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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, Machine Learning, Analytics

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: Data Science, predictive analytics

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, Signal Hub Technologies, Analytics, Data Equity

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: Big Data, Data Science, 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: Big Data, Analytics, Marketing

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

Why Retailers Must Win Millennials — And How to Do It

Posted by Sarah Anderson on Wed, Apr 12, 2017

What’s the big deal about Millennials? For starters, they’re now the biggest demographic in America. But their needs are unlike any generation before them. Here’s what retailers must know to succeed in marketing to this 80-million–strong population.

Millennials are now the largest living generation in the United States, according to the US Census Bureau. Defined loosely as those born from the early 80s to the early 2000s, Millennials account for roughly 25% of the US population at 83.1 million, exceeding the 75.4 million Baby Boomers in the country. They grew up with household computers, cell phones, and myriad other forms of technology, and they entered the work force during the Great Recession, which has made them price-conscious, tech-savvy shoppers — and robust savers. About half of them still live at home, and they’ve taken over the work force. While most are holding off on marriage, nearly 40% have children and 9% live with domestic partners, indicating that they’re starting families and establishing households (i.e. shopping and spending). Perhaps the most telling stats come from a study from FutureCast, which says that Millennials’ purchases are most influenced by their social circles and that they’re the first generation to influence older generations.

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Topics: Big Data, Data Science, Marketing

Data Enablers: Opera Solutions Signals Big Data Value

Posted by Laks Srinivasan on Thu, Apr 06, 2017

In this interview with PYMNTS, Laks Srinivasan discusses the challenges and opportunities the Big Data brings to the world's largest enterprises.

Big Data may be everywhere, but that doesn’t mean that companies are able to actually get the most out of it in an efficient and scalable way.

With the notion that the world’s flow of computable information would eventually become the oil of the twenty-first century, Opera Solutions was launched back in 2004 with the goal of addressing the challenges and opportunities emerging as a result of the influx of data that came from more people having increased access to technology and a greater ability to generate even more data.

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Topics: Big Data, Data Science, Machine Learning