Posts

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

 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

On June 3rd, Ironside hosted a webinar on Donor Optimization Techniques with IBM Business Analytics.  Industry experts from IBM, along with special guest Monique Dozier, the Assistant Vice President of Advancement Information Systems & Donor Strategy at Michigan State University, demonstrated techniques for successfully increasing and accurately targeting fundraising efforts. Read more

Public data is everywhere, and if you know where to look, you’d be surprised at the insights it can give you. In fact, when paired with the right tools, this freely available information can enrich and complement your internal data resources to reveal compelling patterns of behavior and trends that you can act on to drive growth at your organization. To showcase what public data can do in the hands of professional analysts, we’re kicking off the Ironside Public Data Powered article series. These publications will periodically take you behind the scenes to show you how our consultants think about and interact with public data using the skills and technologies at their disposal. In this inaugural article, we’ll explore what it takes to start understanding patterns and relationships within a combined public and internal data set through IBM Watson Analytics.

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IBM SPSS Modeler 17 and Statistics 23 were officially released at the beginning of March 2015. There are several important changes in licensing structure and system infrastructure, as well as many innovative new functionality enhancements. This article will briefly introduce the enhancements added to Modeler 17 from a practical perspective. Something worth noting as you begin exploring this most recent round of upgrades is that Analytics Server will be heavily leveraged in a lot of these new features. Read more


You might have missed it if you weren’t closely following the situation or already using the platform, but around the middle of last month IBM forever changed the concept of self-service analytics when they quietly moved Watson Analytics out of beta and into general availability. Watson Analytics is a new cloud-based application that enables an individual to work with their data at a level of ease and intuitiveness that we’ve not seen before. As a sibling of the IBM Watson family, it has inherited a powerful natural language processing capability and as such it enables an individual to query data not with a programming language like SQL, but in the form of a natural language dialogue. Read more

IBM estimates that most businesses utilize about 20% of the data they collect, with the other 80% of a company’s stored information remaining under-utilized or inaccessible.

Today, businesses are storing more data than ever before. Data is vital for record keeping and to provide competitive advantage through reporting and analysis tools. So why the low utilization? 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