predicting your employee feelings with data science

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PREDICTING WHAT YOUR EMPLOYEES FEEL WITH DATA SCIENCE SAMPLE PREDICTIVE ANALYTICS FROM PUBLICLY AVAILABLE EMPLOYEE DATA Sankar Nagarajan TEXTIENT ANALYTICS Twitter : @_textient

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Page 1: Predicting your employee feelings with data science

PREDICTING WHAT YOUR EMPLOYEES FEEL WITH

DATA SCIENCE SAMPLE PREDICTIVE ANALYTICS FROM PUBLICLY AVAILABLE EMPLOYEE DATA

Sankar NagarajanTEXTIENT ANALYTICS Twitter : @_textient

Page 2: Predicting your employee feelings with data science

The Sample Candidate

A Mid size IT Services company

Page 3: Predicting your employee feelings with data science

Feeling detected :: Sadness◦ Experience Level: Disappointment, Frustration, Neglect

‘Context Sense’ of Feelings (What is it related to..?)

Predicted Employee Feelings

Sample detected context : * *… don’t line up for snacks. its a mess in the evening. Most of the times we don’t even get it!

* …. can’t expect any kind of onsite work,no such opportunity

Page 4: Predicting your employee feelings with data science

Feelings of Sadness are triggered in response to some-one or something (events) in an organisation. Strongly indicates a DIS-CONNECTION to some one or something including Org. ideals.◦ Refer Context: slide# 2

Impact to Employees experiencing Sadness◦ Sadness can impact how employees perceives others

(Organisation / Members )◦ Tends to decrease confidence in first impressions ◦ Experience of Sadness can lead one to struggle with the

organisational environment and/or in a self-role perspective.◦ Lowers Productivity : Tiredness, Can’t muster up energy to get

things done etc.

Sadness & Psychological Impact

Page 5: Predicting your employee feelings with data science

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Series1

Top 3 Employee Needs driving Sadness

Predicted Factors

Page 6: Predicting your employee feelings with data science

Significance of Employee Needs

Predicted Factors

Page 7: Predicting your employee feelings with data science

Aspect #1 Indicates a need for sense of total possibility and new things. It relates strongly to modern methods and approach evens recycling something old in a new form, avoiding repetition.

Aspect #2: It also signifies an urge to deal with the latest things related to the field or work including innovation and invention.◦ Companies coud tap this need, unless it evolves and changes

constantly.. Aspect #3 : This can indicate a need for escape

anything that breaks the familiar or rigid routines or needs that can take them out of your normal environment or routines.

FreedomNEED : HIGH

Page 8: Predicting your employee feelings with data science

Employees(s) need Appreciation including on their viewpoints and feelings. Signifies expectations of politeness and etiquette .Courtesy, friendliness and adaptability are other related important expectations of employees from the organisation or management

Balance, Equality and sharingNEED : HIGH

Page 9: Predicting your employee feelings with data science

Strongly signifies a need to getting the job done and the related environment, skill and efficiency

‘Practicality’ in this context can indicate the need for “speed” and “ease” as against experiencing just the ideals..

Indicates a need to experience “Do what You say or Claim!” or being grounded …

It can indicate the need to lower barriers in an organisational environment◦ For instance, the speed with which a process or policy can be changed.

Flexibility and ease in carrying out job or to obtain resources needed to do a job.

Indicates a need to celebrate achievements or values Also potentially Indicates a connect to Freedom and progressive,

open-minded spirit slide#6

Quality of Being PracticalNEED : HIGH

Page 10: Predicting your employee feelings with data science

Thank Yousankar[at]textient.com

Page 11: Predicting your employee feelings with data science

The views mentioned in this presentation my own.Data if used, in the document have been sourced from available information in the public domain and has not been authenticated by any statutory authority.

Although every reasonable effort is made to present current or appropriate information, there are no guarantees of any kind.  Data accuracy cannot be guaranteed.  All analysis included herein are based on data from public sources, but no representation or warranty, expressed or implied, is made as to their accuracy, completeness, timeliness, or correctness. I am not liable for any errors or inaccuracies, regardless of cause to you (readers, users).

Disclaimer