Ever wonder how you can create meaningful insights with your data? Tired of being asked what's your big data strategy, are you doing predictive analytics, or how you are making use of the latest technologies? There is a way for you to get what you need with Data Science. The key to new knowledge is to make sure you use Data Science in your organization.…

What are we trying to predict? Where is the data? How are we measuring if we are getting it right? Which departments are a part of this Data Science project? Who is my internal customer? When does this have to be completed? How clean is the data?

Data Science projects seem to go through a natural set of phases which model the inquisitive spirit of Data Science.…

"Move out of the way – I am ready to model.” That is the typical sentiment of a Data Science team when given a business…

Business goals are no doubt important, but in an analytic project it makes sense to balance the organization's goals with…

Most Data Scientists like to get their hands dirty with data just as quickly as possible, but it is important to practice…

Businesses around the world are involved in a multitude of projects at any given time. As Data Scientists come into the business fold, it becomes more important with each passing day to have both parties – “the business” and “the…

Business professionals of all levels have asked me over the years what it is that they should know that their Data Science departments may not be telling them. To be candid, many Data Scientists operate in fear wondering what they should be doing as it relates to the business. In my judgment, the questions below address both…

Does this sound familiar?  

Your organization is ready to develop more increasingly sophisticated analytics, but finds it difficult to get its data all in one place in a form that is usable. It is probably not a stretch to imagine this is poor quality data, filled with errors or incomplete. Also, there may be a reluctance to share data across the organization. The data may not be collected in a consistent way, or it might be locked in rigid data silos that are…

So many data scientists select an analytic technique in hopes of achieving a magical solution, but in the end, the solution simply may not even be possible due to other limiting factors. It is important for organizations working with analytic capabilities to understand the various constraints of implementation most real-world applications…