With the global workforce being more candidate-driven, HR practitioners are having a tough time filling vacant positions with top talents. This is because recruitment usually requires a long time and is, therefore, more competitive in a candidate-driven industry.

In order to stay competitive, recruiting managers and employers have no alternative but to evolve and reinvent conventional strategies of talent management because of the developments in the industry. One of these emerging approaches is the application of data quality in the case of hiring and recruitment.

Approximately 30 percent of recruiting practitioners do not take full advantage of data consistency that is often seen in data quality, according to a 2013 survey by Dynistics, a management market analytics software firm. According to the report, employers incorporating data quality are twice as likely to strengthen their recruitment procedures, and three times as likely to minimize the resources and amount of time it takes to fill an available position.

Furthermore, according to an analysis of more than 1,200 firms by independent business analytics provider SAS, the use of data analysis across various markets will continue to grow well into the coming years. The study showed that bigger companies are more likely to adopt data quality analytics with at least 100 workers. The absence of reliable evidence doesn’t have to be a cause of frustration for HR recruiters. Unique to the HR sector, data quality has helped in predicting hiring needs. It has increased the quality and retention of new workers and align recruitment success with company success.

Growing the rate of recruits

For enterprises, recruiting the wrong employees may have significant repercussions. Twenty-seven percent of U.S. companies estimated that one bad recruit cost the organization over $50,000, as shown in CareerBuilder study of more than 6,000 HR professionals. Costly recruiting flaws can be eliminated with the incorporation of data quality into the procurement process. When selecting the right applicant, data quality helps employers to be more objective and competitive. Employers are able to collect and classify prospective recruits by processing the details and narrowing down the applicant pool through links to online resume databases, job reports, social media accounts, resumes, assessments, and other details.

Employees’ success rate and Enhancement in training

Quality data helps employers assess the possible success of a given training program to ensure that they make wise improvements in their workers’ training and growth. Conducting regular performance reviews will also allow HR practitioners to better appreciate the impact of their attempts to improve workers.

Improving Resource Utilization

Human resource management is a major priority in today’s workforce. Organizations that use their resources effectively, including their talents, can achieve more than their competitors that are wasting their resources. For example, bad timing can mean the collapse of a field services organization when field technicians get dissatisfied with inefficient routes and have to deal with disgruntled customers because they did not appear within the planned service window.

Several technologies allow HR to utilize data for improved resource use and control of the workforce, from tools that support data-driven scheduling and dispatching to those that help align revenue-generating activities with expense-generating activities. It’s all about productivity in the modern industry.

 

 

If you need help, iData is here! You can also check out the Getting Started with Data Quality  and Advancing Your Career in Data courses from the iData Quality Academy.

 

Read more about our recruitment opportunities: iResource | Intelligent Delivery Solutions (intelligent-ds.com)

 

 

Your Data Quality Primer: Everything you need to know

Welcome to your Data Quality Primer from the iData Quality Academy – in this useful guide you’ll find everything you need to know to improve your understanding of data quality, assorted into useful categories for you. You don’t have to read through it in order, you can jump right to the section you’re interested in!

Your Data Quality Primer: Everything you need to know

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