How CFOs Can Achieve Data and Analytics Maturity

High-value data will always be a highly sought-after asset for business leaders and leading CFOs. And with the exponential rise in business data and the development of analytics capabilities, finance has become the figurehead in the push towards the data-driven enterprise.

However, research by Harvard Business Review revealed the extent of the mission facing CFOs. The survey of 162 finance function managers revealed that 90% had experienced an increase in the volume of data collected and used in the last two years, and understandably many CFOs are struggling to get to grips this new surge of information and analytics.

According to the survey, the top three challenges finance teams face in handling data include:

• Accurately preparing, reconciling, and accessing high volumes of information (68%)
• Integrating recent or real-time data into analyses (55%)
• Analyzing data, forming recommendations, and communicating them (55%)

Unsurprisingly, 88% recognized that fostering a data-driven culture in finance was critical to ensuring future performance, with 55% identifying this as a high priority for finance leadership today.

What’s clear is that finance’s ability to manage data and associated processes has never been more critical. Yet despite this, most respondents revealed they still rely on manual processes when it comes to collecting and utilizing data, illustrating the journey many organizations have before they can achieve data and analytics maturity.

Here are several ways CFOs are working to get their organizations on the path to data maturity.

1. Overcome reliance on manual processes and legacy technologies

Despite the wealth of technological capabilities on offer for finance teams, it’s surprising to learn that a significant majority still lean on manual processes when it comes to data analysis. Instead of using data analytics tools to inform the decision making process, more than three-quarters (77%) of respondents report relying on labor-intensive manual data collection processes, with a smaller proportion (62%) making use of data analytics tools.

When it comes to sharing data, most teams still rely on old technologies to transmit them, with 84% emailing spreadsheets or slides and 65% hosting regular meetings. However, modern methods of sharing information and insights that help foster data-driven decision making are gaining traction, with 51% sharing via dashboards and 28% using collaboration software. This is supported by the popularity of automated data collection and cleansing application programming interfaces, with four in 10 respondents looking to increase their usage of this solution to allow for tighter data integration.

2. Take full advantage of nonfinancial data

With the increasing importance of CFOs within organizations and businesses seeking a real-time view of performance and analytics, finance functions need to develop an even greater ability to analyze and understand business performance.

Finance teams also have to make use of data from other organizational departments to help drive strategic decision-making across the enterprise. This is made clear by the HBR survey, which revealed that nearly two-thirds of the respondents had made significant use of data from nonfinance functions.

In order to collate and analyze this information effectively, organizations need to develop a flexible data hub capable of accommodating multiple data types. However, only 37% assigned high importance to the leveraging of a flexible data hub, with 49% of respondents rating technological investments to support analysis and data management as a ‘high or ‘very high’ priority.

Despite this, finance leaders do recognize the need to prioritize their strategic investments into analytics capabilities. 53% plan to adopt or increase their usage of data analytics hubs that can consolidate financial and operational data, while 58% are looking to utilize integrated planning, analytics, and forecasting systems.

3. Improve your confidence in data

One of the key attributes of a decision-ready organization is having confidence in data. 59% of the respondents to the survey rated themselves as ‘confident’ or ‘very confident’ in the accuracy and efficiency of the data their finance teams use in decision making, while a significant 41% are not, rating themselves as 3 or lower in a scale of 1 to 5.

Fostering a truly data-driven culture requires confidence in data, and to gain more confidence in the quality of the information they have available, organizations are tuning to using technology like artificial intelligence (AI) and machine learning/predictive analytics.

However, the onboarding of these solutions is still in its infancy; 33% of the survey respondents stated that their finance teams don’t use these technologies at all, with less than a third rating their teams use of these in their day-to-day operations as a 3, 4, or 5 on a 5-point usage scale, with 5 indicating great usage.

In the long term, though, finance leaders recognize the need to develop their teams’ capabilities on this front, with 72% of respondents saying their finance teams are likely to make use of AI, machine learning, and/or predictive analytics to some or a great extent within the next three-years.

Driving a data-driven culture

While every organization is unique and all finance functions are in different stages of their journey to data maturity, finance leaders recognize the need to shift their priorities and embrace new technologies in order to realize the data-driven culture, collate increasingly varied and diverse data, and maximize their effectiveness.

To find out more about what senior finance leaders are doing to make their enterprises data-driven, read the full report here.