Here at Data Tapestry we are here to help organizations looking to understand their
data and make improvements to their organization’s customer service, turnover, or
solve one of the many other challenges facing organizations today. Some of our
specialties include NLP analytics, developing data system architecture tailored for
analytics, predictive analytics, and applying applications to simplify data.
With some of our current and previous clients we have been able to come up with
solutions to assist with turnover by automating the process of analyzing employee
exit surveys, analyzing employee efficiency, and utility corridor management using
machine learning. We have also worked on a project for integration of financial
and contract management systems to improve allocation to company’s KPI, as well
as parking assignments for universities during sports seasons to name a few. We
are able to tackle a variety of software customizations as well as analyze data and
provide solutions to common organizational challenges.
We look forward to the opportunity to serve you soon and to learn more about
challenges your organization is facing so our team can offer solutions for you. If
you are interested in talking with Data Tapestry to learn more about our projects or
how we can help solve your business’ challenge, reach out to us at
Business@datatapestry.ai.
One of the biggest hurdles in data analysis is just getting access to data in the first place. At Data Tapestry, we offer end-to-end analytics services beginning with data acquisition, performing analytics, and providing end user products. Keith Shook walks us through how to maintain data security and integrity when dealing with a variety of situations. Tell us a little about your background and your role at Data Tapestry. Currently, I’m a senior data engineer, but I actually started off as an intern ingesting data into Postgres and SQL databases. I then shifted into visualization using D3, a javascript library, but we found that Tableau was much more efficient. Since then, I’ve gained a variety of experience using scala, Hive, AWS, and building clusters. Can you walk us through a project you’ve worked on? Data engineering is pretty straightforward as far as the process goes. You get the data, ingest it into the database, and then hand it off to the data scientist. You have to be fle
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