Have you ever wondered why change management deliverables as a part of the overall OCM solution are structured and sequenced the way they are in effective change management plans?
Organisational change management deliverables are defined as the data that is put in use in every activity in a change-management. Besides activities, deliverables can form an integral part of any change management project.
There is an inherent logical flow from which change deliverables feed into the next. This means that subpar quality in the deliverable earlier on happens if the work is inadequately carried out. Also, this will likely flow into the rest of the deliverables.
For the change management team, change management deliverables start out very high-level. Earlier in the project development lifecycle, there is a lot of unknown details which stops you from conducting detailed stakeholder management assessment and a communication plan. Moreover, there are lots of questions that cannot be answered about the nature of the change, what the new processes are, and training needs. More details presents itself as the project progresses through each phase. Therefore, the change practitioner is able to populate and document various details, including what the change means and how stakeholders will be impacted (i.e. the change impact assessment).
Eventually, each change deliverable contributes to the next, resulting in a detailed change plan. The change plan is a culmination of a detailed understanding. Also, it’s an assessment of the impacted stakeholders and what the changes will mean to them. Therefore, the respective change interventions within the change initiative that are critical to transition these key stakeholders from the current to future state. Change management communication, change readiness assessment and stakeholder engagement plan as well as effective training plan also form a core part of the change plan.
Along with the change management process as a part of the change strategy, one should create a system for managing scope of the change. Good project managers apply these components effectively to ensure project success through careful planning. Whether it’s a sudden change of personnel, new technology changes, change resistance or an unexpectedly poor quarter; Change managers should be adaptable enough to conduct risk assessment to apply the appropriate mitigations and changes to your plan to accommodate your company’s new needs.
Change management functions encompass planning, implementation, and monitoring of organizational changes. The change process ensures smooth transitions by managing effective communication of change impact, training efforts, and support to ensure positive outcomes. Additionally, it assesses impacts and adapts strategies into change management tasks to minimize resistance, ultimately fostering a culture that embraces change for improved overall performance and employee satisfaction.
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.
An important part of measuring meaningful change is to be able to design effective communication effectiveness change management surveys that measure the purpose of the survey it has set out to measure the level of understanding of the change. Designing and rolling out change management surveys is a core part of what a change practitioner’s role is. However, there is often little attention paid to how valid and how well designed the survey is. A survey that is not well-designed can be meaningless, or worse, misleading. Without the right understanding from survey results, a project can easily go down the wrong path. This is how this survey can be a powerful tool to ensure smooth transition for the change initiative.
Why do change management surveys need to be valid?
A survey’s validity is the extent to which it measures what it is supposed to measure. Validity is an assessment of its accuracy. This applies whether we are talking about a change readiness survey, a change adoption survey, employee engagement, employee sentiment pulse survey, or a stakeholder opinion survey.
What are the different ways to ensure that a organizational change management survey can maximise its validity and greater success?
Face validity. The first way in which a survey’s validity can be assessed is its face validity. Having good face validity is that in the view of your targeted respondents the questions measure what they aimed to measure. If your survey is measuring stakeholder readiness, then it’s about these stakeholders agreeing that your survey questions measure what they are intended to measure.
Predictive validity. If you really want to ensure that your survey questions are scientifically proven to have high validity, then you may want to search and leverage survey questionnaires that have gone through statistical validation. Predictive validity means that your survey is correlated with those surveys that have high statistical validity. This may not be the most practical for most change management professionals.
Construct validity. This is about to what extent your change survey measures the underlying attitudes and behaviours it is intended to measure. Again, this may require statistical analysis to ensure there is construct validity.
At the most basic level, it is recommended that face validity is tested prior to finalising the survey design.
How do we do this? A simple way to test the face validity is to run your survey by a select number of ‘friendly’ respondents (potentially your change champions) and ask them to rate this, followed by a meeting to review how they interpreted the meaning of the survey questions.
Alternatively, you can also design a smaller pilot group of respondents before rolling the survey out to a larger group. In any case, the outcome is to test that your survey is coming across with the same intent as to how your respondents interpret them.
Techniques to increase survey validity
1. Clarity of question-wording.
This is the most important part of designing an effective and valid survey. This is a critical part of the change management strategy. The question wording should be that any person in your target audience can read it and interpret the question in exactly the same way.
Use simple words that anyone can understand, and avoid jargon where possible unless the term is commonly used by all of your target respondents
Use short questions where possible to avoid any interpretation complexities, and also to avoid the typical short attention spans of respondents. This is also particularly important if your respondents will be completing the survey on mobile phones
Avoid using double-negatives, such as “If the project sponsor can’t improve how she engages with the team, what should she avoid doing?”
2. Avoiding question biases
A common mistake in writing survey questions is to word them in a way that is biased toward one particular opinion which may lead to biased employee feedback. This assumes that the respondents already have a particular point of view and therefore the question may not allow them to select answers that they would like to select.
Some examples of potentially biased survey questions (if these are not follow-on questions from previous questions):
Is the information you received helping you to communicate effectively to your team members through appropriate communication channels?
How do you adequately support the objectives of the project
From what communication mediums do your employees give you feedback about the project
3. Providing all available answer options
Writing an effective employee survey question means thinking through all the options that the respondent may come up with regarding the upcoming change. After doing this, incorporate these options into the answer design. Avoid answer options that are overly simple and may not meet respondent needs in terms of choice options.
4. Ensure your chosen response options are appropriate for the question.
Choosing appropriate response options may not always be straightforward. There are often several considerations, including:
What is the easiest response format for the respondents?
What is the fastest way for respondents to answer, and therefore increase my response rate?
Does the response format make sense for every question in the survey?
For example, if you choose a Likert scale, choosing the number of points in the Likert scale to use is critical.
If you use a 10-point Likert scale, is this going to make it too complicated for the respondent to interpret between 7 and 8 for example?
If you use a 5-point Likert scale, will respondents likely resort to the middle, i.e. 3 out of 5, out of laziness or not wanting to be too controversial? Is it better to use a 6-point scale and force the user not to sit in the middle of the fence with their responses?
If you are using a 3-point Likert scale, for example, High/Medium/Low, is this going to provide sufficient granularity that is required in case there are too many items where users are rating medium, therefore making it hard for you to extract answer comparisons across items?
5. If in doubt leave it out
There is a tendency to cram as many questions in the survey as possible because change practitioners would like to find out as much as possible from the respondents. However, this typically leads to poor outcomes including poor completion rates. So, when in doubt leave the question out and only focus on those questions that are absolutely critical to measure what you are aiming to measure.
6.Open-ended vs close-ended questions
To increase the response rate of change readiness survey questions, it is common practice to use closed-ended questions where the user selects from a prescribed set of answers. This is particularly the case when you are conducting quick pulse surveys to sense-check the sentiments of key stakeholder groups. Whilst this is great to ensure a quick, and painless survey experience for users, relying purely on closed-ended questions may not always give us what we need.
It is always good practice to have at least one open-ended question to allow the respondent to provide other feedback outside of the answer options that are predetermined. This gives your stakeholders the opportunity to provide qualitative feedback in ways you may not have thought of. This may include items that indicate employee resistance, opinions regarding the work environment, new ways of working, or requiring additional support.
Writing an effective and valid change management survey best practices for a specific change initiative is often glanced over as a critical skill. Being aware of the above 6 points will get you a long way in ensuring that your survey addresses areas of concern in a way that aligns with your change management process and strategy and will measure what it is intended to measure. As a result, the survey results will be more bullet-proof to potential criticisms and ensure the results are valid, providing information that can be trusted by your stakeholders.
