All posts by Jens Loebbermann

Uncategorized

European businesses forget that the United States is not united at all

1 Comment

I know this article is not really related to any of the categories but I read it and thought it is a great view of what I have experienced in my first 2 years living in the US (more personally though, and not business related)

http://www.telegraph.co.uk/finance/economics/10260066/European-businesses-forget-that-the-United-States-is-not-united-at-all.html

The article basically shows that lots of Europeans still think that the USA is one big country where everything works in a similar fashion everywhere. 

However it is rather the opposite! This might also be a reason the why so many european companies struggle to crack the US , because they do not know this.  

Lots of british supermarket brands like Tesco and Marks& Spencer tried to enter the US market, however none of them was successful.

If european businesses would keep in mind the huge regional differences in the USA and their customer, a successful market entry would be possibly much easier.

 

 

 

 

99-Applying business intelligence

Human Brain Inspires New Cognitive Analytics

2 Comments

I wanted to share this great article, which links the human brain with business intelligence, thereby introducing a potential alternative to traditional analytics. This article gives a new futuristic view of how financial data and business decision might be approached in the future. The article was written by two Deloitte consultants and can be found here: http://deloitte.wsj.com/cio/2014/05/13/human-brain-inspires-new-cognitive-analytics/?KEYWORDS=business+intelligence+analytics

In their article they describe cognitive analytics as innovations, which are inspired by the way the human brain processes information, draws conclusions, and codifies instincts and experience into learning.

The authors state that the benefit of cognitive analytics is based on systems that draw from a broad variety of potentially significant information and relations to generate hypotheses rather than depending on predefined rules and structured queries to reveal answers. This differs clearly from traditional analysis, because the more data is put into a machine learning system, the system remembers and learns, which results in higher-quality insights and more exact hypotheses.

In the article the process of cognitive analytics is basically divided into three main components, machine learning, natural language processing and advancements in enabling infrastructure.

1) Machine learning. Machine learning represents artificial intelligence techniques and is modeled after characteristics of the human brain. Many of today’s implementations represent supervised learning, where the machine must be trained or taught by humans. The system will apply the users feedback on the quality of the conclusions to tune its “thought process” and refine future hypotheses.

2) Natural language processing. Natural language processing (NLP), or the ability to parse and understand unstructured data and conversational requests, is another important component of cognitive computing. NLP makes it possible to include large volumes of raw data—including handwritten content, emails, blog posts, and even voice transcriptions—from multiple sources in an analysis.

NLP can also make it easier for humans to interact with cognitive systems. NLP would make it possible to basically ask the program real questions e.g. “What are the sales projections for this quarter?” instead of being forced to look through a pile of excel data sheets and databases.

3) Enbabling infrastructure. This means basically to create low cost, high-end servers and large appliances to ensure continuously collection, storage, and analysis of the massive amounts of data.

One can say that cognitive analytics might revolutionize the way information is analyzed and applied, as more human activity is expressed digitally, resulting in evolving data forms. However, cognitive analytics is still in its early stages, and is for now not a replacement for more traditional information and analytics programs. Yet exploring this powerful new approach in the analytics arsenal might be a new valuable tool for businesses in fighting with massive amounts of unstructured data.