What Is People Analytics?
People analytics can be characterized as the profoundly information-driven and objective centered strategy for contemplating all people forms, capacities, difficulties, and openings at work to lift these frameworks and make supportable business progress.
People analytics is often referred to as talent analytics or HR analytics as well. Essentially, gathering and assessing people analytics leads to better decision-making through the application of statistics and other data interpretation techniques.
More astute, increasingly key, and information sponsored ability choices are hence nearer close by, and this is relevant all through the worker lifecycle – from settling on better procuring choices and progressively compelling execution the board to better maintenance.
People analytics has advanced significantly from when it was first utilized in quite a while in the mid-1900s. There has been a reasonable progress from prescriptive analytics to prescient analytics, with which associations would now be able to be better arranged to confront the dynamism of their operational condition and be proactive instead of receptive. For instance, complex information science, intuitive information perception, and AI – every fundamental piece of people analytics today – were no place a piece of the procedure until a couple of decades back.
What Are the Benefits of People Analytics?
People analytics causes associations to make more astute, progressively vital and increasingly educated ability choices. With people analytics, associations can discover better candidates, settle on more brilliant enlisting choices, and increment worker execution and maintenance.
People analytics applies sophisticated data science and machine learning to help organizations more efficiently and effectively manage their people. It give organizations options for viewing, understanding and acting on talent data across the entire employee lifecycle. This includes an interactive data visualization application that gives business leaders deeper intelligence about their people, Planning, an intuitive workforce planning application that helps organizations easily create, manage and execute accurate hiring plans over multiple time horizons, as well as Insights, it’s predictive and prescriptive analytics solution that equips business leaders with the intelligence to better recruit, train, manage and develop their people.
The Process of People Analytics
People analytics today is a lot more intuitive and predictive. With that expectation to live up to, the process involves the following steps.
Step 1: Dig data that matters
The core question to ask here is, “What data is relevant to our business goals?”
and to set the key performance indicators (KPIs) accordingly. This allows you to save major resources by only investigating areas that need direct monitoring, such as operational tasks within the people management spectrum, and can lead to tangible business success.
If it does not add strategic value, digging that data could be a waste of time. Knowing what to focus on also helps in applying the right statistics, data mining, machine learning, survey management, and strategic workforce management tools.
Step 2: Experiment, explore, enrich
In a crowded and visibly fragmented market, it is imperative to choose a people analytics tool by exploring the market, experimenting with different options, and analyzing which option would enrich the organization the most. Multiple offerings include data mining, data transformation, and data visualization techniques, all merged into a user-friendly self-service interface.
Platforms that offer a wide range of features often require a lot of manual manipulation to access important data, and these aspects can be tested only through systematic experimentation.
Step 3: Have an action plan ready
Once you know what your end goal is, which data is relevant, and what the available options are (based on clear pros vs. cons analysis), create an action plan. Applying big data and predictive analytics to talent management, leadership development, and organizational capabilities often helps in fine-tuning the action plan.
Moreover, having a well-defined plan of action enables a better understanding of why certain changes may be taking place and where the organization is headed and can thus help garner more stakeholder support.
Step 4: Avoid legal loopholes
Ensuring that legal compliance is maintained in the collection of all data is crucial. Before you start on the analytics project, have a legal team validate the data sourcing techniques and processes. It does not end here.
Once the raw data has been gathered and treated, the results gleaned need to be approved as well before they can be applied or published. In our digital ecosystem, with data protection and privacy laws still evolving, it is prudent to keep abreast of the changes and double-check on legal compliance.
Step 5: Create leaner systems
Irrespective of the complexity of the project at hand, the broader strategy that the processes must adhere to needs to be simple and lean. The basic process of data analysis and interpretation should allow for easy application, updating, and readability.
For example, create the basic outline simplified as intake and design (data collection and the design of the analysis), data cleaning (removing irrelevant or unreliable data), data analysis (quantitative and qualitative exploration), and sharing insights (interpretation and presentation of the data). This can help avoid unnecessary complications such as confusion about the flow of steps involved, time wastage, or repetition of sub-processes that occur with unstandardized process structures, while still allowing room for tweaks where necessary.
The idea is to find the right balance between the limited moving parts (people and the dynamism of the environment) and fluid, customizable systems and processes of people analytics. When you have the right team with the relevant skillset in place, it is easier to streamline the whole process and apply quality controls.
Step 6: Build a fact-based, measurable HR business strategy
A realistic HR business strategy avoids functional silos and can align talent to business seamlessly. Having clear KPIs and ROI expectations from people analytics endeavors ensures that the impact is measured often and with transparency. A winning strategy needs to be backed by data and an effective plan of action.
Step 7: Take tech support
Technology is interspersed with every aspect of life today and more so with processes like people analytics, where often a bulk of analytical data is to be treated with little or no room for error. New-age HR tech tools make real-time data easily accessible. And this is an opportunity that needs to be milked because today, agility and real-time intelligence can truly set you apart from the competition.
Three Key Level-based Checks to Choose the Right People Analytics Tools
With a vast array of available vendors, options, and subscription plans, choosing the right people analytics tools can often seem like a rather daunting task. Here’s a three-level need-based check to make the right decision.
Level 1: A working HR dashboard
To get started with people analytics, use a basic dashboard that allows you to capture, aggregate, and visualize data.
Tools like Power BI, Tableau, and Qlik allow ease of use and ease of data access. With a level 1 requirement, your priority should be to keep your people analytics system as simple as possible.
Level 2: An insightful HR dashboard
You may have a steady dose of relevant data and need basic insights to analyze better and make stronger decisions. Statistical tools like Excel or SPSS are effective as well, though they may not come with quirky visual aids and social-media style interfaces. Tools like Visier, while taking some time to be set up, come with holistic analytics solutions.
Level 3: A predictive HR dashboard
Your organization is at the third requirement level when you seek not only to analyze data but also to make intuitive predictions based on upcoming trends. These tools help you study behavior in a way that you can predict the next course of action.
For example, there might be some correlation between your employees updating their LinkedIn page, taking frequent leaves, and with them not being very content at work. While this is a very simplistic situation, predictive tools could help you make connections with behavior and decision patterns that you might have missed otherwise.
Python or RStudio can help with advanced analyses for large quantities of data, though they might require you to hire data scientists specialized in the field.