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What is advanced analytics?

Advanced analytics involves moving beyond traditional business intelligence tools and using statistical techniques based on data science. In the era of big data, these more sophisticated approaches to analysis are crucial to enable finance teams to become strategic business advisers.

Data analytics is the process of analysing information and drawing out trends and metrics that can provide business insight. Although it can be applied to any type of data processing, it is now often used to extract meaning from big data, the rise of which has led to more dispersed, varied and complex data sets.

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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For the finance function, converting the mountains of data available into actionable insights – relating to anything from financial reporting to operations and governance – can help support better business decisions.

Analytics activities tends to fall into three areas:

  • Descriptive analytics (hindsight): The analysis of past performance to help the business understand its position and past performance.
  • Predictive analytics (insight): Predicting or identifying the probability of future events.
  • Prescriptive analytics (foresight): Prescribing the best course of action in a given situation.

Advanced analytics techniques can be used in support of all of these outcomes, but are potentially most useful in helping finance teams offer their organisations more predictive and prescriptive outputs.

Advanced analytical techniques

Here are a few examples of common statistical methods that might be used to solve an analytical problem with finance:

Statistical method What does is it do? 
Linear regression
Establishes a linear equation between the dependent (the outcome variable) and the independent variable. This equation is then used for predicting the future values of the dependent variable.
Logistic regression Predicts the probability of success or failure of a pre-defined event.
Time series forecasting
Predicts the future values of a time series using its own historical data.
Clustering models
Clustering algorithms are used to group similar data together.
Classification models Classification algorithms are used to categorise new data samples into pre-defined classes.

More support on statistics

You can find more support on data mining, predictive analysis and statistics in a free online resource provided by StatSoft. The Electronic Statistics Textbook provides user-friendly guidance on the pivotal concepts, with more in depth exploration of specific areas of statistics.

Business benefits of advanced analytics

Research shows that CFOs are second only to Chief Information Officers in having overall responsibility for analytics within their businesses. Some of the key benefits of using advanced analytical techniques include:

  • Offering forward-looking strategic insights based on accurate and detailed forecasts, rather than traditional historical financial reporting.
  • Using financial and non-financial datasets to provide more comprehensive reporting – this might involve combining financial information with external datasets gathered via social media, for example.
  • Presenting results visually and interactively, so they are easier to understand and have more impact.
  • Mitigating risk by using facts to make important business decisions with more confidence.

Examples of how data analytics can be specifically used in audit include: planning and risk assessment; identifying and measuring exceptions; inconsistencies and outliers; and visual representation of data to enhance management understanding (such as graphs, dashboards and charts).

The limitations of advanced analytics

While advanced analytics can provide greater insight and predictive capability, it is not without downsides or risks. The outputs from analytics tools are only as good as the data being analysed and the people who are interpreting it.

Tools need to be properly tailored to requirements and staff adequately skilled and trained in their use. Poor quality initial data and/or poor visual presentation can produce inaccurate results or misleading outputs.

As with any data use, there are also security, privacy and reputational risks associated with sourcing and collating data for analytics, meaning a robust governance and cybersecurity framework is crucial. 

Moving forward with advanced analytics

Data analytics entails long-term investment but, by unlocking the potential in data, the finance function can help inform wider strategic decisions and help better understand customers, competitors, suppliers and employees.

On the audit side, extensive use of analytics is still mainly confined to large and mid-tier firms, with listed companies increasingly asking prospective auditors during tendering how they are going to use data analytics.

Smaller audit firms still have limited experience of analytics, can be suspicious of the costs in re-tooling and re-skilling, and are unclear about how it might be applied to business advantage. This may change, however, as the technology moves on and smaller firms have access to more and better data sets.

One thing is clear for any size or type of organisation: strong data and technology skills will be a pre-requisite to understanding, extracting and manipulating data, and perhaps to challenge the assumptions and output of analytics. Finance professionals will also need training in how best to leverage and present the insights provided.

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.