DataThink’s Mission
Data are often as complicated and rewarding as the people they measure. DataThink thrives on building solid and thriving relationships between your corporate goals and your data.
Our Team
We are data scientists, analysts, programmers, statisticians, and problem solvers with over 20 years of combined industry experience. Our core team can expand to meet your needs as we are located next to one of the nation’s top undergraduate data science programs at Brigham Young University - Idaho. Whether you need 20 hours for a small project or ten full-time data scientists for the next year, our team can deliver.
Eli Johnson (LinkedIn, resume) and J. Hathaway (LinkedIn, resume) are the co-owners and primary contacts to make your data dreams a reality.
Our History
Data
If you have seen one dataset, you have only seen one dataset
There is an old idiom that goes ‘A mall’s a mall. If you’ve seen one, you’ve seen them all’ that Merriam-Webster even references. One of the unique things about data problems is that they don’t fit this idiom. They require thorough understanding of their issues and a way to interpret what they want to say to you. That’s why we strive to have a data diversity that allows us to meet your needs.
We are comfortable with data wrangling and analysis that leverages spatial and temporal dimensions. Each industry tackles the challenges of the interaction of spatial, temporal, and subject level measurements differently. The data formats often exemplify their unique challenges. The list below highlights data spaces where we have partnered.
- Sales and Finance Data: Time series data shines in this space. Helping clients see and understand seasonal and annual trends as well as leveraging their historical data to optimize future value.
- Agricultural: Leveraging the John Deere API, integrating weather data, and supporting displays that are heavily spatial and temporal.
- Medical records: Electronic medical records and the new space of real world data requires data wranglers that understand the domain and the needs. We are comfortable with Cerner’s Real-World Data products.
- Academic records: Student records data is strongly nested. In addition, there are lots of interactions between varied data environments.
- Environmental: These data often come in smaller bites but have their own needs with understanding measuring limits of detection, spatial precision, and data quality.
- Climate: These data often come in bigger bites. Climate scientists have built complex data formats to handle large Spatio-temporal data objects.
- Banking: Heavily transactional that often requires a bit of munging to get the data from the day-to-day storage to modeling and dashboarding.
Languages
Languages evolve much faster than the constructs
- R: We have partnered with R since it was birthed from S. We are big fans of the Tidyverse and Shiny.
- Python: Python has it all. We focus on the data science space. Whether you want machine learning, data hanlding, or other needs like pyspark, streamlit, or data visualizations.
- SQL: Almost every data project requires SQL to get it going and keep it working.
Tools
Where code meets the business need
We focus on the dashboarding tools that connect analysis with decision-makers — PowerPoints give once while dashboards keep giving. We can connect to and deliver from a wide arrange of products.
Some Examples
Much of our current and historical work is business sensitive. We are more than willing to discuss high-level details of our history using Pyspark, Shiny, PowerBI, and Streamlit. We have linked to a few of our public-facing examples below.