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The Highs and Lows of Being a Female Tech Leader

Elizabeth Becker

[fa icon="calendar'] March 17, 2017

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According to a recent Reuters study, 30% of 450 technology executives stated that their groups had no women in leadership positions. Only 25% of the IT jobs in the US were filled by women and considering the fact that 56% of women leave IT in the highlight of their career, it’s no surprise that there’s so few women leading the tech industry. There has been momentous push to highlight gender inequality within tech, yet the question still remains: Why are there so few women in tech leadership roles?

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Can Government Analytics Curtail the Opioid Epidemic?

Andrew Lutes

[fa icon="calendar'] March 10, 2017

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In 2015, there were over 2.5 million Americans addicted to opioids; 33,000 of them died of overdose. Nationwide, that rate is over ten deaths per 100,000 people, and the rate is over 30 in some states, such as West Virginia and New Hampshire.  At the height of the crack epidemic, in comparison, crack was “only” causing about four deaths per 100,000 people. The vast majority of opioid abusers are addicted to legal prescription pain killers such as Vicodin or Percocet, and many of those eventually become addicted to illicit drugs like heroin. As analytics experts, we outline ways that technology can help the Federal Government reverse this trend and curtail this destructive epidemic.

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Risk Analytics and the Danger of Playing It Safe

Ryan McGibony

[fa icon="calendar'] February 24, 2017

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Research shows that people tend to be overly risk averse when weighing the potential success or failure of a decision. This tendency is compounded when we consider the vast number of decisions being made across an organization. For various reasons, both individuals and groups are often cautious at the expense of their long-term success. From an analytics practitioner’s point of view, this misalignment presents opportunities to improve outcomes through the strategic use of data.

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Using Agile Data Science Methods to Manage Shifting Priorities

Andy Janaitis

[fa icon="calendar'] February 10, 2017

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After working with a client’s data for over three weeks with no real progress, you finally hit upon a real breakthrough. You’ve been searching for insights that will help identify which customers are most likely to turn into regular purchasers; the ultimate goal is to focus the company’s marketing efforts on this group in order to earn more revenue per advertising dollar. Studying customer purchase history has been unfruitful. Suddenly, you find that customer geography seems to be a better predictor of future purchases. You have a few more weeks to explore that connection, so you should be able to find some real value for the customer, right?

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Taking Action on Technical Success: A Fable of Data Science and Consequences

William Proffitt

[fa icon="calendar'] January 31, 2017

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TalkThree’s new Analytics Director, Michael, has had a sobering month. What he had hoped would be his first major contribution to his company has fallen flat. His team created a model which was intended to address a pressing challenge for TalkThree: a steady stream of departing cellular phone service customers, known as “churners.” Their model predicts who is most likely to leave, and though he delivered it enthusiastically, it received an unexpectedly lukewarm reception from the customer retention team. The churn solution was a poorly conceived data product that didn't reach its audience in a way that worked for them. 

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Analytics 4.0 - Are You Ready For The Future of Competition?

Jordan Barr, Ph.D.

[fa icon="calendar'] January 13, 2017

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The evolution of analytics is categorized by three distinct eras (i.e. 1.0, 2.0, and now 3.0), but a new era, Analytics 4.0 looms on the horizon.  Before we are catapulted into the future, let us visit the present and past of analytics.  As we will see in Analytics 3.0, the most successful companies incorporate data analytics into all aspects of their business processes to gain and sustain a competitive advantage.  But such advances are very recent. 

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Empathy and Data Science: A Fable of Near-Success

William Proffitt

[fa icon="calendar'] December 28, 2016

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Michael is an analytics director. This evening we find him frazzled. As he pulls out of the TalkThree parking lot, contemplating his next move, his radio whimpers a slow rock ballad. Michael let down the executives of his company today. The recipients of his first project aren’t experiencing the success he loudly promised, and his team has ended up back at the drawing board. How did he get here? Michael failed to consider his audience. Let’s rewind the story.

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Operationalizing Analytics Deployment with SPSS Collaboration & Deployment Services

Joy McKinney

[fa icon="calendar'] December 16, 2016

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As analytics professionals, we can work days or weeks building and validating predictive or descriptive models.  However, the success of an analytics project does not just depend on the model building.  To truly be a success, we need to deploy our models, integrate them into workflows, and use them to change business processes.  Operationalizing the analytic assets can be the most rewarding part of the process, but how do we know if we are successful?  How can we ensure that once analytics are deployed in production that the environment will remain stable and continue to produce the desired results?

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DoD’s Third Offset – Science Fact or Fantasy?

Jordan Barr, Ph.D.

[fa icon="calendar'] December 6, 2016

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An offset strategy is critical to the US Department of Defense’s plan to gain and maintain a strategic defense advantage over our enemies, real or potential.  Since the beginning of the Cold War, the vision casting and implementation of these offsets has followed three distinct phases.  The first was nuclear, the second depended on precision-guided missiles, and the third and most ambitious, currently underway, relies on emerging technologies centered on human-machine integration. 

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How Do You Know Your Predictive Model Results Are Valid?

Will Goodrum

[fa icon="calendar'] November 28, 2016

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Across our diverse portfolio of clients, we have repeatedly seen the value of getting an early second opinion on the validity of predictive analytics models. Why bring in an outside expert? Even well-intentioned, highly-capable, technically-sophisticated data analytics initiatives can fail if they lack proper context and support from within the business.1 Having an accurate model is not good enough. Are you confident that you’re on the right track with respect to your analytics initiatives? What changes does the model demand of the organization? Most importantly, how do you know the model results are valid?

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