TDW Insights

Creating equal opportunities for women in tech must start early and continue throughout their careers to ensure diversity and equality in the workforce.

Refined, standardized data is the key to train machine learning in order to achieve meaningful results.

Understand why we built our Ingest pipeline the way we did, and why generic ETL solutions fall short when it comes to handling data variety.

What's it like working at a startup like ThinkData? We ride that line between delivering enterprise solutions and keeping the spirit of fun and creativity.

Data projects are failing, but the problem isn't your data team – find out why business goals and data outcomes aren't lining up and what to do about it.

Open data cannot live up to its potential until available data becomes accessible information – explore the historical gap and the progress being made.

Use public sources of data to better understand the electoral process and apply data science to drill down into the socio-economic profiles of contributors.

There's a world of difference between available data and accessible data, and it's not your data team's fault that the two don't intersect more often.

Entity resolution is simple enough conceptually, but exceedingly difficult to achieve in practice and at scale, so we built er² for master data management.

There's value in external data, but getting it, and getting it into shape, is not so easy. DataOps is the missing piece of the puzzle.

Landgrid.com introduces USPS Nationwide Residential and Vacancy Data, the first-time that vacancy data has ever been released at the parcel level.

ThinkData's data partner, WatrHub, provides insight into water infrastructure across North America to mitigate water infrastructure problems early.