People Analytics For All: Building a Data-Driven Business Revolution
A Journey in Crafting Insightful Data Solutions for Business Growth
You're drowning in data but starving for insights. Your HR team juggles multiple systems. Each holds a piece of the workforce puzzle.
This fragmented approach is costly. You miss chances to boost employee satisfaction. Predicting future hiring needs becomes a struggle. Top talent might slip away unnoticed. Every day without a unified view means missed opportunities and uninformed choices.
The solution to these problems is People Analytics. It transforms HR from a cost centre into a strategic powerhouse.
Ready to dive in? Today's article will teach you why People Analytics is so hard.
But wait, there's more. I will also tell you how my coworker Dimitar and I approached the problem and how we built our internal People Analytics data product.
Let's get this show on the road.
Reading time: 13 minutes
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🧑📊 What People Analytics Is
Description 🖼️
You are likely aware of Business Analytics areas like marketing, product, and sales. These help you understand how people find you, which features they use, and how much they pay you.
Unlike these, People Analytics looks inside the business. It's a way to see patterns and trends in people's work.
At its core, People Analytics involves gathering data from various sources, such as employee surveys, performance reviews, and HR systems. Then, as Business Analytics, you analyse this data to uncover insights and support your stakeholders' decision-making.
For example, you might use People Analytics to identify factors contributing to employee turnover. You can also measure the impact of training programs or predict future hiring needs.
These and other People KPIs lead to cutting all unnecessary HR processes (I know you love them) and focusing on what matters.
Why Is It Important ❗
Why should you care about People Analytics? Because it helps you make better decisions. But in this case, instead of virtual KPIs, you improve the lives of actual people.
Here are some key benefits:
Objective Decisions: People Analytics helps you move beyond gut feelings and biases. For example, you can use data to identify the best candidates for a promotion rather than relying solely on subjective opinions.
Early Problem Detection: By analysing trends and patterns, you can spot problems before they become serious. For example, if you notice a rise in employee turnover in a particular department, you can investigate and address the issue before it spreads.
Employee Satisfaction: Understanding what your employees need and want can help you create a better work environment. For example, if data shows that employees value flexible working hours, you can implement policies to support this.
Productivity: By identifying the factors contributing to high performance, you can replicate these across the organisation. For example, if data shows that employees who receive regular feedback perform better, you can enforce regular feedback sessions.
Cost Savings: People Analytics can help you make smarter decisions about hiring, training, and retention, which can save money. For example, you can invest in the best return on investment by identifying the most effective training programs.
In short, People Analytics turns your HR department from a cost centre into a strategic powerhouse.
Why Is It Hard 🗃️
Before you rush off to start crunching numbers, let's talk about why People Analytics isn't a walk in the park. But People Analytics isn't easy.
Here are some of the challenges:
Complexity of Human Behavior: People are complex, and their behaviour doesn't always fit neatly into a spreadsheet. Understanding human behaviour requires a deep understanding of psychology and organisational behaviour.
Data Quality: The data you need is often scattered across multiple systems, inconsistent, and sometimes just plain messy. Cleaning and integrating this data can be a significant challenge.
Privacy Concerns: Privacy is a primary concern when handling employee data. You must ensure that data is handled securely and that employees' privacy is protected. This can involve complex legal and ethical considerations.
Interpreting Data: Correct data interpretation is a skill in itself. Misinterpreting data can lead to costly mistakes.
Change Management: Implementing People Analytics requires a cultural shift within the organisation. People need to trust the data and be willing to act on the insights. This can involve overcoming resistance and changing established practices.
So, People Analytics requires technical skills, HR knowledge, and business acumen. However, the right approach can transform how you manage your workforce.
Now that you have a basic understanding of People Analytics let me tell you how we approached it recently.
👷 The Projects
Background 🗂️
Our company uses many tools to manage job postings, employees, and internal eNPS. Each tool serves a specific purpose and provides valuable data.
However, these tools are scattered, which makes it hard to see the clear picture. For example, if we want to understand the impact of our hiring process on employee performance, we need to combine data from Workable and HiBob. This can be a time-consuming and error-prone process.
We realised that we needed a single source of truth to make the most of our People data. This would allow us to see and report all our People data in one place. As a result, it's so much easier to analyse and act on the insights.
Aim 🎯
Our goal was to create a single source of truth for all our People data. We wanted one place to see and report everything. This would allow us to:
Improve Decision-Making: By having all the data in one place, we could make more informed decisions. For example, we could understand what factors improve tenure in certain regions.
Increase Efficiency: Automating data collection and reporting could save time and reduce errors. This would free up our HR team to focus on more strategic tasks.
Enhance Security: We could implement more robust security measures to protect sensitive information by centralising our data. This would help us comply with data privacy regulations and build trust with our employees.
Problems 🔐
The biggest issue we faced was data sensitivity. We store names, addresses, salaries, and other confidential data. Only a few people should have access to this information. This made our project challenging from the start.
We needed to ensure that our solution was secure and that only authorised people could access the data. This required careful planning and the implementation of robust security measures.
We came up with three solutions. Let me tell you about these.
🧙 Solution #1: Existing Data Infrastructure
Description 🖼️
Our first thought was: Hey, we already have a pretty sweet data setup. Why not use that?
We do have a robust data infrastructure that we use for other parts of the business. Here's what it looks like:
Meltano to pull data from various sources.
Snowflake as a data warehouse.
dbt, which transforms our raw data into something useful.
Looker to present the data.
Sounds pretty good, right? Well, let's look at the pros and cons.
Pros 👍
Using our existing tools means we can leverage our data team's expertise. Integrating new data into our existing infrastructure is straightforward. This approach allows us to take advantage of the features and capabilities of our enterprise-grade tools. We have everything: scalability, automation, and advanced analytics.
Using our existing setup, we can also ensure consistency and accuracy in our data. Our data team is familiar with these tools. We have established processes for data integration, transformation, and reporting. This means we can quickly and efficiently integrate People data into our existing workflows.
As a bonus, we could integrate this with other data we already work with like GitHub and Jira.
Cons 👎
The downside is security. Every Looker admin can see the data, even if we limit access in our data warehouse. This is a considerable risk when dealing with sensitive employee information.
We would need to implement stringent access controls and monitoring to address this. However, this would add complexity and increase the risk of human error. Additionally, the more people with potential access, the higher the risk of a data breach.
Using our existing infrastructure also means that we may need to compromise data privacy and security. For example, we may need to limit the level of detail in our reports to protect sensitive information. This could reduce the value of the insights we can gain from the data.
👷Solution #2: A Parallel Data World
Description 🖼️
We considered building a separate data platform just for People data. This would require a new process for ingesting, storing, and reporting data. By creating a parallel system, we could limit access to just the handful of people who absolutely need it.
This approach involves setting up a separate data pipeline, data warehouse, and reporting tool. We would use tools different from those in our existing infrastructure to ensure complete separation of People data from other business data.
Pros 👍
This approach offers ultimate security. We can control who accesses the data and ensure that only authorised personnel can access sensitive information. By creating a separate system, we can implement more robust security measures and reduce the risk of data breaches.
We would still get all the benefits of a modern data stack, such as scalability, automation, and advanced analytics. However, we would have complete control over who sees what. This allows us to protect sensitive employee information while gaining valuable insights from the data.
And because of the low data volumes and user numbers, we could build this platform at a low price.
Cons 👎
Building a separate system takes time and resources. Our HR team would need to handle data modelling and reporting, which is a lot to ask. Plus, we'd need to maintain these new tools.
This approach also requires significant investment in terms of time and money. We would need to set up and maintain a separate data pipeline, data warehouse, and reporting tool. This adds complexity and increases the workload for our HR and data teams.
Additionally, by separating People data from other business data, we may lose some of the benefits of integrated analytics. For example, it may be more challenging to analyse the impact of HR practices on business outcomes if the data is stored in separate systems.
📑 Solution #3: Spreadsheets
Description 🖼️
Using Google Sheets is the quickest way to start. This approach allows us to get up and running quickly without complex setups or new tools.
All tools we use offer dumps in the form of CSV files. We could manually download those files and create dashboards in Sheets.
Google Sheets is a familiar tool that our HR team is comfortable using. It provides basic data analysis and visualisation capabilities.
Pros 👍
It's fast and easy, and we don't need to involve anyone outside the People team. This approach allows us to control the data and ensure that only authorised personnel have access.
By using Google Sheets, we can quickly create a working solution and start gaining insights from our People data. This allows us to demonstrate the value of People Analytics and build support for more advanced solutions in the future.
I don't want to sign NDAs and access my coworkers' data. I count this as a huge advantage.
Cons 👎
Google Sheets has limitations in terms of data storage and processing power. This method doesn't scale well. It requires a lot of manual work and slows down with more data.
Additionally, the manual nature of this approach increases the risk of human error. Manually downloading and updating data is time-consuming and prone to mistakes. This can lead to inaccuracies in the data and reduce the reliability of the insights.
