You've never had so many metrics about your team, and yet many key decisions are still made late or on the back foot. In this episode of Jugando en Serio (in Spanish), Blanca Castán, a people leader with a background in technology, data analytics and international banking, explains why data-driven decision making only works when you add context, listening and judgment.
Why data isn't enough to make decisions about people
Today you can measure almost everything: performance, engagement, productivity, turnover. But deciding about people has never been a math problem. Castán learned this through a specific case: the data showed a sharp drop in productivity in one area and pointed to one person in particular, so her team drew up an improvement plan for them.
With the plan almost finalized, a conversation with the manager changed everything. That person was going through a difficult personal situation, the team had changed its processes without enough training and was overwhelmed, and on top of that, they were helping out another team. None of it showed up in the data, and all of it pointed to a different decision.
In my experience, there has never been a decision based solely on a dashboard. Dashboards give you information and point you in a direction, but you don't have the full context.
Data, context and humanity: the three layers
That doesn't mean data is useless: in that very case, it was the data that flagged the problem. For Castán, people analytics shows what the eye can't see, context explains where that data sits, and thinking about people is the finishing touch that completes the decision.
Context starts with asking the right question. If you're analyzing turnover, do you care about overall turnover or about losing your key talent? And at the other extreme lies analysis paralysis: analyzing and analyzing until you discover the decision has already been made without you.
When data becomes a shield
With so much data available, it's easy to hide behind it to avoid the responsibility of deciding. In leadership committees, Castán always argues for looking at the context and thinking about people, guided by a clear mantra: when people grow, companies grow.
Turnover taught her the same lesson. An analysis seemed to prove that every voluntary departure came down to one specific cause; action was taken, and turnover didn't drop. Only when the team stepped back and listened to managers and employees did the real causes emerge. For her, active listening is essential.
If the data looks too perfect, I always want to take another look, or at least stop for 30 seconds: why is it so perfect? Because there's probably something hiding behind it, and you can't hide behind that to make a decision.
Decision making can be trained
In her team, judgment starts with hiring: they look for soft skills such as decisiveness and the autonomy to turn data into useful insight. It then develops on the job, with senior profiles mentoring juniors and sharing the mistakes they've made, because for Castán a mistake is a fantastic learning opportunity.
That's why she believes decision making should carry the same weight as negotiation, communication or leadership in the training of anyone who works with data, and that people should be free to make mistakes within clear limits set by company culture. For the AI era, she adds curiosity and the judgment to tell what's real from what isn't.
That human focus is also a cultural choice: when she was hired, the CEO explicitly asked her to be «a human person». And according to Castán, it shows in the business, because happy people pass it on to customers.
The context pause: two questions before you decide
The habit she recommends was born from a mistake: when she analyzed what had gone wrong, two questions emerged that any leader can start asking tomorrow.
The first: what do I need this data for? If you can't answer, it's noise. The second is what she calls the context pause: stop for 30 seconds before deciding and ask yourself where to focus that data, what reality lies behind it, what people are telling you and how to factor that into the decision. Her conclusion: a company is powerful when it has data, but above all when its leaders are trained to decide, to listen and to be human.
