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Introducing Insights

As a People Ops person without a people analytics platform up and running, you are often conducting analysis by hand. That tiresome process often includes gathering, checking the quality, and crunching your data. This process requires technical skills, domain knowledge, and most importantly — a lot of time.

People Ops teams are usually spread too thin when it comes to operational priorities, so it’s difficult to find the time to invest in building up reporting infrastructure and crunching numbers in Excel.

This is why we designed Insights — a new feature to serve you actionable reports just when you need them!

Let’s see how it works.

Orgnostic Insight card

Why we created Insights

Once you start using Orgnostic’s people analytics platform — you have your data connected and cleaned up, you’ll see the Story section, which provides an overview of your organization’s headcount, talent acquisition, DE&I, talent management rewards, culture, employee wellbeing, team effectiveness, turnover, and leadership metrics.

But how do you do more in-depth, timely, specific analysis?

Our answer to this question is creating Insights — reports we generate only if we notice that there is a trend that needs your attention at a particular moment.

What do we mean when we say Insights?

Orgnostic connects your data from various sources and turns it into insights, metrics and predictive metrics. What is the difference between these three?

🕷 Metrics, Predictive metrics and Insights 🕸

Insights are calculated using a specific segment of your data, and they offer a snapshot of its current state.

Metrics are the data you get either out of the box by connecting your HR apps with the Orgnostic Platform or by running our Diagnostic or Data Filler surveys. For example, Number of Terminations is a metric which offers you an overview of employees exiting the organization.

Predictive metrics are built on top of Story metrics using various statistical and machine learning algorithms to predict issues related to employee turnover, engagement, and burnout. For example, they allow you to predict which employees are at risk of leaving and mitigate the risk.

What differentiates Insights from Survey metrics and Predictive metrics the most are suggested actions — tips on how to act in order to improve or maintain a specific area of interest — for example, the current pay gap in your company.

How do Insights work?

Whenever your data meets a few predefined assumptions, we run a relevant statistical or Machine Learning algorithm which checks whether we can identify an actionable insight.

If a specific insight is detected in your data, the results of the analysis will be shown in Orgnostic.

These results are also accompanied by suggested actions which could be rolled out by an HR team. Suggestions are extracted from research and results in behavioral economics, psychology, and public policies.

However, if an actionable insight is not detected in your data, you won’t be bothered with unnecessary notifications!

Starting with the Pay Gap Insight

Gender pay inequity can cause various issues such as increased employee turnover in your organization. This is why we chose to offer pay gap analysis as our first insight.

Pay Gap Insight

If your data meets a predefined set of assumptions and if a useful insight is detected, you will get the report shown above in Orgnostic.

This insight shows you the differences in salaries between male and female employees, while taking into consideration other relevant employee characteristics. If a gap is detected, Orgnostic will share a report on the difference in average salary, number of salary increases, salary increase amounts, and salary increase percentages.

This insight does not currently take into account other genders for the simple reason that there are often not enough employees of some other specific gender which would allow anonymizing data and extracting reliable information using various statistical methods.

Under the hood

Pay Gap analysis relies on a statistical method that tries to explain how employees’ salary differs with respect to different employee characteristics.

For example, a specific employee’s salary might be explained in terms of the department in which they’re working — different departments are often paid differently. Furthermore, with age often comes more experience which is also tied to an employee’s salary. Employee tenure is another variable that is closely tied to their salary — loyal employees are often rewarded with salary increases.

Our analysis takes into account such variables and extracts the contribution of each employee characteristic to their salary. In that situation, an employee’s gender shouldn’t account for any differences in salaries. If, however, an employee’s gender indeed accounts for a specific portion of differences in salaries, it’s worth exploring why that occurs.

Maybe you have a lot of fresh female employees or a lot of horizontal change in female employees’ roles — scenarios which are perfectly fine. However, it could also be worth exploring your company’s hiring and promotion process more thoroughly — that’s where our suggested actions come into play.

What’s next for Insights?

With Insights as a new tool in your pocket, you will be able to make data-driven decisions, present new findings to your colleagues in a clear manner, and sketch an actionable plan!

We are constantly looking for informative ways to present what’s hiding in your data and we’re relying on statistical rigour and fresh scientific articles to give you the best possible experience. If you are interested in insights regarding employee wellbeing, your organizational culture, or the structure of your organization (to name a few) — stay tuned, there’s obviously more where this insight came from!

Want to get the pulse of your team and move your HR efforts in the right direction? 👉 Start your people analytics journey →