Everyday new data visualizations are built, highlighting information in a way that is interesting and meaningful for the people consuming it. As visualization libraries have improved, the breadth of visualizations has grown tremendously. Since the beginning of the year we've been amazed by some of the visualizations that have been built. Today, we're presenting 5 of our favorites (in no particular order).

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AuthorRyan Harrington

On October 17, 1991, Walt Mossberg published the first installment of his weekly column for the Wall Street Journal, Personal Technology.  In his first words, he succinctly summarized popular sentiment at the time towards personal computers. Now, 30 years later, his sentiments apply just as well to artificial intelligence as they once did to personal computing.

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AuthorRyan Harrington

Over the past seven years "mobile first" has taken over design philosophies across the country. This past week, Google launched a new product signaling their transition to a new philosophy: AI first.

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AuthorRyan Harrington

“Predictive analytics”. If you've paid any attention to the business or tech world over the past several years, then you’ve heard the phrase. It's a phrase that has likely been accompanied by other, equally intimidating sounding words like “big data” or “artificial intelligence” or “machine learning”.  Over the past decade, these phrases have only become more popular and even more important for businesses to understand. Don't believe us? Here's what the Google Trends for both "big data" and "machine learning" look like over the past 13 years.

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AuthorRyan Harrington

At its heart, predictive analytics is about making better choices.  If your company has the opportunity to make an informed decision, then it is likely to be more successful in the long run.  There are plenty of ways that predictive analytics does this – from artificial intelligence to clustering to regression.  One particularly important tool for predictive analytics is time series analysis.

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AuthorRyan Harrington

Each week at CompassRed, we take a few minutes to share what we've been reading with you.  Some of it's technical, some of it's topical, all of it's interesting. Want these fresh in your inbox every Monday? Subscribe now!

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AuthorRyan Harrington

Machines walkingComputers tutoringCars drivingAlgorithms diagnosing.  As we outlined in Part One of this series, each of these examples highlights some of the amazing progress that we have made using artificial intelligence.  In Part Two, we discussed how these artificial intelligence systems work.  Beyond understanding what artificial intelligence is and how it works, we must also understand its pros and cons.

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AuthorRyan Harrington

Each week at CompassRed, we take a few minutes to share what we've been reading with you.  Some of it's technical, some of it's topical, all of it's interesting. Want these fresh in your inbox every Monday? Subscribe now!

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AuthorRyan Harrington

by Dr. Steve Poulin

Over the past several years, artificial intelligence (AI) has received an increasing amount of attention (and scrutiny) recently because of the exciting new ways it is being used.  Many people are familiar with the technologies that AI has enabled, but how many people actually know how it works?  In this post, we'll explore what makes artificial intelligence...artificial intelligence.

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AuthorRyan Harrington

by Dr. Steve Poulin

Those who are working with Predictive Analytics are always trying to find a better more effective way. We have chosen a field that that can get better and better every day. And in the last few years, with the development of new technologies and approaches to acquiring data – we are finding the spotlight on us to do just that: find better ways. Traditionally, there are three primary ways to develop models and algorithms for predictive analytics: (1) the expensive SAS solution, (2) the cheaper but just as effective IBM SPSS, or (3) the open source “R”. We, at CompassRed, think there is a fourth: Leverage the best of all.

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AuthorPatrick Callahan

by Steve Poulin and Patrick Callahan

As a Data and Analytics company, we at CompassRed have seen the full cycle of leveraging data for insight with each phase of development being just as important as any other. Deployment is the part of the predictive analytics process that puts the predictions to work.  Predictive analytics requires a very time-consuming process of data preparation and a very complex process of finding the best algorithms (also known as “modeling”) for producing predictions.  However, all of this effort is for naught if the predictions are not used by an organization to meet their objectives, which typically means increasing revenue and decreasing costs

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AuthorPatrick Callahan

By Patrick Callahan and Noah Baker

A major component to CompassRed’s  predictive analytics capabilities - or any data analysis, is the method of delivery for the insights we unlock from a customer’s data. . Gathering insights without making them useful is a waste. Delivery can come in many flavors: via application programming interface (“API”) integration into your company’s software, custom triggers that alert via email/mobile notification/tweet, or in most situations, through dashboards delivered via “Business Intelligence (BI)”. 

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AuthorPatrick Callahan

In our CompassRed Data Lab, on behalf of our clients, we are always looking for a better way to “Predict” with our algorithms, including Machine Learning (ML), and Artificial Intelligence (AI). As data becomes more ubiquitous and complicated, and as the systems that manage the data become more fluid, the process and methodology become ever more important (as long as they’re flexible). (read more)

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AuthorPatrick Callahan

How CompassRed’s Dr. Steve Poulin and his team leveraged Unstructured Data and External Data to uncover success and save $10 million dollars for every percentage point reduction in turnover.

When 92% of the agents hired by a life insurance company were leaving the company within their first year, this large Insurance Company realized their current approach to retention was not sustainable. This was causing significant expenses for continuous recruitment and hiring costs.  Using unstructured data from the applicant’s resume and US Census data about the area in which they live, a predictive analytics process was developed to identify which applicants were most like to sell the amount of insurance required to become a successful agent with the company.  (read more)

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AuthorPatrick Callahan