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?

1. 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.

2. 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.

3. 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.

4. 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. 

5. 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?

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?
• 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.

Check out The Ultimate Guide to Measuring Change.

Also check out this article to read more about using change management software to measure change.

If you’re interested in applying data science to managing change by leveraging digital tools have a chat to us.