Uncategorized

Phil Simon 0
Simon's First Law of Data Visualization

There's no shortage of hype and confusion surrounding big data. Plenty of companies are starting to dip their toes in the pool despite the relative paucity of documented case studies – at least compared to ERP, CRM and BI applications. Sometimes people ask me, "Can you give me one tip

Jim Harris 0
The architects of the invisible

In the era of big data, Kenneth Cukier and Viktor Mayer-Schonberger noted in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, “we are in the midst of a great infrastructure project that in some ways rivals those of the past, from the Roman aqueducts

David Loshin 0
Organizing entity and identity data

Almost by definition, a customer-centric strategy demands identification of each unique customer within the customer community. Creating a representative model of the customer is a necessary prelude to developing customer profile models and analyzing any characteristics and behaviors for classification. That model must, at the very least, incorporate these aspects:

Phil Simon 1
Big data and the project mentality

Is big data becoming too big to ignore? An increasing number of organizations seem to think so. As Matt Asay on ReadWriteWeb writes: According to a recent Gartner report, 64% of enterprises surveyed indicate that they're deploying or planning Big Data projects (emphasis mine). Yet even more acknowledge that they

Jim Harris 0
The antimatters of MDM (part 4)

In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a

David Loshin 0
Managing customer attribution and classification data

In my last post, I suggested that there is a difference between data attributes used for unique identification and those used for attribution to facilitate customer segmentation and classification. An example of some attributes used for segmentation are those associated with location, such as home address or package delivery address.

1 73 74 75 76 77 105