Skip to main content

This Week in the DoT (Data of Things)

Every Friday, starting today, I will try to post some of what in my view were the relevant events during the week for the Data of Things, including news, videos, etc.

For today, I have a short list of influencers on Twitter — in no particular order — that you might want to follow for all data-related topics. I’m sure you will enjoy their tweets as much as I do:


Claudia Imhoff       @Claudia_Imhoff

Merv Adrian           @merv
Neil Raden             @NeilRaden
Marcus Borba         @marcusborba
Howard Dresner     @howarddresner
Curt Monash           @curtmonash
Cindi Howson         @BIScorecard
Jim Harris              @ocdqblog
Julie Hunt              @juliebhunt

Of course, the list will grow in time. For now, enjoy following this group of great data experts.


Bon weekend!


Comments

Popular posts from this blog

Machine Learning and Cognitive Systems, Part 2: Big Data Analytics

In the first part of this series, I described a bit of what machine learning is and its potential to become a mainstream technology in the industry of enterprise software, and serve as the basis for many other advances in the incorporation of other technologies related to artificial intelligence and cognitive computing. I also mentioned briefly how machine language is becoming increasingly important for many companies in the business intelligence and analytics industry. In this post I will discuss further the importance that machine learning already has and can have in the analytics ecosystem, especially from a Big Data perspective. Machine learning in the context of BI and Big Data analytics Just as in the lab, and other areas, one of the reasons why machine learning became extremely important and useful in enterprise software is its potential to deal not just with huge amounts of data and extract knowledge from it—which can somehow be addressed with disciplines such as data

Next-generation Business Process Management (BPM)—Achieving Process Effectiveness, Pervasiveness, and Control

The range of what we think and do is limited by what we fail to notice. And because we fail to notice that we fail to notice there is little we can do to change until we notice how failing to notice shapes our thoughts and deeds. —R.D. Laing Amid the hype surrounding technology trends such as big data, cloud computing, or the Internet of Things, for a vast number of organizations, a quiet, persistent question remains unanswered: how do we ensure efficiency and control of our business operations? Business process efficiency and proficiency are essential ingredients for ensuring business growth and competitive advantage. Every day, organizations are discovering that their business process management (BPM) applications and practices are insufficient to take them to higher levels of effectiveness and control. Consumers of BPM technology are now pushing the limits of BPM practices, and BPM software providers are urging the technology forward. So what can we expect from the next

SAP Data Hub and the Rise of a New Generation of Analytics Solutions

“Companies are looking for a unified and open approach to help them accelerate and expand the flow of data across their data landscapes for all users. SAP Data Hub bridges the gap between Big Data and enterprise data, enabling companies to build applications that extract value from data across the organization, no matter if it lies in the cloud or on premise, in a data lake or the enterprise data warehouse, or in an SAP or non-SAP system.” This is part of what Bernd Leukert, SAP’s member of the executive board for products & innovation mentioned during SAP’s Big Data Event held at the SAP Hudson Yards office in New York City as part of the new SAP Data Hub announcement and one that, in my view, marked the beginning of a small yet important trend within analytics consisting on the launch or renewed and integrated software platforms for analytics, BI and data science. This movement, marked by other important announcements including Teradata’s New Analytics Platform as well