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
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.
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 firstname.lastname@example.org