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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 investing in data and analytics 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 and machine learning. 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 to 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 data and analytics maturity among survey respondents. Though more immediate, tactical challenges may be 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.

Quote by Ron Walker

Key actions: 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 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 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.

Coexisting in the cloud

Cloud has begun to reach maturity, with two-thirds or more of organizations using at least some cloud solutions across all major Finance systems. Organizations have most heavily adopted cloud as part of their automation initiatives, while adopting it least frequently for core and peripheral financial systems.

Adoption strategy of cloud-based solutions for the following technologies

Cloud-based, as-a-service software for enterprise performance management (EPM) and enterprise resource planning (ERP) is increasingly being used to enable integrated end-to-end global processes. These technologies can help finance standardize processes and improve efficiency in areas such as budgeting and planning, management reporting, and payroll processing. For example, instead of embedding financial planners in each part of the business, Financial planning and analytics (FP&A) can use advanced analytics and cloud-based EPM to create an integrated view of the front, middle, and back offices.

In fact, as Cloud and automation technologies continue to evolve, there will almost certainly come a time when managed service providers are able to provide “Finance-as-a-service,” running almost the entirety of the Finance function at scale.

Footnote: High-performing organizations are defined as ranking in the top 16 percent on a combined measure of revenue and profitability growth

Contact:

Stephanie Terrill
Partner, Global Lead Financial Management