3 minute read 24 Aug 2019
Data analytics and the new opportunities for finance

Data analytics and the new opportunities for finance departments to drive value

By

Eoin O'Reilly

Ernst & Young — Ireland (EY Ireland) Partner and Head of Data Analytics

Passionate about innovation, data and advanced analytics.

3 minute read 24 Aug 2019

As the world embraces a digital future, finance departments and CFOs must consider leveraging analytics and emerging technologies to help lead change within their organisations.  

Changes in the business landscape are accelerating fast, and a continually changing business environment requires a new approach.  

It’s a very exciting time to be involved in digital finance transformation as greater access to technology is enabling new approaches to how we deliver value.  

The recently published National Analytics Maturity Study 2018* reveals that 42% of Irish organisations surveyed agreed that the application of data analytics technology would streamline internal operations. 

A further 34% report that analytics insights are not well integrated into current business processes.  
In finance, the technologies are being applied across a range of activities including; 

  • Dynamic Forecasting 

  • Revenue and Cost Optimisation 

  • Working Capital Analysis 

  • Contract Compliance and Automation 

  • Automated Reporting through Robotic Process Automation 

One of the main challenges for finance departments is trying to add more insight in their forecast reporting. Analytics have the potential to make forecasts more dynamic and insightful, to answer the ‘so what’ questions and help organisations make more informed decisions.  

Analytics driving value 

There are always opportunities to apply analytics and emerging technology to drive value for finance departments.  

I haven’t come across an organisation yet that couldn’t make changes to improve the quality and intelligence of their forecasting.  

New tools can help extract information from historical datasets and overlay a new level of insight in your forecasting. Here are some examples; 

New ways of visualising - a very simple transformation is to present your results in an interactive dashboard that allows you to modify analysis depending on your audience. 

Enhanced processing - advanced workflow tools (like Alteryx) or RPA can be implemented to ensure data capture and transformation of information are timelier and less arduous. 

Scenario analysis - automate your analysis to generate scenario analysis to help answer the ‘so what’ questions. 

Recommendations generation - use Artificial Intelligence (AI) to help automate recommendations on what you should do in the future. As you continue to forecast, your AI model keeps learning and becoming more accurate.  

There are many benefits of implementing a dynamic forecasting approach including stakeholders being better informed, process improvement around data capture, reduced time and effort and the upskilling of finance teams.  

The main adoption challenge is senior stakeholder buy-in and ownership of transformation. Developments like this are far more successful and easier to embed when you have a senior buy-in driving the change.  

In fact, 51% of respondents in the National Analytics Maturity Study cited lack of collaboration and senior leadership support as a major pain point in moving to analytics maturity.  

Here at EY, we have delivered a number of finance transformation projects for our clients and would be happy to discuss how you can make the most of the opportunities in this space. 

*Study by the Analytics Institute in collaboration with EY & UCD Smurfit School.

Summary

Data analytics have the potential to make forecasts more dynamic and insightful, to answer the ‘so what’ questions and help organisations make more informed decisions.

About this article

By

Eoin O'Reilly

Ernst & Young — Ireland (EY Ireland) Partner and Head of Data Analytics

Passionate about innovation, data and advanced analytics.