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.
Data visualization can be a tough undertaking for many organizations. Choosing which metrics and how to visualize them in an effective manner can be challenging when you are limited to just tools like excel. Additionally, how do you keep them up to date with the everchanging demands of your business? Alexa Tipton explains how she partnered with a client to achieve just that. Can you tell us about your background and current role at Data Tapestry? Currently, I’m a data scientist at Data Tapestry. Before that, I was a research assistant at UT Knoxville for the center of ultra-wide area resilient electric networks or CURENT. While there, I used a number of techniques including text mining, machine learning, and NLP to analyze tweets from Twitter. The goal was to understand the public’s sentiment towards their energy providers around natural disasters, but more importantly improve electric grid structures overall as part of the national science foundation project. Tell me about how you
Comments
Post a Comment