subscribe: Daily Newsletter

 

Big data follows the BI evolution curve

0 comments

Big data analysis in South Africa is early in its maturity levels, and has yet to evolve in much the same way as BI did 20 years ago, writes Mervyn Mooi, director at Knowledge Integration Dynamics (KID).
Big data analysis tools aren’t ‘magical insight machines’ spitting out answers to all business’s questions: as is the case with all business intelligence tools, there are lengthy and complex processes that must take place behind the scenes before actionable and relevant insights can be drawn from the vast and growing pool of structured and unstructured data in the world.
South African companies of all sizes have an appetite for big data analysis, but because the country’s big data analysis segment is relatively immature, they are still focused on their big data strategies and the complexity of actually getting the relevant data out of this massive pool of information. We find many enterprises currently looking at technologies and tools like Hadoop to help them collate and manage big data. There are still misconceptions around the tools and methodologies for effective big data analysis: companies are sometimes surprised to discover they are expecting too much, and that a great deal of ‘pre-work’, strategic planning and resourcing is necessary.
Much like the early days of BI, big data analysis started as a relatively unstructured, ad hoc discovery process, but once patterns are established, models are developed, and the process becomes a structured one.
And in the same way that BI tools depend on data quality and relationship linking, big data analysis depends on some form of qualifying prior to being used. The data needs to be profiled for flaws which need to be cleansed (quality), it must be put into relevancy (relationships) and it must be timeous in context of what is being searched or reported on.  Methods must be devised to qualify much of the unstructured data, as a big question remains around how trusted and accurate information from the internet will be.
The reporting and application model that uses this structured and unstructured data must be addressed, and the models must be tried and tested. In the world of sentiment analysis and trends forecasting based on ever-changing unstructured data, automated models are not always the answer. Effective big data analysis also demands human intervention from highly skilled data scientists who have both business and technical experience.  These skills are still scare in South Africa, but we are finding a growing number of large enterprises retaining small teams of skilled data scientists to develop models and analyse reports.
As local big data analysis matures, we will find enterprises looking to strategise on their approaches, the questions they want to answer, what software and hardware to leverage and how to integrate new toolsets with their existing infrastructure. Some will even opt to leverage their existing BI toolsets to address their big data analysis needs.  BI and big data are already converging, and we can expect to see more of this taking place in years to come.