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When you're dealing with data, think about how the data was collected and what kind of data it is.
But that would have required it to be able to quickly process huge volumes of digital data (think about companies with millions of customers and millions of products in the field).
In category 1 (more data), think about all the data that does not live in CRM, but that could be useful in prioritizing prospects and activities.
Think about what you are not seeing from your data; think about what benefits you can harvest with faster and deeper insights; think about the value of taking your trained machine learning models and operationalizing them; and think about what augmenting your classical data analytics workloads outside the lab would bring to the business.
As you go through your data, think about how you can put what you've found into a thesis-like statement.
Similar(55)
What I try to do is to find new facts and data, things you haven't thought about, and turn them into new stories.
It's often helpful to step back from the data and think about things logically.
Going up a dimension means you've got a lot more data to think about.
"So it's no one thing in isolation, and that's why so many different organisations have to take note of this data, and think about what they can do".
When you're working through a data problem, think about how you'd explain it at Thanksgiving dinner (in a way that doesn't make everyone's eyes glaze over).
For example, a clinical researcher asked the data analyst, "think about yourself reading this article.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com