As finance augments its traditional role as a control function and seeks to position itself as a trusted business partner, perhaps its most critical competency is to enable better decision making across the enterprise by providing accurate, timely, and high-quality data analysis. This reality is strongly reflected in the survey results, with investment in D&A capabilities ranked as a top priority among survey respondents across all industries, geographies and company sizes. Improving planning and forecasting capabilities, another top priority, also requires high-quality data and analysis.

Top three drivers for investing in AI and ML

The primary means through which respondents have sought to improve these capabilities is through the use of advanced technologies, in particular, Artificial Intelligence (AI) and Machine Learning (ML). These technologies place an emphasis on generating forward-looking predictive and prescriptive insights rather than backward-looking descriptive ones.

In the future, finance organisations likely to be successful will harness data from multiple systems to create automated user-friendly dashboards and reports. Rather than measuring past performance, they will likely rely on a combination of both external and internal data sources to predict demand, highlight areas of opportunity and provide critical input on how their companies’ most important

Mastering your data – the foundation is fundamental

A number of challenges, however, stand in the way of improving the quality of insights generated by the finance function. Foremost among them is issues with data quality. All too often, finance is presented with data sources that conflict with one another or are inconsistent in format. The lack of clear data standards combined with manual analysis processes lead to analysis and reporting that cannot be fully relied on. Survey respondents may underestimate the importance of having a strong data management governance structure, which ranks as the least important challenge to improved D&A maturity among survey respondents. Though more immediate, tactical challenges may currently be of great concern, in order to help ensure ongoing success, it is critical to establish the foundation by having clear ownership and expertise in where data should be housed and how it should be analysed.

Dealing with the avalanche of data by solving the “basics” is the first step on the path to enhanced analytics.

Biggest barriers to improving D&A maturity

High-performing organisations have begun to masterdata quality issues, and see it as much less of a challenge than others: while data accuracy and quality rank as the top challenges for companies overall, for high-performing companies they are almost atthe bottom of the list. While high performers still struggle with integrating newanalytics tools with legacy systems, they have largelymastered the data 'basics', and have turned their attention to determining what business questions they should answer, and how to best present and disseminate the results of their analysis.

Biggest barriers to improving D&Amaturity

Key actions

Solving the D&A dilemma

Put in the work to harmonise data sources. While ensuring data consistency across multiple systems can be a tedious, painstaking process, poor quality data can only lead to poor quality analysis. Organisations need to put in the hard work to create a “single version of truth” that can be relied on to generate meaningful insights.

Start with the end state, then work backwards. Rather than resolving to adopt a high-impact analytics technology and then determining where best to apply it, first ascertain what business questions the company most struggles to answer, then determine what data, technologies and other capabilities are required to solve them.

Create non-traditional KPIs to measure business performance. More sophisticated analytical techniques facilitate the creation of more sophisticated performance measures. Measures such as customer lifetime value and customer experience profitability are being used by exemplar organisations to uncover the true drivers of business performance.

Consider CoEs and other centralised resources to solve governance issues. Data-focused CoEs can provide enterprise-wide expertise on how to source and integrate data, how to govern it, and the methods and technologies to analyse it. The finance function is uniquely well positioned to create and manage such a CoE.