
               
                   
                   
               
            
           
         
       
     
     The following table illustrates an example where an analysis based on the user profile data are disabled: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 Case 2: 
                 The system determines that the quality of the user profile data is poor. 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Function ƒM 
                   
                   
                   
                   
                   
                   
               
               
                 (parameters): 
                 A 
                 R 
                 F 
                 L 
                 SM 
                 SR 
               
               
                   
               
               
                 ƒM inputs for 
                 73% 
                 17 
                 0.2% 
                 3 
                 L1: 20 
                 L1: 400 
               
               
                 this company: 
                   
                   
                   
                   
                 L2: 2 
                 L2: 80 
               
               
                   
                   
                   
                   
                   
                 L3: 1 
                 L3: 30 
               
               
                   
               
            
           
           
               
               
            
               
                 ƒM (output): 
                 FALSE 
               
            
           
           
               
               
               
            
               
                 Function ƒO 
                 D 
                 E 
               
               
                 (parameters): 
               
               
                 ƒO inputs for 
                 54% 
                 61% 
               
               
                 this company: 
               
            
           
           
               
               
            
               
                 ƒO (output): 
                 FALSE 
               
               
                 Conclusion: 
                 The outputs for the two decision functions are false. 
               
               
                   
                 Both manager and organization experiences are disabled. 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Behavior: 
                 Enabled/Disabled 
               
               
                   
                   
               
               
                   
                 Focus 
                 Disabled 
               
               
                   
                 Decision Making Meetings 
                 Disabled 
               
               
                   
                 Cross group collaboration 
                 Disabled 
               
               
                   
                 Manager connectedness 
                 Disabled 
               
               
                   
                 Influencers 
                 Disabled 
               
               
                   
                 Wellbeing (Work-life Balance) 
                 Disabled 
               
               
                   
                 Manager coaching 
                 Disabled 
               
               
                   
                   
               
            
           
         
       
     
     The following table illustrates an example where some results metrics of an analysis based on the user profile data are displayed: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 Case 3: 
                 The system determines that the quality of the user profile data is mixed (good and poor). 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Function ƒM 
                   
                   
                   
                   
                   
                   
               
               
                 (parameters): 
                 A 
                 R 
                 F 
                 L 
                 SM 
                 SR 
               
               
                   
               
               
                 ƒM inputs for 
                 70% 
                 14 
                 0.025% 
                 6 
                 L1: 10 
                 L1: 201 
               
               
                 this company: 
                   
                   
                   
                   
                 L2: 2 
                 L2: 15 
               
               
                   
                   
                   
                   
                   
                 L3: 1 
                 L3: 12 
               
               
                   
               
            
           
           
               
               
            
               
                 ƒM (output): 
                 FALSE 
               
            
           
           
               
               
               
            
               
                 Function ƒO 
                 D 
                 E 
               
               
                 (parameters): 
               
               
                 ƒO inputs for 
                 85% 
                 95% 
               
               
                 this company: 
               
            
           
           
               
               
            
               
                 ƒO (output): 
                 TRUE 
               
               
                 Conclusion: 
                 The outputs for the two decision functions are false for ƒM and true for ƒO. 
               
               
                   
                 As such, only the insights for Organization are enabled for this customer. 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Behavior: 
                 Enabled/Disabled 
               
               
                   
                   
               
               
                   
                 Focus 
                 Enabled 
               
               
                   
                 Decision Making Meetings 
                 Enabled 
               
               
                   
                 Cross group collaboration 
                 Enabled 
               
               
                   
                 Manager connectedness 
                 Disabled 
               
               
                   
                 Influencers 
                 Disabled 
               
               
                   
                 Wellbeing (Work-life Balance) 
                 Enabled 
               
               
                   
                 Manager coaching 
                 Disabled 
               
               
                   
                   
               
            
           
         
       
     
       FIG. 5  is a flow diagram illustrating a method for determining a quality of an active directory in accordance with one example embodiment. Operations in the method  500  may be performed by the heuristics validation engine  126 , using components (e.g., modules, engines) described above with respect to  FIG. 3 . Accordingly, the method  500  is described by way of example with reference to the heuristics validation engine  126 . However, it shall be appreciated that at least some of the operations of the method  500  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. For example, some of the operations may be performed at the enterprise application  122 . 
     At block  502 , the heuristics module  304  accesses collaboration data and topic data for user of an enterprise application from the enterprise application interface  302 . At block  504 , the heuristics module  304  accesses enterprise organization data from a directory application from the directory application interface  310 . At block  506 , the quality assessment module  306  determines a quality of the active directory based on the collaboration data and the enterprise organizational data. 
       FIG. 6  is a flow diagram illustrating a method for quantifying a quality of an active directory in accordance with one example embodiment. Operations in the method  600  may be performed by the heuristics validation engine  126 , using components (e.g., modules, engines) described above with respect to  FIG. 3 . Accordingly, the method  600  is described by way of example with reference to the heuristics validation engine  126 . However, it shall be appreciated that at least some of the operations of the method  600  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. For example, some of the operations may be performed at the enterprise application  122 . 
     At block  602 , the heuristics module  304  determines a quality of user profile data from the directory application  124  based on the collaboration data and the enterprise organizational data. At block  604 , the quality assessment module  306  identifies a percentage of active directory considered to be reliable for the analytics module  308 . 
       FIG. 7  is a flow diagram illustrating a method for providing validated users in accordance with one example embodiment. Operations in the method  700  may be performed by the heuristics validation engine  126 , using components (e.g., modules, engines) described above with respect to  FIG. 3 . Accordingly, the method  700  is described by way of example with reference to the heuristics validation engine  126 . However, it shall be appreciated that at least some of the operations of the method  700  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. For example, some of the operations may be performed at the enterprise application  122 . 
     At block  702 , the heuristics module  304  forms a user hierarchy based on organization data. At block  704 , the rules module  402  removes isolated users in the user hierarchy. At block  706 , the rules module  402  removes looped users in the user hierarchy. At block  708 , the rules module  402  forms a first group and a second group after removing the isolated and looped users. At block  710 , the rules module  402  validates a value for a manager attribute for a user in the first group based on collaboration data. At block  712 , the rules module  402  identifies a value for a manager attribute for a user in the second group based on collaboration data. At block  714 , the rules module  402  provides validated and identified managers to the analytics module  308 . 
       FIG. 8  is a flow diagram illustrating a method for determining a quality of an active directory in accordance with one example embodiment. Operations in the method  800  may be performed by the heuristics validation engine  126 , using components (e.g., modules, engines) described above with respect to  FIG. 3 . Accordingly, the method  800  is described by way of example with reference to the heuristics validation engine  126 . However, it shall be appreciated that at least some of the operations of the method  800  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. For example, some of the operations may be performed at the enterprise application  122 . 
     The method  800  starts at done block  804 . At block  802 , the quality assessment module  306  determines a quality of the active directory based on the collaboration data and the enterprise organizational data. At decision block  806 , the quality assessment module  306  determines whether the quality level is below a preset threshold. If the quality level is higher than the preset threshold, the quality assessment module  306  identifies metrics dependent on the active directory as reliable in block  808 . At block  810 , the quality assessment module  306  provides the reliable metrics to the analytics module  308 . If the quality level is lower than the preset threshold, the quality assessment module  306  identifies metrics that depend on the active directory as unreliable at block  812 . At block  814 , the quality assessment module  306  filters unreliable metrics from the analytics module  308 . The method  800  ends at done block  816 . 
       FIG. 9  is a flow diagram illustrating a method for generating a recommendation in accordance with one example embodiment. Operations in the method  900  may be performed by the heuristics validation engine  126 , using components (e.g., modules, engines) described above with respect to  FIG. 3 . Accordingly, the method  900  is described by way of example with reference to the heuristics validation engine  126 . However, it shall be appreciated that at least some of the operations of the method  900  may be deployed on various other hardware configurations or be performed by similar components residing elsewhere. For example, some of the operations may be performed at the enterprise application  122 . 
     The method  900  starts at done block  904 . At block  902 , the quality assessment module  306  determines a quality of an active directory (e.g., user profile data) based on collaboration data and enterprise organizational data. At block  906 , the quality assessment module  306  determines that the quality of the active directory is below a threshold. The quality assessment module  306  generates one or more recommendation from block  908 , block  910 , or block  912 . At block  908 , the quality assessment module  306  generates a recommendation to use another directory source (e.g., another user profile data). At block  910 , the quality assessment module  306  generates a recommendation to provide an alternative user interface that is not dependent on the active directory data. At block  912 , the quality assessment module  306  generates a recommendation to modify or alter a user experience workflow that would have depended on unreliable user profile data (e.g., manager attribute). 
     In block  1002 , routine  1000  accesses user collaboration data from a plurality of users of an application. In block  1004 , routine  1000  accesses a directory application that manages user profile data for each user of the plurality of users. In block  1006 , routine  1000  applies a set of heuristics on the user profile data. In block  1008 , routine  1000  validates the results of the applied set of heuristics on the user profile data with the user collaboration data. In block  1010 , routine  1000  assesses a quality of the user profile data based on the validation. 
       FIG. 11  illustrates a graphical user interface  1100  in accordance with one example embodiment. The graphical user interface  1100  indicates a mapping quality graph mapping quality bar  1104  that indicates a quality of a manager  1106  of user profile data from active directory A  1110 . The graphical user interface  1100  indicates a mapping quality bar  1102  that indicates a quality of a department attribute  1108  of user profile data from active directory A  1112 . 
       FIG. 12  is a diagrammatic representation of the machine  1200  within which instructions  1208  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1200  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1208  may cause the machine  1200  to execute any one or more of the methods described herein. The instructions  1208  transform the general, non-programmed machine  1200  into a particular machine  1200  programmed to carry out the described and illustrated functions in the manner described. The machine  1200  may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1200  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1200  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1208 , sequentially or otherwise, that specify actions to be taken by the machine  1200 . Further, while only a single machine  1200  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1208  to perform any one or more of the methodologies discussed herein. 
     The machine  1200  may include processors  1202 , memory  1204 , and I/O components  1242 , which may be configured to communicate with each other via a bus  1244 . In an example embodiment, the processors  1202  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  1206  and a processor  1210  that execute the instructions  1208 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 12  shows multiple processors  1202 , the machine  1200  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory  1204  includes a main memory  1212 , a static memory  1214 , and a storage unit  1216 , both accessible to the processors  1202  via the bus  1244 . The main memory  1204 , the static memory  1214 , and storage unit  1216  store the instructions  1208  embodying any one or more of the methodologies or functions described herein. The instructions  1208  may also reside, completely or partially, within the main memory  1212 , within the static memory  1214 , within machine-readable medium  1218  within the storage unit  1216 , within at least one of the processors  1202  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1200 . 
     The I/O components  1242  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1242  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1242  may include many other components that are not shown in  FIG. 12 . In various example embodiments, the I/O components  1242  may include output components  1228  and input components  1230 . The output components  1228  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1230  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1242  may include biometric components  1232 , motion components  1234 , environmental components  1236 , or position components  1238 , among a wide array of other components. For example, the biometric components  1232  include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  1234  include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1236  include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1238  include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1242  further include communication components  1240  operable to couple the machine  1200  to a network  1220  or devices  1222  via a coupling  1224  and a coupling  1226 , respectively. For example, the communication components  1240  may include a network interface component or another suitable device to interface with the network  1220 . In further examples, the communication components  1240  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), WiFi® components, and other communication components to provide communication via other modalities. The devices  1222  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1240  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1240  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1240 , such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (e.g., memory  1204 , main memory  1212 , static memory  1214 , and/or memory of the processors  1202 ) and/or storage unit  1216  may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  1208 ), when executed by processors  1202 , cause various operations to implement the disclosed embodiments. 
     The instructions  1208  may be transmitted or received over the network  1220 , using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components  1240 ) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1208  may be transmitted or received using a transmission medium via the coupling  1226  (e.g., a peer-to-peer coupling) to the devices  1222 . 
     Although an overview of the present subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present invention. For example, various embodiments or features thereof may be mixed and matched or made optional by a person of ordinary skill in the art. Such embodiments of the present subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or present concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are believed to be described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present invention. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present invention as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. 
     EXAMPLES 
     Example 1 is a computer-implemented method comprising: accessing user collaboration data of a plurality of users of an application; accessing a directory application that manages user profile data for each user of the plurality of users; applying a set of heuristics on the user profile data; validating results of the applied set of heuristics on the user profile data with the user collaboration data; and assessing a quality of the user profile data based on the validation. 
     Example 2 includes Example 1, further comprising: forming a tree comprising leaf nodes and parent nodes based on the user profile data of the plurality of users, the tree indicating a manager hierarchy, a leaf node representing a user, and a parent node representing a manager of the user; applying a set of rules from the set of heuristics to the tree; updating the tree based on the set of rules; and assessing a quality of the updated tree based on a number of missing manager attributes from the user profile data of the updated tree. 
     Example 3 includes any of the above examples, wherein the set of rules comprise: identifying and removing isolated users from the user profile data; and identifying and removing users caught in a loop from the user profile data. 
     Example 4 includes any of the above examples, further comprising: forming a first group for users that have a value in a corresponding manager attribute of their user profile data based on the updated tree; and forming a second group for users that are without a value in the corresponding manager attribute of their user profile data based on the updated tree. 
     Example 5 includes any of the above examples, further comprising: identifying a manager for a user in the first group based on the user collaboration data; and updating a manager attribute of the user profile data of the user in the first group with the identified manager for the user in the first group. 
     Example 6 includes any of the above examples, further comprising: identifying a manager for a user in the second group based on the user collaboration data; and assigning the identified manager to the manager attribute of the user in the second group in the user profile data from the directory application. 
     Example 7 includes any of the above examples, wherein identifying the manager further comprises: identifying a plurality of candidate users with whom the user in the second group has a recurring one-on-one meeting based on the user collaboration data; computing a total duration value for each of the plurality of candidate users within a preset period; and identifying a candidate user with a highest total duration value from the plurality of candidate users; verifying that the identified candidate user is a manager based on the user profile data; and in response to the verifying, assigning the identified candidate user as the manager for the user in the second group. 
     Example 8 includes any of the above examples, wherein assessing the quality of the user profile data further comprises: modifying the user profile data based on a set of rules from the set of heuristics applied to the user profile data; identifying manager attribute values for each user from the user collaboration data; and validating manager attribute entries for each user in the modified user profile data with the manager attribute values for each user from the user collaboration data. 
     Example 9 includes any of the above examples, further comprising: determining that the quality of the user profile data is below a preset threshold; and in response to determining that the quality of the user profile data is below the preset threshold, generating a recommendation, the recommendation indicating at least one of a recommendation to use another directory source, to provide an alternate user interface that present metrics that do not depend on a manager attribute from the user profile data for each user of the plurality of users, or to modify a user experience workflow. 
     Example 10 includes any of the above examples, wherein the set of heuristics further comprises a manager heuristic and an organization heuristic, the manager heuristic comprising a first set of rules that validates a mapping of a manager attribute with the user from the user profile data, the organization heuristic comprising a second set of rules that validates a mapping of an organization attribute with the user from the user profile data, wherein the user collaboration data comprises metrics that measure a time and frequency of interactions between a first user of the plurality of users and a second user of the plurality of users.