data science

DecisionCamp 2017 Recap: DMN and Analytics

I was pleased to be able to both attend and present at DecisionCamp 2017 in London on refinements to DMN and decision modeling for analytics. I want to thank my fellow members of the organizing committee and I would like to recognize the efforts of the conference Chair, Dr. Jacob Feldman, for making this important […]

Predictive Analytics World SF 2017 – Analytics Blocking and Tackling

Along with the many topics at Predictive Analytics World SF this week representing the leading edge of analytic sophistication, there were also presenters speaking to the blocking and tackling necessary to nurture analytic thinking and scale adoption within their organization. Since many organizations are facing these same analytic adoption and scale problems, I’d like to highlight […]

Harness Data-Driven Decisions with Decision Management

Get out of the reporting quagmire, be explicit about decision making with decision modeling and integrate analytics in BI and operational systems. We’re Not There Yet “The activities of analytics teams and the investments made to support them aren’t in sync with what executives expect or desire.” International Institute for Analytics, February 2017 “If analytics does […]

From BI to Predictive Analytics with Decision Centric Dashboards

Many organizations are keen to improve data-driven decision making with predictive analytics but they are trapped by operational demands for traditional BI reporting and dashboards. They are asking how do they get “there” – better data-driven decisions – from “here” – traditional BI reporting and dashboards. This challenge is readily addressed with decision modeling and modern […]

Great Examples of Integrating BI and Data Science

We talk a lot about the power of predictive analytics*. Data-driven decision making is the goal, but to get there, organizations need to learn how to extract actionable information from their data. We also talk a lot about how this is different from traditional business intelligence (BI), where the focus is on historical reporting. But […]

Decision Modeling and CRISP-DM for Modern Data Science Projects

Many data science projects use the popular and well established CRISP-DM methodology. However, CRISP-DM has limitations especially regarding business understanding and deployment. The decision modeling process and the graphical decision requirements diagram addresses these challenges. CRISP-DM Popular, but with Limitations Gregory Piatetsky of KDnuggets writes following the KDnuggets Data Mining Methodology Poll: “CRISP-DM remains the […]

Great New Case Study – Bringing Clarity to Data Science and Analytic Projects

We have been helping several organizations improve their analytic and data science projects. Like many users of analytics, these organizations find that their analytic teams often lack a clear understanding of the business problem, resulting in projects that lose their way or produce analytic  models that don’t get operationalized, deployed or used. We have helped […]

Predictive Analytics World 2017: The Role of Decision Modeling in Creating Data Science Excellence

Join me and Tina Owenmark of Cisco when we speak on The Role of Decision Modeling in Creating Data Science Excellence at Predictive Analytics World in San Francisco. Cisco’s Data Science Office focuses not just on data science, but also on shaping the questions and answers for Cisco’s operational groups. They focus not on technology or […]

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