Augmented Analytics for HR: Six Use Cases Transforming People Management

HR + AI = Exploring the new possibilities of data.

Augmented analytics—an AI-driven technology enabling automated data preparation and insight generation—has recently seen increased adoption across industries such as BFSI, telecom, retail, and IT, providing actionable insights on demand planning, inventory levels, investments, and customer experience strategies, among others.

However, in addition to aiding high-level business decisions, this technology also has the potential to become an integral part of everyday business functions, including finance, sales, and HR. Facilitating data management, integration, and governance practices, augmented analytics has reduced organizations’ dependence on data scientists, giving rise to a new class of data science users: those who do not possess—or need—formal training. According to research, by 2025, businesses will be able to independently adopt AI & ML technologies, despite the projected scarcity of data scientists, with the help of augmented analytics.^[1]

Augmented analytics is therefore set to make data more accessible across functions, and with the exponential growth of HR data—from profiles and interviews to employee lifecycle information—the potential for use cases is immense. The range of benefits unlocked for HR professionals includes the following:

• Finding links between disparate data sources
• Eliminating time-consuming tasks
• Providing accurate results and actionable insights
• Helping address attrition and bolster retention
• Boosting employee engagement and productivity
• Reducing costs

How does augmented analytics work?

Augmented analytics is defined as “the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”^[2]

However, of note to HR functions here are automated insight generation and explanation, with AI & ML helping extract insights from varied kinds of data, including employee performance, engagement metrics, and attrition data, and turning these into tailored, easily consumable, and actionable “stories” for HR teams.

For instance, if teams were to ask questions such as “What are the departments facing high attrition?” and “What are the patterns in organizational diversity over the last three years?”, these can be immediately answered through augmented analytics applications. Further, Natural Language Processing (NLP) capabilities can formulate answers to such questions in natural language as well, allowing HR professionals, including payroll specialists, recruiters, and L&D specialists, to directly interact with and act upon their data, without intervention from data scientists required.

What are the use cases for augmented analytics in HR?

Finding links between varied data: With a combination of AI-driven tools, including optical character recognition (OCR) and NLP capabilities, data from across various siloed sources, including databases in other functions, solicited information including surveys, performance data, profiles, and interview documentation, as well as unsolicited information including online reviews and social media posts can be examined. Augmented analytics here can help find meaningful links between such data and translate them into actionable insights: for instance, tracing the impact of policy changes on employee satisfaction, or understanding the effect of increased working hours on employee productivity.

Reducing time-consuming tasks: As data scientists can take between weeks and months to collect and clean data as well as develop models, augmented analytics can reduce this time significantly by automating processes, freeing up time for higher-order tasks and analyses. Similarly, from a user’s perspective, augmented analytics can help recruiters source data such as candidate information, qualifications, hiring requirements, suitable profiles, and so on, with a single command, enabling instant decision-making. With NLP capabilities making this data accessible to lay users, the time taken to manually sort through documentation can reduce from days to a few seconds.

Providing accurate results and actionable insights: With data integrated from various sources, augmented analytics can help provide answers to pertinent HR questions, such as the quality of hiring efforts, the limitations faced in hiring for particular geographies, and shortcomings in recruitment targets, in terms of time and numbers. By quantifying these various metrics for recruitment performance, hiring strategies can be further fine-tuned. For instance, candidates with relevant skillsets, experience levels, and qualifications can be identified from across an organization, regardless of their field, to fill a particular position by weighing their suitability against the job requirements. Augmented analytics can help highlight profiles that may go unnoticed by humans, providing a richer talent pool to choose from.

Helping address attrition and bolster retention: AI can help predict the risk of attrition among current and prospective employees, and even highlight pain points in the workplace that may lead to attrition. It can examine a range of information, from working hours and tenure to survey responses and commute times, to uncover reasons for voluntary attrition. For instance, if high turnover is identified within a particular sales department, factors such as working hours, incentives, and employee growth can be examined to make correlations between data and attrition rates. Importantly, it can help differentiate between regrettable and non-regrettable attrition, helping HR professionals keep track of optimal rates for both.

In addition to providing insights into the nature and cause of attrition, analytics can also help develop improved strategies to ensure employee retention. Upon identifying high-performing employees, their needs, such as increased flexibility at work, parental leave, benefits, and even changes in office spaces, can be pre-empted and implemented using augmented analytics to improve employee retention. In addition, factors contributing to high employee satisfaction in certain departments can be analyzed and thereafter replicated to ensure better retention across the board.

Reducing costs: With data scientists’ tasks of collating, cleaning, and analyzing data reduced through augmented analytics, the need to hire data scientists in turn reduces significantly, which lowers costs. Further, with HR professionals and departments requiring minimal training to handle data and becoming self-sufficient, collaborative efforts, a culture of knowledge sharing, and data democratization becomes possible in the long run, leading to several benefits for any organization.

Boosting employee engagement and productivity: Augmented analytics can be utilized to examine a variety of engagement-related metrics, documentation, feedback, and so on to determine both successes and pain points for employees, further enabling HR professionals to develop relevant strategies. Further, factors such as overtime, reporting structure, workload, and so on can also be similarly examined to find factors hindering productivity. For instance, a complex organizational hierarchy could hinder decision-making—augmented analytics can uncover the relationship between reporting structures and reduced productivity, enabling action on the same. Similarly, incentivizing factors, such as learning sessions, recognition programs, and bonuses, that would boost employee productivity, can also be identified and implemented in this manner.

Case in point: MathCo’s Amber Chatbot

To ensure the wellbeing and health of its employees, MathCo introduced Amber, an augmented analytics-driven chatbot, in 2019, to obtain feedback from employees at important touchpoints in their career. This intelligent application obtains data on a real-time basis and is automatically triggered to reach out to employees during important milestones in their journey, including at the 15-day, 45-day, 90-day, 120-day marks and more.

Amber asks each employee a set of curated questions that are tailored to the most recent milestones achieved. For instance, employees are asked questions on organizational culture or their teams as part of the 15-day feedback cycle, whereas they may answer questions on work culture and learning, as part of the 45-day reach out. Amber then records confidential feedback on seven key categories: work, the organization, managers, teams, career & learning, senior leadership, and organizational culture, to help human resource and people success teams understand overall organizational health.

The data received is transformed into consumable and summarized views, with the option available to filter based on metrics such as teams, feedback scores, individual managers, and more. In this process, Amber automatically categorizes information and flags off potential concerns, at the category as well as the individual employee level, to generate reports on function-level and team-level health.

The reports created for the former are shared with the heads of functions, highlighting summarized feedback on areas of success and areas requiring progress. Not only does this help managers take quick action for their teams, but with the localized, custom data provided by Amber, the nature, pain points, and growth of individual teams can be understood better as well.

While Amber does not completely replace the element of human interaction at MathCo, it facilitates regular touchpoints with 1000+ employees. Helping the team connect the dots between feedback and employee sentiment, it closes the loop between AI and humans to provide a seamless employee experience.

Augmented HR: Future Directions

From providing actionable insights and reducing costs to accurately predicting turnover and uncovering factors affecting employee performance and engagement, the utility of augmented analytics for HR is immense. But perhaps its greatest advantage lies in the data democratization it makes possible, with lay users now able to tap into their data and use it to drive everyday decision-making. With augmented analytics allowing for keen insights into everyday functioning as well as long-term growth, HR functions can now engage with digital transformations to improve everyday processes and make the most of cutting-edge tech.

Bibliography:

[1] Gartner_Inc. “Augmented Analytics Is the Future of Data and Analytics.” Gartner, July 27, 2017.
https://www.gartner.com/en/documents/3773164.

[2] Gartner_Inc. “Augmented Analytics Is the Future of Data and Analytics.” Gartner, July 27, 2017.
https://www.gartner.com/en/documents/3773164.