Change management professionals often struggle with proving the worth of their services and why they are needed. There are certainly plenty of reasons why change management professionals are required and most experienced project managers and senior leaders would acknowledge this. However, for the less mature organisations that may not have had effective change management experts leading initiatives, the rationale may be less clear.
When we look across different project members, it is easy to argue that without developers, the technical project cannot progress. Without business analysts, we cannot understand and flesh out the core business steps required in the initiative. And of course, we definitely need a project manager for a project. But, what’s the justification for a change manager? Many projects have other project or business representatives do the change work instead.
As an attempt to justify in a very direct way, the value of change management, many resort to ROI calculations. This may seem like a great way to convey and show in a very direct and financial way, the value of change management. After all, we use ROI for calculating projects, why not use the same for change management as well?
There are plenty of articles on how to best calculate change management ROI. Here are a couple:
1. PROSCI
Prosci has a good, clear way of calculating change management ROI within a project. You simply evaluate to what extent people adoption is important to the project. Then you take the overall expected project benefits and deduct the part of the expected benefits if there was no adoption. This is termed “people side benefit contribution”.
People Side Benefit Contribution = Expected Project Benefits – Expected Project Benefits (if adoption and usage = 0)
People Side Benefit Coefficient = People Side Benefit Contribution / Expected Project Benefits
2. Rightpoint
Rightpoint has a variation to this calculation. They have added ELV (Employee Lifetime Value) to the calculation.
Using ROI may be useful when the cost of the initiative is the critical focus for the organisation. However, it is not the only way to convey the overall value of change management. In addition, the ROI method limits the value of change management to focus on the cost invested versus the value created. Also, this type of calculation limits the value of change to a project by project perspective.
So, how else do we show the direct financial value of change management? Let’s look to research. It turns out there are plenty of research examples. Here are some:
McKinsey & Company. (2016). The people power of transformations. This study found that transformation initiatives are 5.8 times more successful if CEOs communicate a compelling change story, and 6.3 times more successful when leaders share messages about change efforts with the rest of the organization. Link here.
Korn Ferry. (2018). Engaging hearts and minds: Preparing for a changing world. This study found that calls out change as a key trend found that companies with high levels of employee engagement had 4.5 times higher revenue growth compared to companies with low levels of engagement, noting that all companies are undergoing change. Link here.
IBM. (2016). Making change work … while the world keeps changing. This study found that 76% of successful projects include change management activities at the beginning of their overall project plans, which is 33% more than less successful projects. Link here.
IBM. (2015) Why a business case for change management. The article references a survey carried out in 2010 where companies that apply a value (benefit) realization approach (of which change management is a core component) complete projects at least twice as quickly and under budget by a factor of at least 1.9 times, Compared to those that don’t. Link here.
Towers Watson. (2013). Change and communication ROI. Organizations with highly effective communication and change management practices are more than twice as likely to significantly outperform their peers in total shareholder returns, versus organizations that are not highly effective in either of these areas. Link here.
Prosci. (2020). Best Practices in Change Management 11th Edition. The paper referred to a Prosci study that found that projects with excellent change management practices 6 times more likelihood of meeting project objectives than those that are poor. Link here.
So let’s take a comparison to see the difference in using a ROI calculation of the value of change management versus using findings from the above research findings to demonstrate the derived value.
Let’s take a typical project example. Company A has ….
Annual revenue of $1 billion with 5% profitability
The revenue growth is 1%
Project A costs $1Million and is targeted for $3 million in benefits.
If the expected project benefits without adoption would be $1Million, then, the people-side contribution is …
$2Million / $3Million = $667K.
Let’s contrast this to other calculations using research.
Research findings
Calculation
Korn Ferry study where companies with high levels of employee engagement had 4.5 times higher revenue growth compared to companies with low levels of engagement. Taking a very conservative approach of portioning on 1/3 of employee engagement linked to change, this means 1.5 times higher revenue growth.
Taking a very conservative approach of portioning 1/3 of employee engagement as linked to change, this means 1.5 times higher revenue growth.
This means if the revenue growth is 1%, then the additional revenue is $15 Million per year.
You can see that $15 million in value is much higher than the $667K in initiative ROI. From these examples, you can see that the financial value dwarfs that from the ROI calculation. On top of this, these are from research findings, which may have a stronger perceived validity and be easier to be trusted by stakeholders than the ROI calculation.
To point out, it is not an apple-to-apple comparison between the change management ROI from one initiative to the organisational value of change management across initiatives. However, the call out is that:
The financial value of change management does not need to be limited to individual initiatives
The sum may be greater than its parts. Rather than measuring at initiative levels, research findings are looking at organisational-level value
The value of change management may be more than cost, but also other value drivers such as revenue
As change management practitioners we should not shy away from calling out and citing the value of change management. Cost may be one value, but the true benefit of change management is both the top line as well as the bottom line. Directly referring to the research-backed findings also helps to highlight its value size and importance.
To do this, we should also work to deliver organisational value in managing change and not limit ourselves to one initiative. Focus on uplifting change management capability in the forms of leadership styles, change governance, change analytics, and change champion network capability, just to name a few.
To read more about calculating the financial value of managing a change portfolio click here.
Have a problem in delivering change using data? Chat with us to find out how Change Compass might be able to help.
Change management is often seen as a ‘soft’ discipline that is more an ‘art’ than science. However, managing change, like managing a business, relies on having the right data to understand if the journey is going in the right direction. The data can inform whether the objectives will be achieved or not.
Data science has emerged to be one of the most sought-after skills in the marketplace at the moment. This is not a surprise because data is what powers and drives our digital economy. Data has the power to make or break companies. Companies that leverages data can significant improve customer experiences, improve efficiency, improve revenue, etc. In fact all facets of how a company is run can benefit from data science. In this article, we explore practical data science techniques that organizations can use to improve change outcomes and achieve their goals more effectively.
What are some of the benefits of using data science in change?
Improved decision making
One of the significant benefits of using data science in change management is the ability to make informed decisions. Data science techniques, such as predictive analytics and statistical analysis, allow organizations to extract insights from data that would be almost impossible to detect or analyse manually. This enables organizations to make data-driven decisions that are supported by empirical evidence rather than intuition or guesswork.
Increased Efficiency
Data science can help streamline the change management process and make it more efficient. By automating repetitive tasks, such as data collection, cleaning, and analysis, organizations can free up resources and focus on more critical aspects of change management. Moreover, data science can provide real-time updates and feedback, making it easier for organizations to track progress, identify bottlenecks, and adjust the change management plan accordingly.
Improved Accuracy
Data science techniques can improve the accuracy of change management efforts by removing bias and subjectivity from decision-making processes. By relying on empirical evidence, data science enables organizations to make decisions based on objective facts rather than personal opinions or biases. This can help reduce the risk of errors and ensure that change management efforts are based on the most accurate and reliable data available.
Better Risk Management
Data science can help organizations identify potential risks and develop contingency plans to mitigate those risks. Predictive analytics can be used to forecast the impact of change management efforts and identify potential risks that may arise during the transition. For example, change impacts across multiple initiatives against seasonal operations workload peaks and troughs.
Enhanced Communication
Data science can help facilitate better communication and collaboration between stakeholders involved in the change management process. By presenting data in a visual format, such as graphs, charts, and maps, data science can make complex information more accessible and understandable to all stakeholders. This can help ensure that everyone involved in the change management process has a clear understanding of the goals, objectives, and progress of the transition.
Key data science approaches in change management
Conduct a Data Audit
Before embarking on any change management initiative, it’s essential to conduct a data audit to ensure that the data being used is accurate, complete, and consistent. For example, data related to the current status or the baseline, before change takes place. A data audit involves identifying data sources, reviewing data quality, and creating a data inventory. This can help organizations identify gaps in data and ensure that data is available to support the change management process. This includes any impacted stakeholder status or operational data.
During a data audit, change managers should ask themselves the following questions:
What data sources do we need to support the change management process?
Is the data we are using accurate and reliable?
Are there any gaps in our data inventory?
What data do we need to collect to support our change management initiatives?
Using Predictive Analytics
Predictive analytics is a valuable data science technique that can be used to forecast the impact of change management initiatives. Predictive analytics involves using historical data to build models that can predict the future impact of change management initiatives. This can help organizations identify potential risks and develop proactive strategies to mitigate those risks.
Change managers can use predictive analytics to answer the following questions:
What is the expected impact of our change management initiatives?
What are the potential risks associated with our change management initiatives?
What proactive strategies can we implement to mitigate those risks?
How can we use predictive analytics to optimize the change management process?
Example of predictive analytics from The Change Compass
Leveraging Business Intelligence
Business intelligence is a data science technique that involves using tools and techniques to transform raw data into actionable insights. Business intelligence tools can help organizations identify trends, patterns, and insights that can inform the change management process. This can help organizations make informed decisions, improve communication, and increase the efficiency of change management initiatives.
Change managers can use business intelligence to answer the following questions:
What insights can we gain from our data?
What trends and patterns are emerging from our data?
How can we use business intelligence to improve communication and collaboration among stakeholders?
How can we use business intelligence to increase the efficiency of change management initiatives?
Using Data Visualization
Data visualization is a valuable data science technique that involves presenting data in a visual format such as graphs, charts, and maps. Data visualization can help organizations communicate complex information more effectively and make it easier for stakeholders to understand the goals, objectives, and progress of change management initiatives. This can improve communication and increase stakeholder engagement in the change management process.
Change managers can use data visualization to answer the following questions:
How can we present our data in a way that is easy to understand?
How can we use data visualization to communicate progress and results to stakeholders?
How can we use data visualization to identify trends and patterns in our data?
How can we use data visualization to increase stakeholder engagement in the change management process?
Example of a project dashboard from Change Automator
Monitoring and Evaluating Progress
Monitoring and evaluating progress is a critical part of the change management process. Data science techniques, such as statistical analysis and data mining, can be used to monitor progress and evaluate the effectiveness of change management initiatives. This can help organizations identify areas for improvement, adjust the change management plan, and ensure that change management initiatives are achieving the desired outcomes.
Change managers can use monitoring and evaluation techniques to answer the following questions:
How can we measure the effectiveness of our change management initiatives?
What data do we need to collect to evaluate progress?
How can we use statistical analysis and data mining to identify areas for improvement?
How can we use monitoring
The outlined approaches are some of the key ways in which we can use data science to manage change. Change practitioners should invest in their data science capability and adopt data science techniques to drive change success. Stakeholders will take more notice of change management status and they may also better understand the value of managing change. Most importantly, data helps to achieve change objectives.
In this Change Practitioner Q&A series we talk to change managers to ask them how they approach their work. This time we are talking to Fiona Johnson.
Change Compass: Hi Fiona, describe yourself in 3 sentences.
Fiona: I’m a ‘seasoned” change practitioner who has survived many types of workplaces relatively unscathed ! Honestly, I could write a book about it. I always try and see the positive aspects of any workplace and do my best to enhance and support the cultural norms AND keep a sense of humour. I like to collaborate with professional and supportive team members and coach and mentor team members.
Change Compass: What has been the most challenging situation for you as a change practitioner? Tell us what happened and how you fared through it.
Fiona: I’ve had a lot of challenges, but I think the key is getting leaders to lead the change and supporting them.
I had an instance where I had to “sell” the benefits of change management to a very resistant Financial Controller. At the start of the project ( basically an operating model change) , he was totally focussed on the numbers and not the people and lacked the insight that change is always about people.
I had a team made up of business representatives and I set up regular fortnightly meetings to get his attention on issues we needed resolving and keep him up to date. I made the meetings short and sharp and each team members gave an update on the work they were doing to give them visibility. He realised the value of change management once the project delivered as that was when the gaps became evident. I think we were able to prepare him for the implementation but once the project wrapped up it was evident there was a lot of embedment activities not planned for and I think this would have caused more pain.
Although change initiatives are clearer now about the roles and responsibilities of the Sponsor and Business Owner, there is a still a reluctance amongst senior leaders to lead from the front in case it’s a failure and reflects negatively on them. I think this is an education piece and leaders need to trust change managers.
Change Compass: What are the most critical and most useful things to focus on when you first start on a project, and why.
Fiona: These tend to be the questions I focus on … • What are the business drivers? Why? Because this helps form the narrative and links to strategy and then to the frontline – “What’s in it for me?” • Who is the Sponsor and how actively engaged are they? They need to be involved and advocating throughout the project. • How much funding is set aside for Change Management ? I’ve implemented change on a shoestring but its better if there is funding for communication and training as this indicates consideration for the recipients. • What’s the organisations history of managing change – is there a “good” change example and what made it stand out, conversely what was a poor experience and what factors contributed to it ? • What is the culture like ? Take note of employees’ surveys as they provide markers on morale and pain points. • Finally identify a network of strong champions and advocates to help the change and provide them with the tools to do this.
Change Compass: As change practitioners we don’t often get to stick around to see the fruits of our labour, but from your experience what are the top factors in driving full change adoption?
Fiona: For me …. • Understanding the future state and identifying existing organisation metrics that can monitor and measure, or if there are gaps, ensuring these are filled before the change. • Handover to a committed business owner to manage and maintain momentum and who understands their role and responsibilities. • Building governance structures to review and report on the changes to the Executives or using existing forums. • Reporting and tracking are key but also other types of controls such as operating procedures and training. • Involving other areas such as QA, Compliance, HR and Finance in the discussions relating to embedment
Change Compass: You’re known to be great at explaining complex changes to stakeholders. What’s your secret?
Fiona: I have the grandmother test … would your grandmother understand this? Also, use basic communication rules such a targeting your audiences – there’s a difference between communicating to white collar and blue collar. Other tips include … • Use storytelling and personas your audience can relate to • Use your advocates and sponsors to spread the message. • Keep it simple and use a variety of mediums
One of the most feared aspect of change by organisations is its impact on performance. There is a wide variety of change which can determine the potential for performance dips during the change process. However, there is a significant body of research on the phenomenon of performance dip during system implementation. This refers to a temporary decrease in performance or productivity that often occurs when a new system is introduced or a significant change is made to an existing system. In this article we review key research studies on performance dips during change.
What are some of the research studies on performance dips during system implementation? Here are a few research studies that provide some insight into the degree of performance dips during system implementation:
A study published in the Journal of Computer Information Systems in 2019 found that performance dips during ERP implementation projects can range from 10% to 25% on average, with some organizations experiencing dips as high as 40%.
A study published in the Journal of Information Technology Management in 2011 found that performance dips during enterprise system implementation can range from 5% to 50% on average, depending on the organization and the type of system being implemented.
A study published in the International Journal of Information Management in 2016 found that performance dips during electronic health record (EHR) system implementation can range from 5% to 60% on average, depending on the organization and the level of customization required for the EHR system.
What about for transformation programs? What are some of the findings on how much performance could dip during the transformational change process?
Here are some examples of the percentage of performance dips observed in various transformation programs:
A study by McKinsey & Company found that organizations undergoing digital transformations typically experience a 10% to 15% dip in productivity during the implementation phase.
A research report by the Hackett Group found that companies implementing large-scale enterprise resource planning (ERP) systems experience an average performance dip of 5% to 15% during the implementation phase.
A case study of a large Australian bank’s transformation program found that the organization experienced a 10% to 20% dip in productivity during the implementation phase.
A study of 10 organizations that had implemented new supply chain management systems found that they experienced an average productivity dip of 12% during the implementation phase.
The percentage of performance dips
The percentage of performance dip with transformation programs can vary widely depending on a variety of factors, such as the size and complexity of the transformation, the industry, the specific processes and systems being impacted, and the level of planning and support provided during the implementation.
It’s important to note that these percentages are only rough estimates, and the actual performance dip can vary widely depending on the specific context of the transformation program. Organizations can minimize the impact of performance dip by carefully planning and managing the implementation process, providing appropriate training and support to employees, and monitoring performance closely during and after the implementation.
Why causes the performance dip?
One key factor that contributes to performance dip is the learning curve associated with the new system. Users need time to become familiar with the new software or hardware and may initially struggle to complete tasks at the same speed or with the same level of accuracy as they did with the previous system.
Another factor is the disruption to established workflows and processes that can occur during system implementation. When a new system is introduced, it often requires changes to the way work is done, which can lead to confusion and delays until everyone adjusts to the new way of doing things.
Research has found that performance dip tends to be most pronounced in the initial stages of system implementation and can last anywhere from a few days to several months, depending on the complexity of the system and the level of support provided to users during the transition.
Overall, it is largely change management factors that can cause performance dips. For example:
Resistance to change. When employees are asked to change the way they work, they may resist the change, leading to a decline in performance. Resistance can be due to various reasons, including fear of the unknown, lack of understanding of the reasons for the change, and concerns about job security.
Implementation issues: When new processes or technologies are not implemented correctly, they may not work as intended, leading to a decline in performance. Implementation issues can be due to various reasons, including inadequate planning, insufficient resources, and unrealistic timelines.
Communication breakdowns: When communication between stakeholders breaks down, it can lead to confusion and misunderstandings, leading to a decline in performance. Communication breakdowns can be due to various reasons, including inadequate planning, insufficient resources, and unrealistic expectations.
Organizational culture: Organizational culture can also contribute to performance dips during transformation programs. When the organizational culture does not support change, employees may be resistant to it, leading to a decline in performance. Organizational culture can be due to various reasons, including leadership style, history, and values.
What about performance dips when there are multiple changes going on?
Research has shown that implementing multiple changes simultaneously can lead to a higher risk of performance dips. Here are some examples of research studies that have explored this issue:
“The Effects of Multiple Change Initiatives on Perceptions of Organizational Change: Implications for Employee Outcomes” by Michael Tushman and Philip Anderson (2004): This study found that implementing multiple change initiatives at the same time can lead to increased uncertainty and confusion among employees, which can lead to a decline in performance.
“The Effect of Multiple Change Programs on Employee Well-being and Work Outcomes: A Longitudinal Study” by Michal Biron and Yair Bamberger (2012): This study found that implementing multiple change programs simultaneously can lead to increased stress and burnout among employees, which can negatively impact their performance.
“The Impact of Multiple Change Initiatives on Perceived Organizational Performance” by Matthew Davis and Stephen Taylor (2008): This study found that implementing multiple change initiatives simultaneously can lead to a decline in perceived organizational performance, which can impact employee morale and motivation.
“Managing Multiple Organizational Changes: The Role of Prior Change Implementation and Timing of Change Initiatives” by Sebastian Kunert and Christiane Stenger (2019): This study found that implementing multiple changes simultaneously can lead to a higher risk of performance dips, but that prior experience with change implementation and careful timing of change initiatives can help to mitigate this risk.
Overall, these studies suggest that implementing multiple changes simultaneously can lead to a higher risk of performance dips. However, it is not that organisations should simply avoid implementing simultaneous changes. Morever, implementing simultaneous change is a fact of corporate life and continuous development. No modern organisation can survive by implementing only one singular change at a given time.
How to avoid performance dips across the portfolio of change initiatives
“Managing multiple change initiatives: the role of planning, sequencing, and implementation” by Jelena Spanjol and Susan Ashford (2018): This study found that careful planning and sequencing of change initiatives can help to reduce the negative impact of multiple changes on employee performance. The authors suggest that organizations should prioritize changes based on their strategic importance, and implement changes in a way that minimizes disruption to employees.
In particular, the following 3 points have been highlighted.
Prioritization: Organizations should prioritize changes based on their strategic importance, and implement changes in a way that minimizes disruption to employees. This can involve aligning changes with the organization’s overall strategy, and ensuring that employees understand how the changes will benefit the organization.
Timing and sequence: The timing and sequence of changes can have a significant impact on employee performance. Organizations should consider the timing of changes relative to other initiatives, as well as the sequence of changes. For example, changes that are more disruptive to employees may be better implemented after other, less disruptive changes.
Coordination: Effective coordination of multiple change initiatives is crucial to minimize the negative impact on employee performance. Organizations should ensure that there is clear communication and coordination between different departments and teams involved in the changes, and that there is adequate support and resources available to employees to help them adapt to the changes.
In fact similar findings have been concluded across various McKinsey studies as well. Having clear prioritisation and sequencing is absolutely integral to deliver significant value to the organisation across the initiative portfolio. 40% more value. That is correct. Organizations that are focused on prioritizing and sequencing across the initiative portfolio can gain 40% more value than those that do not.
If you’re keen on achieving 40% more value across your change portfolio have a chat to us about how The Change Compass digital solution can help you do just this.
How to avoid performance dip during system implementation change initiatives
Here are some research findings from different articles on how to reduce performance dips during system implementation projects:
1. “Reducing Performance Dip During Implementation of Large-Scale Information Systems” by David Straub and James King (1996): • Encourage and support employee participation in the implementation process. • Provide adequate training and education on the new system. • Communicate effectively with employees about the changes and their impact. • Provide adequate technical support and resources. • Establish clear and specific goals for the implementation process.
2. “Managing multiple change initiatives: the role of planning, sequencing, and implementation” by Jelena Spanjol and Susan Ashford (2018): • Develop a comprehensive change management plan that includes communication, training, and support. • Prioritize and sequence change initiatives to minimize disruption and avoid overload. • Provide clear and consistent communication about the changes and their impact. • Involve employees in the design and implementation process. • Monitor and address resistance to change.
3. “A multi-level model of employee attitudes toward organizational change” by W. Matthew Bowler et al. (2010): • Foster a positive attitude toward change by providing clear and consistent communication, support, and training. • Encourage employee participation and involvement in the change process. • Provide resources and tools to help employees adapt to the change. • Monitor and address resistance to change. • Recognize and reward employee efforts to adapt to the change.
4. “Reducing the Performance Impact of Software Upgrades” by Albert J. Simard and Lionel P. Robert Jr. (2004): • Develop a comprehensive training program that focuses on the most relevant features of the new system. • Provide ample opportunities for practice and feedback. • Establish a clear and specific timeline for the implementation process. • Communicate effectively with employees about the changes and their impact. • Provide technical support and resources to address any issues that arise.
In conclusion, research suggests that organizations that use a combination of these change strategies are more likely to avoid performance dips during transformation programs at a portfolio level. By carefully managing and monitoring the portfolio of initiatives, providing appropriate training and support to employees, and continuously improving performance, organizations can ensure a successful transformation that delivers the desired benefits.
There is no change curve. A single change curve doesn’t exist in most organisations. The concept of a single change curve means you’re always looking at it from the myopic lens of a single project or a single change. If we adopt a humanistic and human-centred view, what we see is that at any one time there are likely multiple change curves happening, to the same person, the same team, the same organisation.
At any one time, an impacted stakeholder maybe undergoing the 3rd iteraction of changes in one project, whilst partially adopting the new behaviours of another project, whilst just learning about the details of yet another project. And it may not even be projects or programs. It could be smaller team-led continuous improvement initiatives.
The concept of Agile methodology has revolutionized the way organizations approach software development and project management. It emphasizes flexibility, adaptability, and continuous improvement. However, the frequent introduction of multiple Agile changes within an organization can lead to multiple ‘S’ curves, which can result in several challenges related to adoption and business performance and capacity.
Multiple S curves refer to the continuous introduction of new Agile changes, each of which leads to a new adoption process and a corresponding performance improvement. This results in a series of S-shaped curves, each representing a different stage of the Agile adoption process.
The S curve is assuming that all of the changes are well implemented with good people experiences. The initial curve shows the slowness of the change adoption in the beginning, followed by a faster change adoption process, and finally capering off.
However, when the change is not well implemented due to various reasons the experience can be more like a V curve, where the experience and performance dips down into the ‘valley of despair’, followed by a ramp-up of improving experiences and change adoption.
The introduction of multiple Agile changes within an organization can lead to several challenges related to adoption and business performance and capacity. Firstly, continuous change can lead to confusion and uncertainty among employees. It can be difficult for employees to keep up with the latest changes and understand how they should adjust their work processes accordingly. This can result in decreased productivity and morale among employees.
Moreover, frequent changes can also result in increased cognitive strain and workload for employees. They may need to continuously learn new processes and techniques, leading to burnout and decreased job satisfaction.
Another challenge of having multiple Agile changes is that it can lead to decreased consistency in processes and outcomes. Each change may result in different outcomes and different ways of working, making it difficult to standardize and measure performance. This can result in a lack of accountability and a decrease in the organization’s overall efficiency.
In addition to the challenges related to adoption and performance, multiple Agile changes can also result in a decreased business capacity. The frequent changes can disrupt established workflows, making it difficult for teams to complete projects in a timely manner. This can lead to decreased project velocity and increased project risk, making it challenging for the organization to meet its goals and objectives.
So, while Agile methodology is a powerful tool for organizations, the frequent introduction of multiple Agile changes can result in several challenges related to adoption, performance, and capacity. To mitigate these challenges, organizations should take a strategic approach to Agile adoption, ensuring that changes are well-planned, communicated effectively, and implemented in a controlled manner. By doing so, organizations can ensure that the benefits of Agile methodology are realized while minimizing the risks associated with multiple changes.
To truly manage the multiple change curves, data is key. Without understanding which change curves are happening at what time it is not possible to manage change holistically. With data, you can easily drill into what is happening when, to whom, to what extent, and in what way. It is only with data that we can effectively orchestrate change across the board.
If you are going on a journey to capture change impacts across the organisation, be aware of how you are capturing the data so that you are truly addressing business issues critical to the organisation. For example:
Ensure that the data captured can be easily formatted and visualised to support a range of business decision-making contexts without too much manual work. The more manual the set up of the data is, the more time and effort it requires to answer the various data cuts that stakeholders may be needing
Balancing critical data points required versus having too many data fields and therefore too Cumberland and difficult to capture the data. The more data you are required to collect, the more complex the process is for those whom you are collecting the data from
Thanks to the nature of agile projects, the data will change constantly. The tracking of constantly changing change data is critical. However, it should also be easy and quick to update the data
Organisations under changes will invariably have changes in organisational structures, teams or roles. Ensure that your data-capturing process makes it easy to update the structure as they change.
Have a chat with us to understand more about how to leverage digital solutions to multiple change impacts across the organisation, and how to leverage AI and automation to make your lives easier in leveraging a data platform to make critical business decisions using change impact data.
So next time you talk about THE change curve, just be aware that you’re likely not adopting a people-centric view of change. You may want to look more holistically at what your impacted stakeholders are undergoing or about to undergo. Adopt a holistic mindset of what impacted stakeholders are going through as you plan out your change approach.
If you’re interested in exploring more about managing agile changes check out the following:
