The road to real and sustainable change is paved with many critical decisions. These decisions could be about where to focus resources or investment, what activities to prioritize, how to schedule change interventions, which change levers to pull, what optimum structures look like, which learning strategies might be most effective and which tools or digital solutions might be needed. Difficult decisions could result in vastly different outcomes. The complexity and interrelationships of these decisions make it important to gather evidence and base choices on meaningful data. Data which tells us about the different employee populations, the success of past changes, the efficiency of processes, the cultural traits, the learning behaviors, real-time employee sentiment, employee productivity, or which communication channels are most effective. Leveraging the existing data already within the organization sounds obvious, however, change management strategies are far too often left to the intuition and the ‘way things are always done here.’

Today, we have access to more comprehensive data collection tools and data libraries than ever before, and we must adapt our practices so that data is useful and recognized for the vast opportunities it presents. Organizations undergoing large and complex transformations will often undertake a number of actions all at the same time to solve the same problems, and it’s easy to see how chaos can reign in this kind of environment. Data can bring some order to the chaos in grounding decisions and minimizing inefficiencies, reducing unnecessary costs, improving speed, and aligning leaders on one unified path. Decisions based on data can reduce delivery risk and accelerate execution, even for experienced executives and change managers.

Using data to inform and iterate change strategies

Traditional methods of detailing the impacts of change and understanding change history to drive strategy, whilst still important, are only some of the data sources that are available to optimize decision making. When we hear about data, we tend to think of big data, complex analysis, and overwhelming arrays of digital transformation and visualization tools, but small data is far more accessible and can be actively gathered from observation, interaction, or employee feedback.

Of course, there is a need for both big and small data in driving change, such as when we are trying to understand the needs of a particular impacted population based on a deep understanding of the employee profile utilizing big data metrics such as tenure, age, attrition, remuneration and performance, all playing an important role in the decision making process. Small data is useful in informing perspectives on where to focus, when, for how long, what to do next, and importantly, what to stop doing.

Deciding how to measure success

Deciding and agreeing on indicators of successful outcomes at the very beginning of a change journey will assist with charting the right course. Knowing what good looks like at the end will focus attention and energy on the things that truly matter at the beginning, and once the change has been implemented, ongoing monitoring of the indicators can facilitate quick and specific course correction. Indicators may be simple data points such as how many employees logged into a new technology on day one and then continue to use it once the novelty wears off, process efficiency measures, turnaround times, or a reduction in errors, or they may require more complex combinations of data.

Using predictive indicators to highlight the need for change

Point in time, data collection and analysis is slowly becoming a thing of the past as a way to inform people strategies, with engagement and culture survey tools moving towards ongoing monitoring solutions. Replacing lag indicators with more predictive models of behaviors allow organizations to stay a step ahead. This has never been more critical in areas where behaviors previously thought to be innocuous now come under intense scrutiny as posing a risk to an organization (increasingly being referred to as risk culture). Organizations such as banks who find themselves under an increasing regulatory spotlight must find ways to leverage their existing data to continuously monitor risk in their business and spring to targeted action.

"Take an honest look at your virtual table. Who’s there and who’s not? Go find people to join the conversation and be their champion"

The key to building a predictive model is understanding what you are trying to predict and then collecting the data sets that enable you to do so. In the case of risk culture, what are the predictive indicators that may trigger concern about conduct or technology risk, for example, and how can those indicators be used to continuously monitor the risks in real-time? Perhaps an unexpected surge in sales of a particular product, patterns of time and attendance that are out of the ordinary, or a sudden rise in phishing activity.

When managing change, the use of data creates an opportunity to look beyond the surface and bring order. Of course, this is not to say that experienced change practitioners are not critically important in driving change, but the right data in the hands of a skilled changed professional with years of lessons learned is a powerful tool indeed and one that helps to accelerate progress and cannot be replaced with data alone.

Better balancing of scientific evidence, experience, and intuition will shift the investment in change so that is no longer seen as an optional extra or act of goodwill but rather a science of data-informed judgment and decision making that is truly central to the planning, implementation, and monitoring of change. True insights and evidence of anticipated outcomes will legitimize the investment and cement change management as the most powerful factor in successful transformation.

In our complex world where organizations must continuously transform themselves, data plays a critical role in driving rapid sustainable and systemic change, however, to harness the power of data for transformation efforts, organizations will need to build or source business capability (people, processes, tools) in several key areas to complement their change management capabilities:

● The ability to access, combine and analyze multiple data sources to inform strategic decision making

● The ability to build analytics models and visual dashboards for predicting and monitoring the outcomes that transformation will achieve.

● The ability to build predictive models that can continuously monitor in real-time and highlight the need for targeted change interventions

As it becomes more and more accessible and essential, the use of data is critical in accelerating and informing decision-making, predicting the need for change, and mitigating the risks of change. Relevant data is not necessarily complex to gather, and structure and the right capabilities will lead to better choices, better leadership, and better, more sustainable outcomes for all. Over time, this will have a significant positive impact on one very important data point, whether or not your transformation programs succeed and realize their benefits.