Policing in the United States and around the world is rapidly changing.  Just as there have been paradigm shifts in law enforcement procedures in the past, we are now on the brink of another transformation of how communities are policed.  Current national narratives and recent events are motivating these changes, and like it or not, a new era of law enforcement is upon us.  One of the main solutions that helps law enforcement adapt to this change is adopting a sound data driven policing strategy. Read more

For the second year in a row, Ironside has been named one of IBM’s Beacon Award finalists, this time in the category of Outstanding IBM Analytics Line-of-Business Solution. This recognition comes in honor of the compelling results that our IronShield predictive policing platform has generated.

About IronShield

IronShield Ironside predictive policing logo

IronShield provides turnkey predictive hot spots policing and analytics for law enforcement. It enables data-driven, evidence-based policing that stops crime before it happens and is customizable to the environment in which it’s implemented, going beyond its initial hot spots module to target each community’s needs. Our CEO Tim Kreytak recently highlighted the impact IronShield has had in Manchester, NH helping the city’s police department combat the heroin crisis. Read more

Suppose you’re trying to build a model to predict respondents, and in your data set, about 3% of the population will respond (target = 1). Without applying any specific analysis techniques, your prediction results will likely be that every record is predicted as a non responder (predicted target = 0), making the prediction result insufficiently informative. This is due to the nature of this kind of information, which we call highly imbalanced data. Read more

Do you ever wonder how Netflix makes recommendations for you? Or how the drug store decides which coupons to offer you when you make a purchase? Behind the scenes they have a data scientist conducting what is called market basket analysis, which searches through vast amounts of purchase history information to find patterns in people’s purchases, web searches, or Netflix viewing preferences. The data mining technique used for market basket analysis is called Association Rules (AR). This is the actual algorithm designed to detect probabilistic if- then statements, such as “If you watched Breaking Bad and House of Cards, then you are also likely to enjoy Mad Men.” Read more

Law enforcement is a place where data science and predictive analytics have the chance to truly change lives. These strategies and technologies can make a huge difference in crime prevention and public safety efforts, improving people’s wellbeing in communities of all sizes. The Manchester, NH Police Department wanted to make this kind of impact in their city, and chose to implement Ironside’s Predictive Policing platform to achieve their crime reduction goals. Read more

How Data Scientists Demystify Complex Questions

To help you understand Ironside’s philosophy on advanced analytics, data science, and predictive/prescriptive technologies, we thought it would be helpful to share insights directly from our data scientists.

Win FullerThis week I talked with Win Fuller, who has over three decades of experience with advanced analytics and business intelligence at a wide array of companies including Staples, VistaPrint, Upromise, Stax, and Bain & Company. Win holds a PhD in Econometrics from Tufts University, and specializes in predictive models addressing questions around churn, customer retention, and demand generation among many other analysis topics. He is also an expert in all aspects of data extraction, integration, and manipulation. His goal in any engagement is to use his experience to clearly understand and realize his clients’ business and analysis goals. Read more

How Data Scientists Reveal the Right Actions

To help you understand Ironside’s philosophy on advanced analytics, data science, and predictive/prescriptive technologies, we thought it would be helpful to share insights directly from our data scientists.

Chi ShuThis week I spent some time with Chi Shu, a 5-year veteran in the data science and advanced analytics space with experience across both the public and private sectors. Prior to joining Ironside, Chi worked as a marketing analyst evaluating performance through segmentation and direct marketing models and as a government analyst using business analytics platforms to make critical metrics available to key stakeholders. Chi is one of our brightest programming minds with extensive knowledge of systems such as MATLAB, Python, R, SAS, and SPSS. Her top priority in any engagement is to consistently deliver personalized, relevant results that unlock an organization’s true potential. Read more

Ironside’s client, a leading New England technical institute, wanted to get predictive with their admissions strategy, leveraging advanced analytics to generate projections that would have measurable impacts on the number of successful students continuing on past their freshman year. They also wanted these projections to integrate seamlessly with the admissions process, providing counselors with actionable guidance and standards for admittance. Read more

 How Data Scientists Find Order in Chaos

To help you understand Ironside’s philosophy on advanced analytics, data science, and predictive/prescriptive technologies, we thought it would be helpful to share insights directly from our data scientists.

Pam Askar BWThis week I spoke with Pam Askar, who has over 10 years of quantitative research and predictive modeling experience and holds a PhD in Developmental Psychology from UConn. She worked in academia and the private sector as a psychology professor and a data modeler for a pharmaceutical market research firm before joining Ironside. This makes Pam uniquely qualified to both implement analysis strategies for clients and teach solutions in ways that ensure success. Regardless of the project, Pam’s deepest source of enjoyment is always the same: finding powerful solutions to complex problems that provide actionable business results. Read more

Earlier this year, we published a newsletter article scratching the surface of the Text Analytics Premium add-on for IBM SPSS Modeler. It introduced the fundamentals of text analytics and the idea of extracting information from unstructured text data. In this follow-up article, we will dive more deeply into some of the more advanced topics to fully leverage the add-on’s functionalities. Read more