The global big data market revenues for software and services are expected to increase from $42 billion to $103 billion by year 2027.1 Every day, 2.5 quintillion bytes of data are created, and it’s only in the last two years that 90% of the world’s data has been generated.2 If that’s any indication, there’s likely much more to come.
The world is driven by data, and it’s being analysed every second, whether it’s through your phone’s Google Maps, your Netflix habits, or what you’ve reserved in your online shopping cart – in many ways, data is unavoidable and it’s disrupting almost every known market.3 The business world is looking to data for market insights and ultimately, to generate growth and revenue. Although data is becoming a game changer within the business arena, it’s important to note that data is also being utilised by small businesses, corporate and creative alike. A global survey from McKinsey revealed that when organisations use data, it benefits the customer and the business by generating new data-driven services, developing new business models and strategies, and selling data-based products and utilities.4 The incentive for investing and implementing data analysis tools and techniques is huge, and businesses will need to adapt, innovate, and strategise for the evolving digital marketplace.
Every day, 2.5 quintillion bytes of data are created, and it’s only in the last two years that 90% of the world’s data has been generated.
What is data analysis?
Data analysis, or analytics (DA) is the process of examining data sets (within the form of text, audio and video), and drawing conclusions about the information they contain, more commonly through specific systems, software, and methods. Data analytics technologies are used on an industrial scale, across commercial business industries, as they enable organisations to make calculated, informed business decisions.5
Globally, enterprises are harnessing the power of various different data analysis techniques and using it to reshape their business models.6 As technology develops, new analysis software emerge, and as the Internet of Things (IoT) grows, the amount of data increases. Big data has evolved as a product of our increasing expansion and connection, and with it, new forms of extracting, or rather “mining”, data.
Six big data analysis techniques
Big data is characterised by the three V’s: the major volume of data, the velocity at which it’s processed, and the wide variety of data.7 It’s because of the second descriptor, velocity, that data analytics has expanded into the technological fields of machine learning and artificial intelligence.8 Alongside the evolving computer-based analysis techniques data harnesses, analysis also relies on the traditional statistical methods.9 Ultimately, how data analysis techniques function within an organisation is twofold; big data analysis is processed through the streaming of data as it emerges, and then performing batch analysis’ of data as it builds – to look for behavioural patterns and trends.10 As the generation of data increases, so will the various techniques that manage it. As data becomes more insightful in its speed, scale, and depth, the more it fuels innovation.
The world is driven by data, and it’s being analysed every second, whether it’s through your phone’s Google Maps, your Netflix habits, or what you’ve reserved in your online shopping cart.
McKinsey’s big data report identifies a range of big data techniques and technologies, that draw from various fields such as statistics, computer science, applied mathematics, and economics.11 As these methods rely on diverse disciplines, the analytics tools can be applied to both big data and other smaller datasets:
1. A/B testing
This data analysis technique involves comparing a control group with a variety of test groups, in order to discern what treatments or changes will improve a given objective variable. McKinsey gives the example of analysing what copy, text, images, or layout will improve conversion rates on an e-commerce site.12 Big data once again fits into this model as it can test huge numbers, however, it can only be achieved if the groups are of a big enough size to gain meaningful differences.
2. Data fusion and data integration
By combining a set of techniques that analyse and integrate data from multiple sources and solutions, the insights are more efficient and potentially more accurate than if developed through a single source of data.
3. Data mining
A common tool used within big data analytics, data mining extracts patterns from large data sets by combining methods from statistics and machine learning, within database management. An example would be when customer data is mined to determine which segments are most likely to react to an offer.
4. Machine learning
Well known within the field of artificial intelligence, machine learning is also used for data analysis. Emerging from computer science, it works with computer algorithms to produce assumptions based on data.14 It provides predictions that would be impossible for human analysts.
5. Natural language processing (NLP).
Known as a subspecialty of computer science, artificial intelligence, and linguistics, this data analysis tool uses algorithms to analyse human (natural) language.15
This technique works to collect, organise, and interpret data, within surveys and experiments.
Other data analysis techniques include spatial analysis, predictive modelling, association rule learning, network analysis and many, many more. The technologies that process, manage, and analyse this data are of an entirely different and expansive field, that similarly evolves and develops over time. Techniques and technologies aside, any form or size of data is valuable. Managed accurately and effectively, it can reveal a host of business, product, and market insights. What does the future of data analysis look like? It’s hard to say with the tremendous pace analytics and technology progresses, but undoubtedly data innovation is changing the face of business and society in its holistic entirety.
- 1 Burris, P. (Mar, 2018). ‘Wikibons 2018 big data and analytics market share report’. Retrieved from Wikibon.
- 2 Marr, B. (May, 2018). ‘How much data do we create every day? The mind-blowing stats everyone should read’. Retrieved from Forbes.
- 3 Kambhampaty, S. (Jul, 2018). ‘It’s all about data’. Retrieved fromForbes.
- 4 Gottlieb, J & Khaled, R. (Jan, 2018). ‘Analytics comes of age’. Retrieved fromMcKinsey.
- 5 (Nd). ‘Data analytics (DA)’. Retrieved from Search Data Management.
- 6 Kambhampaty, S. (Jul, 2018). ‘It’s all about data’. Retrieved from Forbes.
- 7 (Nd). ‘Big data. Retrieved fromSearch Data Management.
- 8 (Nd). ‘Big data. Retrieved fromSearch Data Management.
- 9 Erl, T & Khattak, W & Buhler, P. (2016). ‘Big data fundamentals: Concepts, drivers & techniques’. Retrieved from Safari Books Online
- 10 Erl, T & Khattak, W & Buhler, P. (2016). ‘Big data fundamentals: Concepts, drivers & techniques’. Retrieved from Safari Books Online
- 11 Manyika, J, et al. (2011). ‘Big data: The next frontier for innovation, competition, and productivity’. Retrieved from McKinsey.
- 12 Manyika, J, et al. (2011). ‘Big data: The next frontier for innovation, competition, and productivity’. Retrieved from McKinsey.
- 13 (Nd). ‘Big data mining’. Retrieved from
- 14 Reavie, V. (Aug, 2018). ‘Do you know the difference between data analytics and AI machine learning?’. Retrieved from Forbes.
- 15 Manyika, J, et al. (2011). ‘Big data: The next frontier for innovation, competition, and productivity’. Retrieved from McKinsey.