Aligning Tomorrow's Knowledge Worker™

Top Gun Talent in Business Analytics, Data Science and Big Data

Pontificating Parser Newsletter

  • Speed of Thought

    Back in the early 1990’s when OLAP was gaining traction, the term ‘speed of thought’ was used to describe the slicing/dicing retrieval time from cube.

    In the current era of business analytics, organizations are still grappling with getting insights from their ever-increasing data volumes.

    Today, in-memory analytics are enabling knowledge workers explore this data at previously unheard-of speeds, what some - once again - are referring to as being at the “the speed of thought.”

    In-memory analytics can process and analyze large data sets exponentially faster than what was previously possible, eliminating the need for a number of database optimization tasks.

The symbioses between data science and analytic technologies demands knowledge workers who are aligned with the way enterprises need to understand their customers - and the way they want to engage knowledge workers to solve business problems.

Technology propels business incrementally through the successful completion of discrete projects. The quantifiable success of data science relates directly to aligning knowledge workers properly – the ‘fit’ between people and project, analytic results and content.

Alignment naturally narrows the gap the between your current state and desired future state of Enterprise Information Management.  But equally as important, it provides an alternative to the approach to moving projects forward, so that your existing investments are realized more fully.

Data Science broadly covers many 'traditional' areas of decision support, but perhaps with an emphasis that insights derived from large volumes of data may be structured or unstructured. 

The field of data science borrows from various approaches to performing analysis on large volumes of data, then deriving insights from the models rendered.  Some of the techniques that are the backbone of data science include predictive analytics, data mining, data engineering, data visualization, statistical analysis, econometric modeling and machine learning, to name a few.  

Given that both structured and unstructured content are used to render insights via data science, RM Dayton collectively refers to the derived analysis of data science as Content-Powered Analytics.  

By understanding the broad differences among the underlying disciplines of data science, as well as the tangential, and at times interrelated, fields of Enterprise Information Management, we're in a good position to help our customers.  This core understanding enables our customers to align enterprise intelligence in a manner that transcends the iterative approach to deployed solutions.

We Thank Our Recent Clients

“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

The ETCC on LinkedIn

The Executive Technology Convergence Council is a forward-thinking community of knowledge workers who share a passion for providing technology convergence of structured and unstructured content.  Founded in 2009, The ETTC was Big Data before it was cool.  The group has limited a membership of 2500 and only permits thoughtful posts from industry executives and thought leaders relating to data analytics and data science.  

Request below to join The Enterprise Technology Convergence Council forum on LinkedIn.

The Enterprise Technology Convergence Council