Email and Calendar Data are Helping Firms Understand How Employees Work
Email and Calendar Data Are Helping Firms Understand How Employees Work
Using data science to predict how people in companies are changing may sound futuristic. As we wrote recently, change management remains one of the few areas largely untouched by the data-driven revolution. But while we may never convert change management into a “hard science,” some firms are already benefiting from the potential that these data-driven techniques offer.
One of the key enablers is the analysis of email traffic and calendar metadata. This tells us a lot about who is talking to whom, in what departments, what meetings are happening, about what, and for how long. These sorts of analyses are helping EY, where some of us work, by working with Microsoft Workplace Analytics to help clients to predict the likelihood of retaining key talent following an acquisition and to develop strategies to maximize retention. Using email and calendar data, we can identify patterns around who is engaging with whom, which parts of the organization are under stress, and which individuals are most active in reaching across company boundaries.
Understandably, there may be privacy concerns about examining an individual’s email or calendar, even in a work context. However, you can also get powerful insights using anonymous metadata, where the individual names and specific content are removed. It’s possible to analyze the metadata for content themes and frequency of contact between departments, and to correlate this data with more traditional indicators of process effectiveness, cycle time, right-first-time, and so on. What this gives us is hard data on how processes fail in the organization. We no longer need to rely on anecdotes or employee surveys — instead, we can pinpoint precisely where the breakdowns are occurring just by examining data on day-to-day workflows. We can say precisely what behavior change is needed to make a new process work, and then monitor improvement in real time.
An early example comes from an organization restructure we have been working on. These sorts of projects are usually motivated by a desire to improve strategy execution and reduce costs. Traditionally, only the financial element was measurable, which could easily drive decision-making. For one EY client, we are using data science to make organizational design decisions that accelerate strategy goals. The client wanted to increase collaboration across units, for example, between sales and product development. We used an analysis of anonymized email and calendar data to predict what impact the number of direct reports a manager had on the ability of specific teams to collaborate. That helped us to optimize work design to achieve the result the client wanted.
The potential of these techniques is to change the way managers interact with employees. Today, most managers are doing their best to engage and motivate employees. However, we have to wait for “formal triggers” before we can respond, such as an employee survey or a one-on-one with a manager. Analyzing activity in email traffic might allow us to intervene much faster and find out whether what we are doing actually works. This can become a sort of “real-time employee sentiment analysis” that would transform the quality of insight managers have at their disposal.
Let’s take the example of the recent executive order in the United States that imposed a travel ban on seven mostly Muslim countries. This was a major concern for many technology companies that have a large number of employees on H1-B visas, both from the countries involved and from their neighbors in Asia and the Middle East. If companies were using an artificial intelligence solution that provided real-time insight, they would be able to monitor the level of concern in the organization, perhaps even anticipate the sorts of concerns that employees were having. Many employers set up dialogue sessions with employees to answer questions and attend to their concerns. The only evidence we have on the impact of these sessions was anecdotal. With a “real-time employee sentiment” system, we’d be able to say precisely and respond accordingly, and measure the impact of those responses.
We will always need professional change managers to interpret this data and to design the right sorts of ways to work with employees during transformation or external emergencies, such as the travel ban. What these data science tools can do is make our responses faster and more targeted and tell us what worked in a faster, more reliable, and less invasive way than was previously achievable. In the organization restructuring referenced above, it took only three weeks to analyze a year’s worth of behavioral data to be included in the design of the future processes and structure. In the past, we would have relied on invasive techniques, such as interviews and employee surveys, that not only take up time but also introduce all kinds of bias. Our advice for the change manager of the future is to make data your friend; never reorganize without it.