We understand the plight of frustrated data scientists

We conduct a data DNA survey every year to better help you understand your data analysts and scientists, and the challenges they face. To respond, we’ve created a data science communication toolkit so you can glean the tools needed to solve recurring challenges.

Insights from our Data DNA survey

Data science touches every group of an organization.

We asked for demographic information about industry, company size, and job titles. Out of 400 responses, over half identified themselves as either data analysts or scientists but we had a wide range of other respondents from CEOs to consultants and psychologists

Insights from our Data DNA survey

The knowledge that data scientists wished their leadership knew about data science

People who work with data or use data in their work want their leadership to understand the power and limitations of data science as well as how it can be leveraged to better serve their team. More often than not, managers believe that “data is easily available” and that “data is clean“and usable for analysis without scrubbing.” However, it is a rare case when both of those statements hold true.

While building a data science team is a strong start, it is only a piece of a puzzle.

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Insights from our Data DNA survey

30% data scientists spend substantial time answering simple data questions

Time travel aside, 30% of anyone’s time is a substantial amount to spend on communicating the results of an analysis or helping colleagues understand data, and half of our respondents spend more than that. In fact, data literacy and data standards are key areas that can accelerate or hinder an organization’s path to becoming data-driven.

Insights from our Data DNA survey

Top 3 reasons data scientists are leaving their jobs

Expectation does not match reality

Many companies hire data scientists without a suitable infrastructure in place to start getting value out of AI.

Go to person about anything data

Non-technical executives often don’t understand what a data scientist truly is and end up making assumptions about data science skills.

Working in an isolated team

Just “plugging in” a data scientist in your databases won’t deliver the expected results.

Inside our data science toolkit you’ll find

Why become data-driven

Becoming data driven is how we can improve efficiency and stay competitive.

Unsupervised vs supervised ML

Review two powerful umbrella terms that data scientists use to describe their methods: unsupervised and supervised machine learning.


Data Collection best practices

Some best practices that how to standardize data collection and naming conventions across the organization


Data-driven organizations

Highlight the common knowledge and vocabulary that data-driven organizations need

Typical data science process

The typical data science process that we should go through every time we want to build a new data model.

Data scientists' time allocation

Due to no centralized repository, lack of standardization across different data sets, and not using the correct data, a large portion of a data scientist’s / analyst’s time is spent collecting and cleaning the data. 

Common misconceptions

These are the top misconceptions that were listed in the toolkit survey 

Phases of being data-driven

What phase is your company in?

 Not ready to talk to us?

We get it. Creating a swell around data takes a big commitment from everyone at your company. Because of that, we’ve created a toolkit to start a conversation around transforming the way organizations approach data transformation and data training — download it, and let us know if you have any questions. You can always find us at hello@datasociety.com