In the part 1 of the HR Tech Series blog, we discussed the role of people analytics and how it has is rapidly becoming the new currency of HR. In Part 2, we will evaluate how people analytics can help HR teams in unlocking the true potential of your workforce in today’s data-driven workforce. People analytics is a deeply data-driven and goal-focused method that studies all people processes, challenges, functions, and opportunities at work to upgrade the systems and achieve sustainable business success. People analytics is often also called HR or talent analytics.
According to studies by Deloitte, a few of the functions that people analytics comes to aid are increasing job offer acceptance rates, and optimizing compensation reducing HR help tickets. Essentially, collecting and assessing people analytics results in better decision-making with the application of statistics and other data interpretation techniques. Strategic decisions and data-backed decisions throughout the employee lifecycle: hiring, performance, and retention can be made with people analytics.
Organizations have learned to be proactive rather than reactive with the shift in analytics from prescriptive to predictive. For example, machine learning, interactive data visualization, and sophisticated data science are all integral parts of people analytics now though they were not a part of the process earlier.
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Process of people analytics
Today people analytics is much more intuitive and predictive. The process involves the below-mentioned steps:
Data that matters
Find the data that is relevant to your business goals and set key performance indicators (KPIs) accordingly. This saves resources by only investigating areas which need direct monitoring, like operational tasks within the people management spectrum.
Digging data that adds no strategic value would be a waste of time. Identifying the focus areas helps in applying the right statistics, data mining, survey management, machine learning, and strategic workforce management tools.
Experiment, enrich
In a fragmented market, choosing a people analytics tool requires exploring the market, experimenting with available options, and analyzing which option would best enrich the organization. Platforms that offer a broad range of features often require manual manipulation to access important data. Such aspects can only be tested through systematic experimentation.
Action plan
Create an action plan after you know the goal, relevant data and available options (based on pros vs. cons analysis). Apply big data and predictive analytics to organizational capabilities, talent management, and leadership development. Having a plan ready also helps in garnering more stakeholder support.
Avoid loopholes
Ensure maintaining legal compliance in the collection of all data. Before starting an analytics project, get a legal team to validate the data sourcing techniques and processes. After treating the raw data as well, the results need to be approved before they are applied or published. It is prudent to keep abreast of the changes in data protection and privacy laws and double-check on legal compliance.
Leaner systems
The strategy that the processes must adhere to must be simple and lean. It should allow for easy application, readability, and updating. This will help avoid confusion about the flow of steps involved, repetition of sub-processes, or time wastage while still leaving room for tweaks where needed.
When you have the right team possessing relevant skill-sets, it is simpler to streamline the whole process and apply quality controls.
Fact-based HR business strategy
A realistic and measurable HR business strategy reduces functional silos and can align talent to business seamlessly. Set clear KPIs and ROI expectations from your people analytics endeavors to ensure the impact is measured often and with transparency.
Tech support
Processes like people analytics often have a bulk of analytical data to be treated with the least room for error. The latest HR tech tools make real-time data easily accessible and today agility and real-time intelligence can surely set you apart from the competition.
Choosing the right tools
Choosing the right people analytics tools may seem a rather daunting task. Follow this 3 leveled need-based check for making the right decision.
Level 1
Use a basic dashboard, when starting with people analytics, to capture, aggregate, and visualize data. Tableau, Power BI, Qlik, and such other tools allow ease use and of data access. Focus on keeping your people analytics system as simple as possible in Level 1.
Level 2
With a steady inflow of relevant data, the need for basic insights to analyze and make stronger decisions arises. Excel, SPPS, and other statistical tools are effective but tools like Visier offer holistic analytics solutions apart from social-media style interfaces and quirky visual aids.
Level 3
When your organization requires not only to analyze data but also to make intuitive predictions, Python or RStudio can be of help. These tools help in studying behavior to predict the next course of action. Also, they provide advanced analyses for large quantities of data and make connections with behavior and decision patterns that might have missed your eye.
In conclusion, there are 3 things that should be your focus while implementing a people analytics function – availability of quality data, data security, and data privacy. Though data about people has long been available, the transition of people analytics to a more structured approach has been a challenge recently undertaken. The main challenge with using a data-driven decision-making approach is that it is important to compare employees and performance on a level playing field which proves hard to do. Here comes the need to adjust for context. In other words, it would mean considering factors that may not be obvious or present in the data. For instance, while comparing performance for similar jobs, environmental factors like working conditions, managers, etc. outside the data must be considered.
Data-driven decisions within the People Analytics space may sometimes create tensions since people favor human judgment over algorithmic judgment. But data-driven decision-making removes bias in a level playing field, given the data is accurate. Further, the availability of data offers the organization with new innovations and insights.
The end goal for an organization is to embed people analytics in its business decisions. For this, the data must be reliable, consistent, and secure, however, it need not be perfect. HR must identify people problems that impact the business and can be eliminated altogether. The HR and the Business Partners should work collaboratively to identify opportunities, form deep partnerships, and advance organizational maturity.