WHo is able to complete this discussion?

Open Posted By: surajrudrajnv33 Date: 05/05/2021 Graduate Report Writing

   Current Human Capital Management: Predictive Analysis   Twitter    

Prior to beginning work on this discussion,

Using the two articles you researched on human capital predictive  analysis as well as any of this week's required or recommended articles,  discuss how predictive analysis is being used to help make human  resource decisions. Additionally, address how, as managers, you might  use predictive analysis to create a strategic global competitiveness  from a company’s human assets. Be sure to give specific company examples  to support your discussion and position on the topic.

Your initial response should be a minimum of 220 words. Support your response with at least one scholarly resource in addition  to the text.

 Cited Source: Cascio, W. F., & Aguinis, H. (2019). Applied psychology in talent management (8th ed.). Retrieved from https://www.vitalsource.com 

Category: Accounting & Finance Subjects: Finance Deadline: 12 Hours Budget: $120 - $180 Pages: 2-3 Pages (Short Assignment)

Attachment 1

Data science and predictive analytics enabling better hiring mechanisms for enterprises Aggarwal, Varun; CTO; Aspiring Minds . Dataquest ; Gurgaon (Dec 24, 2015).

ProQuest document link


According to a Deloitte research, companies that are incorporating analytics in HR have two to three times better

results in the quality of hire, leadership development, and employee turnover.\n FULL TEXT  

It is essential to leverage the predictive power of data science to bring in structured information and enable

building of an effective recruitment mechanism for the future

The cost of a bad hire an organization bears is beyond monetary-time spent in recruiting/training, loss of

productivity, impaired employee morale are some of the aftermaths of a bad decision. At present, the HR function

is adversely affected by the lack of structured information which if analyzed can provide key insights into the

system. While effective recruitment is at the core of their responsibilities and may seem intrinsic, hiring the right

candidate for the right job in a cost and time effective manner is one of the key challenges faced by modern day


Further adding, more than a million candidates are entering the workforce every year, thus increasing the process

of talent filtering.

All recruiters typically receive a resume which has a lot of information but no clear mention of skills or

competencies. The candidates are further subject to scrutiny by couple of line managers who may or may not be

trained for interviewing and may form an opinion based solely on their interaction. In this entire process the

decision making becomes quite subjective. The question is how can we bring objectivity to this process?


With the growing amount of data present around us, data science and predictive analytics can revolutionize hiring

mechanisms to make hiring more objective and democratic. The problem here is simple-there is supply (pool of job

seekers exists) and demand (pool of jobs exist) but there is no match-making of job seekers with the right jobs.

The answer lies in inculcating a conscious movement towards a culture of data science. Data science in simple

terms is an inference science which helps us make objective decisions and also allows us to know how effective

those decisions would be.

Many organizations have just begun to invest in analytics, but the benefits associated with data-driven decisions,

the time is not far when data science will seep into every aspect of recruitment. To streamline hiring, an

organization can extract data for all the employees it hired in the previous year and quantify basis parameters like

educational qualification, experience, test scores, skills and more to predict which of them have been successful

and which of them have not.

The insights collected from such analytics will allow organizations to move beyond subjective hiring and recruit

the right talent from a more scientifically shortlisted pool. In a recent case, Xerox was able to reduce the attrition in

its call centers by using algorithm driven recruitment techniques. The insights showed that employees without any

call center experience were just as successful as those that had experience and that creative people were more

likely to stay longer. This allowed the company to widen its hiring pool and improve the quality of hires and was

able to cut down on attrition by 20%.


Gathering the vast amount of data can help recruiters identify the right talent by classifying information into trends

and narrow down the talent pool. This will also help save cost, time, and resources that would have been otherwise

spent in the recruitment process. However, many organizations still rely on conventional hiring methods which are

largely un-scalable and thus leading to them lose out on the right talent. Enterprises can significantly improve

output and efficiency in hiring with data science enabling them to hire the right talent with least effort with the help

of numbers.

Data science coupled with objective scores becomes a very powerful tool for companies to determine the right

recruitment standards. In a recent case, A Fortune 500 company wanted to establish hiring criteria based on

objective measures. They worked with an assessment partner to conduct job analysis to understand the

Knowledge, Skills, Ability and other prerequisites for the profile. Based on the analysis, they hired through a set of

skill based pre-employment tests. In the next year, the company noticed that in new hires, the percentage of high

performers had increased to 39% from 23% thereby leading to a 70% increase in high performers and 65%

reduction in low performers.

Recruitment is not the only aspect where HRs can benefit from data analytics but it can also help enterprises deal

better with retention, once a candidate is on the job. Usually, a higher salary package is offered to retain an

employee which may only be a short-term solution. According to a Deloitte research, companies that are

incorporating analytics in HR have two to three times better results in the quality of hire, leadership development,

and employee turnover.

Studying resignation patterns, common features of exiting employees, job satisfaction levels among other

information can lead to insights which will help HRs adopt the right approach with employees and cut down on the

attrition rate which is critical in today's increasingly competitive landscape. Predictive analytics can help

employers understand the workforce, the way marketing teams analyze data to understand and predict customer

behaviour and patterns. These issues were earlier unquantifiable but modern data science methods are changing

the way organizations can benefit from this data.

Human resource is relatively a new domain which is seeing this technological invasion but structuring this vast

data can benefit not just organizations but also the employment ecosystem on the whole. Investing in resources

and knowledge should be a priority for enterprises as the value-driven insights will help address real business


Copyright 2015 Cyber Media (India) Ltd., distributed by Contify.com

Credit: Varun Aggarwal, CTO, Aspiring Minds DETAILS

Subject: Recruitment; Skills; Science; Decision making

Identifier / keyword: top stories aspiring minds data science features

Publication title: Dataquest; Gurgaon

Publication year: 2015

Publication date: Dec 24, 2015

Publisher: Athena Information Solutions Pvt. Ltd.

Database copyright  2021 ProQuest LLC. All rights reserved. Terms and Conditions Contact ProQuest

Place of publication: Gurgaon

Country of publication: India, Gurgaon

Publication subject: Computers--Data Base Management, Computers

ISSN: 0970034X

Source type: Trade Journals

Language of publication: English

Document type: News

ProQuest document ID: 1751474203

Document URL: https://search.proquest.com/trade-journals/data-science-predictive-analytics-


Copyright: Copyright 2015 Cyber Media (India) Ltd., distributed by Contify.com

Last updated: 2015-12-24

Database: ProQuest Central

  • Data science and predictive analytics enabling better hiring mechanisms for enterprises