Digital HR and People Analytics – A Technical Perspective 0

Digital HR
Dr Anu Binny is an HR technology practitioner having more than 15 yrs of experience in training and L&D functions. She was recently awarded the HR Future Leader Award. She is a thorough Learning and Training professional known and appreciated for her dedication to continuous improvement and strategic orientation. She has international exposure in managing multi-domain training programs and leading all training initiatives by leveraging new technology. She specializes in project based training solutions, frameworks, blueprints for training programs including product, functional and behavioral and leadership programs. She played a pivotal role in the implementation of Knowledge Management project at Capital Market of Saudi Arabia. She is currently heading the Training of a large diversified group.

Millennials will comprise approximately half of the world’s workforce in two years, and roughly 75 % by 2025. Employers looking to attract and retain top-notch talent need to better understand what motivates this generation. Would the simpler initiatives like a plan to cross-train employees, prepare for changing roles, create programs focused on employee retention, reorient the rewards and recognition work? Only the company that employs the right metrics and analytics will have the answers. Let’s understand why HR analytics is the way forward.

Businesses are increasingly relying on data to scout the right talent. According to a research report by PwC about 80% of business leaders don’t believe talent analytics are focused on the right issues, but they are certain that talent analytics can transform businesses. According to the same research, using analytics and focusing on key talent, organisations have shown an increase in employee performance, improvement in the quality of hire and employee engagement. Is the reality any different for you? Does every HR presentation you present to the CXO ends with – where are the insights? How did you come to this decision? Is it your gut feeling? Where is the data supporting it? If yes, then read on. As a part of this article, I attempt to break down HR analytics and put across its significance in ways you wish you could explain it to your CXO.

What is HR Analytics?

HR analytics refers to applying analytical processes to human resource department of an organisation with an intent to improve employee performance and get a better ROI on hiring efforts. It specifically focuses on applied methods and techniques with an output orientation for improving the human resource function. There are two main purposes of HR analytics:

1. Provide Actionable Insights

It provides insights to leaders and helps them make decisions to create engaging work environments, maximize employee productivity, and at a strategic level, ensure business goals are met efficiently within a certain time-frame. These insights facilitate decision making across the board.

2. Deliver Business Outcomes

The second key function of HR analytics is to help identify the data, provide the models for predicting the different ways the organisation is able to receive an optimal return on investment (ROI) on its human capital.

HR Analytics – How Does it Work?

Before applying analytics and related insights to the HR function, it is important to fetch the right data. Establishing a reliable and functional HR analytics framework involves the following steps (as shown in figure 1).

HR Analytics Framework

  1. Identify key strategic objectives that will drive talent acquisition strategy
  2. Quantify using metrics like Lead and Lag indicators, critical outcomes, cost determinants
  3. Analyze and select a model – Descriptive, Operational, Predictive, or Prescriptive analytics – to facilitate decision making
  4. Create Outputs for decision making ( Pivots, charts, graphs, dashboards, exploratory data visualization tools)
  5. Create Actionable Tasks/goals
  6. Track / achieve the stated Business outcome as per time guidelines and deliverables.

Now that you have the framework ready, Let us understand how to apply it to the HR function. To begin with, let’s take the example of a million-dollar logistics firm where the leadership decided that strategically, they want to be one of the largest warehousing company in the US. But very soon realized that its opportunities of expansion was being dangerously shrouded by lack of staff and high attrition. It was bleeding of current distribution of workers and finding it hard to recruit new ones. To overcome these challenges, the company turned to Analytics. Here’s a step-by-step breakdown of how they went about it –

Step 1- Business Question

The board asked among themselves an important question – in the sourcing, hiring, and on-boarding process what were the common factors shared by our best performers?

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Step 2 – Quantify

To answer this the HR folks used people analytics and got together to discover actionable correlation among recruiting sources, hiring methods, selection process, and employee performance. The best employees were identified from the appraisal cycle recorded in the Human Resource Information System (HRIS), similarly the recruitment data through the recruitment software and onboarding from the HR and the training team. Some metrics applied were as follows

  1. Time to hire
  2. Cost per hire
  3. Offer acceptance rate
  4. Employee retention rate
  5. Early turn over
  6. Time since last promotion
  7. Revenue per employee
  8. Engagement rating etc.

Step 3 – Choose an Analytics Model

The company chose the descriptive analytics model using the existing employee data and HR appraisal data.

Step 4 – Outputs

Based on the discussions with stakeholders and delivery heads, the HR department came up with the following outcomes –

  1. Create a recruitment theme: How would you like an exciting place to work with great benefits, right in your own
    neighborhood?
  2. Improve onboarding experience
  3. Increase recruitment through job reference

Step 5 – Actionable Tasks

Based on these findings, the HR team began a new recruitment program centred on the theme. The question appeared in all their advertising Billboards and flyers posted in nearby restaurants diner stores etc. Informal walk-ins with even some snacks were scheduled on Tuesdays and Thursdays during the shift change. Lead employees were trained in interviewing and were provided with handy question templates. A special employee benefits information packet was prepared for all the employees.

Step 6 – Track / Achieve the Stated Business Outcomes

Within a few months, the flow of job applications increased by 20% and the level of offer acceptance went up by more than 18%. What was more impressive was that after the next review cycle, as predicted, the number of high performing employees increased by 35%.

Which Data Analysis Model Do I Need?

A great way to explain the value of analytics is to understand the analytics maturity model. This model contains two parts. First, analytics is a two-step process: insights are generated from data, then decisions are made based on these insights. Second, we distinguish four maturity levels, depending on how much of the analytics process is automated. Basic – Operational Analytics Automated – Descriptive (Diagnostic), Predictive, and Prescriptive. Operational Analytics helps in the correlation of disparate data. It basically uses excels, and statistical modeling and static dashboards for visualization.

The other forms of analytics use statistical modeling and machine learning and are broadly classified as (refer to figure 2) –

Analytics Landscape

1. Descriptive Analytics Focuses On the Past

Analytics use data to provide insight into the past and answer: “What has happened?” using statistical modeling. Operational HR analytics focuses on reporting operational basic metrics, key highlights and tracking the efficiency of the HR function reports that provide historical insights, etc. What behaviours did these employees take during appraisal period? Or how do I retain talent?

Figure 3 explains how strategies lead to objectives and the way it is achieved through Lead and Lag indicators:

How Strategies Lead To Objectives

2. Predictive Analytics Focuses on Understanding the Future

They analyze historical data, forecast what might happen in the future. It employs techniques from statistics, data mining, machine learning, and artificial intelligence to analyse current and historical facts to make predictions about the future (as shown figure 4). The first phase is about creating a model using training data and implement it in the real world. The below table explains how strategies lead to the formation of objectives and the way it is achieved through predictive modeling:

Analyse Current And Historical Facts

Once the data is gathered, HR analysts feed workforce data into sophisticated data models, algorithms, and tools to gain actionable insights. These tools provide insights in the form of dashboards, visualizations,and reports. An ongoing process is put in place to ensure continued improvement.

3. Prescriptive Analytics Advise on Possible Outcomes

The relatively new field of prescriptive analytics goes beyond descriptive and predictive analytics by recommending options or possible courses of action. A very small percentage of companies (<3%) are using prescriptive analytics as it is relatively complex.

Prescriptive analytics not only predicts what will happen but also why it will happen, providing recommendations regarding actions that will take advantage of the predictions. They provide users with advice on what action to take.

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How Do I Prepare to Face the Challenges of HR Analytics

Retain Talent

While analytics may change the face of HR as we know it, organisations may face some challenges while adopting them. Let’s explore these challenges one by one and also look at the possible actions HR professionals can undertake to overcome them.

Challenge #1 – Incorrect Prioritization Of Business Goals

For HR analytics to drive real value, it has to be aligned with an organisation’s business goals. Everyone contributing to HR analytics process needs to be briefed on the overarching business strategies, so they can understand how their analysis contributes. For example, reduce turnover on a particular team or organisation-wide or Ensure the organisation has talent aligned to expansion plans or planned new product offerings, etc.

REQUIRED ACTIONS: Define a Business challenge to solve don’t be overambitious. Assess the organisation’s HR metrics capabilities. Focus on insights into potential gaps. Create a fact-driven culture focused on creating business value.

Challenge # 2 – Poor Change Management

Every change made, introduces additional incremental business risk. Knowing how and when to communicate change to users and stakeholders requires significant planning and strategizing.

REQUIRED ACTIONS: Emphasize the need for clear and open communication early in the process. This will help stakeholders and their teams understand expectations upfront and establish relationships between teams for success. Automate the delivery of key information to stakeholders. Focus on user experience. Create outputs that deliver end users really need with easy accessibility and minimal training. Focus on a user interface that is high on user engagement and visualization.

Challenge #3 – Poor Data Quality Garbage in is garbage out.

HR data issues like validity, duplication, outliers, poorly defined definitions, formulas, data-bias etc. make the interpretations and breaks the trust of stakeholders.

REQUIRED ACTIONS – Conduct basic data hygiene practices from the beginning. Assess the quality of data, gauge the need for data clean- up and document data-gathering and reporting processes. A word of caution here – respect privacy rules as employee data is personal. Define business and metric glossary.

Challenge # 4 – Flexibility

Economic and business climates are not static; even the best Talent analytics program may miss emerging talent shortages or inadequately take certain contingencies into account.

REQUIRED ACTIONS – To make sure talent analytics program reflects the latest internal and external developments, organisations should regularly re-calibrate and revise assumptions made from previous HR data analysis. Create a common repository of definitions and what they mean so that customization and standardization are possible.

Challenge #5 – Building the Right Team

Getting the right team on board, which is made up of psychologists, statisticians, etc., could be challenging.

REQUIRED ACTIONS – Create a partnership with Business leaders, Data science team, etc., Get the team trained on professional effectiveness skills (Problem-solving, critical thinking, business acumen, communication skills, etc).

Challenge # 6 – Return on Investment

The key to successful HR analytics relies on the understanding that the size of the measured data isn’t the key to success, but rather, the impact the data can have on decision-making in the organisation. The price band for HR analytics tools is varied and the platform costs can range from $400,000 to $1. 5 million for a company with 5,000 full-time workers. HR leaders must be aware of the cost challenge and should be committed to visible phase wise ROI.

REQUIRED ACTIONS – Focus on lower hanging fruits while developing long term roadmap. Ask the right questions. Celebrate small wins.

Conclusion

With the increase in scope of digitization and automation, technology is integrating with all business functions. Human Capital being the centre of success and sustainability of every business, HR analytics becomes imperative for all analysis and decision making. It not only aligns HR function to the business to deliver value but also to empower people who are associated with the organisation. It provides insights not only to improve efficiency, productivity at organisation level but also proactively works on creating an ecosystem for talent development. HR analytics brings in a scientific, fact-driven result-oriented approach to the overall decision making.

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