In The Forrester Wave™: Enterprise BI Platforms With Majority Cloud Deployments, Q3 2017, Forrester Principal Analysts Boris Evelson and Martha Bennett noted that, “The winners in the digital economy will be those that are able to gain insights the fastest and ...
[This is the first in a series of blogs on the Birst Value-Based Design (VBD) process. This installment focuses on identifying the top metrics for any analytics deployment, and future blogs will cover other aspects of the VBD process.]
Data is becoming so critical to all business processes today. Making use of the data that we create as part of marketing to customers should help us all do a better job. But this is not just “our” data.
Machine Learning has gone from a niche area of technology to being the savior of companies across multiple industries. Others see it as a technology that is overhyped and doesn’t deliver. Indeed, the Gartner Hype Cycle for Emerging Technologies has Machine Learning, or ML, at the Peak of Inflated Expectations. This is where technologies end up when there are huge amounts of attention and marketing exuberance on display but not necessarily the results to show adoption.
In my last blog post, I looked at the potential impact that the General Data Protection Regulation (GDPR) will have on marketing teams. In this post, I’ll go through how the new regulation may affect your overall approach to using data and how to prevent the regulation from negatively affecting you.
In this podcast interview with Ronan Leonard of Irish Tech News, Richard Neale, Birst Director of EMEA Marketing, discusses how Birst’s cloud-based, business analytics platform enables people to extend their analytics, data models and visualizations through a seamless, networked integration of data and analytics deployments across their organizations.