Autonomous Analytics Alignment


The best way to predict the future is to invent it.

- Alan Kay


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
  • AI Rules in Effect

    After decades of process automation, stability and productivity gains have been delivered, but not new growth.

    Enterprise yield is a new AI-enabled discipline - setting the stage for a new cross-enterprise executive discipline.

    Enterprise AI is changing the rules, enabling the discover of perishable opportunities hidden in diverse data.

    New growth is achieved by continuously sensing what people want and then delivering continuously adaptive value.

Calendar for RM Dayton Meeting

Ingrained in our DNA is that optimal talent does not necessarily refer to the top achievers in a company’s permanent workforce.  

The top talent driving the technology may best be viewed as a collective – the combined force of all the analytic knowledge workers striving to get the most out of company’s analytic technology investments regardless of how they are deployed.

Our approach is to view the collective workforce holistically, not subscribing to the doctrine of pre-defined classifications that may limit an organization’s effectiveness in aligning analytic knowledge workers.  

We were, from day one, created specifically to focus exclusively on artificial intelligence (AI), data science, big data and business analytics. 

We maintain technological independence so that we may serve our customers without enterprise software vendor bias.

The symbioses between data science and artificial intelligence (AI) demands knowledge workers aligned with the way enterprises need to understand their customers - and with AI solutions that impact the enterprise bottom line. 

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AI Micro-Concept Innovation

Internal and external data is mapped to rich attributes. Enterprise AI provides big data automation, machine learning clustering, collaborative filtering, and cluster engineering.  Clustering may then discover patterns, rate offerings/content and predict actions.
Clusters may be grouped along the most relevant consumer drivers to create micro-concept archetypes.
Machine learning actions then guide people with micro-forecasted yield optimizing recommendations, such as for personalized loyalty, omnichannel orchestration and demand forecasting.

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