Skip to main content

Posts

Showing posts from June, 2020

Data Discovery and Understanding across an Organization

Much of what we do at Data Tapestry is helping our clients gain an understanding of their own data. If you work at a large organization, data access can be limited. This means you may not know what questions to ask of your data or what you can measure with your data. Hillary Rivera walks us through how she partnered with multiple stakeholders to gain insight from a variety of data sources. Can you tell us about your background and current role at Data Tapestry? I started out my career thinking I would eventually go to medical school, but I quickly realized I wanted to work in analytics. I studied public health and worked in healthcare data management for a while. Then I went back to school to study business analytics at UT. Since then, I’ve enjoyed working in ecommerce and software and technology. Now I’m back in healthcare working as a data scientist at Data Tapestry.   Tell me about how your project started with the client. I had a unique experience with my client because I had two v

Automating Visualizations and Implementing Standardized Data Collection Practices

Creating automated visualizations can be difficult when working data that has not been standardized. In a large hospital system, standardization requires multi-level communication across many departments as well as strict adherence to those standards so that processes can be implemented. Alex Ratliff talks us through how he created a dashboard around ever changing standardization issues. Tell me about your background and role at Data Tapestry. I’m currently a data scientist at Data Tapestry. My background is in math and analytics. During the first three years of my career, I worked at a company that contracted with the department of defense. While I was there, I worked on making dashboards to monitor data flow to make sure the data was being processed correctly. There were different sites that make data transfers. Our job was to make sure each job had the proper amount of bandwidth. I mostly used R and SQL to manage that.   Tell me about the dashboard project you worked on. When I star

Transforming and Accessing Data through Custom Built Pipelines

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

From Spreadsheets to Tableau: Creating Dynamic Data Visualizations

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

Consulting, Designing, and Coding: the Many Roles of a Developer

At Data Tapestry, we pride ourselves in deeply understanding your business needs and delivering solutions in a hands-on or consulting capacity. Our staff not only performs analytics, but we also build and support custom software products. Philip Vacarro, our full-stack software developer, explains how he partners with multiple clients in different capacities to deliver production software solutions, advise on data architecture, and provide product support.   Can you tell us about your background and current role at Data Tapestry? I’m a full stack software developer, and I serve as the first stop for new clients when it comes to consulting on data architecture and other products we’ve built. I’ve worked as a software engineer for Siemens in the research and development. We focused on interventional imaging. After that, I worked as a full stack developer at ORNL. We collected terabytes of data submitted from scientists all over the world.  The data was then centralized in an application

Reducing Workforce Turnover using Anomaly Detection and NLP

Maintaining an engaged workforce is essential to any organization looking to not only minimize the costs associated with hiring new personnel but also maximize productivity through engaged employees. Our senior data scientist, Jeremiah Lowhorn, partnered with one of our clients to analyze the risk factors that lead to employee turnover and how to mitigate them. We sat down with him to learn about how he was uniquely suited to solve such a complex problem. Can you tell us about your background and current role at Data Tapestry? My title is senior data scientist, and I’m currently working on my second master of science, this time in information management. Before Data Tapestry, I worked as a senior software engineer at Cigna focusing mainly on big data and data science projects. Prior to that role, I was working at US cellular as a data scientist. While there, I focused mainly on time series analysis and predictive modeling. Tell me about the problem you were asked to solve and what were