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Tuesday 10 December 2019
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Analytics in finance

CLIVE WEBB, ACCA senior professional insights manager

In the race for relevance, analytics has a head start over some of the other technologies. Yet there are still things that can be done to take it to the next level; to take advantage of emerging technologies and to bring greater insights to the business.

Analytics applications have progressed from the business intelligence tools that supported queries run from core accounting applications and offered the ability to correlate data, into more sophisticated tools that deliver visual representation of data on a real time basis.

In the next iteration the addition of machine learning will improve the ability to incorporate unstructured and external data and deliver another level of predicative modelling.

All this, however, relies on quality data.

Why analytics in finance?

Business leaders are increasingly turning to shorter decision making horizons. Performance against objectives is being monitored ever more closely. The need to react to the latest trend is more immediate. The risks from competition are broader and more timely.

Finance teams need to be able to deliver greater insight based upon the data available to them. To assist the decision making process by being forward looking as well as backward glancing. As the volume of data available increases so does the processing requirement of the analytics tools.

The data explosion opens up new opportunities for the incorporation of big data – data that comes from devices such as those that are part of the internet of things (IoT). These offer the next level of insight.

Implementing Analytics

There are many analytics tools in the market. Choosing the right one for the circumstances of your organisation often requires independent advice and opinion.

Implementation requires a quality of data appropriate to the level of decision making that is being made. Efforts to achieve 100 per cent accuracy in data are frequently not met by the same level of improvement in analysis.

In many organisations data sits in silos. This is a barrier to effective analytics. Data should be open to those who wish to access it (with the exception of data that genuinely needs to be restricted). Siloing data restricts the insights available.

Using external and unstructured data can add a dimension to the level of insight. Customer data can be obtained and there are a number of data sources that can be used through open APIs.

The constraint, perhaps, is the ability to visualise the solution to the problem. Having the right mix of talent to manage the data and understand the business issues.

The following impacts and issues need to be considered:

• How data quality is managed?

• Do you have the right procedures over data governance?

• Is your data open?

• Have you sufficiently protected personal data, which may be subject to regulation?

• Do you have the skills and resources?

• Reliance on manipulated data

Key considerations:

• Map your analytics to your business goals

• Have your end goal in mind – have you identified the business issues?

• Ensure that your data is ‘open’ within the organisation

• Data needs to as clean as it needs to be for the purpose it is used – not necessarily 100%

• Consider the skill sets that finance needs to be effective business partners

• Use relevant internal and external data sources

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