Daniel D. Gutierrez

Recent Posts

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

The Growing Government Open Data Movement

Posted by Daniel D. Gutierrez on Mon, Jul 07, 2014

It was 7:00 a.m. on a Saturday morning, and the 10th floor of Los Angeles City Hall was filled with more than 450 people gathered to spend their day off of work with their noses buried in their laptops. They were data scientists, and they had come to innovate new technologies to solve complex social problems using the city’s newly open data.

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Topics: Big Data, Data Science

The 5 Best Data Science Tools from the 2014 Hadoop Summit

Posted by Daniel D. Gutierrez on Fri, Jun 20, 2014

The Hadoop Summit conference, hosted by Hortonworks and Yahoo, has become a must-see Big Data event. The Hadoop distributed computing architecture is now an integral part of what it means to be a data scientist, and a few days of concentrated effort each year is enough to get a vision for where the industry is headed. The Hadoop Summit serves this purpose well by providing thought-provoking technical sessions, keynote addresses, and a vendor exhibition that brings many of the major players in the Hadoop ecosystem together under one roof.

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Topics: Data Science, Machine Learning, Events

Data Science As the Panacea for Healthcare Fraud, Waste, and Abuse

Posted by Daniel D. Gutierrez on Thu, Jun 12, 2014

Edited by Yan Zhang

Many pundits speaking about the state of the nation’s healthcare infrastructure routinely point to fraud, waste, and abuse (FWA) as major reasons for many of the problems the public witnesses every day – increased healthcare costs and the resulting increase in insurance premiums. The net effect is an annual loss of billions of dollars, and these losses affect the public in very real ways. 

The size of the healthcare sector, the enormous amount of money involved, and the lack of surveillance and monitoring mechanisms across the healthcare ecosystem make it an attractive target for FWA. According to the Office of Management and Budget, in 2010, about 9%, or $47.9 billion was lost to fraud in Medicare alone. It is therefore imperative to develop effective FWA technologies and solutions for reducing the costs associated with our healthcare system.

Data science and its primary enabling methodology of machine learning represent the country’s best chance for detecting FWA to avoid extraordinary sources of loss. Data science possesses the facilities to make a significant difference healthcare industry budgets and their impact to the public. Opera Solutions is the industry leader in applying Big Data technologies to the most challenging and significant business problems. We are the company charged with developing the analytics to identify fraud for the Centers for Medicare & Medicaid Services (CMS) on the health insurance exchanges. Here’s a look into just how big this challenge is — and some of the approaches we’re taking to overcome one of the costliest burdens in America.

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Topics: Healthcare, Big Data, Data Science, Machine Learning

Data Science: Recommender Engines with Collaborative Filtering

Posted by Daniel D. Gutierrez on Mon, Jun 02, 2014

Ever wonder how services like Netflix or Pandora choose media to suggest to you? If you’ve been reading this blog for a while, you’re familiar — at least a little bit — with recommender engines. In our post “How Machine Learning Will Affect Your Next Vacation,” we talked about the impact machine-learning recommender engines have on regular consumers. But here, we want to dive deeper and talk about the math and science behind recommender engines.

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Topics: Data Science, Analytics, Marketing

Data vs. Gut Instinct: How Will You Drive Your Business?

Posted by Daniel D. Gutierrez on Tue, May 20, 2014

Corporate executives make dozens of business decisions every day — most of which are invisible to the general population. But one business decision of late stands out as a stark exception: CNN’s decision to focus on missing Malaysian flight 370 (MH370) long after other news sources moved on. Some CNN watchers grew tired of the endless coverage, especially as other big stories fought for attention elsewhere. Yet CNN seemed to be stubbornly obsessed with the missing flight. For the first time in a long time — possibly ever — people were questioning why an entire network was ignoring major human interest stories — including a sunken ferry with nearly 300 teenage casualties in South Korea and 200 kidnapped schoolgirls in Nigeria — in favor of one human interest story that was no longer news. CNN even became the butt of jokes for its coverage.

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Topics: Big Data, Analytics

What Is Data Science?

Posted by Daniel D. Gutierrez on Tue, May 06, 2014

As a relatively new term, “data science” can mean different things to different people due in part to all the hype surrounding the field. Often used in the same breath, we also hear a lot about “big data” and how it is changing the way that companies interact with their customers. This begs the question — how are these two technologies related? Unfortunately, the hype can cloud our understanding for how these technologies are working to shape our increasingly data-driven society. Rest assured, there truly is something deep and profound representing a paradigm shift in our society surrounding data, but the hype isn’t helping to clarify data science’s exact role in Big Data. In this article, we strive to put to rest many of the misunderstandings surrounding data science.

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Topics: Data Science, Machine Learning, Analytics

How Data Science Impacts Intelligent Retargeting

Posted by Daniel D. Gutierrez on Thu, Apr 24, 2014

Conversion rates for online retailers are generally considered pretty dismal by most common measures. Imagine if only 1–3 percent of shoppers entering your store ended up making a purchase. Maybe you’d think of trying a new strategy. The new strategy employed by many online retailers is called retargeting — the use of search and display campaigns to target the 97 percent of visitors who came to your e-commerce site but didn’t convert, meaning they did not make a purchase, fill out a form, or request a demo or call. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit other sites online.

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Topics: Big Data, Data Science, Analytics

Local Data on a Global Scale

Posted by Daniel D. Gutierrez on Fri, Apr 11, 2014

With the help of technology and a hankering for local Big Data, Hyperlocal Data lets you be there without going there.

In a world where most things are only a click away, humans have a newfound appreciation for local knowledge. Farmers congregate at the local grain elevator to talk corn prices. Local growers and bakers gather to sell their goods (and eye the competition) at local farmers’ markets. You can judge a melon’s quality by the sound it makes when you tap it or a pineapple’s ripeness by how easy it is to pluck out a top leaf. There’s a lot of value in being in a place, in person, using all your senses. 

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Topics: Big Data

Increase Revenue and Improve Business Strategy with Retail Analytics

Posted by Daniel D. Gutierrez on Mon, Apr 07, 2014

When people shop online, they know they’re sharing personal data to be collected and used for marketing purposes. And yet we’re surprised by how many companies fail to do even the bare minimum of analysis with the data they’ve painstakingly gathered. By implementing just a few basic analytics techniques into your marketing practices, you can quickly gain insight into your site’s performance, your customers’ satisfaction, and the effectiveness of your marketing campaigns. And if you take your analytics a few steps further, you can actually predict what your customers want and gain specific recommendations about how best to market to them — whether you have hundreds of customers or millions of them.

Here, we’ll show you the basic building blocks of retail marketing analytics. The insights gathered from these techniques can help you optimize business strategies and increase revenues. We’ll show you which metrics, or key performance indicators (KPIs), matter the most and how they can be used to measure the success of a retail enterprise. We’ll also shed some light on what’s possible once you establish the basics: the more advanced analytics techniques that deliver predictive insights and recommended actions. Your crash course starts now.

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Topics: Big Data, Data Science, Analytics, Signals, Marketing