In the movie ‘Back to the Future’, Marty Mcfly travels to the past and accidentally interacts with his mom before she married his dad. This accidental interaction altered the future in unexpected ways which he then tried to fix. People Analytics can learn from Mcfly about our use of predictive analytics.
Specifically, predictive and prescriptive statistics are like a ‘data-driven time machine’ that we want to use in order to change the future by acting in the present.
We use ‘big data’ and look for answers to questions such as
- What are the early signs of people’s intentions to quit and what interventions may be most effective in retaining them (e.g IBM’s Proactive Retention program)?
- How can we detect long-term fit between job applicants and organizations to solve grief for both parties?
With data related to the right questions, we can use our ‘People Analytics time machine’ to predict the future and act to alter it. However, time traveling has implications, and that is the other lesson People Analytics should take from Mcfly.
When we interact with others in one-time point, we alter the reality of future times.
Similarly, when we measure and collect data to build and use our predictive models, we interfere with the reality which we measure. This can mean that, like Marty, we will need to ‘fix’ unexpected outcomes.
However, it can also mean that we can take the opportunity to expand our frame of mind and use measurement, not just to collect data, but rather to create a deliberate impact – cultural and behavioral.
Measurement – Not Just About Data Collection
Measurement is an effective tool to induce change, even regardless of the insights that can be derived from the collected data!
For example, in the famous Hawthorne studies (1924-1932) scientists were observing factory workers to understand the effects of lighting. While there were no differences between lighting conditions, a different unintended yet interesting finding came up: performance rose across all groups.
Later analyses of the data (1958) concluded that the observations themselves induced the increase in performance. This is known as the ‘Hawthorne/Observer Effect’ which simply means that people react and alter their behavior when they are aware of being observed.
Similarly to the ‘observer effect’, the ‘mere-measurement effect’ indicates that asking an individual about their intentions changes their subsequent behavior and decisions. These two effects taken together demonstrate the need to understand the effects of measurement beyond collecting data.
When you stop to think about the examples above, it is rather amazing actually. With hardly any resources invested people changes their behavior! This is powerful. And this is a great opportunity for people analytics professionals to become a proactive partner in shaping organizational culture and the employee experience by thinking of measurement as a cultural signal.
People analytics professionals should become a proactive partner in shaping organizational culture and the employee experience by thinking of measurement as a cultural signal.
Research the Measurement of People
To understand the cultural signaling of our measurement methods we should consider two elements – ‘what we measure’ and ‘how we measure it’.
In today’s technological world, the possible answers to both these questions have grown exponentially. There are so many things we can measure and so many ways to collect this data via technology – wearables, email, social networks, and collaboration tools.
People leave data trails behind them every online move they make. Such big data is the fuel of our time machine, and time traveling is really exciting!
Moreover, it seems people have less time and willingness to answer long/multiple questionnaires. Thus, collecting available online data may seem to be a win-win solution and the best way to go. But is it really the case?
The recent data protection regulations in the EU may point out to a different story. A story in which data collection methods induce negative perceptions. The focus on measurement from a ‘data collection’ perspective neglects the effects measurement may have on people and culture – does it make us suspicious? More guarded?
There is an important research to be held here about different methods of measurement and their emotional and behavioral effects. Such research is relevant to any method we may use for data collection.
Understanding Measurement Creates Opportunities
If we take questions as an example, we can find research that looked into how questions alter behavior and found that asking the right question is a powerful and cost-effective way to influence behavior.
Asking questions is also a known coaching tool to induce change. When we are asked a (good) question our brain comes to life, and depending on the framing of the question, different boxes in our heads are triggered. Once opened, these boxes shape our perceptions of reality, and direct our behavior. If we take note of this effect of questions, then we can think ‘end to start’ about what are the behaviors we want to encourage in the realm of what we focus on (e.g. employee engagement), and how can we frame questions to trigger these behaviors.
In addition to their effect on people’s behaviors, questions also affect how people perceive the entity who is asking the question. Think of Voltaire’s (1694-1778) quote – “Judge a man by his questions, rather than his answers” – within the organizational context. How do the questions we ask to collect data shape the perception of our organization?
Taken these two effects – alter behavior and change perceptions of organization – we can see that questions are a very powerful tool we can leverage to support our role as proactive culture-drivers.
Questions are a very powerful tool we can leverage to support our role as proactive culture-drivers
For example, imagine an organization in which all people are regularly asked about those who help them succeed in their work (e.g. who do you turn to when you want to brainstorm?). What effect such questions may have?
According to Wayne Baker’s research, gratitude motivates people to ‘pay it forward’. If we take note of the help we received, we will be inclined to do good for others. Moreover, if everyone in the organization recognize others, the more likely we are to adapt to the culture of ‘giving and recognizing’. Thus, questions that make us stop and take note of those who help us can induce behaviors beneficial for both individuals and the organization as a whole.
Moreover, as people are social entities seeking to be meaningful to others, recognized, and included, such questions may also trigger self-reflection (e.g. will I be recognized as a co-thinking partner?), which in turn can lead to improvements in social interactions.
Measurement can have a huge impact! Doc knew to warn McFly about interacting with others, he knew the ripple effect one minor interaction can have in the system. We can take the same lesson and apply it to measurement – one small intervention can have a ripple effect. Now the question is – how can people analytics take on the opportunity and make it a great one?