For both 'currents' the solution is: a conversation with the customer, to verify the image from the data before a decision is made. But in both cases the threshold is very high to discuss that image of a customer with that customer. What is therefore especially needed here, in addition to the aforementioned overview of impact and insight into consequences, is to have a really good customer conversation . A good customer manager does that in principle, of course, but the content of the conversation may change considerably due to the new possibilities of data. This also touches on the cultural issue: people must be able to do it and want to do it.
Learning to work with data
There are several points of interest in learning to work with data. Let's stay with the execution for a moment. The counterargument that it takes too much time? Time follows priority . And besides, investing time 'up front' usually yields time gains at a later stage.
What underlies the resistance more strongly is the fact that people prefer not to change. In the process of learning to work with data, they have to learn skills , but also unlearn them . And nobody likes that. In working with data, there are 7 skills that you have to learn, and 3 ingrained patterns that you have to unlearn:
Thinking about ethical frameworks and remaining steadfast in applying them (even when under pressure).
Awareness of the fact that all data sets and all categorizations that follow from them are not objective.
Asking targeted questions to the data to make sharp choices.
Gaining an overview of everything that affects data and decisions and gaining insight into all the implications of choices (in the system and living environment).
Critically examine the results of your questions: test from different angles.
Awareness of how your judgment works and actively applying it.
In implementation: thoroughly reviewing a picture of a south africa telegram data customer situation in a critical and equal conversation with the customer in question.
3 Ingrained Patterns You Need to Unlearn
Rock-solid confidence in the outcomes of 'objective' data.
The belief that data has intrinsic 'predictive value', and that this does not depend on the lens through which you look at the data.
Reactive viewing: allowing yourself to be surprised by data and outcomes and letting them guide your decisions without asking control questions.
Implementing data-driven work requires a programmatic approach
In the organizations I work for, data-driven work is often a project. That suggests that there is a defined scope, that at some point you can 'implement' 'dashboards', and then you are done. I doubt that is true.
In my experience, implementing data-driven work should not only be a matter of visualizing data and giving people access to those insights. I am convinced that this should be accompanied by a solid program around understanding and being able to deal with data. With awareness of how your human decision tree works, and what you can (or should) do to not blindly sail on data. In fact: with actively guiding you in using your common sense.
Well, seen like that, data-driven work is actually very human.
