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Opera Solutions Wins First Place in Kaggle Competition

Posted by Sarah Anderson on Mon, Feb 26, 2018

Michael Jahrer, VP of applied machine learning at Opera Solutions, proves his data science mettle by using deep learning to predict who will be a safe driver in the year ahead. 

Porto Seguro, the third-largest insurance company in Brazil, set out to improve its predictions of who would file an insurance claim in the next year. The company sponsored a competition through Kaggle, the premier platform for predictive modeling and analytics competitions, and our own Michael Jahrer, VP of applied machine learning, took home the win. 

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Topics: Machine Learning, Artificial Intelligence, Data Science, Analytics

Decreased Attention Spans: A New Reason to Perfect Personalization

Posted by Laks Srinivasan on Wed, Jan 10, 2018

Online shoppers are getting antsy. They’re spending less time thinking about purchases before buying — or leaving a site — and drastically shrinking marketers’ opportunity time. We explore the latest trends in online personalization — and how to keep up.

We spend a lot of time talking about personalization in marketing. That’s because it’s both the most profitable way for our customers to leverage artificial intelligence and the most challenging. A new paper by Seth Earley, published in IEEE’s November/December issue of IT Professional, lays out some of these challenges and addresses how best to overcome them.“AI-Driven Analytics at Scale: The Personalization Problem” touches on many of the issues our customers face every day, including maximizing data scientists’ time, developing use cases in a timely fashion, and even using a Signal Layer to help expedite the process. It also cites a new study that examines exactly how much time companies must apply their personalization insights in a real-time setting.

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Topics: Analytics, Artificial Intelligence, Big Data, Marketing

8 Steps to Executing a Machine Learning Solution

Posted by Anatoli Olkhovets on Tue, Sep 12, 2017

Managers and executives at all levels are now expected to be at least familiar with how machine learning models are built and deployed. However, if you don’t have a formal data science education, reading through industry publications is not very helpful: High-level use case descriptions and marketing materials too often present machine learning as somewhat of a dark magic powering their products; technical publications tend to be incomprehensible for a nonspecialist, and how-to guides simply list the steps without giving sufficient background as to why each step is needed, which limits understanding. 

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Topics: Big Data, Analytics, Machine Learning

Ensuring Predictive Analytics Success with Data Preparation & Quality

Posted by Daniel D. Gutierrez on Tue, Aug 01, 2017

Data is the lifeblood of most organizations these days. The insights that come from a company’s data can help drive major — and minor — decisions, providing incremental boosts in company performance on a regular basis and even drastic boosts on occasion. But if you’re not seeing these kinds of results from your data, your problem might not be the analytics. It’s more likely that you’re missing key steps in preparing the data and ensuring its quality.

Proper data preparation ensures the ability to access both internal and external sources of data and transform these data sets into a form that’s ready for analysis. This might involve various forms of data transformation, including processes for improving data quality. Data scientists spend up to 80% of their time preparing data for analysis and ensuring its quality, leaving only 20% to do the actual modeling and analysis that deliver the relevant, actionable insights companies are after.

These numbers are not new, and they shouldn’t be a surprise to anyone reading this. But for those wondering what exactly goes into the process, why it takes up so much of scientists’ time, and why data prep and data quality are so important, we spell it out here.

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Topics: Big Data, Data Science, Data Equity

How Predictive Analytics Can Rescue the Retail Industry

Posted by Georges Smine on Tue, Jul 25, 2017

Many people are under the impression that great marketing is an art, but the field of data science has introduced a scientific component to marketing campaigns. Clever marketers are now relying on data more than ever to assess, test, and plan their strategies. Although data and analytics will never replace the creative minds behind the best marketing campaigns, they can provide marketers with the tools to help improve performance. 

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Topics: predictive analytics, Data Science

The Data-Driven CMO: 7 Steps to Overcome Data Pains in Marketing

Posted by Sarah Anderson on Tue, Jul 18, 2017

In our new podcast series, The Data-Driven CMO, Opera Solutions Senior Vice President John Kelly interviews Julie Cary, CMO of La Quinta Inns and Suites, who has developed a strong reputation as both a traditional and digital marketer in her dde-long tenure with the company. She discusses the steps taken to integrate Big Data and data science into La Quinta’s everyday marketing operations.

Julie shares how she balanced marketing, technology, data science, and IT and secured the necessary budgets to develop a robust digital marketing environment with tangible marketing results.

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Topics: Data Science, Marketing

Get More Mileage Out of Your Third-Party Data

Posted by Nicholas Wetherbee and Alissa Zhang on Thu, Jun 01, 2017

Here at Opera Solutions, we often refer to data equity, which we define as not just the amount of data you have, but also the ability to derive value from it. And to get value from your data, you need to ensure it is high quality. But how? Knowing the answer could make the difference between data equity and data bust. 

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Topics: Big Data, Data Equity, Signal Hub Technologies, Analytics

Why You Need Multiple Clustering Techniques

Posted by Anatoli Olkhovets on Tue, May 30, 2017

Shopping around for a Big Data analytics solution is a daunting task for anyone. But for those who are somewhat familiar with data science, a common area of misunderstanding — and underestimating — is clustering techniques. Whether the assumption is that all clustering techniques are created equal or that a company needs only one or two clustering techniques, business buyers are often left scratching their heads. The fact is several types of clustering techniques exist — each with its own strengths and weaknesses — and companies need access to a variety of techniques to accomplish optimal results.

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Topics: Data Science, Big Data, Analytics

The Data-Driven CMO: Opera Solutions Announces New Podcast Series

Posted by Sarah Anderson on Tue, May 09, 2017

Opera Solutions is pleased to announce its new podcast series, the Data-Driven CMO. With an increasing focus on technology and Big Data, the marketing profession must rapidly learn how to use such technologies as predictive analytics, machine learning, and artificial intelligence. 

At Opera Solutions, we have helped several global companies transform their marketing using our predictive analytics software. However, technology and products are only part of the story, and we wanted to bring the organizational and human perspective on this transformative shift to light. Our new podcast series opens the discourse through direct conversations with some of the most technically adept marketing leaders. 

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Topics: Marketing, Big Data, Analytics

Analysis Paralysis — How to Turn Big Data into Profits

Posted by Laks Srinivasan on Wed, May 03, 2017

As the amount of data accumulated by businesses continues to grow, one of the often confounding questions asked is “How do we turn it into insights and then profits?” Sooner or later, most businesses find themselves confronted with analysis paralysis and become unable to extract meaningful insights or monetary value from the data that should be fueling their growth.

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Topics: Big Data, Analytics