Change managers are not just facilitators of change transition; they are strategic partners who must understand and navigate complex organisational landscapes. One key skill that is often under-emphasised in this role is analytical capability. By adopting a strategic consultant’s mindset and employing robust analytical skills, change managers can significantly enhance their effectiveness throughout the project lifecycle. Let’s explore how change managers can leverage analytical skills at each phase of the project lifecycle, emphasising frameworks like MECE and TOSCA to drive successful change initiatives.
The Importance of an Analytical Lens
Change management involves facilitating transitions while ensuring that stakeholders are engaged and informed. However, to do this effectively, change managers must analyse complex data sets, identify patterns, and make informed decisions based on evidence. This analytical lens can be applied through every stage of the project lifecycle: commencement, planning, execution, monitoring, and closure.
Gone are the days when change practitioners are making recommendations ‘from experience’ or based on stakeholder input or feedback. For complex transformation, stakeholders now (especially senior stakeholders) demand a more rigorous, data-driven approach to drive toward solid change outcomes.
1. Project Commencement Phase
At the project commencement phase, the groundwork is laid for the entire change initiative. Change managers need to scan the organizational environment through the lens of impacted stakeholders, gathering relevant information and data.
Example: Consider a company planning to implement a new customer relationship management (CRM) system. The change manager should begin by analysing the existing state of customer interactions, assessing how the change will impact various departments such as sales, marketing, and customer service. This involves conducting stakeholder interviews, reviewing existing performance metrics, and gathering feedback from employees.
Using a MECE (Mutually Exclusive, Collectively Exhaustive) framework, the change manager can categorize stakeholder concerns into distinct groups—such as operational efficiency, user experience, and integration with existing systems—ensuring that all relevant factors are considered. By identifying these categories, the change manager can articulate a clear vision and define the desired end state that resonates with all stakeholders.
The above is from Caseinterview.com
Hypothesis: Sales Team Will Resist the New CRM System Due to Lack of Training and User-Friendliness
Step 1: Identify the Hypothesis
Hypothesis: The sales team will resist the new CRM system because they believe it is not user-friendly and they fear insufficient training.
Step 2: Break Down the Hypothesis into MECE Categories
To validate this hypothesis, we’ll break it down into specific categories that are mutually exclusive and collectively exhaustive. We’ll analyse the reasons behind the resistance in detail.
Categories:
User Experience Issues
Complexity of the Interface
Navigation Difficulties
Feature Overload
Training and Support Concerns
Insufficient Training Programs
Lack of Resources for Ongoing Support
Variability in Learning Styles
Change Management Resistance
Fear of Change in Workflow
Previous Negative Experiences with Technology
Concerns About Impact on Performance Metrics
Step 3: Gather Data for Each Category
Next, we need to collect data for each category to understand the underlying reasons and validate or refute our hypothesis.
Category 1: User Experience Issues
Data Collection:
Conduct usability testing sessions with sales team members.
Administer a survey focusing on user interface preferences and pain points.
Expected Findings:
High rates of confusion navigating the new interface.
Feedback indicating that certain features are not intuitive.
Category 2: Training and Support Concerns
Data Collection:
Survey the sales team about their current training needs and preferences.
Review existing training materials and resources provided.
Expected Findings:
Many team members express a need for more hands-on training sessions.
A lack of available resources for ongoing support after the initial rollout.
Category 3: Change Management Resistance
Data Collection:
Conduct focus groups to discuss fears and concerns regarding the new system.
Analyse historical data on previous technology implementations and employee feedback.
Expected Findings:
Employees voice concerns about how the CRM will change their current workflows.
Negative sentiments stemming from past technology rollouts that were poorly managed.
Step 4: Analyse Data Within Each Category
Now that we have gathered the data, let’s analyse the findings within each MECE category.
Analysis of Findings:
User Experience Issues:
Complexity of the Interface: Usability tests reveal that 70% of sales team members struggle to complete certain tasks in the CRM.
Navigation Difficulties: Survey responses show that 80% find one step of the navigation counterintuitive, leading to frustration.
Training and Support Concerns:
Insufficient Training Programs: Surveys indicate that only 40% of employees feel adequately trained to use this part of the new system.
Lack of Resources for Ongoing Support: Focus groups reveal that team members are unsure where to seek help after the initial training.
Change Management Resistance:
Fear of Change in Workflow: Focus group discussions highlight that 60% of participants fear their productivity will decrease with the new system, at least during the post Go Live period.
Previous Negative Experiences: Historical data shows that past technology rollouts had mediocre adoption rates due to insufficient support, reinforcing current fears.
Step 5: Develop Actionable Recommendations
Based on the analysis of each category, we can create targeted recommendations to address the concerns raised.
Recommendations:
User Experience Issues:
Conduct additional usability testing with iterative feedback loops to refine the CRM interface before full rollout.
Simplify the navigation structure based on user feedback, focusing on the most frequently used features.
Training and Support Concerns:
Develop a comprehensive training program that includes hands-on workshops, tutorials, and easy-to-access online resources.
Establish a dedicated support team to provide ongoing assistance, ensuring team members know whom to contact with questions.
Change Management Resistance:
Implement a change management strategy that includes regular communication about the benefits of the new system, addressing fears and expectations.
Share success stories from pilot programs or early adopters to demonstrate positive outcomes from using the CRM.
By following this detailed step-by-step analysis using the MECE framework, the change manager can thoroughly investigate the hypothesis regarding the sales team’s resistance to the new CRM system. This structured approach ensures that all relevant factors are considered, enabling the development of targeted strategies that address the specific concerns of stakeholders. Ultimately, this increases the likelihood of successful change adoption and enhances overall organizational effectiveness.
Data-Driven Decision Making:
At this stage, change managers should work closely with the project sponsor and project manager to determine effective positioning. A data-driven approach allows the change manager to form a hypothesis about how the change will impact stakeholders. For instance, if data suggests that the sales team is particularly resistant to change, the manager might hypothesize that this resistance stems from a lack of understanding about how the new CRM will enhance their workflow.
2. Planning Phase
Once the project is initiated, the planning phase requires detailed strategy development. Here, analytical skills are essential for conducting stakeholder analysis and impact assessments.
Example: In our CRM implementation scenario, the change manager must analyse the data collected during the commencement phase to identify the specific impacts on different departments. This involves grouping and sorting the data to prioritize which departments require more extensive support during the transition.
Using the TOSCA (Target, Objectives, Strategy, Constraints, Actions) framework provides a structured approach to guide the change management process for the CRM implementation. This framework helps clarify the overall vision and specific steps needed to achieve successful adoption. Below is a detailed exploration of each component:
1. Target
Definition: The target is the overarching goal of the change initiative, articulating the desired end state that the organization aims to achieve.
Application in CRM Implementation:
Target: Improve customer satisfaction and sales efficiency.
This target encapsulates the broader vision for the CRM system. By focusing on enhancing customer satisfaction, the organization aims to create better experiences for clients, which is crucial for retention and loyalty. Improving sales efficiency implies streamlining processes that enable sales teams to work more effectively, allowing them to close deals faster and serve customers better.
2. Objectives
Definition: Objectives are specific, measurable outcomes that the organization intends to achieve within a defined timeframe.
Application in CRM Implementation:
Objectives: Increase customer retention by 20% within a year.
This objective provides a clear metric for success, enabling the organization to track progress over time. By setting a 20% increase in customer retention as a target, the change manager can align training, support, engagement and system adoption with this goal. This objective also allows for measurable evaluation of the CRM’s impact on customer relationships and retention efforts.
3. Strategy
Definition: The strategy outlines the high-level approach the organization will take to achieve the objectives. It serves as a roadmap for implementation.
Application in CRM Implementation:
Strategy: Implement phased training sessions for each department, with tailored support based on the unique impacts identified.
This strategy emphasizes a thoughtful and structured approach to training, recognizing that different departments may face distinct challenges and needs when adapting to the new CRM. By rolling out training in phases, the organization can focus on one department at a time, ensuring that each team receives the specific support they require. Tailoring the training content based on the unique impacts identified earlier in the MECE analysis helps maximize engagement and effectiveness, addressing concerns about usability and fostering greater adoption of the CRM.
4. Constraints
Definition: Constraints are the limitations or challenges that may impact the successful implementation of the strategy. Recognizing these upfront allows for better planning and risk management.
Application in CRM Implementation:
Constraints: Limited budget and time restrictions.
Acknowledging these constraints is critical for the change manager. A limited budget may affect the types of training resources that can be utilized, such as hiring external trainers or investing in advanced learning technologies. Time restrictions might necessitate a more rapid rollout of the CRM system, which could impact the depth of training provided. By recognizing these constraints, the change manager can plan more effectively and prioritize key areas that will deliver the most value within the available resources.
5. Actions
Definition: Actions are the specific steps that will be taken to implement the strategy and achieve the objectives.
Application in CRM Implementation:
Actions: Develop a communication plan that includes regular updates and feedback mechanisms.
This action focuses on the importance of communication throughout the change process. A well-structured communication plan ensures that all stakeholders, particularly the sales team, are kept informed about the implementation timeline, training opportunities, and how their feedback will be incorporated into the process. Regular updates foster transparency and help build trust, while feedback mechanisms (such as surveys or suggestion boxes) allow team members to voice concerns and share their experiences. This two-way communication is essential for addressing issues promptly and reinforcing a culture of collaboration and continuous improvement.
By applying these frameworks, change managers can make informed recommendations that align with organizational objectives. This structured approach helps ensure that all relevant factors are accounted for and that stakeholders feel included in the planning process.
3. Execution Phase
As the project moves into the execution phase, the change manager must remain agile, continually collecting organizational data to confirm or reject the hypotheses formed during the planning stage.
Example: In an agile setting, where iterative processes are key, the change manager should implement mechanisms for ongoing feedback. For instance, after each sprint of CRM implementation, the manager can gather data from users to assess how well the system is being received. Surveys, usage analytics, and focus groups can provide rich insights into user experiences and pain points.
This ongoing data collection allows change managers to adjust their strategies in real-time. If feedback indicates that certain features of the CRM are causing confusion, the change manager can pivot to provide additional training or resources targeted specifically at those areas. This iterative feedback loop is akin to the work of strategic consultants, who continuously assess and refine their approaches based on empirical evidence.
Example in Practice: Imagine a situation where the sales team reports difficulties with the new CRM interface, leading to decreased productivity. The change manager can analyse usage data and user feedback to pinpoint specific issues. This data-driven insight can guide the development of targeted training sessions focusing on the problematic features, thus addressing concerns proactively and fostering user adoption.
4. Monitoring Phase
Monitoring the change initiative is crucial for ensuring long-term success. Change managers need to analyse performance metrics to evaluate the effectiveness of the implementation and its impact on the organization.
Example: For the CRM project, key performance indicators (KPIs) such as sales conversion rates, customer satisfaction scores, and employee engagement levels should be monitored. By employing data visualization tools, change managers can easily communicate these metrics to stakeholders, making it clear how the change initiative is progressing.
A fact-based approach to analysing these metrics helps in making informed decisions about any necessary adjustments. If, for instance, customer satisfaction scores are declining despite an increase in sales, the change manager may need to investigate further. This might involve conducting interviews with customers or analysing customer feedback to identify specific areas for improvement.
Suppose the organization observes a drop in customer satisfaction scores following the CRM implementation. The change manager could work with other stakeholders to conduct a root cause analysis using customer feedback and service interaction data to identify patterns, such as longer response times or unresolved issues. By addressing these specific problems, the change manager can refine the CRM processes and enhance overall service quality.
5. Closure Phase
The closure phase involves reflecting on the outcomes of the change initiative and drawing lessons for future projects. This is where the analytical skills of change managers can shine in assessing the overall impact of the change.
Example: After the CRM system has been fully implemented, the change manager should conduct a comprehensive review of the project along with the project team (retro). This involves analysing both qualitative and quantitative data to evaluate whether the initial objectives were met. Surveys can be distributed to employees to gather feedback on their experiences, while sales data can be analysed to determine the financial impact of the new system.
Using frameworks like MECE can help in categorizing the lessons learned. For instance, feedback could be sorted into categories such as user experience, operational efficiency, and overall satisfaction, allowing the change manager to develop clear recommendations for future initiatives.
Lessons Learned: If the analysis shows that certain departments adapted more successfully than others, the change manager could investigate the factors contributing to this variance. For example, departments that received more personalized support and training may have demonstrated higher adoption rates. This insight can inform strategies for future change initiatives, emphasizing the importance of tailored support based on departmental needs.
Building Relationships with Senior Leaders
In addition to the technical aspects of change management, the ability to communicate effectively with senior leaders is crucial. Seasoned change managers must clearly understand organizational objectives and be able to articulate how the change initiative contributes to these goals.
Example: During discussions with senior leadership, a change manager along with the rest of the project team can present data showing how the CRM system has improved customer retention rates and increased sales. By positioning this information in an easily understandable and rigorous manner, the change manager demonstrates the value of the initiative and its alignment with broader organizational objectives.
Effective communication ensures that leaders remain engaged and supportive throughout the change process, increasing the likelihood of success. By continuously linking the change initiative to organizational goals, change managers can build trust and credibility with stakeholders at all levels.
Leveraging Analytical Frameworks
Throughout the project lifecycle, incorporating structured analytical frameworks can enhance the decision-making process. Here are two key frameworks that change managers can leverage:
MECE Framework
MECE (Mutually Exclusive, Collectively Exhaustive) helps in breaking down complex information into manageable parts without overlap. By ensuring that all categories are covered without redundancy, change managers can identify all relevant factors affecting the change initiative.
TOSCA Framework
TOSCA (Target, Objectives, Strategy, Constraints, Actions) provides a comprehensive roadmap for change initiatives. By clearly defining each component, change managers can develop coherent strategies that align with organizational goals. This framework not only clarifies the change strategy but also ensures that all team members understand their roles in achieving the objectives.
Continuous Learning and Adaptation
Change management is not a static process; it requires continuous learning and adaptation. As organizations evolve, change managers must stay attuned to emerging trends and best practices in the field. This involves seeking feedback, conducting post-project evaluations, and staying updated on analytical tools and methodologies.
Change managers can attend workshops, participate in industry conferences, and engage with professional networks to enhance their analytical skills and learn from peers. By sharing experiences and insights, change managers can refine their approaches and incorporate new strategies that drive successful change.
The Transformative Power of Analytical Skills
The role of a change manager is multifaceted and requires a broad range of skills. However, one skill that stands out as particularly critical is the ability to think analytically. By adopting a strategic consultant’s mindset and applying analytical skills at each phase of the project lifecycle, change managers can significantly enhance their effectiveness.
From project commencement to closure, employing frameworks like MECE and TOSCA allows change managers to approach challenges in a structured way, making informed decisions that drive successful change. Continuous data collection, stakeholder engagement, and effective communication with senior leaders are essential components of this analytical approach.
In an era where organizations must adapt quickly to change, the ability to analyse complex data sets and derive actionable insights will distinguish successful change managers from the rest. Emphasizing this critical skill not only positions change managers as strategic partners within their organizations but also ensures that change initiatives lead to lasting, positive transformations.
As change practitioners, let us elevate our analytical capabilities and drive impactful change with confidence and clarity. By embracing this essential skill, we can navigate the complexities of organizational change and lead our teams toward a successful future.
In the world of change management, Go Lives are often seen as significant milestones. For many project teams, these events represent the culmination of months or even years of hard work, signaling that a new system, process, or initiative is officially being launched. It’s common for stakeholders to view Go Lives as a key indicator of the success of a change initiative. However, while Go Lives are undeniably important, relying on them as the primary measure of change impact can be misleading and potentially harmful to the overall change effort.
Go Lives are just one piece of the puzzle. Focusing too heavily on these milestones can lead to an incomplete understanding of the change process, neglecting crucial activities that occur both before and after Go Live. Let’s outline the risks associated with using Go Lives to report on change management impacts and offers best practices for a more holistic approach.
Go Lives: A Double-Edged Sword
Go Lives are naturally a focal point for project teams. They represent a clear, tangible goal, and the success of a Go Live can boost morale, validate the efforts of the team, and provide a sense of accomplishment. From a project delivery perspective, Go Lives are critical. They signal that the project has reached a level of maturity where it is ready to be released to the broader organization. In terms of resourcing and business readiness, Go Lives ensure that everything is in place for the new system or process to function as intended.
However, the very attributes that make Go Lives attractive can also make them problematic as indicators of change impact. The simplicity and clarity of a Go Live event can lead stakeholders to overestimate its significance, from a impacted business perspective. The focus on Go Lives can overshadow the complex and often subtle changes that occur before and after the event. While a successful Go Live is necessary for change, it is not sufficient to guarantee that the change will be successful in the long term.
The Pre-Go Live Journey: Laying the Foundation for Change
A significant portion of the change management journey occurs long before the Go Live date. During this pre-Go Live phase, various engagement and readiness activities take place that are critical to shaping the overall impact of the change. These activities include town hall meetings, where leaders communicate the vision and rationale behind the change, and briefing sessions that provide detailed information about what the change will entail.
Training and learning sessions are also a crucial component of the pre-Go Live phase. These sessions help employees acquire the necessary skills and knowledge to adapt to the new system or process. Discussions, feedback loops, and iterative improvements based on stakeholder input further refine the change initiative, ensuring it is better aligned with the needs of the organization.
These pre-Go Live activities are where much of the groundwork for successful change is laid. They build awareness, generate buy-in, and prepare employees for what is to come. Without these efforts, the Go Live event would likely be met with confusion, resistance, or outright failure. Therefore, it is essential to recognize that the impact of change is already being felt during this phase, even if it is not yet fully visible.
Post-Go Live Reality: The Real Work Begins
While the Go Live event marks a significant milestone, it is by no means the end of the change journey. In fact, for many employees, Go Live is just the beginning. It is in the post-Go Live phase that the true impact of the change becomes apparent. This is when employees start using the new system or process in their daily work, and the real test of the change’s effectiveness begins.
During this phase, the focus shifts from preparation to adoption. Employees must not only apply what they have learned but also adapt to any unforeseen challenges that arise. This period can be fraught with difficulties, as initial enthusiasm can give way to frustration if the change does not meet expectations or if adequate support is not provided.
Moreover, the post-Go Live phase is when the long-term sustainability of the change is determined. Continuous reinforcement, feedback, and support are needed to ensure that the change sticks and becomes embedded in the organization’s culture. Without these ongoing efforts, the change initiative may falter, even if the Go Live event was deemed a success.
The Risk of Misleading Stakeholders
One of the most significant dangers of focusing too heavily on Go Lives is the risk of misleading stakeholders. When stakeholders are led to believe that the Go Live event is the primary indicator of change impact, they may not fully appreciate the importance of the activities that occur before and after this milestone. This narrow focus can lead to a number of issues.
Firstly, stakeholders may prioritize the Go Live date to the exclusion of other critical activities. This can result in insufficient attention being paid to pre-Go Live engagement and readiness efforts or to post-Go Live adoption and support. As a consequence, the overall change initiative may suffer, as the necessary foundations for successful change have not been properly established.
Secondly, stakeholders may develop unrealistic expectations about the impact of the change. If they believe that the Go Live event will immediately deliver all the promised benefits, they may be disappointed when these benefits take longer to materialize. This can erode confidence in the change initiative and reduce support for future changes.
Finally, a narrow focus on Go Lives can create a false sense of security. If the Go Live event is successful, stakeholders may assume that the change is fully implemented and no further action is required. This can lead to complacency and a lack of ongoing support, which are essential for ensuring the long-term success of the change.
Best Practices for Reporting Change Management Impact
To avoid the pitfalls associated with relying on Go Lives as indicators of change impact, change management practitioners should adopt a more holistic approach to reporting. This involves considering the full scope of the change journey, from the earliest engagement activities to the ongoing support provided after Go Live. Here are some best practices for reporting on change management impact:
Integrate Pre-Go Live Metrics:
Track and report on engagement activities, such as attendance at town hall meetings, participation in training sessions, and feedback from employees.
Monitor changes in employee sentiment and readiness levels throughout the pre-Go Live phase.
Report on aggregate pan-initiative change initiative impost on business units, pre-Go Live
Emphasize Post-Go Live Support:
Develop metrics to measure the effectiveness of post-Go Live support, such as the number of help desk inquiries, employee satisfaction with the new system, and the rate of adoption.
Highlight the importance of continuous feedback loops to identify and address any issues that arise after Go Live.
Communicate the need for ongoing reinforcement and support to stakeholders, emphasizing that change is an ongoing process
Report on post-Go Live adoption time impost expected across initiatives
Provide a Balanced View of Change Impact:
Ensure that stakeholders understand that Go Live is just one part of the change journey and that significant impacts occur both before and after this event.
Use a combination of quantitative and qualitative data to provide a comprehensive view of change impact.
Regularly update stakeholders on progress throughout the entire change journey, not just at the time of Go Live.
Manage Expectations:
Clearly communicate to stakeholders that the full impact of the change may not be immediately visible at the time of Go Live.
Set realistic expectations about the timeline for realizing the benefits of the change.
Prepare stakeholders for potential challenges in the post-Go Live phase and emphasize the importance of ongoing support.
While Go Lives are important milestones in the change management process, they should not be used as the sole indicator of change impact. The journey to successful change is complex, involving critical activities before, during, and after the Go Live event. By adopting a more holistic approach to reporting on change management impact, practitioners can provide stakeholders with a more accurate understanding of the change journey, manage expectations more effectively, and ensure the long-term success of the change initiative.
The key takeaway is that change management is not just about delivering a project; it’s about guiding an organization through a journey of transformation. Go Lives are just one step in this journey, and it is the responsibility of leaders to ensure that every step is given the attention it deserves.
Change management professionals are increasingly requested to provide measurement, data, and insights to various stakeholder groups. Not only does this include tracking various change outcomes such as business readiness or adoption, but stakeholder concerns also include such as change saturation and visibility of incoming initiative impacts.
To become better at working with data there is much that change managers can learn from data scientists (without becoming one of course). Let’s explore how change management can benefit from the practices and methodologies employed by data scientists, focusing on time allocation, digital tools, system building, hypothesis-led approaches, and the growing need for data and analytical capabilities.
1. Time Allocation: Prioritizing Data Collection and Cleansing
Data scientists spend a substantial portion of their time on data collection and cleansing. According to industry estimates, about 60-80% of a data scientist’s time is dedicated to these tasks. This meticulous process ensures that the data used for analysis is accurate, complete, and reliable.
In the below diagram from researchgate.net you can see that for data scientists the vast majority of the time is spent on collecting, cleansing and organising data.
You might say that change managers are not data scientists because the work nature is different, and therefore should not need to carve out time for these activities? Well, it turns out that the type of activities and proportions of time spent is similar across a range of data professionals, including business analysts.
Below is the survey results published by Business Broadway, showing that even business analysts and data analysts spend significant time in data collection, cleansing, and preparation.
Lessons for Change Management
a. Emphasize Data Collection and Cleansing: For change managers, this translates to prioritizing the collection of reliable data related to change initiatives. This might include stakeholder feedback, performance metrics, impact data and other relevant data points. Clean data is essential for accurate analysis and insightful decision-making. Data projects undertaken by change managers are not going to be as large or as complex as data scientists, however the key takeaway is that this part of the work is critical and sufficient time should be allocated and not skipped.
b. Allocate Time Wisely: Just as data scientists allocate significant time to data preparation, change managers should also dedicate sufficient time to gathering and cleaning data before diving into analysis. This ensures that the insights derived are based on accurate and reliable information.
It also depends on the data topic and your audience. If you are presenting comparative data, for example, change volume across different business units. You may be able to do spot checks on the data and not verify every data line. However, if you are presenting to operations business units like call centres where they are very sensitive to time and capacity challenges, you may need to go quite granular in terms of exactly what the time impost is across initiatives.
c. Training and Awareness: Ensuring that the change management team understands the importance of data quality and is trained in basic data cleansing techniques can go a long way in improving the overall effectiveness of change initiatives. Think of scheduling regular data sessions/workshops to review and present data observations and findings to enhance the team’s ability to capture accurate data as well as the ability to interpret and apply insights. The more capable the team is in understanding data, the more value they can add to their stakeholders leveraging data insights.
2. Leveraging Digital Tools: Enhancing Efficiency and Accuracy
Data scientists rely on a variety of digital tools to streamline their work. These tools assist in data collection, auditing, visualization, and insight generation. AI and machine learning technologies are increasingly being used to automate and enhance these processes.
Data scientists rely on various programming, machine learning and data visualisation such as SQL, Python, Jupyter, R as well as various charting tools.
Lessons for Change Management
a. Adopt Digital Tools: Change managers should leverage digital tools to support each phase of their data work. There are plenty of digital tools out there for various tasks such as surveys, data analysis and reporting tools.
For example, Change Compass has built-in data analysis, data interpretation, data audit, AI and other tools to help streamline and reduce manual efforts across various data work steps. However, once again even with automation and AI the work of data checking and cleansing does not go away. It becomes even more important.
b. Utilize AI and Machine Learning: AI can play a crucial role in automating repetitive tasks, identifying patterns, data outliers, and generating insights. For example, AI-driven analytics tools can help predict potential change saturation, level of employee adoption or identify areas needing additional support during various phases of change initiatives.
With Change Compass for example, AI may be leverage to summarise data, call out key risks, generate data, and forecast future trends.
c. Continuous Learning: Continuous learning is essential for ensuring that change management teams stay adept at handling data and generating valuable insights. With greater stakeholder expectations and demands, regular training sessions on the latest data management practices and techniques can be helpful. These sessions can cover a wide range of topics, including data collection methodologies, data cleansing techniques, data visualisation techniques and the use of AI and machine learning for predictive analytics. By fostering a culture of continuous learning, organizations can ensure that their change management teams remain proficient in leveraging data for driving effective change.
In addition to formal training, creating opportunities for hands-on experience with real-world data can significantly enhance the learning process. For instance, change teams can work on pilot projects where they apply new data analysis techniques to solve specific challenges within the organization. Regular knowledge-sharing sessions, where team members present case studies and share insights from their experiences, can also promote collective learning and continuous improvement.
Furthermore, fostering collaboration between change managers and data scientists or data analysts can provide invaluable mentorship and cross-functional learning opportunities. By investing in continuous learning and development, organizations can build a change management function that is not only skilled in data management but also adept at generating actionable insights that drive successful change initiatives.
3. Building the Right System: Ensuring Sustainable Insight Generation
It is not just about individuals or teams working on data. A robust system is vital for ongoing insight generation. This involves creating processes for data collection, auditing, cleansing, and establishing governance bodies to manage and report on data.
Governance structures play a vital role in managing and reporting data. Establishing governance bodies ensures that there is accountability and oversight in data management practices. These bodies can develop and enforce data policies, and oversee data quality initiatives. They can also be responsible for supporting the management of a central data repository where all relevant data is stored and managed.
Lessons for Change Management
a. Establish Clear Processes: Develop and document processes for collecting and managing data related to change initiatives. This ensures consistency and reliability in data handling.
b. Implement Governance Structures: Set up governance bodies to oversee data management practices. This includes ensuring compliance with data privacy regulations and maintaining data integrity. The governance can sponsor the investment and usage of the change data platform. This repository should be accessible to stakeholders involved in the change management process, promoting transparency and collaboration. Note that a governance group can simply be a leadership team regular team meeting and does not need to be necessarily creating a special committee.
c. Invest in system Infrastructure: Build the necessary system infrastructure to support data management and analysis that is easy to use and provides the features to support insight generation and application for the change team.
Data scientists often use a hypothesis-led approach, where they test, reject, or confirm hypotheses using data. This method goes beyond simply reporting what the data shows to understanding the underlying causes and implications.
Lessons for Change Management
a. Define Hypotheses: Before analyzing data, clearly define the hypotheses you want to test. For instance, if there is a hypothesis that there is a risk of too much change in Department A, specify the data needed to test this hypothesis.
b. Use Data to Confirm or Reject Hypotheses: Collect and analyze data to confirm or reject your hypotheses. This approach helps in making informed decisions rather than relying on assumptions or certain stakeholder opinions.
c. Focus on Actionable Insights: Hypothesis-led analysis often leads to more actionable insights. It is also easier to use this approach to dispel any myths of false perceptions.
For example: Resolving Lack of Adoption
Hypothesis: The lack of adoption of a new software tool in the organization is due to insufficient coaching and support for employees.
Data Collection:
Gather data on the presence of managerial coaching and perceived quality. Also gather data on post go live user support.
Collect feedback from employees through surveys regarding the adequacy and clarity of coaching and support.
Analyse usage data of the new software to identify adoption rates across different departments.
Analysis:
Compare adoption rates between employees who received sufficient coaching and support versus those who did not.
Correlate feedback scores on training effectiveness with usage data to see if those who found the training useful are more likely to adopt the tool.
Segment data by department to identify if certain teams have lower adoption rates and investigate their specific training experiences.
Actionable Insights:
If data shows a positive correlation between coaching and support, and software adoption, this supports the hypothesis that enhancing coaching and support programs can improve adoption rates.
If certain departments show lower adoption despite completing coaching sessions, investigate further into department-specific issues such as workload or differing processes that may affect adoption.
Implement targeted interventions such as additional training sessions, one-on-one support, or improved training materials for departments with low adoption rates.
5. Building Data and Analytical Capabilities: A Core Need for Change Management
As data and analytical capabilities become increasingly crucial, change management functions must build the necessary people and process capabilities to leverage data-based insights effectively.
Lessons for Change Management
a. Invest in Training: Equip change management teams with the skills needed to manage data and generate insights. This includes training in data analysis, visualization, and interpretation.
b. Foster a Data-Driven Culture: A lot of organisations are already on the bandwagon of Encourage a culture where data is valued and used for decision-making. The change management needs to promote this equally within the change management function. This involves promoting the use of data in everyday tasks and ensuring that all team members understand its importance. Think of incorporating data-led discussions into routine meeting meetings.
c. Develop Analytical Frameworks: Create frameworks and methodologies for analyzing change management data. This includes defining common key metrics, setting benchmarks, and establishing protocols for data collection and analysis for change data. Data and visual templates may be easier to follow for those with lower capabilities in data analytics.
Practical Steps to Implement Data-Driven Change Management
To integrate these lessons effectively, senior change practitioners can follow these practical steps:
Develop a Data Strategy: Create a comprehensive data strategy that outlines the processes, tools, and governance structures needed to manage change management data effectively.
Conduct a Data Audit: Begin by auditing the existing data related to change management. Identify gaps and areas for improvement.
Adopt a Hypothesis-Led Approach: Encourage the use of hypothesis-led approaches to move beyond descriptive analytics and derive more meaningful insights.
Invest in Technology: Invest in the necessary digital tools and technologies to support data collection, cleansing, visualization, and analysis.
Train the Team: Provide training and development opportunities for the change management team to build their data and analytical capabilities.
Collaborate Across Functions: Foster collaboration between change management and data science teams to leverage their expertise and insights.
Implement Governance Structures: Establish governance bodies to oversee data management practices and ensure compliance with regulations and standards.
By learning from the practices and methodologies of data scientists, change management functions can significantly enhance their effectiveness. Prioritizing data collection and cleansing, leveraging digital tools, building robust systems, adopting hypothesis-led approaches, and developing data and analytical capabilities are key strategies that change management teams can implement. By doing so, they can ensure that their change initiatives are data-driven, insightful, and impactful, ultimately leading to better business outcomes.
Successful change management relies on having the right metrics to measure progress, gauge impact, and communicate with stakeholders. Moreover, the right metrics can drive continuous improvement and help directly achieve change outcomes. However, not all metrics are beneficial, and some can mislead or fail to meet stakeholder needs. Let’s check out the top change management metrics to avoid and go through examples to take note.
Understanding the Disconnect: Change Managers vs. Business Stakeholders
A significant reason certain change management metrics fall short is the differing perspectives between change managers and business stakeholders. Change managers are trained to view metrics through the lens of change management frameworks and methodologies, focusing on detailed assessments and structured approaches. These include applying ratings and judgments on aspects such as impact levels.
In contrast, business stakeholders prioritize business operations, strategic outcomes, and practical implications. The busy business stakeholder is often looking for practical implications from metrics that can be used to directly drive decision making, meaning “what do I do with this data to improve the ultimate business outcome”.
Of course, different stakeholder have different data needs, and you need to show the right metric to the right type of stakeholder. For example, operations focused stakeholders expect fairly detailed metrics and data and what that means in terms of organisation, coordination, capacity and performance perspectives. Senior managers may prefer higher level data with focus on strategic impacts, overall progress and adoption indicators.
This disconnect can lead to the use of metrics that do not resonate with or are misunderstood by stakeholders.
Metrics from a Change Manager’s Perspective
Change managers may leverage metrics that are derived from the various change management documents such impact assessments, training plan or communications plan. Metrics also often chosen for ease of use and ideally are not overly complicated to execute.
For example, impact assessments typically involve rating stakeholder groups and initiatives on a traffic light system (red, amber, green) based on their impact. While this approach is systematic, it can be problematic for several reasons:
Lack of Sufficient Stakeholder Context: Business stakeholders might not understand the practical implications of these ratings. For instance, an “impact rating per initiative” may not clearly convey what the rating means for day-to-day operations or strategic goals. For example, if an initiative has a red impact rating, stakeholders might not grasp the specific operational changes or strategic adjustments needed, in essence, “what do I do with this?”.
Misinterpretation of Traffic Light Ratings: The red, amber, green system can be misleading. Stakeholders might interpret red as an indicator of alarm or imminent risk, while green may be seen as a sign that no action is needed. This is because stakeholders are trained to interpret traffic light ratings this way (from the various project/business updates they’ve attended). In reality, red might simply mean high impact, requiring focused attention, and green might indicate a low impact but still require monitoring. For instance, a red rating might indicate significant process changes that need careful management, not necessarily a negative outcome.
Hard to defend ratings if prompted: Business stakeholders may also want to drill into how the ratings are determined, and based on what basis. They may expect a logical data-backed reasoning of how each colour scheme is determined. If a rating is based on an overall ‘personal judgment’ this may be hard to defend infront of a group of stakeholders.
Examples of Potentially Misleading Metrics
Certain metrics, although straightforward, can be easily misinterpreted and fail to provide a realistic picture of change impacts. Often these are selected because they are easy to report on. However, easy, make not give you the outcome you are looking for.
Number of Go-Lives: Tracking the number of Go-Lives over time might seem like an effective way to represent change volume. However, the most significant impacts on people often occur before or after the Go-Live date. For example, the preparation and training phase before Go-Live and the adoption phase afterward are critical periods that this metric overlooks. A Go-Live date might indicate a milestone but not the challenges, progress or impacts faced during the implementation phase.
Number of Activities Implemented: Similar to Go-Lives, this metric focuses on quantity rather than quality. Simply counting the number of activities does not account for their effectiveness or the actual change they drive within the organisation. For example, reporting that 50 training sessions were conducted does not reveal whether employees found them helpful or if they led to improved performance.
Number of impacts or stakeholders impacted: Again, using a numerical way to indicate progress can be very misleading, or unmeaningful. This is because it may be ‘interesting’ but with no real action for your stakeholder to take in order to somehow lead to a better overall change outcome. If metrics do not result in some kind of action, then over time it will not shape your change(s) toward the targeted outcomes. Or worse, your stakeholders may lose interest and lose confidence in the strategic impact of these metrics.
Another common way to report change metrics is to use the number of impacts or number of stakeholders impacted. This can be in terms of the following:
Number of divisions impacted
Number of stakeholder groups impacted
Number of employees impacted
Number of initiatives per division/stakeholder
Metrics That May Be Too Operational
Metrics that are overly operational can fail to capture meaningful progress or adoption. Perhaps if the metric are for reporting within the Change Management team that may be OK. However, when you are showing metrics to stakeholders, a different set of expectations should be cast.
If you are presenting metrics to senior managers, you need to ensure that they hit the mark for that audience group. If the group is more interested in strategic impact, and higher level progress outcomes, you need to tailor accordingly.
Examples of metrics that may be too operational include:
Number of Communications Sent: This metric measures activity but not effectiveness. Sending numerous emails or messages does not guarantee that the message is received, understood, or acted upon by stakeholders. For instance, stakeholders might receive 100 emails, but if the content is unclear, the communication effort is wasted. Or worse, the emails may not even have been read.
Number of Training Sessions Attended: This one is a classic. While training is crucial, the number of sessions attended does not necessarily reflect the attendees’ understanding, engagement, or the practical application of the training. For example, employees might attend training but not apply the new skills if the training is not relevant to their roles for various reasons.
Number of workshops/meetings: Another way of articulating the change management progress in terms of activities is the number of workshops or meetings conducted with stakeholders. Again, this may be good to track within the change management team. However, presenting this metric to stakeholders may not be appropriate as it may not mee their needs.
The way metrics are presented is just as important as the metrics themselves. Poor visualization can lead to misinterpretation, confusion, and misguided decisions. Here are some common pitfalls to avoid:
Ineffective Use of Pie Charts
Pie charts can be misleading when used to show data points that are not significantly different. For example, using a pie chart to represent the percentage of divisions impacted by a change might not effectively communicate the nuances of the impact if the differences between the divisions are minimal. A pie chart showing 45%, 30%, and 25% might not convey the critical differences in impact levels among divisions.
Misleading Traffic Light Ratings
Using red, amber, and green to indicate high, medium, and low impacts can send the wrong message. Stakeholders might associate these colours with good and bad outcomes rather than understanding the actual levels of impact. Stakeholder may be used to interpreting these in the context of their usual project or business updates where red indicated alarm and ‘bad’. This can lead to unnecessary alarm or complacency. For instance, a green rating might suggest no need for action, while in reality, it might require ongoing monitoring.
Overuse of Colours
Using too many colours in charts and graphs can overwhelm stakeholders, making it difficult to discern the key message. Using colours in data visualisation can be two-edged sword. Colour can effectively point your stakeholders are the area where you want them to focus on. But, too many colours can lose your audience. A cluttered visual can obscure the critical data points and lead to misinterpretation. For example, a graph with ten different colours can confuse stakeholders about which data points are most important.
Practical Takeaways for Senior Change Managers
To ensure that change management metrics are effective, consider the following practical takeaways:
Align Metrics with Stakeholder Perspectives
Understand Stakeholder Priorities: Engage with stakeholders to understand their priorities and concerns. Tailor your metrics to address these aspects directly. For example, if stakeholders are concerned about operational efficiency, focus on metrics that reflect improvements in this area.
Use Business Language: Frame your metrics in a way that resonates with business stakeholders. Avoid change management jargon and reference, and ensure that the implications of the metrics are clear and actionable. For example, instead of using technical terms, explain how the metrics impact business outcomes. Think in terms of business activities, milestones, busy periods, and capacity challenges.
Focus on Meaningful Metrics
Measure Outcomes, Not Just Activities: Prioritize metrics that reflect the outcomes and impacts of change, rather than just the activities performed. For example, instead of counting the number of training sessions, measure the improvement in employee performance or knowledge retention post-training.
Example: Instead of reporting that 100 employees attended training sessions, report that 85% of attendees showed improved performance in their roles after training, or that certain level of competencies were gained.
Track Engagement and Adoption: Monitor metrics that indicate the level of engagement and adoption among stakeholders. This could include surveys, feedback forms, or direct measures of behaviour change.
Example: Use post-training surveys to measure employee confidence in applying new skills or managerial rating of application of learnt skills. Track the percentage of employees who actively use new tools or processes introduced during the change.
Improve Metric Visualization
Simplify Visuals: Use clear, simple visuals that highlight the key messages. Avoid clutter and ensure that the most important data points stand out.
Example: Use bar charts or line graphs to show trends over time rather than pie charts that can be harder to interpret.
Contextualize Data: Provide context for the data to help stakeholders understand the significance. For example, instead of just showing the number of Go-Lives, explain what each Go-Live entails and its expected impact on operations. Or better, focus on showing the varying levels of impact on different stakeholders across time within the initiative.
Example: Accompany a Go-Live count with a visual showing the varying impact level of various implementation activities of the changes.
Narrative Approach: Combine metrics with a narrative that explains the story behind the numbers. This can help stakeholders understand the broader context and implications.
Example: Instead of presenting raw data, provide a summary that explains key trends, successes, and areas needing attention.
Educate your stakeholders: Depending on stakeholder needs you may need to take them on a phased approach to gradually educate them on change management metrics and how you ultimately want them to drive the outcomes.
Example: You may start the education process to focus on more simplistic and easy-to-understand measures, and as your stakeholders are more change-mature, move to drill into more detailed metrics that explain the ‘why’ and ‘how’ to drive outcome success.
Continuously improvement: Provide regular updates on key metrics and adjust them based on feedback from stakeholders. Continuous communication ensures that everyone remains aligned and informed.
Example: Hold monthly review meetings with stakeholders to discuss the latest metrics, address concerns, and adjust strategies as needed.
Examples of Effective Metrics
Employee Adoption and Engagement
Percentage of Employees Adopting New Process/System: This metric measures the rate at which employees are using new processes or systems introduced during the change. High adoption rates indicate successful integration.
Implementation: Use software usage analytics or surveys to track tool adoption rates.
Visualization: A graph showing adoption rates over time.
Employee Feedback Scores: Collect feedback on change initiatives through surveys or stakeholder ratings to measure sentiment/feedback and identify areas for improvement.
Implementation: Conduct regular surveys asking employees about their experience with the change process. Do note that depending on the change you may expect negative feedback due to the nature of the change itself (vs the way it was implemented).
Visualization: Bar/Line charts comparing feedback scores across different departments or time periods. Bar/Line charts are the standard go-to for data visualisation. They are easy to understand and interpret.
Impact on Business Outcomes
Improvement in Key Performance Indicators (KPIs): Track changes in KPIs that are directly impacted by the change initiatives, such as productivity, customer satisfaction, or financial performance.
Implementation: Identify relevant KPIs and measure their performance before and after change initiatives.
Visualization: Use line/bar graphs to show trends in KPI performance over time.
Operational Efficiency Metrics: Measure improvements in operational processes, such as reduced cycle times, error rates, or cost savings.
Implementation: Track specific operational metrics relevant to the change initiatives.
Visualization: Bar charts or heatmaps showing improvements in efficiency metrics across different operational areas.
Effective change management requires metrics that not only measure progress but also resonate with business stakeholders and accurately reflect the impact of change initiatives. Avoiding common pitfalls such as relying on easily misinterpreted or overly operational metrics is crucial. By aligning metrics with stakeholder perspectives, focusing on meaningful outcomes, improving visualization, and communicating effectively, senior change and transformation professionals can ensure that their metrics truly support the success of their change initiatives.
The top change management metrics to avoid are those that fail to provide clear, actionable insights to business stakeholders. By understanding and addressing the disconnect between change managers and business stakeholders, and by prioritizing metrics that truly reflect the impact and progress of change, you can drive more effective and successful change management efforts by influencing your stakeholders in your organisation.
Chat with us if you would like to discuss more about leveraging AI and technology to generate high-impact change management metrics and data for your stakeholders, both at project and portfolio levels.
Enterprise change management reporting is changing. It no longer consists of general updates of change streams of project progress or updates on various change capability training session volumes and satisfaction rates. Executives are demanding more value from enterprise change functions. The pace of change since Covid has not slowed down. For many, it has increased in pace and volume. To gain better insight into how the change management function is supporting the success of organisations, reports and dashboards have often become a visible linchpin of what value an enterprise change management delivers.
Having the right content and format for your enterprise reports can make or break your reputation. Do it right and you could start a ripple of high-impact and strategic conversations across senior stakeholders that drive focus on improving change. You can be in the spotlight in influencing change leadership and the achievement of change and transformation goals. Do it wrong and you may never have another opportunity to have the room to talk about change management to senior leaders. You may be associated with not providing much value and too ‘operational’.
Unlocking the Potential of Enterprise Change Management Reporting:
At its core, enterprise change management reporting goes beyond merely tracking progress. It encompasses a holistic approach that considers various factors crucial to the success of organizational initiatives. While monitoring progress, readiness, and the amount of work done may be interesting components, true impact comes from focusing on impacts, adoption and predictors for benefit realization.
Executives and stakeholders are not just interested in receiving progress updates; they seek insights into the likelihood of initiative success and the potential risks that may impede desired outcomes. These risks extend beyond project timelines and budget constraints to encompass broader business implications such as performance impacts, capacity constraints, prioritization effectiveness, and the sustainability of behavioural change.
Impacts of change:
Quantifying and visualising impacts are not new to change practitioners. The key is how the data is presented over time. A lot of change practitioners would settle with a standard heatmap based on personal ratings. This does not deliver much value as the data cannot be easily substantiated by evidence (since it is more of a finger in the air estimation). Standard heatmaps also are too high level and does not really support key decision making.
Decision making requires specific data points such as:
Change saturation or change tolerance levels (these levels need to be substantiated based on business indicator reference to justify the levels, and not be someone’s personal opinion)
What division, team, role and which week the saturation points are forecasted
Corresponding data on what initiatives, and their respective impact activities that contribute to the saturation risk, and therefore proposed options
A key part of representing change impacts should not just be at an operational level, which is more concerned about capacity and bandwidth. Impact should also be tied to strategic levers, portfolio types, benefit types and readiness.
Predictive Indicators for Success:
To create impactful change management reports, organizations must incorporate predictive indicators that go beyond change volume and risk assessment. These indicators should provide insights into business performance, strategy achievement, and the realization of intended benefits.
These are some of the ways you can incorporate predictive indicators:
Forecast lines. With sufficient data you can forecast such as impact or capacity levels (which may be seasonal), or even readiness levels across the initiative lifecycle historically across initiatives.
The types of factors that can be included as predictive indicators can include readiness. It could be that readiness levels only get lifted just before go live or at go live. Adoption levels can also be forecasted if you have trend data across initiatives
Change tolerance levels across different parts of the business can also be seen as a predictive way of forecasting how much capacity there is for change beyond which saturation may be a key risk
Adoption and Behaviour Tracking:
Central to successful change management is the adoption and sustained implementation of new processes or technologies. Tracking adoption rates, user engagement, and behavioural changes are crucial indicators of initiative success. However, it’s essential to strike a balance between capturing relevant metrics and overwhelming stakeholders with unnecessary data.
Capturing behaviour change data can be key for larger initiatives or transformations. Behavioural change can be central in a range of changes such as customer centricity, efficiency, team collaboration or effectiveness. Measuring key behaviour changes that drive the initiative outcome the most is critical. For example, having effective conversations with customers to improve customer experience is a behaviour that can be rated, tracked and reported. Depending on the change, there may also be system features that can aid the tracking of these behaviours.
Effective change management goes hand in hand with strategic alignment. Reports should assess how initiatives contribute to overarching business objectives and whether they align with the organization’s strategic direction. Metrics related to revenue growth, cost savings, customer satisfaction, and employee productivity can provide valuable insights into the impact of change initiatives on business performance.
You can also link your change impacts to each strategic lever. In this way you can visually show the size of the impact per strategic lever. This will give your executives a way to examine whether the right level of impacts in the right areas of business are planned as a part of the course of each strategic lever.
The other angle is to visually show the pace of change against the strategic levers. Are certain key initiatives being driven at the right pace at the right time? Will the velocity of change exceed the ability of the business to absorb the changes? Or is the velocity not sufficiently meeting leadership expectations?
Benefit Realization:
Ultimately, the success of change initiatives is measured by their ability to deliver tangible benefits. Change management reports should include metrics that track the realization of expected benefits, whether they are financial gains, process efficiencies, or competitive advantages. By monitoring benefit realization, organizations can course-correct as needed and ensure that investments in change deliver the intended outcomes.
A key responsibility for change is to focus on those foreward looking measures that predict eventual benefit realisation, including readiness, adoption, engagement and behaviour change. Be sure to link these specifically to high benefit initiatives to provide strategic oversight.
Balancing Complexity and Clarity:
While incorporating a diverse range of metrics is essential for comprehensive reporting, it’s equally important to maintain clarity and focus. Reports should be structured in a way that highlights key insights and trends without overwhelming stakeholders with excessive detail. Visualizations such as charts, graphs, and dashboards can help distill complex data into actionable insights, facilitating informed decision-making at all levels of the organization.
All aspects of chart and dashboard designs are critical. These range from colour scheme chosen, number of charts, commentary, titles, legends, sequencing of charts, and type of charts chosen all act to either contribute to simplicity and clarity or complexity and confusion. Your narrative as you talk through the charts also plays a key role in building the story-line, and simplifying the key messages and actions you would like to impart to the audience.
Charts and dashboards tell a story and in presenting them you should always incorporate any actions required from the audience. If this is not done then it will always remain a FYI. FYI content will be deemed lower in the value curve over time and your stakeholders will lose interest. Instead, you should work on crafting a continual story that ebbs and flows. The following are key questions you should be asking when crafting you ongoing charts and dashboards:
Is there an emerging risk or opportunity that warrants specific focus for this month?
How are we tracking the effectiveness of stakeholder actions through data? This feedback loop is critical and gives your stakeholders a reference point for their own effectiveness
Is your data-based story uni-dimensional? Are there other dimensions beyond what you have been presenting that stakeholder should be aware of?
Are you giving stakeholders what they are most interested in? Whether it be strategic success progress, or benefit realisation?
Are you presenting change data in a holistic way in terms of how the business is run? Vs. just focused on standard change management function-specific metrics such as training sessions, or number of workshops completed?
Enterprise change management reporting is a critical tool for navigating the complexities of organizational change. By focusing on predictive indicators, including adoption and behavior tracking, business performance alignment, and benefit realization, organizations can unlock the full potential of their change management initiatives. However, achieving impactful reporting requires a careful balance between complexity and clarity, ensuring that stakeholders receive actionable insights without being inundated with unnecessary information. Ultimately, by harnessing the power of enterprise change management reporting, organizations can drive successful outcomes and thrive in an ever-evolving business landscape.
To find out more about enterprise change management reporting leveraging digital automation and analytics chat to us here.
