For some time it was the consensus that new data tools and technologies were going to be the thing that finally helped data teams break through, get executive buy-in, and drive the successful outcomes that we all knew they could. (View Highlight)
when I hear those calls for focus on people and process, I can’t help but feel skeptical that either of those things will be the thing that changes the circumstances of data teams. (View Highlight)
many companies are not environments where data teams can be successful, no matter which people, processes or technologies they put into place. Simply said, data teams being left on the sidelines is a problem of company culture. (View Highlight)
It takes a lot of energy, persistence and positive belief, but it’s absolutely possible. Companies may not be sure how to best incorporate data teams into their processes of value creation, but that doesn’t mean we can’t elbow our way in. (View Highlight)
They’ve been elbows of data—folks who have insisted on being involved in driving the company forward, whether they were invited to or not. This way of working doesn’t come naturally to a lot of data people, who are often careful and non-confrontational by nature, and it can feel daunting to try to change your environment when there will always be people who don’t want it to change. (View Highlight)
Elbows of data make a habit of fact finding. If teams at their companies send weekly updates or quarterly newsletters, they engage with them. (View Highlight)
Elbows of data also think about the second life of their work. They of course do their work with a specific customer or audience in mind, but they are also keenly aware that one of the most valuable things about data work is how cross-cutting it is. (View Highlight)
. Elbows of data demonstrate the importance of data teams and data work by solving the company’s problems, even and especially if the company hasn’t recognized that a problem is a data problem yet (View Highlight)
Elbows of data are proactive about explaining their constraints and asking for what they need. If an elbow of data is overwhelmed with stakeholder requests, they tell everyone who’s asking how much they’re being asked for so that those stakeholders understand why things are taking so long. (View Highlight)
They make it clear that data work isn’t free by helping their colleagues understand the reasons why something takes as much time as it does. (View Highlight)
