4 Things Your Data Scientist Should Not Be Doing

4 Things Your Data Scientist Should Not Be Doing

4 Things Your Data Scientist Should Not Be Doing
It’s the age of big data.

Sets of data so vast, so overwhelming that traditional data processing applications aren’t able to handle them.

In fact, according to statistics, more data has been produced just in the last couple of years than in the entire previous history of the human race.

What’s more is that big data is so proliferating that by 2020, 1.7 megabytes will be created each second for every human on the planet.

That’s a lot of data. So what’s an organization to do with it all?

These professionals are responsible for analyzing and interpreting data, then helping their employers put it to good use.

This requires a unique range of skills, including a love of math, algorithms and statistical modeling, along with insights into basic human psychology.

Not only that, but data scientists need to be great storytellers.

In other words, they have to be able to put their findings into context so they can identify trends and patterns based on various parameters, develop useful recommendations, and communicate them in a visual and non-technical way to company stakeholders.

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It’s certainly a tall order. And that’s why these professionals are so in demand.

That said, the role of the data scientist is new to most organizations and can therefore be easily misunderstood.

To get the most out of your data scientists, make sure you’re not expecting the following from them.

EIM professionals should be the ones defining and preparing data that a data scientist uses.

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