January 26, 2017

Getting Started with Workforce Analytics

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There’s a lot of information on the web about how workforce analytics can change your company, but it’s much harder to find practical advice. The internet offers surprisingly little in the way of step-by-step instructions. You’re not going to find a handbook that outlines what data to mine, how to analyze it, and how to build the findings into your company culture.

ALSO READ: 3 Predictions for the Future of Performance Management

This article isn’t going to completely solve that problem, but we will provide you with a roadmap. Your company’s goals, values, and culture should always inform the nuances of your chosen strategy. Nevertheless, here’s our common-sense process for getting started with workforce analytics.

1. Start with Reality

You’ll need a plan, which means understanding where you want to go with your data and analytics. This involves defining the business outcomes you want your analytics to inform. Greta Roberts, CEO at Talent Analytics, points out that HR goals don’t necessarily match business goals, but your business should focus on running more efficiently.

HR goals don’t necessarily match business goals.

You may have already outlined specific business goals for the coming year, so this means less work for you. HR professionals can figure out how to help achieve these outcomes and what data they will need to get a full picture of their position.

Building on existing business goals will also help your team develop questions and hypotheses about the data you already have. This approach will save you from from aimlessly digging through workforce data and coming up empty.

2. Execute with Simplicity

Our Google-able world makes us impatient for knowledge, impatient to digest and master the complexity of the world. But workforce analytics need simplicity, at least when you build your first model. Find the most important question — the one you think will make the most impact on your business outcomes — and investigate from that point.

Thinking about how HR analytics could affect the bottom line means thinking beyond those typical HR objectives of diversity and engagement to the larger trends that show deficiencies and value:

  • Does the company spend a lot of time and money on employee churn?
  • Which managers have the highest team member productivity?
  • What is the correlation between salary, benefits, and employee satisfaction levels?

Finding metrics that correlate with these questions can help your team make better decisions in the future. Are there trends in hiring that result in high employee turnover? How do your outstanding managers work with teams, and can these tactics spread to other less productive departments?

Simple projects mean you can find outcomes faster and show the project’s worth, which in turn boosts stakeholder buy-in. It will also make many of the other suggestions on this list easier, as smaller teams with less sophisticated tools can execute simple analytics.

3. Use Internal Talent

One of the most interesting pieces of advice I ran across in my research was to tap the financial and IT experts in your company to help get your workforce analytics off the ground. These folks have a higher comfort with data science and probably already run some form of analytics within their departments. When you bring these folks onto your team for your first projects, they can provide guidance and emphasize the weight of the project to others.

Remember that you can always hire new dedicated data scientists and specialists, but you shouldn’t pull the hiring trigger too early. Wait until your project successfully gets off the ground. It’s so much easier to get support for increased investments when you can show your worth, rather than making the case on the front end.

4. Plan Security Measures

Nothing gets people to back out of a project faster than thinking that their sensitive personal information is at risk. Your company probably already collects lots of data on your employees, but what sort of security measures do you have in place to protect that data?

Many human resource management systems (HRMS) and business intelligence tools provide encryption for cloud or on-premise data, but who has access to your data? Use your access control settings to keep personal data private from unauthorized parties. Although conventional wisdom suggests that siloing your data hinders collaboration, keeping user access to a minimum also protects data from leakage.

In addition to encryption, firewalls, on-premise databases, think about anonymity. How much of your data should connect to identifying factors? If you plan to send feedback directly to employees, you’ll need to come up with protocol on how to store and transmit that data.

5. Use Technology; Don’t Let It Use You

Technology is fun and exciting, and it’s awesome to say you’re rolling out a new initiative. But the fastest way to lose a project is to let technology run the show. Spend enough time running after the coolest and fastest technology, and you’ll never get any actual analysis completed. Chasing the best modern tools can quickly derail your intentions of solving real business problems — which, if you remember from the beginning of this article, is your actual goal. Stick to the plan.

Many HR professionals are able to build simple workforce analytics models from the data they’ve collected through HRMS platforms, workforce management software, internal surveys, and performance management tools. When your team outgrows those existing systems, you might graduate to

  • a business intelligence platform to analyze your data within the larger context of your business outcomes
  • a dedicated workforce analytics program(e.g. PeopleInsight, WorkForce, Glint) that draws from your other systems

These systems require more from your team including IT’s help with installation and implementation, and might result in your team hiring a dedicated data scientist to sift through the mounds of data.

While we’re on it the subject of chasing technology for its own sake, try to build models with your technology that inform human decisions; don’t let the technology make the decisions itself. We build data models to help us make better informed decisions, not to replace decision-makers.

6. Implement Your Findings

Once you mine and interpret your workforce data, you have to implement it. This is the ultimate test of your project’s success, as your stakeholders want to see measurable results.

Ready yourself and your staff to support your high performers and even implement leadership changes, where necessary. This stage shouldn’t devolve into throwing money at a problem, but you may need to invest time and resources into improvement. Then again, it could be something as simple as a change in communication styles. What would it take to train and follow up on improved communication between managers and teams?

7. Be Transparent About Results

Let people know what your project does and why. Understanding that the project targets specific business outcomes and company goals will only help smooth your road. Want employees to take surveys? Tell them the purpose of the data collection and what you intend to do with it. Publish anonymous company records, and think about giving feedback to individuals.

Prasad Setty of Google gave a really interesting talk about how Google employees took HR feedback regarding various self-generated and sourced performance metrics; those employees actually made choices in their lives to help change their performance.

* * *

Workforce analytics sounds a lot harder than it actually is. Ask a question, look for the answer in your data, and implement change. You don’t need fancy data analysts and expensive tools, although they can certainly help for more difficult projects and long-term growth.

Looking for a tool to help you get started with workforce analytics? Find reviews and software suggestions using our HR Software Product Selection Tool, or call for a free consultation.

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