Data can offer many change management insights, that can help you improve results, boost productivity, and decrease waste.
However, change management is not historically a data-driven field…
How would one introduce analytics into change management?
Below, we’ll explore:
- Why and how analytics can improve change programs
- What change management insights you can expect to gain
- What metrics and change management KPIs to use
- When and what to analyze
First, let’s look at one major reason to use change management analytics:
According to Deloitte, analytics can significantly mitigate risk.
To do this, cover a few bases up front, such as:
- Ensure leadership is aligned with your goals and visions
- Have tools to assess employee readiness
- Map and track change impacts into change management strategy
- Develop a user adoption measurement strategy
To truly understand a program and reduce risk, data should be collected before, during, and after a change program.
Keys to mitigating risk and ensuring success include:
Using trusted, quality change management tools, such as Deloitte’s own change readiness tool
Measuring alignment, readiness, and adoption – early and often
Understanding risks and creating strategic solutions to prevent problems
When it comes to change readiness measurement, Deloitte naturally favors its own approach.
However, change readiness is not the only metric to focus on.
What Types of Data Can Offer Change Management Insights?
Below, we’ll look at a few dimensions to focus on when measuring change.
Deloitte’s software tool, As One, measures readiness in three ways:
- The extent to which employees and leaders support HR priorities
- How strongly leaders and employees identify and connect with different parts of the organization
- How people in the organization prefer to work together
With this tool – and by following the steps mentioned above – Deloitte claims it is possible to greatly reduce risk and “ensure a positive HR transformation experience.”
However, change readinesschange readiness is only one dimension to measure when analyzing your change program.
Let’s look at a few others.
Their research suggests that only 32% of US employees are engaged at their jobs.
When this statistic is put next to the high cost of employee recruitment, training, and onboarding, it becomes clear why engagement metrics matter so much.
There are a few ways to measure engagement:
- Use tools specifically designed for this purpose, such as polls, surveys, or real-time engagement platforms
- Manually collect data via “old-fashioned” methods such as discussion groups, meetings, or through email
- Use digital adoption platforms or other tools that track engagement in apps, software, on websites, and so forth
Measuring engagement is just the first step, as Gallup mentions.
Once you measure engagement, you’ll need to define the problem and develop a solution.
Social media analytics can offer change management insights by tracking stakeholder sentiment.
Social media can help you measure a number of channels, including:
- Channel partners
Today, it is possible to leverage AI, natural language processing, and sentiment analysis to understand how people feel about a chosen topic.
In this case, such tools can be used to understand how people feel about a change program.
Those insights, in turn, can be used to prevent or solve problems.
Today, many companies use data-driven recruitment processes.
The same approach can be used to select employees for your change team.
Today, tools are emerging that can be used to predict team performance, productivity, and engagement.
Some have even suggested that pre-emptive psychometric testing could improve program results.
However, such steps are not necessary to gain insights into employees.
Employee data can come from a variety of sources, including internal data, resumes, recruiters, and so on.
To many change managers, the idea of data-driven predictive modeling may sound a bit far-fetched.
However, predictive models are used daily in AI applications, such as:
- Search engines
- Product or content recommendation engines
- Competitive intelligence
- Digital adoption platforms and employee training software
And, as we have seen, predictive models are commonly used in HR and recruitment.
Naturally, predictive modeling has its own risks – and it is not perfect.
However, with fine-tuning, careful implementation, and ongoing improvement, predictive tools can greatly improve program results.
Putting It All Together
- First, analyze risk and measure change readiness across the organization
- Second, measure engagement and sentiment for all stakeholders
- Finally, use this data to develop predictive models that can improve results
For traditional change managers, such a data-driven approach may seem counterintuitive. Or it may seem impersonal.
However, a data-driven approach does not remove humans from the equation.
On the contrary, it can offer significant change management insights and improve the quality of your change program.
As a result, employees and stakeholders will be more satisfied and less resistant to change.
And, most importantly, you stand a better chance of creating a change program that succeeds.