Here is a big question: How big is big data? Is it as big as the 2016 gross domestic product of Bahrain (US$31.8 billion)? Or Costa Rica (US$57.4 billion)? Or Qatar (US$152.4 billion)? Or Bangladesh (US$221.4 billion)?
Incidentally, Malaysia’s GDP was US$296.3 billion while Singapore’s was US$269.9 billion, as per the World Bank’s estimates.
So, the big answer: It depends who you ask.
According to Hamburg, Germany-based Statista, the global market for big data was worth just under US$34 billion last year, closer to the 2016 GDP of Bahrain. However, by 2026, the global big data market is likely to jump to US$92.2 billion, just below Ukraine’s 2016 GDP of US$93.2 billion.
Another agency, Wikibon, notes that the global big data market is on track to grow from US$18.3 billion in 2014 to a whopping US$92.2 billion by 2026, representing a compound annual growth rate (CAGR) of 14.4%. Wikibon is a global community of consultants who believe technology adoption can be improved through an open-source sharing of free advisory knowledge.
The most bullish outlook comes from International Data Corp. Last March, IDC updated its Worldwide Semiannual BDA (Big Data & Analytics) Spending Guide, which stated that the global BDA spend would reach US$150.8 billion in 2017 (equal to Qatar’s 2016 GDP), up 12.4% from the previous year. Commercial purchases of BDA-related hardware, software and services are set to grow at a 12% annual clip between now and 2020, with sales crossing the US$210 billion mark (Bangladesh’s 2016 GDP) by then.
The devil is in the details, or more precisely, in the definition. Gartner defines BDA or “advanced analytics” as the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence.
BDA helps one to discover deeper insights, make predictions or generate recommendations. Gartner’s definition includes data and text mining, machine learning, pattern matching, forecasting, visualisation, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing and neural networks.