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Invited Talk at the Forum for Information Retrieval Evaluation (FIRE 2012), Indian Statistical Institute, Kolkata, India on 19-Dec-2012. iWork : Analytics for Human Resources Management. Girish Keshav Palshikar Tata Consultancy Services Limited

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iwork analytics for human resources management

Invited Talk at the Forum for Information Retrieval Evaluation (FIRE 2012), Indian Statistical Institute, Kolkata, India on 19-Dec-2012

iWork: Analytics for Human Resources Management


Tata Consultancy Services Limited

54B Hadapsar Industrial Estate, Pune 411013, India.


human resources management
Human Resources Management
  • HR Management is a crucial function within any organization
    • More so in services, IT and BPO industries
    • HR is a cost center; everyone interacts with HR!
  • HR function is characterized by a large number of
    • Business processes
    • IT systems that automate these business processes
    • Huge databases of employee activities
    • Many employee contact initiatives
    • Close interactions with L&D
    • Associated metrics and KPI
    • Monitoring and regulatory compliance
phases in hr management
Phases in HR Management
  • Talent Acquisition
    • Requirement gathering, recruitment planning
    • Campus recruitment, EP interviews and recruitments
  • Talent Management and Utilization
    • Allocation (e.g., to project), team formations, monitoring
    • Roles and tasks, Utilization tracking (billing, timesheets)
    • Transfer, deputation, Travel
    • Training, Knowledge management
    • Performance appraisal, Promotion, Salary
    • Communications
    • Administration (leave, medicals, …)
  • Talent Retention
    • Feedback, complaints, grievances, …
    • Resignation handling, retention, knowledge transfer, succession
business goals of hr management
Business Goals of HR Management
  • HR is responsible for maintaining a high-quality workforce
    • Well-aligned and competitive for the business of the organization
    • Effective in performing the business tasks and services, delivering required value and meeting client expectations;
    • Well-trained in the required and emerging skills
    • Highly responsive to emerging business requirements
    • Stable (low attrition, low impact of attrition; successful succession and knowledge transfer)
    • Cost-effective (low salary and overhead costs)
    • Able to evolve into leadership
    • Agile and Mobile (quickly form effective and distributed teams)
    • Motivated, high on initiative and ownership, highly proactive
    • Happy with their environment, work, roles, salaries, career paths
    • Follows professional ethics, codes of conducts etc.
    • Well-integrated and diverse; highly communicative
what makes hr management challenging
What Makes HR Management Challenging?
  • The human factors!
  • Large and varied backgrounds of the workforce
  • Globalization, diversified and distributed workforce
  • New business demands (services, products, …)
  • New business models
  • New customers across the globe
  • Mergers and acquisitions across the globe
  • Changing skills requirements
  • Innovation (disruptive / incremental, technical / domain)
  • Risks (ethical violations, data privacy, client confidentiality)
iwork analytics for hr
iWork: Analytics for HR




Data Repositories





Text Repositories

HR Domain Knowledge

  • Vision
  • Make effective use of historical databases and document repositories for solving HR domain-specific problems
      • Use analytics-driven decision making to meet HR business goals
  • Combine data and text mining to build innovative HR domain-specific solutions for significant enterprise
  • Deliver analytics-derived to the right users at the right point in the HR business processes
  • Technology Areas
  • Data Mining, Machine Learning, Pattern Recognition, Statistical Analysis, Natural Language processing, Computational Linguistics, Text Mining; Optimization
iwork opportunities
iWork: Opportunities
  • Reduce effects of attrition: understanding of root causes, accurate prediction, targeted retention strategies, backup/replacement plans, …
  • Improve project team formation: optimal mix of experience and expertise for all projects, optimal cost team, maximize match with associates’ interests
  • Improve associate satisfaction: better understanding of drivers, identify concrete action items, cost-effective improvement plan, what-if analysis
  • Talent acquisition: reduce delays and costs, maximize match with requirements, school evaluation, identify patterns of long-term stay, …
  • Team profiles: in terms of backgrounds, skills, roles, domains, …
  • Effective RFI Responses/proposals: locate experts, experience, tools
  • Available HR Datasets: resumes (internal, external), timesheets, project details, in-house tool/document repositories, allocations, trainings, surveys, …
iwovierk overview
iWovierk Overview
  • QUEST: employee/customer survey analytics
  • iRetain: Attrition analytics; retention analytics
  • Resume Center: extract structured information from resumes; match job requirements; find experts; team skill profiles; …
  • ExBOS: optimal project team formation
  • iTAG: analytics for talent acquisition
  • Analytics for improving effectiveness of training programs
iwork vision mine hr data to drive improvements to workforce management
iWork Vision: Mine HR data to Drive Improvements to Workforce Management

Complaints Management System







RMG Tracker



Visa Tracker


Workforce Planning

HR Datasets


iwork strategy and offerings
iWork: Strategy and Offerings
  • Build a set of integrated offerings that identify specific improvement opportunities for Workforce Management
  • Transcend silos in HR systems and data
  • Integrate the offerings with existing HR systems to deliver the actionable recommendations to the users when they need

Talent Retention

Talent management

  • As-is state dashboard for attrition
  • Discovering high-attrition groups
  • Predictive models for attrition
  • Identifying root-causes of attrition
  • Plan for reducing attrition impact
  • Optimal retention plan
  • Quest: employee survey analytics
  • EXBOS: optimal team formation
  • Visa analytics
  • Resume Center: competency extraction; find right people for positions; find experts; find misinformation; enrich RFP response (past projects, tools); talent pool profiling; find customer intelligence;
  • Bench analytics

Talent Acquisition

  • TA analytics: improve cost, quality, timeliness of TA
  • ILP analytics

ITIS workforce management: team sizing + shift planning; optimal team skill profile; service level rationalization; expert finding; training plans; DC transformation planning

Automation of survey responses tagging Nielsen

practical applications need a lot more than ir
Practical Applications Need a Lot More than IR

Document retrievals; ranking

Fine-grained retrieval

Goal-directed retrieval


Information extraction

Cross-linking and information fusion (e.g., with FB, LinkedIn)




Analytics (problem-specific)

Learning to rank

Business Solution

Various databases and text repositories

using resumes in hr functions
Using Resumes in HR Functions
  • Resumes: a valuable source of information for people’s work
    • ~250,000 TCS employees’ resumes
    • ~2 million candidates’ (applicants’) resumes
  • Business goals
    • Use information extraction: extract personal, job history, project details, education, training, awards etc. from given resumes
      • Validate information in resumes
      • Create gazettes (colleges, degrees, certifications, tools, companies)
    • Update employee experience profile and skills/competencies
    • Identify top K best matches for a given job requirement / position
      • Learning to rank (poster in FIRE 2012)
      • improve team formation; shorten recruitment cycle
    • Perform mining of data extracted from resumes to derive novel, actionable insights about the available talent pool
      • reduce bench; reduce attrition; improve utilization
      • identify training opportunities; help in career planning
resume center information extraction
Resume Center: Information Extraction

On-the-fly extraction using an IR engine

resume center powerminer
Resume Center: PowerMiner
  • Given a set of resumes, provide facilities to help in filing RFI/RFP responses, form project teams etc.
  • locate relevant projects for a given project description
  • locate relevant tools for a given project description
  • identify expert persons for a given technical area
  • assign domain(s) to each resume (e.g., insurance, railways, banking, telecom etc.)
  • Identify "unusually high quality" resumes in terms of a set of pre-defined quality criteria
    • Special tools, niche skills, extra qualifications (e.g., domain-related), top-quality academic performance, awards, publications
resume center team profiler
Resume Center: Team Profiler
  • Given a resume repository, help HR executives in building an “understanding” of their teams:
    • What are the strengths and weaknesses of my team in terms of technical skills, domain knowledge, roles etc.?
    • What should I do to improve the quality of my teams?
  • Create a summary profile of a team, in terms of technology skills, domains, experience etc.;
  • Group the given resumes into clusters (from different perspectives), with specific interpretation for each cluster
    • Similar to customer segmentation?
  • Document repository visualization and exploratory facilities

R. Srivastava, G. K. Palshikar, RINX: Information Extraction, Search and Insights from Resumes, Proc. TCS Technical Architects' Conf., (TACTiCS 2011), Thiruvanthapuram, India, Apr. 2011.

  • S. Pawar, R. Srivastava, G.K. Palshikar, Automatic Gazette Creation for Named Entity Recognition and Application to Resume Processing, Proc. ACM COMPUTE 2012 Conference, Pune, India, 24-Jan-2012.
  • G.K. Palshikar, R. Srivastava, S. Pawar, Delivering Value from Resume Repositories, TCS White Paper published on www.tcs.com, Feb. 2012. (c) Tata Consultancy Services Limited.
quest overview
QUEST: Overview
  • Advanced analytics tool to mine survey response data and derive novel, actionable insights for improving workforce management
  • Surveys are a direct and effective mechanism to gauge concerns and issues that affect satisfaction of employees or customers
  • Motivation: TCS conducts an annual in-house employee survey
    • 250,000 employees, ~100 questions (structured, free-form)
    • 250,000 textual responses to each of ~20 questions
    • Challenges: volumes; dependencies; mixed structured/text responses
  • Business goals: improve satisfaction levels among employees
  • Benefits: deeper insights, objective results, reduced time/efforts
  • Impact: Satisfaction levels affect projects quality, client satisfaction
  • Status: Currently deployed in-house
  • Vision
    • QUEST should be an integral part of all HR contact and feedback programs throughout TCS (ISU, geographies, clients etc.)
    • Deploy for customer / product satisfaction surveys
quest approach
QUEST: Approach
  • Dashboards and standard reports
  • Drill-down exploratory analysis
  • Visualization
  • Summarize responses to specific questions/categories
  • Identify specific issues, concerns and suggestions
    • Characterize low-satisfaction groups (discover common characteristics of employees with high/low satisfaction)
  • Identify factors (root causes) that affect satisfaction
  • Design optimal plans to improve satisfaction levels
  • Use survey results in team planning and other workforce management tasks

G.K. Palshikar, S. Deshpande, S. Bhat, QUEST: Discovering Insights from Survey Responses, Proc. 8th Australasian Data Mining Conf. (AusDM09), Dec. 1-4, 2009, Melbourne, Australia, P.J. Kennedy, K.-L. Ong, P. Christen (Ed.s), CRPIT, vol. 101, published by Australian Computer Society, pp. 83 - 92, 2009.

quest results1
QUEST: Results…

Things you don’t like about TCS

quest results2
Quest: Results…

PULSE 2008-09 Responses for TCS Mumbai

  • Groups having unusually low ASI
  • EXPERIENCE_RANGE = ‘4-7’ (60.4; global avg. = 73.8)
  • Root causes for low ASI
  • Canteen, Transportation, RMG

Interesting subset discovery: finding bumps in a large-dimensional distribution

  • M. Natu, G.K. Palshikar, Interesting Subset Discovery and its Application on Service Processes, Proc. Workshop on Data Mining for Services (DMS 2010)held as part of the Int. Conference on Data Mining (ICDM 2010), Australia, 2010, pp. 1061-1068.
quest results3
QUEST: Results…
  • Actionable suggestions made by associates
  • TCS can have tie ups with best Schools in the near by locations for their employee kids
  • … the moment you step out there is only garbage and randomly parked autos around
  • TCS can engage with lease agreement … with TATA Housing itself and provide economical accommodation.
  • I don`t have any leg space...n my knees are hurting badly

S. Deshpande, G.K. Palshilkar, G Athiappan, An Unsupervised Approach to Sentence Classification, Proc. Int. Conf. on Management of Data (COMAD 2010), Nagpur, 2010, Allied PublishersPvt. Ltd., pp. 88 - 99.

sentence classification
Sentence Classification
  • Sentence class labels are usually domain-dependent
  • Unsupervised classification of sentences: specific / general
sentence classification1
Sentence Classification…
  • A SPECIFIC sentence is more ”on the ground”
  • A GENERAL sentence is more ”in the air”
  • Example:
    • My table is cramped and hurts my knees.
    • The work environment needs improvement.
    • Travel vouchers should be cleared within 2 working days.
    • Accounts department is very inefficient.
sentence classification2
Sentence Classification…
  • Compute a specificity score for each sentence:
    • Unsupervised (knowledge-based), without the need for any labeled training examples.
    • Define a set of features and compute their values for each sentence.
    • The features are lexical / semantic.
    • The features are context-free: their values are computed exclusively using the words in the sentence and do not depend on any other (e.g., previous) sentences.
    • Then combine the feature values for a particular sentence into its specificity score.
  • Rank the sentences in terms of their specificity score.
sentence classification3
Sentence Classification…
  • Sentence features
    • Average semantic depth (ASD)
    • Average semantic height (ASH)
    • Total occurrence count (TOC)
    • Count of Named Entities (CNE)
    • Count of Proper Nouns (CPN)
    • Sentence Length (LEN)
sentence classification4
Sentence Classification…
  • Semantic depth (SD) SDT(w) of a word w is the distance (number of edges) from the root of ontology T to word w in T
    • We use T = WordNet ISA ontology
    • More semantic depth  more specific word
sentence classification5
Sentence Classification…
  • Semantic depth of a word changes with its POS tag and with its sense;
    • SD(bank) = 7 for financial institution
    • SD(bank) = 10 for flight maneuver sense.
  • Solution:
    • Apply word sense disambiguation (WSD) during pre-processing; or
    • Take average of the semantic depths of the word for top k of its senses

Average semantic depthS.ASD for a sentence S = <w1 w2 . . . wn> containing n content-carrying words = the average of the semantic depths of the individual words

  • My tablehurts the knees.
    • (8 + 2 + 6)/3 = 5.3
  • The workenvironmentneedsimprovement.
    • (6 + 6 + 1 + 7)/4 = 5.

Semantic height (SH) SHT(w) of a word w is the length of the longest path in T from word w to a leaf node

    • We use T = WordNet hyponym ontology
    • Lower semantic height  more specific word

Average semantic heightS.ASH for a sentence S = <w1 w2 . . . wn> containing n content-carrying words (non stop-words) = the average of the semantic heights of the individual words

  • Semantic height of a word changes with its POS tag and with its sense;
  • Solution: use WSD or take average of the semantic heights of the word for top k of its senses

Intuition: more specific sentences tend to include words which occur rarely in some reference corpus

    • apple (2), fruit (14), food (34)
  • More the number of rare words in a sentence, more specific it is likely to be.
  • OC(w) = occurrence count of word w in WordNet;
    • if w has multiple senses, then OC(w) = average of the occurrence counts for top k senses of w
  • Total occurrence countS.TOC for a sentence S = <w1 w2 ... wn> containing n content words is the sum of the lowest m occurrence counts of the individual words, where m is a fixed value (e.g., m = 3).
  • OC of a word changes with its POS tag and with its sense;
    • Solution: use WSD or take average of the OC of the word for top k of its senses

Named entities (NE) are commonly occurring groups of words which indicate specific semantic content

    • Person name (e.g., Bill Gates)
    • Organization name (e.g., Microsoft Inc.),
    • location (e.g., New York),
    • date, time, amount, email addresses etc.
  • Since each NE refers to a particular object, an NE is a good indicator that the sentence contains specific information.
  • Another feature S.CNE for a sentence S is the count of NE occurring in S

Proper Nouns (PN) are commonly occurring groups of words which indicate specific semantic content

    • Abbreviation (IBM or kg), domain terms (oxidoreductases), words like (Apple iPhone), numbers etc.
  • Since each PN may refer to a particular object, an PN is a good indicator that the sentence contains specific information.
  • Another feature S.CPN for a sentence S is the count of PN occurring in S

Sentence length, denoted S.Len, is a weak indicator of its specificity in the sense that more specific sentences tend to be somewhat longer than more general sentences.

  • Length refers to the number of content carrying words (not stopwords) in the sentence, including numbers, proper nouns, adjectives and adverbs

Features have contradictory polarity.

    • We want higher values  more specificity.
    • Not true for features ASH and TOC
    • Lower values  higher specificity for these
  • Scales of values for various features are not the same, because of which some features may unduly influence the overall combined score.
    • E.g., ASD is usually  10, whereas TOC is a larger integer.
  • Uniform scaling: map x[a, b] to y  [c, d]

Scaling + reversal of polarity


Some specific sentences identified by our

algorithm from 110,000 responses in

an employee satisfaction survey


Some specific sentences identified by our algorithm from 220 sentences from 32 reviews of a hiking backpack product by Kelty.

  • Domain-driven IR = IR + text-mining of retrieved documents
  • Enterprise document repositories offer good scope for Domain-driven IR to deliver solutions and insights relevant for real-life business problems and decisions