Data and analytics have taken the guesswork out of the decision-making process and are redefining how companies approach change. To remain innovative and competitive, companies must have data at the core of their change management processes.
The real-time nature of data forces organizations to implement changes at a quicker pace to keep up with industry changes. This has a big impact on program managers who are guiding strategic projects. It often falls to program managers to serve as change agents and guide the change management process.
By adopting a data-centric approach to change management and implementing software solutions that ensure the integrity of data and automate the change process, program managers help increase the likelihood that their change initiatives will succeed.
Data can help inform the change management process in multiple ways:
1. In Helping Recognize When Change Is Necessary
With robust data-collection tools, businesses can collect better market and customer intelligence to identify any trends signaling the need for change. In order to identify these new opportunities, organizations must understand their markets and audiences, which can be clearly defined through data.
The problem is not all data is good data. Data is only as valuable as the insights that can be drawn from it. No particular data is inherently better or worse than others. It is all about finding the data that will best answer an organization’s questions and deliver on strategic objectives. Alex Birkett, writing for conversion optimization company CXL, has a handy five-point rubric for assessing the usefulness of a dataset:
- Is the data accurate and trustworthy? It is important to question the quality and integrity of the data.
- Is anything missing from the data? Data must present the full picture for it to be accurate.
- Is the data meaningful? Data is not useful unless it can be acted upon.
- Is there any way to get more out of the data? There may be more data points that can be measured to gain more meaning from the data.
- Has the data been through rigorous quality assurance evaluation? Bad data quality assurance can lead to bad business decisions.
From there, analyzing the useful data through the lens of company strategies and goals will deliver the kind of insights that identify reasons and opportunities for change.
There are a variety of different metrics that can speak to the impetus reasons for change, including customer satisfaction surveys, market changes, the obviation of a key performance indicator or competitive analysis.
Jeanne Harris, in an article for ComputerWeekly.com, suggests that companies should be focusing their analysis on the capabilities and processes that are distinctive for them and differentiated from competitors. These are the elements that fuel an organization’s formula for success, and are the prime targets for data analysis.
2. By Informing Change Approval
Once the necessity for change is recognized, program managers are often tasked with implementing the change. Data is a key driver for informing change and helping program managers make good decisions when implementing change.
With the right software and accurate data, program managers are able to make instantaneous and intelligent decisions about changes to projects, says Artemis’ Chief Defense Industry Client Strategist Joe Kerins. Central to those decisions is the ability to run “what if” scenarios based on requested change. Before implementing a change, program managers can see a change’s impact to budget and schedule and decide whether that change moves the project forward.
Kerins explains that having a tool that can run multiple “what if” scenarios provides the most lucrative data for program managers. Essential, though, is the integrity of that data. When program managers are working with multiple software solutions, the integrity of the data can get compromised. With a single software solution that pulls from a centralized database, program managers can trust the integrity of the data, and thus make more informed decisions.
If a company is using a flexible software solution that combines project constraints into a single program, says Kerins, once a decision is made a program manager can automate the approval and baseline changes processes. The integrity of the data is maintained throughout the change process.
3. In Monitoring Process Adjustments
Once a company identifies change opportunities and approves a change, it is time to turn inward and adjust processes to implement the necessary changes. Data can help define and monitor those process changes.
First, identify which processes needs to change and how. Map each process to be sure you understand it and the relationship it may have with other processes. Without this map and a clear understanding of the interaction of processes, the change may fail to meet expectations due to impact on other processes.
Then, for each stage of the identified processes, identify what you want to measure, and which data sources provide that valuable information about those processes. Nick Ismail, in an article for Information Age, identifies a few different ways you could evaluate a process quantitatively:
- Analyze how data varies from a process-performance baseline.
- Check for bottlenecks in different parts of process.
- Look for performance trends.
- See how a process performs against “perfect” performance measurements.
How an organization chooses to analyze the data depends on what part of the process is being improved and how. The most important thing is to be sure the data provides insights that align with new strategic goals.
4. In Creating Employee Training Programs
Process improvements are only one piece of the change management puzzle. People are the other key element for successful change — and often create the most difficult hurdles to overcome. This is where data can be used to train employees as part of the overall change management process.
Training is an inevitable part of change, and program managers should develop training programs for their project teams based on changes being implemented. Data can help program managers create the most effective training tools for their teams by analyzing patterns of behavior that can tell a clear story about how well your training processes are going. Data can also be used to personalize the learning experience by identifying individual learning needs, as well as team and department strengths and weaknesses.
With the right technology, this can all be done in real time, allowing managers to see what material employees are struggling to grasp and intervene if necessary. While change can be difficult for employees, using data in training programs allows program managers to better support employees as they make the adjustments required to meet change goals.
5. By Winning Support Across The Organization
Change cannot happen without organizational support — support for the need to change and the change solution — and data can help get buy-in from everyone. Data helps stakeholders visualize and understand the need for change in an intuitive way. With that better understanding comes acceptance of change and a willingness to see the change initiative succeed.
Human resources consultant Susan Heathfield says one of the tips for getting universal agreement for change is to share as much information as possible about the business to as many employees as possible. If decisions to change are based on relevant data, Heathfield says, stakeholders who are informed will more likely understand and agree with the need for change.
Data Is Improving Change Management
As with other aspects of business, change management has shifted away from intuition-based to data-centric decision making, and program managers are on the front lines of this shift.
Brent Gleeson, founder of leadership and change management consulting firm TakingPoint Leadership, says companies that use data to drive change are more successful because they have the tools and skills to gain real-time insights from data, openly communicate insights across the organization, make data collection a primary activity across departments and use data to diagnose systems and processes.
These companies are leading their industries and innovating at a pace that is keeping up with market demands.