Smarter Analytics Deployment


There is nothing so useless as doing efficiently that which should not be done at all.

- Peter F. Drucker

Yes Jim, There is a High End

“You [have] the techno-functional knowledge of a hiring manager  ...  which saves me an enormous amount of time and frustration.”

- Sr. Director, Fortune 500 Company

Analytics Accolades

20 Most Promising EPM Top Solution Provider
The SR 50 Most Admired Companies of the Year
100 Most Promising Big Data Solution Provider

Propelling Forward Content-Powered Analytics

AI for the Enterprise

Is a bottle of water just a bottle of water? When you’re drinking it, hopefully? But if you’re selling them, it’s really 30-50 attributes, if we’re doing our jobs right.

We’re way, way past broadcasting messages to the average.  

Rather, we’re continuously sensing what people want, and then delivering continuously adaptive value.  

Rapid innovation, faster and faster, for increasingly micro-segmented markets.

It’s about devotion to customers. Enterprise Artificial Intelligence (AI) makes it happen.  The machine can crunch combinations in an ontology that our brains cannot absorb simultaneously, simple as that.

Patterns, clusters and ratings drive automated actions in goal-driven yield management, guided by human judgement and creativity.

Whereas ERP systems enabled innovation that leveraged core assets, and traditional analytics made those transactions systems smarter, new Enterprise AI capabilities are enabling breakthroughs in cross-enterprise precision, speed and alignment.

This alignment provides a compelling ROI alternative to the traditional approach of deploying analytics, so that your existing investments in core ERP, CRM, BI, EPM and other business analytics  technologies are more fully realized.

Enterprise AI fuses data into an AI platform, from which machine learning algorithms recommend goal driven actions, which then are federated back into to existing processes and systems.

Enterprise AI micro-concept innovation translates diverse sets of data into consumer attributes, preferences and behaviors.

The quantifiable success of data science relates directly to aligning it with Enterprise AI – an intersection between people and machines, the ontology and the yield.


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