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Little bit of  a late start for me so I am starting with Geoffrey De Smet from Red Hat talking about constraint planning. He points out that some decisions cannot be easily solved with rules-based approaches – they can be described as decision (and as a DMN decision model in our experience) but not readily [...]

One of our clients was presenting recently at a TDWI conference and was picked up on TechTarget – Analytics teams give data science applications real scientific rigor. It’s a great article with some good tips about using a repeatable methodology like CRISP-DM, especially when combined with decision modeling as a way to capture business understanding and [...]

I recently wrote three articles for KDnuggets on the potential for decision modeling in the context of the CRISP-DM methodology for analytic projects: Four Problems in Using CRISP-DM and How To Fix Them CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key [...]

I am speaking with Tina Owenmark of Cisco 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 algorithms but [...]

An additional blog post here on a session at Building Business Capability that I missed – Business Analysis for Data Science teams. I know Susan Meyer who presented it and we talked several times about her presentation. It’s a really key topic so I wanted to present a summary. Here goes: There is a lot [...]

I am giving a webinar on Analytics Teams: 5 Things You Need to Know Before You Deploy Your Model on November 15 [NEW DATE] at 11am Pacific Many Analytics Teams have experience with building what seems like a great model–valid, predictive, powerful–only to see disappointing or even no business impact. Some models are not deployed, or take [...]

I am giving a webinar on Analytics Teams: 6 Questions to Ask Your Business Partner Before You Model October 13 at 11am Pacific. Analytics Teams know that one of their biggest challenges is effective communication and collaboration with their business partners. Projects are plagued with too many iterations to get to a solution, too many detours [...]

The folks over at ZS Associates sponsored a study by the Economist Information Unit on analytics titled “Broken Links: Why analytics investments have yet to pay off”. This report showed the classic challenge of analytics – 70% think analytics is very or extremely important but only 2% say their analytics efforts have a broad, positive impact. In [...]

I am giving a webinar on “Framing Analytic Requirements with Decision Modeling” April 2, 9am Pacific/Noon Eastern: One of the most important steps in a predictive analytic effort is correctly framing the problem a way that creates a shared understanding of the business problem across business, IT and analytics teams. Established analytic approaches such as CRISP-DM stress [...]

There was a great article in Predictive Analytics Times recently by my friend Dean Abbott – A Good Business Objective Beats a Good Algorithm. Dean, like me, talks about the importance of the “three legged stool” of business, analytics and IT. But it was the title that particularly struck me. As Dean says, it’s easy to [...]

I have been blogging and writing about the Decision Model and Notation standard on the blog for a while now (check out these posts on the goal of a standard from 2011 and these on the approach and the submission itself). Today was a big day because the key boards at the Object Management Group approved our submission, [...]

Earlier this week I posted on the value of decision requirements modeling in analytic projects when it comes to coping with some of the analytic skills shortages people face. But this is not the only reason to focus on decision requirements if you are focused on predictive analytics and data mining.  In fact decision requirements modeling [...]

There are lots of articles these days about the challenges of recruiting enough data scientists, predictive analytic specialists or data miner (whatever you call them). There’s not enough of them and it’s hard for most companies to compete with Google, Amazon, Facebook et al let alone political campaigns and startups. For most companies simply going [...]

Dean Abbot wrote a great post recently “Why Defining the Target Variable in Predictive Analytics is Critical” in which he referenced the CRISP-DM approach to building predictive analytic models and talked about the importance of target variable selection in building an effective model. The thrust of Dean’s post was the crucial point that because The [...]

CRISP-DM – Cross-Industry Standard Process for Data Mining – is the best known data mining methodology out there. It’s been around a long time but ownership/management of the consortium that developed it has gotten complex recently (the CRISP-DM.ORG site is down at present for instance but you can get some details in the CRISP-DM Wikipedia [...]