Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Our Last Hope Before The AI Bubble Detonates: Taming LLMs
  Originally published in Forbes To know that we’re in...
The Agentic AI Hype Cycle Is Out Of Control — Yet Widely Normalized
  Originally published in Forbes I recently wrote about how...
Predictive AI Must Be Valuated – But Rarely Is. Here’s How To Do It
  Originally published in Forbes To be a business is...
Agentic AI Is The New Vaporware
  Originally published in Forbes The hype term “agentic AI”...
SHARE THIS:

9 years ago
AnalyticOps: A New Organizational Role So Your Company Can Monetize Analytics

 

There is no doubt that data science–and predictive analytics– are the next wave of investments aimed to create significant improvements to corporate bottom lines. With the advent of new capabilities, however, generally comes a commensurate amount of complexity and challenge. In the new era of analytics, many companies struggle to properly connect three key divisions: Data Science, IT, and Business Teams. Throughout these divisions time, money, and energy is invested into analytics and model-making with the hope of tangible operational improvement. And while success stories are many, there are many counter examples that show the challenges that new capabilities bring: some models never come to fruition and drive true business decisions, or worse are misinterpreted when passed through various hands to the point of uselessness or harm. I have witnessed such scenarios in my own career as well as in those of friends, colleagues, and others. With these experiences and the emerging importance of analytics, it’s becoming increasingly clear that a new role will be required in many organizations to ensure maximum ROI from data science investments: AnalyticOps.

This content is restricted to site members. If you are an existing user, please log in on the right (desktop) or below (mobile). If not, register today and gain free access to original content and industry news. See the details here.

Comments are closed.