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Facebook / Cambridge Analytics, the Equifax data breach, and political email hackings are just a few examples of the misuse of data and data ethics violations that are constantly making headlines. All except your most hardened hacker would agree that
A large sales organization recently implemented an analytics strategy, with one of their critical metrics being lead conversion. The target number was set and reporting tools were put in place. Each sales team was scored on this metric, and the
The CEO of a fortune five hundred firm found that the feedback her organization was receiving from top customers was negative. Metrics from their customer satisfaction surveys would indicate the opposite. It turns out, some customer service reps were selectively
By: Hannah Arnson These days, we frequently hear about “artificial intelligence” (AI), “neural networks,” and “deep learning.” The media presents these techniques as black boxes that can revolutionize, or destroy, society. At Pandata, we use AI (and neural networks &
More and more of our clients approach us with operational problems that are ripe for machine learning solutions. As such, the data scientists at Pandata spend much of our time maintaining proficiency with the latest results coming out of the
As we usher in 2018, organizations are faced with new challenges but also many of the same that come year after year. What we didn’t get to in 2017 still stares us in the face. Was data strategy one of
It’s been a great year here at Pandata! As we prepare to enjoy holiday family time, we wanted to use data to see who else is celebrating with us. This use case listens to live tweets on Twitter and plots
Since the dawn of humankind, we have found ways to create and utilize new tools to assist in our jobs. Spears and arrows made it easier for humans to hunt. Cars and trains have sped humans around the world. And
Deep learning and cybersecurity As more critical decisions involve information systems and as software continually envelops the world, the security and integrity of our systems are more important than ever. This is especially true of systems that handle sensitive user
Creating a data science team requires painstaking precision. We believe that it should be multifaceted. Not only should it consist of data scientists, with different specializations and disciplines, but it also requires a strong support system to round it out.
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