There is now a lot of attention and focus on data. However, is the same applied to change management data? With the substantial financial investments companies make in change efforts, there’s a growing recognition of the need to leverage change management data strategically. Senior managers and executives are increasingly demanding data-driven insights to make informed business decisions. Here, we explore the challenges associated with change data, the strategic approaches to managing it effectively, and how incorporating it into the decision-making process can drive organizational success.
Common Challenges in Working with Change Data
Ad hoc and Tactical Approaches One common challenge in working with change data is the ad hoc and tactical nature of its collection. Often, data is gathered as needed, primarily at the project level. This can result in a fragmented view of change initiatives, making it challenging to derive meaningful insights. For instance, progress data may be limited to generic metrics such as the number of change impact sessions or completed training sessions, lacking depth and context.
Data Insufficiently Fact-Based Another prevalent issue is the creation of data that lacks a solid factual foundation. Change practitioners sometimes rely on gut-feel ratings or broad categories that are difficult to defend or substantiate infront of stakeholders. Heatmaps, a popular visualization tool, may be based on subjective assessments rather than objective, quantifiable measures, hindering the data’s credibility and utility.
Ineffective Data Visualizations Data visualizations play a crucial role in conveying information effectively. Unfortunately, some visualizations fall short of making a significant impact. Whether they are overly colorful, fail to use the right chart to highlight key points, or present data in a way that obscures the primary message, ineffective visualizations can impede the decision-making processes.
Seeking Easy Fixes Many change practitioners view working with data as a chore and opt for quick fixes. They may collect just enough data to generate a report or dashboard, neglecting the importance of a thorough understanding and management of the data. This short-sighted approach can compromise the quality and reliability of the insights derived from the data.
Strategic Approaches in Working with Change Data
Strategic approaches to manage change data can result in significant value for the organisation. Imagine the power of a range of change management data that highlights anything from impact levels, saturation risks, sentiments, adoption risks and benefit realization progress. Such is the power of change data, if managed effectively. What are some of these strategic approaches?
Managing Data as a Core Routine To address the challenges associated with ad hoc and tactical data collection, organizations must establish routines for managing change data. Monthly data reviews, updates, and audits create a disciplined approach to ensure the data remains accurate, relevant, and valuable. By making data management a core routine, organizations foster a culture of accountability and accuracy. This can be applied across a large program, a business unit, a portfolio of initiatives or across the enterprise.
Leveraging AI for Data Auditing and Cleansing Artificial Intelligence (AI) can play a pivotal role in auditing and cleansing change data. Platforms like The Change Compass offer features that automate these processes, reducing the likelihood of errors and ensuring data integrity. AI-driven tools can identify inconsistencies, outliers, and inaccuracies, providing a more reliable foundation for decision-making.
Linking Change Data with Other Business Sources The true power of change data emerges when it is connected with other relevant business data sources. By integrating change management data with project data, HR data, risk data, and operations data, organizations gain a holistic view of their business landscape. This interconnected approach allows for a comprehensive understanding of key business risks and opportunities, facilitating more informed decision-making.
Incorporating Data into Decision-Making Bodies Change data should not exist in isolation; it should be integrated into key decision-making forums and processes. From executive leadership forums and strategic planning sessions to portfolio planning and operational meetings, incorporating change data into these discussions ensures that insights derived from the data inform critical business decisions. This alignment helps organizations proactively address challenges and capitalize on opportunities.
While recognizing the strategic importance of change data is a significant step forward, change practitioners must actively implement practical measures to enhance their approach to change data management. Here are some recommendations to help change practitioners become more strategic in their utilization of change data:
Standardize/Routinize Data Collection Processes: o Develop standardized processes for collecting change data across different projects and initiatives. o Implement consistent data collection templates and methodologies to ensure uniformity and comparability of data across initiatives and business units
Invest in Training and Skill Development: o Provide training for change practitioners on data management best practices, including data collection, analysis, audit and interpretation. This is critical to drive data capability and maturity. o Foster a data-driven culture within the organization by equipping practitioners with the necessary skills to leverage data effectively.
Utilize Technology and Automation: o Embrace technological solutions, such as data analytics tools and AI-driven platforms, to automate data auditing, cleansing, and visualization processes. o Leverage technology to streamline data collection and reporting, reducing manual effort and minimizing the risk of errors.
Encourage Cross-Functional Collaboration: o Facilitate collaboration between change management teams and other departments, encouraging the sharing of data and insights. o Establish cross-functional teams to integrate change data with project data, HR data, and other relevant business sources.
Implement Data Governance Frameworks: o Develop and implement robust data governance frameworks to ensure the accuracy, security, and compliance of change data. o Define roles and responsibilities for data management within change initiatives, promoting accountability and ownership.
Enhance Data Visualization and Reporting: o Invest in training or hiring professionals with expertise in data visualization to create compelling and impactful reports. o Tailor visualizations to the audience, ensuring that key messages are communicated clearly and effectively.
Conduct Regular Data Reviews and Audits: o Establish a routine for regular data reviews, updates, and audits to maintain the accuracy and relevance of change data. o Use audits as an opportunity to identify and rectify data discrepancies or inconsistencies.
Integrate Change Data into Decision-Making Processes: o Actively participate in executive leadership forums, strategic planning sessions, and other decision-making bodies. o Present change data alongside other relevant business data to contribute to well-informed decision-making.
Measure and Communicate Value: o Develop metrics to measure the value generated by change initiatives and communicate these metrics to key stakeholders. o Regularly assess the impact of change data on decision-making processes and adjust strategies accordingly.
Seek Continuous Improvement: o Foster a culture of continuous improvement within the change management function. o Encourage practitioners to reflect on past experiences, learn from challenges, and refine their approach to change data management over time.
The strategic management of change data is not just a necessity but a critical component of achieving business success in today’s dynamic environment. By addressing common challenges and adopting strategic approaches, organizations can unlock the true potential of change data. As the business landscape continues to evolve, leveraging data-driven insights becomes a strategic imperative for navigating change, mitigating risks, and capitalizing on opportunities. Embracing change data as a strategic exercise positions organizations to not only survive but thrive in an ever-changing marketplace.
Change management is often seen as a ‘soft’ discipline that is more an ‘art’ than science. However, successful change management, like managing a business, relies on having the right data to understand if the journey is going in the right direction toward change adoption. 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.
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 from change leaders and key stakeholders 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, including measurable impact data?
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?
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?
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? (e.g. employee engagement, customer satisfaction, business outcomes, etc.) And what method do we use to collect the data? E.g. surveys or focus groups?
What data do we need to collect to evaluate the change initiative progress?
How can we use statistical analysis and data mining to identify areas for improvement?
How can we use monitoring of ongoing support or continuous improvement?
The outlined approaches are some of the key ways in which we can use data science to manage the change process. Change practitioners should invest in their data science capability and adopt data science techniques to drive effective change management 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.
Captured during a 5-day trek in Tasmania’s southwestern wilderness known as the Western Arthurs, this photograph reflects a journey undertaken four years prior, just before devastating bushfires swept through Tasmania, altering its pristine landscapes. The region, notorious for persistent rain and limited sunshine, graced us with consecutive sunlit days, making it a standout global hiking experience, rivaling trails in the Italian Dolomites, the Himalayas, and the Canadian Rockies.
Embarking on a 5-day expedition in Tasmania’s southwest demands self-sufficiency—carrying all your food, drinking from rivers, and sleeping in a tent with no huts or running water. The solitude is profound, with few fellow hikers; most of the time, it’s just you and Mother Nature.
Childhood lessons painted Mother Nature as a battlefield for survival, where each tree competes fiercely for sunlight, nutrients, and dominance over the land. However, this narrative is challenged by Suzanne Simard, a professor of forest ecology at the University of British Columbia. Over two decades of study revealed that a forest’s essence lies not in individual tree struggles but in subterranean partnerships. Simard unveiled the symbiotic relationship between trees and fungi, known as mycorrhizas—thread-like fungi merging with tree roots. They aid trees in extracting water and nutrients, receiving carbon-rich sugars produced through photosynthesis in return. (For more details, refer to the New York Times article.)
Mycorrhizas serve as the connective tissue of the forest, intertwining trees of different species through an extensive web. This transforms the forest into more than a mere collection of trees. In times of crisis, a tree at the brink of death may altruistically share a substantial portion of its carbon with neighboring trees. The forest thus emphasizes cooperation, negotiation, reciprocity, and selflessness alongside survival and competition.
Remarkably, this ecosystem mirrors the principles of effective change networks. A change network possesses the capacity to reach every individual in a company. Unlike being confined to a specific business unit or hierarchy level, a well-designed change network transcends organizational boundaries.
Let’s delve deeper into the characteristics of a robust and efficient change network…
1) Project-agnostic
In the dynamic landscape of change networks, a paradigm shift from the traditional project-specific model to a project-agnostic approach emerges as a strategic imperative. The conventional methodology, with its exclusive focus on single projects, often results in a staggering 69% of projects achieving initial objectives, while 15% are considered failures. This project-specific model, besides its high failure rate, also contributes to significant resource wastage. Identifying, training, and sustaining a robust change champion network for each project frequently overshoots the project’s lifecycle, hindering desired outcomes and accounting for the 70% failure rate in projects.
Contrastingly, a more efficient paradigm involves nurturing change champions with the ability to support multiple projects. This not only optimizes resource allocation but also aligns with the agile principle, as highlighted by the 56% of companies that exclusively use a single project management methodology.
These versatile change champions, akin to Starbucks’ “My Starbucks Idea” initiative, play a pivotal role in connecting the dots across projects, providing invaluable insights, and fostering a culture of collaboration. Starbucks’ successful implementation of change through customer-driven ideas, resulting in over 5 million monthly page visits, is a testament to the power of adaptable change networks.
Drawing a parallel to the natural world, where mycorrhizas take time to strengthen and fortify the forest, change champions undergo a transformative journey with each project involvement. Their sustained engagement refines their change management skills and delivery expertise, enhancing their proficiency with every endeavor.
The diverse and creative approaches observed in change champions, ranging from themed outfits to innovative reminders, reflect the adaptability crucial for effective end-user engagement. This adaptability serves as the cornerstone of a thriving change champion network, where experimentation and varied strategies contribute to its vibrancy and success. Similar to the ever-evolving forest ecosystem, change networks flourish when nurtured with creativity and adaptability.
2) Cuts across layers
In the realm of change networks, adopting a project-agnostic approach emerges as a strategic shift from the traditional project-specific model. The conventional method involves forming change networks tailored exclusively to a single project, with champions disbanded at the project’s conclusion.
However, this model poses inherent challenges, leading to significant resource wastage. The effort to identify, train, and sustain a robust change champion network for each project often exceeds the project’s lifespan, impeding desired outcomes.
To address this, the change champion network needs to cut across not only different parts of the business but also different layers of the organization. A lot of change champion networks are designed at the mid-layer of the organization, typically involving middle managers. While middle managers can influence the outcome of the change more than frontline staff members, relying solely on this layer may not be sufficient.
Here’s why:
Detail Feedback: Middle managers are often not the ‘end users’ of systems or processes, making it challenging for them to provide detailed feedback on the suitability of the change, sentiments of end users, or necessary adjustments in the change solution.
Signal Loss: Depending on the organization, there may be 1-3 layers between middle managers and end users, resulting in potential ‘signal loss’ where thoughts, emotions, and feedback from the lowest layers of the organization may not be effectively communicated.
Limited Testing Input: Middle managers are usually not directly involved in system or process testing, limiting their ability to provide detailed input to shape the change. Their contributions often focus on higher-level strategies for engaging impacted teams.
To build a strong, vibrant, and extensive change champion network, engagement needs to extend to different layers of the organization, not just the middle layers but also the lower layers. While top layers may be engaged through various committees, middle and lower layers require dedicated change champions.
Similar to the mycorrhizas connecting different trees in a forest, the change champion network, when stronger and more extensive, becomes more capable of influencing and driving change both vertically and horizontally across the company. This inclusivity ensures that smaller business groups are not neglected or deprioritized, contributing to the overall success and adaptability of the change network.
3) Routine interfaces
In the intricate ecosystem of a forest, mycorrhizas play a vital role by providing essential sustenance, and supplying critical nitrogen, water, and other nutrients to plants. In the organizational landscape, change champions serve a similar crucial function. Armed with comprehensive knowledge and a deep understanding of the change, along with the latest updates on its impacts, they possess the ability to interpret messages in a way that resonates with those directly affected, using a language that is tailored to each team’s unique history, priorities, and culture.
Unlike program-level communication, which may be too generalized, the interaction with change champions is a dynamic, two-way process. They engage with impacted employees, actively assessing and understanding where individuals stand in their change journey. This engagement leads to a clear comprehension of the specific communication, learning, or leadership support needs of impacted teams. High-performing change champions delve beyond the surface, understanding the motivations and demotivators of the teams they serve. This wealth of insights becomes a powerful set of messages that can be fed back to the central project mothership.
What sets high-performing change champions apart is not just their ability to communicate and collect feedback; they proactively sense-check and virtually “walk the floor” to feel the pulse of the employees. Often, change champions are directly impacted by themselves, fostering a natural empathy that enables them to connect with others undergoing change. In this dynamic, there is a delicate balance between self-interest and selflessness, as change champions strive not only to navigate their own challenges but also to extend support and assistance to those in need. This nuanced approach mirrors the harmony found in natural ecosystems, where organisms cooperate for mutual benefit.
4) Cross-network collaboration
Within the expansive framework of an extensive change network, diverse sub-teams of change champions naturally emerge, often organized by business units or grade levels. While connecting with peers within the same level might be straightforward, establishing collaboration across hierarchies, especially with those perceived as ‘managers,’ can pose challenges.
To overcome these challenges, intentional routines must be established to facilitate frequent sharing and collaboration among different change champion teams. In the natural world, trees emit chemical alarm signals to warn nearby trees of potential danger. Similarly, within a business context, a team from one business unit may sense a looming risk for change failure based on their experiences, which they can share with other teams yet to undergo the change.
Conversely, successful experiments in one part of the business should be readily proliferated in other areas of the organization. For instance, in a large insurance company, a change champion network recognized the need for frontline staff working virtually to have a platform for immediate queries and responses. The solution was a chat channel implemented under Microsoft Teams, approved by IT. In this channel, frontline staff could freely pose questions about system usage, shortcuts, and outages, and addressing customer concerns.
Initially, the channel had few questions, but as prompt and helpful responses were provided, engagement grew. Today, it stands as one of the most active Teams chat channels in the company, showcasing the effectiveness of cross-network collaboration. This success story has inspired similar initiatives in other businesses, emphasizing the ripple effect of successful collaboration practices within change networks.
5) Nurturing the network
Sustaining a change champion network is an ongoing endeavor that demands continuous nurturing, engagement, support, and leadership. Similar to any community, these networks thrive when provided with the right conditions and resources. Several key activities contribute to the nurturing of a dynamic and effective change champion network:
Onboarding and Expectation Setting: New members need comprehensive onboarding sessions where they receive information about the network’s objectives, core principles, expected time commitments, and other essential details.
Change Capability Sessions: Continuous learning is crucial for change champions. Sessions covering various topics, such as impact assessment, change communication, feedback provision during testing, and engagement with impacted stakeholder groups, help enhance their skills.
Leader Support: The involvement of senior leaders in certain sessions can provide valuable support and visibility to the network’s efforts, emphasizing the importance of their work in the broader organizational context.
Cross-Business Unit Networking: Structured agendas for cross-business unit change champion networking sessions create opportunities for sharing ideas and best practices, fostering a collaborative environment.
Routine Forums: Establishing routine forums for discussing project-specific topics allows members to stay informed and aligned with ongoing initiatives.
Formal Acknowledgments and Prizes: Recognizing key milestones and achievements through formal acknowledgments and prizes not only celebrates success but also motivates members to actively contribute.
Data Access: Providing change champions with access to change data, including impact assessments, readiness metrics, and change roadmaps, empowers them with valuable insights into upcoming changes and their stakeholder implications.
Regular Membership Reviews: Like any dynamic network, regular reviews of membership are essential. Some members may not meet expectations, and their roles might need to be filled by others. Expecting turnover and proactively managing it ensures a continuous influx of fresh perspectives and contributions.
Change champions, armed with comprehensive data on change impact, play a pivotal role in facilitating a clear understanding of impending changes and their ramifications for stakeholders. Regular reinforcement, support, and occasional challenges contribute to the resilience and effectiveness of the change champion network.
6) Supporting multiple initiatives
In the dynamic landscape of organizational change, it’s common for each business unit to undergo multiple initiatives simultaneously. Change champions play a pivotal role in navigating this complex terrain, supporting various initiatives and connecting the dots to form a coherent narrative for the impacted audience. Here’s why having change champions who can support multiple initiatives is crucial:
Holistic Understanding: Change champions, acting as the linchpin between different initiatives, provide a holistic understanding of the changes unfolding within a business unit. This comprehensive view enables them to craft a cohesive story that resonates with the audience, fostering better comprehension and buy-in.
Connecting the Dots: A key function of change champions is to connect disparate initiatives into a unified narrative. By highlighting interdependencies and common goals, they contribute to a more seamless and integrated change experience for stakeholders.
Predicting Crunch Periods: Change champions need to anticipate and understand the crunch periods for their business unit. By supporting multiple initiatives, they become adept at forecasting when the organization might face heightened challenges and risks that could impact daily operations.
Strategic Risk Management: With insights into multiple initiatives, change champions become strategic risk managers. They can identify potential points of friction, overlaps, or resource constraints and proactively address them, mitigating risks that could hinder the success of the initiatives.
Example of a single view of change from The Change Compass
Example of Change Outcome: The Change Compass
In analogy to mycorrhizal networks that span diverse ecosystems, organizations face the challenge of not only developing robust change champion networks internally but also fostering connections with external networks. Just as mycorrhizal networks link various landscapes, change champion networks can extend their impact beyond organizational boundaries.
Research indicates that when change champion networks from different companies link up, a wealth of learning and collaboration unfolds. This interconnectedness leads to a blossoming of reciprocity, negotiation, and even selflessness. Organizations stand to gain immensely by facilitating the exchange of insights and experiences among diverse change champion networks, creating a thriving ecosystem of change management knowledge and practices.
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