Knowledge is power: how big data analytics is transforming our world

In a world dominated by gadgets and smart technology – where lives revolve around swiping, clicking and tapping – it’s little wonder that data is being produced in volumes the likes of which have never been seen. This veritable data mountain contains innumerable pieces of information that, when made accessible and available, have the potential to revolutionise the ways in which we live.

But, how exactly can information from all these sources of data be extracted? What do algorithms, machine learning and artificial intelligence have in common? The answer is big data analytics: a market forecast to increase at an annual growth rate of 29.7% and estimated to be worth $40.6 billion by 2023 (Global Big Data Analytics Forecast to 2023, Frost and Sullivan).

What is big data analytics?

IBM defines big data analytics as the use of advanced analytic techniques against very large, diverse data sets that: include structured, semi-structured and unstructured data; come from different sources; and range from terabytes to zettabytes in size.

It’s a digital ecosystem characterised by both real-time production and sheer scale – big data deals with information that is high volume, high velocity, high variety, or a combination of these ‘three Vs’. Artificial intelligence (AI), mobile, and the Internet of Things (IoT) all feed into its production.

Big data analytics can be applied to numerous sources, including:

• Video and audio
• Databases
• Web interaction
• Web use
• Social media
• Transactions on apps
• Mobile devices and sensors
• Log files
• Networks

It aims to uncover and understand the correlations and relationships that exist between complex information in vast proportions. While more traditional data analysis software and relational databases are unable to process this amount of data at this level of complexity, the systems, applications and tools of big data are designed for this exact challenge.

Why is big data analytics important?

Thanks to big data analytics, organisations across both the public and private sector now have access to large amounts of data collected from consumers, users, providers and suppliers. Big data analytics makes valuable use of data that was previously unusable or inaccessible, allowing businesses to capitalise on the extensive applications offered by advanced analytics. Business leaders can now take the learnings from these untapped datasets and use them to inform decision-making and plan future growth.

The use cases of big data are vast – from healthcare, finance and transport, to retail, communication, hospitality and beyond. To cite just a handful of case studies: Amazon and Tesco can predict customer buying behaviour, promoting products before customers are even aware they want them; banks such as Santander and Monzo can spot fraudulent transactions; and Netflix and YouTube can gain insights into the viewing habits of customers across the world.

While the list is far from exhaustive, this type of analytics makes it possible for businesses to:

• Improve customer experience through personalisation
• Boost customer acquisition and retention
• Pinpoint cost savings and improve efficiency
• Troubleshoot and identify potential risks and fraudulent activity
• Influence business decisions, such as pricing or or product development
• Optimise supply chains, operations and other processes
• Focus and target marketing activities and efforts
• Remain ahead of the competition

Different types of big data analytics

There are four main types of data analysis, and they can be applied to any data-related issue:

Descriptive analysis 

Here, historical data is used to examine what has occurred previously. Examples include: tracking Key Performance Indicators (KPIs), sales leads, and revenue from various demographics.

Diagnostic analysis

Where descriptive analysis can reveal what happened (the results either positive or negative), diagnostic analysis can reveal why it happened. An example of diagnostic analysis is a company observing a reduction in sales leads during a particular month and then pinpointing whether certain marketing campaigns or activities proved ineffective during that period.

Predictive analysis

Predictive analytics examines patterns in past data in order to forecast what is likely to happen in the future. In practice, a business could use this type of analysis to predict the next five years’ revenue, based on data from previous years. Predictive analysis is particularly useful in risk assessment, lead generation, and sales forecasting.

Prescriptive analysis 

This type of analysis – which combines descriptive, diagnostic and predictive variations – seeks to find an appropriate method via which an intended outcome can be achieved. Ultimately, it supports business to make data-driven choices and informs decision-making, and is therefore incredibly powerful.

Careers in big data analytics

There’s currently a huge demand for individuals with skills across data science, machine learning, business intelligence, data management and related fields. With a significant national skills gap within the technology industry, there has never been a better time to take advantage and upskill. 

Career opportunities for those with the right qualifications are available across virtually every sector, as big data, data mining and other new technologies – fast-growing industries that are only forecast to get bigger – are now used by almost every type of employer.

Some of the big data-related roles on offer include:

• Data scientist
• Statistician
• Data analyst
• Machine learning engineer
• Database administrator

A proliferation of sensitive data also requires those with the specialist skills to uphold data protection and ensure that data remains secure, so cyber security analysts and similar roles are also in great demand.

There are a wealth of programmes available aimed at helping individuals to acquire the relevant skills and knowledge to succeed in big data careers. As well as teaching core concepts, tools and methodologies, they provide solid understanding of coding skills, statistical analysis, programming languages (such as SQL), web analytics, advanced database skills, data processing, machine learning, data visualisation and interpretation (such as Tableau). Data storage, data lakes and proper use of data warehouses are also key, using tools such as Apache Hadoop and Spark.

Harness big data technologies to enhance and optimise your professional practice

If you’re seeking to excel in leadership and management – and employ the use of big data and analytics for business success – the University of Sunderland’s online International MBA programme is the choice for you.

Our flexible MBA prepares you for cross-cultural, strategic, senior roles – regardless of your industry, sector or background. Your studies will equip you with the skills and understanding to navigate complex, challenging business environments, exploring corporate strategy, operations, finance, data analytics, organisational behaviour, and more.