Blogs

As your enterprise moves toward being data driven, the ability to derive a domain ontology from your company’s data will become ever more important. In order to move to this deep analytical process, it is important to understand the amount of data required and the state the data must be in before you should attempt any deep analytics studies.

The goal of data science is to translate ALL business problems into scientific problems which can be managed and/or improved in a systematic data-driven way. It is the job of a data scientist to do exactly this. All business problems will benefit from the application of a more scientific and data-driven approach.

When you read the marketing spin on Big Data and the tools available today, you may deduce that there is much upside and not much downside to implementing a Big Data project. Nevertheless, you will quickly find that this is not the case. It’s not as simple as typing “apt-get install Hadoop” into a Linux command window, everything installs, you ask it complex questions, and it gives you sage advice. It is tough to get a Big Data project working.

I often struggle to explain the value of ontologies to my clients. The word ‘ontology’ itself sounds complicated, academic, pompous, bombastic, and irrelevant. For that matter, the word bombastic always struck me as, well,
. . . bombastic.

Nevertheless, when you try to look up ‘ontology’ in everyone’s favorite dictionary, err, google, you get an even less helpful definition:

NOUN

1. 1. the branch of metaphysics dealing with the nature of being.

It really can’t get much more clearly unhelpful than that. At least their 2nd definition seems a little bit more relatable for our purposes:

1. 2. a set of concepts and categories in a subject area or domain that shows their properties and the relations between them.

There are a certain set of timeless skills that one should aim to develop over the course of their career in the field of data science:

A two-blog series on what data science can do, with real world examples.

A two-blog series on what data science can do, with real world examples.

Introduction

 

Let me start by saying that this is not an article about big data.  While the source of big data is external to your organization, it is a topic of its own.  Many of the concepts and approaches discussed will definitely apply to your big data initiatives, but that won’t be the focus of this article.

Introduction

Inter and Intra business communications turn out to be perpetually mind boggling, the need to successfully manage time and assets winds up the key. All things considered, and coupled with huge interest in infrastructure and service, the interest for project management (PM) aptitude in Africa has expanded significantly over a wide assortment of Industries. Africa is a market of middle class consumers which is expected to reach 1.1 Billion by 2060 and will need to cater for maintaining the Master data of the customers. The best possible option is using Master Data Management tools and this will lead to more development projects eventually more project managers to make sure everyone works together as a well-coordinated team. Currently the need for project management is on high in Africa, but MDM project managers are rare.