Slackalytics Part Two: Looping Back on Threads

Posted by Pat Lapomarda on March 2, 2017

In last month's installment of Ask a Data Scientist we introduced the CRISP-DM process and walked through the initial step of identifying a business problem, which in our case was "why are employees leaving important channels in Slack?" 

We were able to pull the content of the messages in Slack and produce Word Clouds using R to gain an understanding of the data.  This month we worked to see if we could quantity the problem of people leaving channels. 

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Topics: Data Science

Why Manufacturers Shouldn't Wait To Move To The Cloud

Posted by Jessica Dugas on February 23, 2017

Manufacturers rely on data to improve process and make production more efficient, so they should have the most efficient data system. Leveraging the cloud for data storage is great way for manufacturers to collect, integrate, and analyze their data to push ahead of the competition. 

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Topics: Data Science, Data Analytics

Risk Management Best Practices That Every Insurance Company Should Adopt

Posted by Jessica Dugas on February 21, 2017

Insurers essentially sell risk, so management of this risk is not taken lightly in the industry. However, because insurance companies have been measuring and weighing risk before the data science industry bloomed, many have fallen behind on the lastest technologies and best practices for data collection, management, and analysis- all of which are required for evaluating risk.

Here are three best practices that every insurance company should adopt to improve risk managment:

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Topics: Data Visualization, Data Science, Data Analytics

How Insurance Companies Can Cut Costs Without Jeopardizing Data Quality

Posted by Jessica Dugas on February 16, 2017

In the insurance industry, data is a vital tool to make decisions around risk, claims, sales and pricing. However, collecting, managing and analyzing data can be extremely costly- but it certainly doesn't have to be. With our experience working with insurance companies, we've come up with three key ways insurance companies can cut costs without affecting their data quality or security- and quite possibly improve it!

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Topics: Data Science

Serverless vs Traditional Architecture

Posted by Nick Marshall on February 9, 2017

 If you have been following the Cloud news lately you may have heard the buzzword "Serverless" passed around, like it is the next big thing. In order to understand what "Serverless" means, let's start by examining what a traditional Server based system entails.

A traditional server is a (frequently large) computer that is accessed over the internet to provide access to information or services. The machines that host websites and deliver them to your browser are a common example of this.

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Topics: Data Tools, Data Science