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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

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

How to Nurture Existing Customer Relationships to Drive Revenue Growth

Posted by John Mack on Tue, Jul 26, 2016

PART 4 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the fourth in a four-part series. The third installment, “Existing Customers vs. New Customers — Exploring the Road Less Traveled,” discussed aspirational value and Big Data analytics’ role in attaining it. Here, we’ll discuss how all the elements described in this series fit together to drive revenue growth.

Businesses that overemphasize or exclusively focus on new business development to drive revenue growth are missing a substantial opportunity: their existing customers. Information about a business’ existing customers already resides in multiple areas of the overall corporate database, not just on a business development list. These customers’ profile information, consisting of demographic and psychographic details, preferences, and behaviors is there, offering a data picture that is far richer than the picture associated with prospective customers.

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Topics: Big Data, Signal Hub Technologies, Marketing

Flipping the Model: Successful Retailers Tailor Campaigns to Each Customer's Needs

Posted by John Mack on Tue, Jul 19, 2016

With 2016 already halfway over, retail marketers are deep into their 2016 campaigns, which are intended to acquire and retain customers, drive sales, and improve overall customer loyalty. But what are marketers doing differently to make 2016 better than 2015? Last year, the Commerce Department recorded only a 2.1% increase in retail sales (excluding automotive) over 2014 — marking the worst such performance since 20091 and a far cry from the 4.1% increase that the NRF projected2. If retailers haven’t changed the way they approach marketing, are we in for more of the same?

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Topics: Big Data, Marketing

Existing Customers vs. New Customers — Exploring the Road Less Traveled

Posted by John Mack on Tue, Jul 12, 2016

Part 3 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the third in a four-part series. The second installment, “Big Data Analytics: Necessary but Not Sufficient,” discussed three new ways companies can use Big Data analytics to improve a business’ ability to consistently improve revenue growth from existing customers. Here, we’ll discuss aspirational value and Big Data analytics’ role in attaining it.

People sometimes fail to notice opportunities that later seem obvious. Even when we do identify such opportunities, we may neglect to pursue them, or we may take an ineffective approach to pursuing them. One example of this phenomenon is how businesses think about driving revenue growth.

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Topics: Big Data, Signal Hub Technologies, Marketing