What Is Data Analytics?
What is data?In its simplest form, data is information of any kind. Data on its own can be vast and confounding; however, when synthesised and analysed it provides a wealth of insight into every aspect of the world – from complex business functions, to interpreting unseen ecological environments, and even positing hypotheses about ancient human history.
What is data analytics?Data analytics is the process of finding, preparing, transforming and modeling data to gain insights that can inform business decision-making. As the complexity of your analysis increases, different types of data analytics can be used to answer a series of questions:
- What happened?
- Why did this happen?
- What will happen?
- How can I make this happen?
The age of analyticsAs a result of widespread adoption of technology across our personal and professional lives, we are now generating volumes of data and, in many cases, have unprecedented access to these data sets. The evolution of analytics, supported by advancements in cloud technology and machine learning, gives us access to highly evolved tools and sophisticated functionality that can disseminate large quantities of data quickly and effectively. Today’s professionals are operating in the age of analytics. According to ‘The future of work’, 90% of all data in existence has been created in the last 2 years. For example:
- 10 billion mobile devices will be in use by 2020.
- Mobile devices are being used to read over 50% of the 294 billion emails sent every day.
- Google processes 63,000 searches a second, which translates into 3.8 million searches per minute, 228 million searches per hour, 5.6 billion searches per day, and at least 2 trillion searches per year.
- Facebook’s data warehouse can now hold over 300 petabytes of data, or about 30,000 times more information than is stored by the U.S. Library of Congress.
- Trillions of sensors monitor, track, and communicate with each other, populating the Internet of Things (IoT) with real-time data.
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Data Analysis and Fundamental Statistics
The demand for data analystsDeloitte Access Economics forecasts that, in Australia alone, the data science workforce will grow from 301,000 people in 2016–17, to 339,000 people in 2021–22. The average annual growth rate of 2.4% is stronger than the 1.5% per annum growth that is forecast for the entire Australian labour force. Although this growth is notable, the growth in demand for data-related skills is much more significant. Our future workforce won’t all be data scientists, but a large percentage of them will need to be able to manage and analyse data to do their jobs. This trend is being seen in finance, marketing, healthcare, education, and most other professions as illustrated in an IBM and Burning Glass report into the American economy. Markow, Braganza, and Taska noted that the number of job postings specifically for data scientists did increase in 2016 (+5%), and there was a dramatic growth in job descriptions requesting data-centred skills including quantitative data analysis, data visualisation, and A / B testing. This diversity points to a new reality: success in the jobs of the future will depend on essential ‘transcendent skills’ – such as data analysis – that are valuable in multiple roles and contexts.
- The future of work: occupational and education trends in data science in Australia [PDF]. Deloitte Access Economics; 2018. Available from: https://www2.deloitte.com/content/dam/Deloitte/au/Documents/Economics/deloitte-au-economics-future-of-work-occupational-education-trends-data-science-170418.pdf
- Markow W, Braganza S, Taska B. The quant crunch: how the demand for data science skills is disrupting the job market. 2017. Available from: https://www.burning-glass.com/wp-content/uploads/The_Quant_Crunch.pdf
Data Analysis and Fundamental Statistics
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