Data Science Techniques You Can Use for Successful Change Management
For your data science investment to succeed, the data science strategy you adopt should include well-thought-out strategies for managing the fundamental change that data science solutions impose on an organization. One effective and efficient way to tackle these data science challenges is by using data-driven change management techniques to drive the transformation itself — in other words, drive the change by “practicing what you preach.” Here are some examples of how to do this in practice.
For companies, there is a new generation of real-time employee opinion tools that are starting to replace old-fashioned employee opinion surveys. These tools can help you manage your data and tell you far more than simply what employees are thinking about once a year. In some companies, employees are surveyed weekly using a limited number of questions. The questions and models are constructed in such a way that management can follow fluctuations in important metrics as they happen rather than the usual once or twice a year. These tools have obvious relevance for change management and can help answer questions like these:
- Is a change being equally well received across locations?
- Are certain managers better than others at delivering messages to employees?
Assume that you have a large travel-and-tourism firm that is using one of these tools for real-time employee feedback. One data-driven approach to use in such a situation is to experiment with different change management strategies within selected populations in the company. After a few changes in the organization, you can use the data collected to identify which managers prove to be more effective in leading change than others. After that has been established, you can observe those managers to determine what they’re doing differently in their change management approach. You can then share successful techniques with other managers.
This type of real-time feedback offers an opportunity to learn rapidly how communication events or engagement tactics have been received, thus optimizing your actions in days (rather than in weeks, which is typical of traditional approaches). The data can then feed into a predictive model, helping you determine with precision which actions will help accelerate adoption of a new practice, process, or behavior by a given employee group.
You can find some commercial tools out there — culture IQ polls, for example — that support this kind of data collection. These kinds of polls sample groups of employees daily or weekly via a smartphone app to generate real-time insights in line with whatever scope you have defined. Another tool, Waggl.com, has a more advanced functionality, allowing you to have an ongoing conversation with employees about a change effort as well as allowing change managers to tie this dialogue to the progress of initiatives they’re undertaking.
These different types of digital engagement tools can have a vast impact on change management programs, but the data stream they create could be even more important. The data that’s generated can be used to build predictive models of change. Using and deploying these models on real transformation projects and then sharing your findings helps to ensure a higher success rate with data-driven change initiatives in the future.
Change managers can also look beyond the boundaries of the enterprise for insights about the impact of change of process management. Customers, channel partners, suppliers, and investors are all key stakeholders when it comes to change programs. They are also more likely than employees to comment on social media about changes a company is making, thus giving potentially vital insight into how they’re responding.
Ernst & Young (now known as EY) is using a tool for social media analytics called SMAART, which can interpret sentiment within consumer and influencer groups. In a project for a pharmaceutical company, EY was able to isolate the specific information sources that drove positive and negative sentiment toward the client’s brand. The company is now starting to apply these techniques to understand the external impact of change management efforts, and it’s a simple leap to extend these techniques within the enterprise. Advances in the linguistic analysis of texts mean that clues about behavior can now be captured from a person’s word choices; even the use of articles and pronouns can help reveal how someone feels.
Applying sentiment analysis tools to data in anonymized company email or the dialogue in tools like Waggl.com can give fresh insight about your organization’s change readiness and the reactions of employees to different initiatives. And, the insights gained from analyzing internal communication will be stronger when combined with external social media data.
Have you ever worked in an organization where different change management programs or projects were compared to one another in terms of how efficiently they made the change happen? Or one where a standard set of measurements were used across different change initiatives? No? Most people haven’t. Why is it that organizations often seem obsessed with measuring fractional shifts in operational performance and in capturing data on sales, inventory turns, and manufacturing efficiency, but show no interest in tracking performance from the differences in change projects and change management, beyond knowing which ones have met their goals?
Some people may claim that you can’t compare change projects or change management within an organization; it would be like comparing apples to oranges. But that’s not accurate: Different projects may have unique features, but you’ll find more similarities than differences between different types of projects. Capturing information about the team involved, the population engaged in the change, how long it took to implement, what tactics were used, and so on is a good idea. It enables you to build a reference data set for future learning, reuse, and efficiency benchmarking. However, remember that although it may not yield immediate benefit, as the overall data set grows, it will make it easier to build accurate predictive models of organizational change of process going forward.
For quite a long time, companies have been using data-driven methods to select candidates for senior change management positions. And today some businesses, such as retailers, are starting to use predictive analytics for hiring frontline staff. Applying these tools when building a change team can both improve project performance significantly and help to build another new data set.
If every change leader and team member would undergo testing and evaluation before a change of process project starts, that data could become important variables to include as you search for an underlying model on what leads to a successful change program. This can even be extended to more informal roles like change leaders, allowing organizations to optimize selection based on what they know about successful personalities for these types of roles.
Along these lines, the California start-up LEDR Technologies is pioneering techniques to predict team performance. It integrates data sources and uses them to help teams anticipate the challenges they may face with team dynamics so that the team can prevent them before they occur.
Picture a company or an organization that has a personalized dashboard it has developed in partnership with the firm’s leadership team — one that reflects the company’s priorities, competitive position, and future plans.
These dashboards should also be used to offer insights related to the different transformation investments you’ve made. Keep in mind that much of the data that can act as interesting indicators for change management are already available today — they’re just not being collected.
When a company builds a dashboard for identifying recruitment and attrition, it’s teaching the executive team to use data to perform people-related decisions. However, it can take quite some time to set it up correctly and iron out the bugs. Want a suggestion? Don’t wait. Start building these type of dashboards as fast as possible now and, where possible, automate them. Why the automation? Change dashboards are vulnerable to version control issues, human error, and internal politics. Automating data management and dashboard generation can make it more transparent and help you keep data integrity.