In a survey conducted by Accounting Today in 2019, 92% of the respondents, primarily senior executives, indicated they planned to invest in data analytics in the subsequent year.1 However, only 28% were of the opinion that their company had a suitable database to use data analytics effectively.
According to the EY survey DNA of the CFO 2020, only one third of companies currently estimate that they have adequate capabilities in advanced data analytics or AI. Perhaps even more importantly, only a small contingent of the CFOs surveyed were of the opinion that their companies had made good progress with regard to data access and its use. A lot of companies would first have to set up an internal infrastructure designed to handle analytics. However, there are ways and means to make this first step considerably easier.
The use of advanced data analytics as part of the finance function is based on three critical success factors.
Firstly, a clear strategic direction must be developed for the use of advanced data analytics. Together with an integrated change management initiative, necessary change processes can then be implemented. A clear, future-oriented target state is necessary and must combine the strategic and technological components of a company-wide uniform data utilization system. This can be closely monitored by management with strong leadership support.
Secondly, companies will need a flexible culture and work methodology with interdisciplinary teaming. The ideal team will have a broad range of core competencies, from data scientists, programmers and business analysts through to on-call experts. These skillsets will be able to address specific questions and drive the project forward with new approaches, value-creating innovations and data-based decisions. The right team composition thus forms the link between the strategy and the operational challenges of the company as a whole, including IT and the finance function.
The third success factor is the right infrastructure and architecture for analytics tools and methods. Strategically valuable information can be extracted from a pool of structured and unstructured internal and external data using statistical models, algorithms and cloud-based tools and solutions. Using specific mathematical methods and toolsets enable companies to model future scenarios and automate decision-making aids. Machine learning, AI, data and process mining as well as sentinel and semantic analysis make it possible to integrate internal data with enriched external data. This combination can produce reliable new findings that can be used to derive KPIs and create company-wide added value.
Cloud-based infrastructure provides access to significant and scalable computing power, making calculations of the most complex data models and solutions possible. More and more plug-and-play solutions are also available, which employees can use without specific programming experience. One of the greatest hurdles in the implementation of advanced data analytics is the high initial cost to implement the infrastructure and architecture. However, this can be reduced thanks to scalable cloud options, thereby lowering the initial start-up threshold compared to just a few years ago – provided the company has the ideal team and a robust strategy in place. The ultimate challenge facing CFOs in this decade is combining all three elements in an appropriate time period.