Data is the new resource for companies looking to gain a competitive advantage in a rapidly evolving market.
Are Australian and New Zealand organisations mature enough to profit from data driven decision making?

Big Data disruption is putting pressure on organisations to become smarter to remain competitive. Here’s what you need to know.

Big Data is not just about data volume; it is about data variety, velocity, and value.
Big Data has moved beyond a disruptive inconvenience to a competitive lever to enhance the way organisations do business. In the information age, data is the raw material from which products and services will be fashioned.
By definition, data sets are unstructured; traditional data capture and storage techniques will not help you.
Machines, devices, sensors, and digital processes are being applied to more and more of our daily activities. This will result in even more unstructured data and extracting value will be the big challenge for organisations.
However, you can use Big Data analytics to find hidden correlations and patterns that will deliver competitive advantage.
Differing and disparate data is challenging traditional methods and giving rise to new analytical tools and disciplines such as data science. Organisations will look towards data to make decisions on corporate strategy.
You will need to access the Big Data Ecosystem effectively — this report explains the roles different suppliers play.
There is no single solution that can solve all of an organisation’s Big Data problems. Organisations will often need to build out an architecture to deliver the complete solution for any Big Data project
Big Data innovation is moving at light-speed, and driving our decisions.
Organisations can no longer afford to make decisions solely on simple research and analysis. Roles such as the Chief Data Officer will become commonplace as successful companies rely more on their data to drive decisions.

The changing definition of Big Data

One of the earliest definitions of Big Data that many commentators adopted is the proliferation of data sets that are too large for traditional data management technologies to capture, store and process. However, this definition tends to be a little misleading in that it restricts the concept of Big Data to growth in the size of data sets. Growth in data has never really been a problem in the past. Organisations such as financial institutions, airlines and utility companies have collected and processed large volumes of data for the past few decades. However, IDC reports that these organisations make up less than 5% of companies in Australia that do, and typically this is historical data residing in traditional data warehouses and mainframes.
If volume were the key criterion to validate the requirement for Big Data solutions, Australian companies would not have a problem, but it is the other three Vs – variety, velocity and value – that contribute to the disruption of Big Data.
Variety and velocity of data gave birth to the early Big Data processing engines at companies like Yahoo and Google, who needed a platform and algorithm to handle the billions of searches and index content from millions of webpages across massive storage clusters, and then have a way to search and disseminate these massively unstructured data sets. Enter Hadoop and MapReduce.
For the majority of Australian organisations, data volume was not initially the driver. They simply did not have huge volumes of data for some very simple reasons. Firstly, the type of data being collected was mainly historical and transactional, and records were only legislated to be kept for a maximum of seven years. Secondly, data storage tended to be terribly expensive. They did however need to figure out how to manage the variety and velocity of data they had begun to collect from sources such as social media, image, audio and video files, emails, web traffic etc. and how to extract value from it. This sent traditional data management vendors scrambling as the sheer force of the data explosion rendered their existing solutions inadequate. There was nothing traditional about Big Data, and the disruption had well and truly begun.