Coming soon

Groups pages will be added as we roll out new features

Coming soon

Blogs will be added as we roll out new features

Coming soon

Jobs pages will be added as we roll out new features

Big Data by itself does not replace conventional knowledge

Thursday, 5th March 2015

The term ‘Big Data’ has captured a lot of peoples as well as organisations imagination over the last few years, leading to an underlying assumption that every sector of society such as business, education, government etc. will experience significant change and even disruption. For problems to be classified as ‘Big Data’ it is now assumed the four V’s, Volume, Velocity, Veracity and Variety must be met. Most of us are well aware of these four V’s and none of these are new. In fact we have seen research in areas such as data mining, cognitive analytics, machine learning etc. for more than 20 years. This highlights some of the key challenges for anyone interested in a career in analytics. ‘Big Data’ by itself does not replace conventional knowledge. ‘Big Data’ also does not replace the business process focus of many organisations. In fact the application of ‘Big Data’ requires an acquisition and application of knowledge for strategic decision-making. Further analytics is sometimes portrait as an almost ‘magical’ instrument that will help organisations to solve all their problems.

Against this backdrop it is evident that analytics is an exciting, diverse and challenging career with the potential to make a significant contribution to an organisations success.

For anyone interested in developing a career in analytics the nascent nature of the industry can present a significant hurdle with advertised roles in analytics or business analytics often deliberately generic. In such an environment there are essentially two approaches to kickstart a career in analytics. The first approach is to find an opportunity within an organisation that will provide on-the-job training in analytics. For this approach to be successful it is important to carefully choose an organisation with analytics proficiency. This proficiency can often be found in banking, telecommunications and consultancies. The second and often more attainable approach is to pursue an analytics qualification to provide the specific knowledge around the current tools, techniques, management practices and strategic decision-making for analytics. An example of such a qualification is the Master of Business Analytics at Deakin University. In addition to such a formal qualification it is advisable to continue reading about analytics, pursue internships, attends industry presentations and join communities of practice.

For anybody already in the profession it is evident analytics is a fast pace, dynamic discipline and it is critical to stay up-to-date with latest developments in technology, strategy and management. As the discipline matures and with increased interest and competition there have been a number of new or updated tools in the marketplace to facilitate various aspects of analytics. Many different aspects of analytics, such as dashboards, visualisation, business intelligence, statistical packages, etc. are supported through an ever-increasing number of commercial as well as open source tools and consolidation and commercialisation in this area is occurring. This availability of tools in addition to an ever-increasing number of application areas for analytics requires analysts to continuously increase their knowledge base and to be able to pick and operate the right tool for the problem.

Another side-effect of maturity in analytics will be an ever stronger separation between the data analysis side of analytics and decision-making. Similarly to the differentiation between Information Technology and Information Systems much of the processing can be outsourced but at the same time there will be increasing demand for instantaneous, real-time, visual access to analytics. Visualisation in analytics is one area of notoriety where a different, more creative representation of analytics can facilitate deeper, more immersive understanding of data and therefore facilitate more focused and faster decision-making. An example of decision making game play can be found on YouTube.

Deakin University of Australia - Master of Business Analytics

Comments are turned off