The key word in the phrase big data is "big." While this might seem obvious on a certain level, it can be easy to take this method and the tools associated with it – such as business intelligence software and platforms – for granted. A major example of this can be seen in the prevalence of unstructured data within many organizations' infrastructure. If a company undertakes a major data initiative and does so without the right software and no concrete plan for organization or structure, this can become a significant problem. It will also be important to pay attention to the differences between master and application data.
Looking to make sense of the unstructured
In a recent blog post, Tim Sheedy, an analyst with the firm Forrester Research, commented on the profligate nature of unstructured data. The essential definition of this term, for Sheedy, is information contained somewhere within a company or organization's IT infrastructure that has no concrete or actionable value.
Most of the businesses out there, ranging from large enterprises to small and medium-sized businesses, have at least some unstructured data. It's almost impossible to have none of it. But beyond a certain point, it becomes notably problematic. It can be a drain on productivity and cut away at the efficiency of BI and analytics. The software that deals with big data as a whole must mine all unstructured information, ranging from software code to messaging data, and find effective purposes for it.
Master vs. application – the differences
In the drive to make complete sense of all of the information that passes through an organization and powers all of its essential processes, it is important to differentiate between all of its disparate categories. Master data and application data are majorly significant among these.
According to Gartner's Andrew White, a research VP with the research firm, it's a fairly basic difference. Master data can be used and distributed throughout multiple business applications, whereas application data is, as its name indicates, specific to a single app or purpose. White argues that it's essential for organizations to utilize BI and data solutions that take this distinction into account and help apply some semblance of governance, which is essential.
The problems that could arise if this issue is not properly addressed are significant. These include the possibility of increased integration, storage and application costs, and can also cause data to become siloed, ultimately limiting its usefulness.