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February 12, 2020

Developing Finance Analytics IQ

By Nilly Essaides  – Senior Research Director, Finance/EPM/FinOps

Improved analytics are essential to the function’s ability to drive superior enterprise performance. Companies and finance organizations recognize the importance of strong analytical skills.

  • 82% of respondents to The Hackett Group’s 2020 Key Issues Study cited enabling enterprise analytics capabilities as an important or highly important enterprise goal that finance must support this year. Only cost efficiency was higher, at 86%.
  • 79% of respondents reported that improving finance’s analytical, modeling and reporting capabilities is their number-one transformation initiative for 2020.

These numbers reflect finance’s expanding role as a strategic advisor and partner to the business, as growing stakeholder demand for decision-support in order to develop and execute enterprise strategies.

 

Five Steps

In The Hackett Group’s 2020 Key Issues Study, we asked finance executives how they intend to develop and deliver actionable insight. Here’s what they said in order of priority.

#1 Developing analytics competencies internally. Finance is beginning to escape its traditional reporting mindset and rethinking how to leverage data to create better insight through an end-to-end data analytics process, which starts with “raw” data curation and ends with actionable insight. For a long time, finance was viewed – and viewed itself – as a collector and processor of internal, mostly historical information. With a fast-changing and uber-competitive digital business landscape, the function needs to expand its view and look forward.

#2 Providing self-service tools. By pushing analytics capabilities into the business, finance can empower end users to get answers to ad-hoc questions more quickly. The rising pressure on finance to deliver advanced decision support is slowing the creation and distribution of critical insights to different stakeholders. By leveraging new, more user-friendly tools, business users can access “blessed” data and either pick analytical models from a library of pre-made algorithms or create their own. Over half of respondents to our study picked the provision of self-service tools as a strategy for improving the organization’s analytical savvy.

#3 Expanding the use of data visualization tools. Data visualization tools provide an interactive way to leverage analyses and drill down into and present information in more digestible ways. Instead of looking at rows and columns of numbers, these tools capture trends in pictures and provide users with a quick and dynamic way to look at data from different directions and share the results of their analyses with business partners and management. The Hackett Group’s research reveals a projected 26% rise in the adoption of data visualization solutions in 2020 vs. 2019 – the highest digital technology growth rate.

#4 Increasing internal and external analytics training resources and time allocation. Finance organizations are recognizing that thinking analytically and shifting from historical variance analysis to forward-looking statistical models requires new skills. The market for data analytics talent is tight. So, many organizations are looking to reskill existing staff. According to our research, 70% of finance organizations hire from within. The most advanced analytics technology is useless without employees who understand how it works and know how to ask the right questions.

#5 Enhancing data quality and accessibility. Legacy systems and rigid storage infrastructures have resulted in the proliferation of data silos that house inconsistent and duplicative information. Today’s finance organizations are modernizing their data architecture by embracing strong data management and governance policies, filtering data to minimize information overload, and virtualizing data storage, to enable direct access to critical information. In a study last year, we found that increasing the adoption of modern data platforms was a top priority for FP&A teams. Cloud-based, data management solutions can be implemented rapidly, while maintaining effective quality controls, and providing user-friendly interface for different users.

 

Understanding the Interdependencies

These steps are by no means sequential. The development of internal expertise by pivoting away from a reporting to an insight-generation mindset is dependent on upskilling and reskilling staff. Minds don’t change because of a management edict or the actions of a few individuals. To create a new model for achieving finance’s analytics objectives, everyone must be on board. That means staff must understand the big picture and feel comfortable they have the skills to moving forward into new roles. According to our research, cultural resistance is the #1 hurdle to transformation success.

Meanwhile, while better data management ranked fifth on the list above, it is a foundational element that must be addressed concurrently. Without a strong layer of “blessed” data, there is no point in rolling out self-service or visualization tools. Both rely entirely on access to clean and standardized data. If data is messy, self-service solutions will produce duplicative and inconsistent answers to similar questions, and visualization tools will paint the wrong pictures.

The lesson is that finance must take a holistic approach to its analytics-improvement efforts, which includes the people, process and technology aspects of transformation.