How Augmented Analytics is Better Than Regular Analytics in Reorganizing HR?

by September 27, 2019 0 comments

The augmented analytics market is growing at a fast pace of 25 percent every year which is making some significant implications for the HR segment. With the rapid rise in data volumes globally, companies are determined to extract deep insights about their workforce and generate value. But the constant dependency on data scientists is still a big challenge for most of them. In particular, small and medium-sized companies do not have the resources and skill set to extract the full potential of data. This condition is true of non-technical functions as well, say HR. Due to the absence of data science talents, HR may struggle to derive meaningful and actionable insights. This may further result in fewer benefits from huge investments in analytics model development.

This is where augmented analytics comes into the picture. The augmented analytics democratizes data-to-insight conversion enabling virtual access to insights in a comprehensible format to any stakeholder.

Augmented Analytics in HR

It can be described as a branch of data science aiming to automate the insight generation process by using cognitive technologies. This enables machines to view and represent data from a human perspective.

Components of Augmented Analytics in HR: Machine Learning, Natural Language Processing, and Insight Automation.

Machine Learning enables technology systems intuitively learn, eliminating the need for intervention of human coders. The system can automatically adapt to different circumstances independent of rule-based programming. ML-powered augmented HR analytics provides better and right insights with every data processing cycle.

Natural Language Processing is quite relevant to HR as it does not require HR professionals to have years of data science experience rather the augmented HR analytics interface can deliver insights in a human-readable format.

Insight automation can take over the tiring work of data scientists who spend 19 percent of their time on collecting data and 60 percent on cleaning and organizing it as per conventional analytics model. Instead, data science professionals can focus on developing more effective training sets and refining analytics algorithms. HR only needs to input correct data into the interface in order to receive the most relevant insights.

These three components make HR analytics quite easier to use.

Use Cases

Augmented analytics is not confined to one HR function or area rather. much like the internet, it has the potential to transform processes across the entire enterprise. For example:

Aligning Hiring Efficiency to Employee Quality

In the current times where the labor market is going through intense competition, HR risks compromising quality due to uneven focus on quantity. There exists a great rush for deadlines, time-to-hire goals and recruitment campaigns to be kept under budget. This results in undermine the quality of hire.

The HR augmented analytics enables the enterprise to feed recruitment data into software and assess where it stands in the quality bandwagon.

Controlling Voluntary Attrition

For enterprises, attrition is a complex issue. In some cases, voluntary and voluntary attrition is not at all regrettable. Therefore, augmented analytics enables the enterprises to deep dive into all such characteristics, sifting through employee tenure information and highlighting the cause and nature of attrition. The consequent attrition insights can help enterprise refine the employee management mechanism for optimal attrition rates, targeted towards most high-performing and ROI-friendly employees.

Employee Engagement 

The company can also apply augmented analytics to virtually any HR use case including employee engagement considering pain-pints and benefits administration. This may lead to various advantages over traditional analytics approach.

Benefits

AI with Analytics

Several companies are eager to embrace AI technology but are not sure of the right utilization. Amid these augmented analytics takes a solution-oriented approach for adopting AI and identifying a measurable HR challenge while using data to solve it. Consequently, the company can obtain tangible returns from its investment in AI.

Reduction in Time

Augmented analytics has a clear take on manually driven analytics systems. As data scientists can take a long time to collect and cleanse the data and building analytics models, augmented HR analytics automates the 50 percent of the work significantly increasing insight generation. Notably, this approach is faster than regular analytics. A person need not spend time converting such insights into action points because the predictive capabilities of augmented analytics will indicate a clear course of action.

Cost Reduction

Augmented analytics paves the way for advanced business intelligence a huge group of stakeholders. Anyone can leverage augmented HR including IT team, payroll division, C-level leaders or third-party employee engagement consultants, to improve processes. This subsequently eliminates the need to hire experienced data scientists on high salaries for analytics function.

Conclusion

Augmented analytics is an exciting area of research, development, and innovation with the global market expected to cross US$22 billion in the next 6 years. The technology is named under ‘top 5 disruptive HR technology to track in 2019’. Several big companies across the globe are already planning to implement augmented HR analytics as part of their larger business intelligence services.

HR

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