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Ensuring Predictive Analytics Success with Data Preparation & Quality

Posted by Daniel D. Gutierrez on Fri, Mar 24, 2017

If you’re in the business of pretty much anything, you’ve got a lot of important data coming in from a lot of different places — both internal and external. What you might be lacking are some best practices that could help you access or see all of that data and be in a position to extract important insights that could nudge your business into new competitive directions.

But what data is relevant to your business and where is it? Can you access it when you want to? Do you know that it’s accurate, current, clean, and complete? Can you easily pull all the data together, no matter what format it’s in or how often it changes?

Basically, is your data ready to support analytics?

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Topics: Big Data, Data Science, Data Equity

Data Science Maturity: A Take on Maslow’s Hierarchy of Needs

Posted by Anatoli Olkhovets on Wed, Mar 15, 2017


In my role of leading Product Management and Presales at Opera Solutions, I am constantly exposed to direct customer interactions, most often in the early stages of the sales cycle. In these meetings, part of my job is to assess our prospective customer’s pain points and needs as much as they are assessing our products, technology, and capabilities. Thus, given the exposure we get at Opera Solutions, I am in a good position to understand real-world business needs around analytics across industries.  

We often talk about corporate cultures, but the experience with hundreds of customers led me to think of corporate psychology, and juxtaposing Maslow’s hierarchy of needs to the current state of data science adoption and readiness in the industry. Companies need to recognize the stage they are in and not be seduced by the hype or promise of the technology. Data science adoption needs not follow a sequential maturity process; dynamic corporations can certainly accelerate things when the need and will exist. So for fun, here’s a take on Maslow’s Hierarchy of Needs adapted to data science.

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Topics: Big Data, Data Science

Big Data Reflections for 2017

Posted by Laks Srinivasan on Thu, Feb 23, 2017

To predict the future, one must look at the past, says the old adage. To determine what to expect in 2017, we thought it was best to draw lessons from 2016 despite our industry’s yearning for dramatic change. Laks Srinivasan, COO at Opera Solutions, shares his insights into the biggest Big Data trends of 2016 and reflects on where the market is going and how companies will react.

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

AI, Machine Learning, and Deep Learning Explained

Posted by Qi Zhao on Tue, Feb 14, 2017

Artificial intelligence is no longer just evolving nomenclature in IT. Everyone is taking interest. With the mainstream press and bloggers from every corner weighing in, it is worth taking stock of the nomenclature and learning how to differentiate three overly used key terms: artificial intelligence (AI), machine learning, and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model (as depicted in the figure below) with AI — the idea that came first and has since been evolving — having the largest area. This is followed by machine learning, which blossomed later and is shown as a subset of AI. Inside both of those is deep learning, which is just one class of machine learning algorithms but one that is currently driving today’s AI explosion.

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Topics: Data Science, Machine Learning, Artificial Intelligence

How to Recruit Data Scientists

Posted by Tim Bridges on Tue, Feb 07, 2017

A guide from the head of HR at a leading analytics company

Data scientists are in higher demand than ever before. According to the latest CrowdFlower survey, 79% of respondents reported a data scientist shortage in 2015. In 2016, that number grew to 83%. The race is on to find skilled people who can organize, structure, and make business sense out of Big Data sets. People with heavy STEM, analytics, and conceptual skills, and the attendant work-friendly personality traits (insatiable curiosity, ability to prioritize, and a healthy dose of skepticism, to name a few), can virtually write their own tickets.

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Topics: Big Data, Data Science, Analytics

7 Steps to Prepare for Data Science Adoption

Posted by Anatoli Olkhovets on Wed, Feb 01, 2017

 

Everywhere you turn, both business and IT talk about data science. But there’s also trepidation about how to get started, especially in the context of attaining an organization’s business goals and objectives beyond the realm of lab or departmental experimentation.

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Topics: Big Data, Data Science

How to Transform Marketing with the Membership Economy and Advanced Analytics

Posted by John Mack and Sarah Anderson on Wed, Nov 30, 2016

Everyone wants to belong. But how can that basic human need coexist with the commercial needs of a business so that both the customer and the business find the relationship beneficial? Big Data analytics makes it possible while also opening new possibilities.

History is replete with examples of human beings finding ways to connect with one another. We form tribes, congregations, clubs, and entire societies. We develop communications channels and pass specialized content through those channels. Even efforts to divide these groups and disrupt these channels simply engender new ones and actually help strengthen our identity as individuals. This pattern has continued to evolve with the advent of the digital age and extends to peoples’ relationships with products and services. Are you loyal to Mac or PC? Do you use Facebook or Instagram? Are you enrolled in Amazon Prime? Are you a Netflix subscriber? This need to identify one’s self with a larger group is a primal human instinct no matter how contemporary the group.

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Topics: Big Data, Analytics, Marketing

The Relentless Progress of Big Data and Machine Learning Technology

Posted by Anatoli Olkhovets on Tue, Oct 04, 2016

“Didn’t you just go to a similar Big Data conference recently?” my wife asked me. “How much could have changed in a few months?” I was hesitating about attending another conference in a short time span. My wife is right about most things, but in this case, I am glad I didn’t listen and went anyway. I learned about many new advances in both commercial and open source tools and across the whole technology stack: new hardware, in-memory databases, and new-and-improved tools.

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Topics: Big Data, Data Science, Machine Learning, Hadoop, Signal Hubs, Spark

CLV and Clumpy Customers: Why Clumpy Behavior Is Changing the Customer Lifetime Value Equation

Posted by Michael Turon on Fri, Sep 23, 2016

Calculating customer lifetime value has been a critical part of the marketing process for years, but new technologies are changing consumer behavior, and marketing professionals need to catch up.

Ever have a weekend where you just can’t stop spending money? Maybe you’re using some free time to stock up, or you’re decorating a room or planning a party. Or maybe you’re not spending more but just consuming more — a good television or movie series or, for example, plowing through a good book on your Kindle. This kind of behavior is called “clumpiness.” And while it’s not necessarily new, new technologies — particularly in the world of digital content — have shed light on this type of behavior for all industries. The problem is that it hasn’t been included in the formula marketers have been using for decades.

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Topics: Signal Hub Technologies, Analytics, Marketing

Data Science for Business Users — An Overview of Popular Techniques

Posted by Anatoli Olkhovets on Thu, Aug 04, 2016

Everybody seems to be talking about machine learning these days, and a quick check of Google returns 21.2 million search results for the term. Clearly, this is a popular topic. Yet the term “machine learning” can have many different meanings, depending on the context in which it is being discussed. It is also associated with an equally lengthy list of data science techniques and technologies. Business leaders often feel overwhelmed by this rather bewildering array of terms, analytical approaches, and technology solutions. There are hundreds of algorithms, with new variants seemingly appearing every day. Researching them online does not seem to clarify the choices or point to an obviously superior decision, as most articles target deep experts and revolve around nuances of a particular model or open source package. As a result, business leaders can be reluctant to adopt something they don’t fully understand, resulting in missed opportunities.

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Topics: Data Science, Machine Learning, Analytics