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

In the future, Finance organizations 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 upon 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 business decisions are expected to impact the future.

 

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. A lack of clear data standards combined with manual analysis processes leads to analysis and reporting that cannot be fully relied upon. 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 top of mind, 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 analyzed.

 

Biggest barriers to improving D&A maturity

High-performing organizations have begun to master data quality issues, and see it as much less of a challenge than others: while data accuracy and quality ranks as the top challenge for companies overall, for high-performing companies it falls near the bottom of the list. While high performers still struggle with integrating new analytics tools with legacy systems, they have largely mastered the data “basics,” and have turned their attention to determining what business questions they should focus on answering, and how to best present and disseminate the results of their analysis.

 

Solving the D&A dilemma

Put in the work to harmonize 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. Organizations need to put in the hard work to create a “single source of truth” that can be relied upon to generate meaningful insights.

 

Start with the end state, then work backwards. Rather than resolving to adopt 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 organizations to uncover the true drivers of business performance.

 

Consider COEs and other centralized 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 analyze it. The Finance function is uniquely well positioned to create and manage such a COE.