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What is big data?

One of the most commonly cited examples of disruptive digital technologies, big data is a constantly moving target. Characterised by its volume, velocity and variety of data, the key to its exploitation is in applying analytics. Read more about the reality of big data.

Advanced analytics and visualisation

Find out how to turn data into actionable insights by completing this eLearning. The module outlines how advanced analytical techniques are changing support for finance; the benefits they bring and examples of how they are transforming finance processes.

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Big data describes the increasingly large amounts of digital information created by the digital world. Every day, quintillions of bytes of data are produced by billions of information technology systems. The list is ever growing, but includes: accounting software, apps, bardcodes, cameras, mobile phone signals, online searches, sat nav units, social media, submissions of financial information to government agencies, transactional databases, video clips and website visits.

The creation, capture, exchange and storage of big data is supported by mutually reinforcing technology trends. These include:

  • pay-as-you-go access to cloud-based storage and other computing resources;
  • increasing processing and computing power;
  • ubiquitous smartphones, tablets, apps and wireless internet; billions of smart internet-connected objects (commonly known as the “the internet of things”); and
  • access to specialist big data hardware and software.

Business benefits of big data

These and other technology developments are creating a global digital infrastructure that enables greater variety and volumes of data to exist and flow around with increasing speed.

As elements in this infrastructure become more widely accessible and affordable, it gets easier for individuals, businesses, governments and other organisations to collect, store and explore big data and apply sophisticated analytics to:

  • derive greater insights;
  • automate and optimise processes;
  • find innovative ways to share and develop knowledge;
  • boost productivity and margins;
  • enhance insights and improve decision-making; 
  • link and interrogate data from new and disparate sources;
  • identify exceptions, patterns, unexpected events and outliers;
  • improve planning, forecasting and predictions of future outcomes; and
  • identify and reduce risks.

Current applications include:

Healthcare: Clinicians using massive quantities of anonymised health data to improve outcomes for individual patients and drive wider improvements in healthcare delivery.

Retail: Shops tracking all of customers as they move between channels to gain insights into optimising and segmenting advertising.

Regulation: Tax authorities drawing data from dozens of internal and external sources (including social media) to improve tax compliance and combat fraud.

As data is core to what accountants do there are many ways to engage with and exploit big data – particularly in combination with technologies, such as artificial intelligence and advanced analytics tools and techniques.

Better outlier and exception analysis can improve internal control and risk management. Combining finance and non-finance data from new and traditional sources can improve: budgeting and forecasting, financial statement audit and tax planning, for example.

Potential risks of big data

Products, services and systems that use big data can bring new risks and amplify traditional risks. Gathering big data from multiple sources offers multiple opportunities for privacy and security problems, infringements of legislation and the potential for financial and reputational damage.

Risks around data quality, organisation, storage, retention, ethics and governance must be carefully assessed and addressed.

Finance teams are experienced in many of the issues that arise around data quality and analysis, but they may need new and improved skills, to help businesses minimise their risk of exposure to misinterpretation of big data and, as a result, making erroneous decisions.

Accountants need to ensure that use of big data and analytics is appropriate, effective and subject to robust challenge, particularly in the case of predictive models. In the world of big data, accountants’ inherent prudence and scepticism may be more important than ever.


Find out more about advanced analytics

Learn more about advanced analytics and visualisation techniques by completing ICAEW and Deloitte's eLearning module. This resource provides more details on the statistical methods that are enabling finance teams to provide their business with more actionable insights.